A Holistic Model for Ergonomic and Sustainable Personnel Scheduling in Urban Transportation

Abstract

Personnel scheduling plays a pivotal role in numerous industries, impacting productivity, job satisfaction, and employee welfare. However, conventional scheduling approaches often neglect ergonomic and sustainability considerations, overlooking their influence on workforce health and environmental impact. This study presents a novel goal programming framework for optimizing personnel scheduling in urban transportation systems, integrating ergonomic risk assessments (REBA method) and sustainability metrics (aligned with SDGs). This model is validated through a case study of an urban transportation company with 140 employees working in a three-shift system. The results demonstrate a 44% reduction in high-risk task assignments, a 45.1% improvement in sustainability balance, and a 37.7% increase in employee satisfaction. This study offers theoretical contributions by expanding scheduling research to include multi-objective workforce optimization and practical implications by providing a decision-support tool for transportation agencies and workforce managers. Future research can explore real-time scheduling adaptations and AI-based predictive workforce planning.

1. Introduction

In contemporary workforce management, personnel scheduling is pivotal for driving productivity, supporting employee well-being, and maintaining operational efficiency. Yet, traditional scheduling models tend to prioritize shift coverage and cost minimization, frequently overlooking critical aspects like ergonomic safety and sustainability considerations. This narrow focus can result in heightened risks of musculoskeletal disorders (MSDs), diminished job satisfaction, and increased absenteeism, especially in physically demanding sectors like urban transportation. These shortcomings highlight a pressing need for innovation, leading to the central research question: How can personnel scheduling be optimized to simultaneously reduce ergonomic risks, enhance sustainability contributions, and maintain operational efficiency in urban transportation systems? Addressing this question requires a shift toward more holistic scheduling practices that integrate employee health and environmental goals alongside operational demands, ensuring a balanced and forward-thinking approach to workforce management.

Traditional personnel scheduling models [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43] have predominantly focused on optimizing workforce allocation based on shift coverage, employee availability [19,20,21,22,23,24,25,26], and cost minimization, often overlooking critical ergonomic risks and sustainability considerations. This study distinguishes itself by presenting a holistic personnel scheduling model tailored for urban transportation systems, integrating ergonomic risk assessments and sustainability metrics. By incorporating Rapid Entire Body Assessment (REBA) scores, this research evaluates and minimizes musculoskeletal risk factors, a departure from conventional models that typically neglect such ergonomic considerations. Furthermore, a novel sustainability scoring system is introduced, aligning scheduling decisions with United Nations Sustainable Development Goals (SDGs), thus addressing responsible consumption, sustainable infrastructure, and employee well-being.

The study’s key innovations extend to its methodology and application. By formulating the personnel scheduling problem as a multi-objective goal programming model, it simultaneously optimizes ergonomic safety, sustainability goals, and operational efficiency, offering a comprehensive approach to scheduling. Unlike generic workforce scheduling models, this study specifically addresses the challenges of urban transportation, such as high shift variability, demanding physical tasks, and compliance with sustainability policies. The model also emphasizes improved scheduling equity and fairness by ensuring a balanced workload distribution. This approach not only balances ergonomic risks and sustainability contributions among employees but also promotes work-life balance and reduces occupational hazards, marking a significant advancement in personnel scheduling practices.

This study not only provides a practical scheduling framework for urban transportation companies but also serves as a foundation for future research on sustainable workforce management. By considering both employee well-being and sustainability objectives, this model offers a comprehensive decision-support tool for optimizing personnel scheduling in demanding work environments.

Personnel scheduling in urban transportation systems is a complex and multi-faceted problem due to the high variability in shifts, physically demanding tasks, and the need for sustainability integration. Unlike traditional scheduling problems, which primarily focus on cost minimization and shift coverage, this study addresses a multi-objective challenge that considers employee well-being, operational efficiency, and sustainability.

One of the major challenges in urban transportation personnel scheduling is balancing ergonomic risks with shift assignments. Many jobs in urban transportation involve repetitive movements, awkward postures, prolonged sitting, and heavy lifting, all of which increase the risk of musculoskeletal disorders (MSDs). Traditional scheduling models do not take ergonomic safety into account, which leads to worker fatigue, increased absenteeism, and long-term health risks. A key question in this context is how to assign shifts while minimizing ergonomic risks using REBA-based task evaluations.

Another significant challenge is incorporating sustainability into workforce scheduling. Most existing scheduling studies do not integrate sustainability considerations, despite companies adopting corporate sustainability goals such as carbon footprint reduction, energy efficiency, and responsible resource management. The challenge is how to optimize scheduling decisions to align with sustainability targets while maintaining operational efficiency. To address this, this study introduces a sustainability scoring system aligned with the United Nations Sustainable Development Goals (SDGs), which pairs high-contribution and low-contribution employees to promote sustainable workforce practices.

Ensuring fair and efficient workload distribution is another critical issue. High shift variability and uneven workload assignments often result in certain employees being overburdened while others receive lighter tasks. A major challenge is how to design a scheduling system that ensures fair exposure to different shifts while also considering ergonomic risk and sustainability scores.

Additionally, managing multi-objective optimization in scheduling presents a technical challenge. Most workforce scheduling models are single objective, focusing on either cost minimization or productivity maximization. However, this study seeks to develop a goal programming model that optimally balances multiple objectives, including ergonomic risk minimization, sustainability-based workforce assignment, and operational efficiency. The proposed multi-objective goal programming model addresses this challenge by simultaneously optimizing these factors.

Finally, adaptability for the urban transportation workforce is crucial, as this sector operates 24/7, requiring personnel to work night shifts, long hours, and physically demanding tasks. The challenge lies in creating a scheduling model that can adapt to highly dynamic, multi-shift environments while ensuring regulatory compliance with work-hour limitations, fair shift rotations to prevent fatigue, and task-specific job assignments (e.g., ensuring trained operators handle vehicle maintenance and operation). The proposed model is customized for urban transportation, ensuring it meets industry-specific constraints while improving both employee well-being and operational efficiency.

Table 1. Summary of the challenges.

Challenge	Problem	Proposed Solution
Ergonomic Risk	High exposure to physically demanding tasks	REBA-based task evaluations and fair job rotation
Sustainability in Scheduling	Lack of sustainability integration	Sustainability scoring system (aligned with SDGs)
Fair Workload Distribution	Uneven shift allocation and overburdening	Balanced workforce optimization model
Multi-Objective Scheduling	Existing models focus only on cost or productivity	Goal programming optimizing ergonomics, sustainability and efficiency
Adaptability to Urban Transportation	High shift variability and industry-specific needs	Customization for 24/7 operations and task-specific roles

summarizes the key challenges identified in current scheduling practices, the specific problems they pose, and the proposed solutions to address these issues. Each challenge highlights a critical area for improvement in personnel scheduling, aiming to enhance worker well-being, promote sustainability, and optimize overall efficiency.

This paper is structured as follows: Section 2 provides a comprehensive review of the existing literature on urban transportation and personnel scheduling problems, laying the groundwork for the research presented. Section 3 delves further into the specific literature relevant to the proposed methodology. Section 4 and Section 5 meticulously define the problem statement and detail the chosen methodology, respectively. Section 6 showcases a real-world case study application within an urban transportation company, demonstrating the practical applicability of the developed model. Section 7 presents a thorough discussion and analysis of the results obtained, evaluating the model’s impact on both ergonomic risk reduction and sustainability improvements. Finally, Section 8 concludes the paper with a summary of key findings and contributions, while Section 9 explores the potential for simulation and real-world implementation of the proposed approach.

2. Urban Transportations and Personnel Scheduling Problems

Urban transportation systems are critical to the functioning of modern cities. They provide essential services that enable people to commute to work, access essential services, and engage in recreational activities. Urban transportation companies operate a wide range of vehicles, including buses, trams, trains, and other forms of public transit, often with large numbers of employees working in various roles such as operators, maintenance workers, administrative staff, and support personnel. Ensuring the optimal scheduling of this diverse workforce is essential to both the operational success of the company and the health and safety of its employees.

Personnel Scheduling Problems (PSP) are combinatorial optimization problems that are classified as NP-hard. This means that finding an exact optimal solution in polynomial time is computationally infeasible for large-scale problems, especially when considering numerous employees, tasks, and constraints. The complexity of the PSP grows exponentially with the number of employees, shifts, tasks, and scheduling horizon, making it increasingly difficult to solve as these parameters increase [7].

Moreover, PSPs often involve multiple conflicting objectives, such as maximizing employee satisfaction, minimizing labor costs, and ensuring fairness in scheduling. Balancing these competing objectives requires the use of sophisticated optimization algorithms that can efficiently handle such trade-offs. To address these challenges, several approximation and heuristic algorithms have been developed, including greedy algorithms, genetic algorithms, simulated annealing, and constraint programming. While these methods may not always yield optimal solutions, they produce high-quality schedules that satisfy most constraints in a reasonable time frame.

Choosing the appropriate algorithm depends on the specific problem requirements. For example, greedy algorithms offer quick solutions but may sacrifice optimality, while genetic algorithms and tabu search excel in navigating complex search spaces. Constraint programming and integer programming are highly effective when precise mathematical formulations of constraints and objectives are available. Each algorithm has its advantages, depending on the size, complexity, and resources available for the problem at hand [6].

An essential aspect of personnel scheduling is addressing ergonomic needs and the well-being of employees. Ergonomic scheduling involves assigning tasks or shifts in a way that minimizes the risk of musculoskeletal disorders while maintaining operational efficiency. Industries such as manufacturing, construction, and healthcare are particularly vulnerable to ergonomic injuries, caused by repetitive movements, prolonged standing, or heavy lifting. By incorporating ergonomic factors such as physical workload, rest breaks, job rotation, and workstation design, ergonomic scheduling helps mitigate these risks and improve worker well-being.

Researchers have proposed various approaches to integrate ergonomic considerations into PSPs [17,18], including heuristic algorithms, constraint programming, and mathematical programming. These methods aim to generate schedules that reduce injury risks while meeting operational constraints, such as task requirements and employee availability. Integrating ergonomic considerations into personnel scheduling not only enhances employee well-being but also improves productivity and reduces absenteeism, making it an essential focus in workforce management.

3. Literature Review

Effective personnel scheduling plays a crucial role in workforce management, impacting employee well-being, productivity, and organizational sustainability. While traditional scheduling models primarily focus on cost and efficiency, recent research has emphasized the importance of incorporating ergonomic risks and sustainability considerations into scheduling frameworks. Ergonomic personnel scheduling tries to create schedules that minimize the risk of injury or musculoskeletal disorders while ensuring operational efficiency. Several approaches have been proposed to address the ergonomic risks associated with personnel scheduling, including scheduling algorithms, job rotation, optimization models, and hybrid approaches. This section reviews notable studies on ergonomic scheduling algorithms, job rotation, optimization models, and hybrid approaches, while also addressing the emerging role of sustainability in personnel scheduling.

(i)

Scheduling Algorithms

Several studies have explored the development of scheduling algorithms that incorporate ergonomic considerations, such as physical workload, rest breaks, and work-related strain reduction. Alakaş et al. [1] and Otto and Scholl [8] proposed scheduling algorithms that integrate the principles of ergonomics and occupational health and safety. These algorithms consider factors such as physical workload, rest breaks, and task duration, aiming to create schedules that reduce the risk of musculoskeletal disorders. In a similar vein, Asensio-Cuesta et al. [3] presented a genetic algorithm that incorporates ergonomic and occupational health constraints. Their case study demonstrated that the algorithm improved working conditions for employees while meeting production goals and reducing labor costs. However, these studies primarily focus on manufacturing environments, with limited applications in urban transportation personnel scheduling.

(ii)

Job Rotation

Job rotation is another key approach to ergonomic personnel scheduling. This strategy involves rotating employees between different tasks or positions to reduce the risks associated with repetitive motions and prolonged exposure to certain physical demands. Adem and Dağdeviren [18] studied a job rotation model for blue-collar employees. Moussavi et al. [9] and Seçkiner and Kurt [33] analyzed job rotation scheduling in healthcare environments, where staff frequently face physical strain from repetitive movements and extended shifts. Their findings suggest that job rotation effectively distributes physical workload, preventing injuries. While these studies highlight the benefits of job rotation in healthcare and manufacturing, research on its application in urban transportation remains scarce.

This study extends job rotation principles by integrating ergonomic risk assessment (REBA scores) into urban transportation personnel scheduling to create a fair and health-conscious rotation strategy.

(iii)

Optimization Models

Optimization models have also been widely used to incorporate ergonomic factors into personnel scheduling. Dewi and Septiana [4] proposed an optimization model that accounts for physical workload and rest breaks. Their approach uses mathematical programming to generate schedules that minimize ergonomic risks while meeting operational needs. In manufacturing settings, Kaçmaz et al. [37], Bedir et al. [42], and Pınarbaşı [38] demonstrated that optimizing schedules to consider workload and rest breaks can significantly reduce musculoskeletal injury risks. Savino et al. [10] used constraint programming to optimize personnel scheduling while incorporating ergonomic considerations in a semiconductor manufacturing facility. Their approach proved effective in improving both employee well-being and operational efficiency. Similarly, Hochdörffer et al. [11] employed a linear programming-based heuristic model to address ergonomic constraints in task scheduling, showcasing its utility in reducing ergonomic risks in a corporate setting. However, they do not account for sustainability metrics or the specific challenges of urban transportation workforce management.

This study bridges this gap by incorporating sustainability scores into an optimization-based scheduling framework.

(iv)

Hybrid Approaches

Several studies have combined different approaches to form hybrid models for ergonomic personnel scheduling. Mousavi et al. [13] proposed a hybrid method that combines mathematical programming and genetic algorithms to generate schedules that minimize ergonomic risks while accounting for employee preferences and labor costs. Their case study in a manufacturing plant demonstrated that this hybrid approach could produce high-quality schedules that effectively balance ergonomic safety with operational efficiency. Although hybrid models enhance scheduling quality, none fully integrate sustainability considerations alongside ergonomic constraints.

This study advances previous hybrid approaches by developing a goal programming model that optimizes ergonomic risks and sustainability metrics simultaneously.

(v)

Comprehensive Surveys and Frameworks

In addition to specific studies, comprehensive surveys of the field have also been conducted. Adem and Dağdeviren [17] reviewed the existing literature on ergonomic personnel scheduling, categorizing different ergonomic constraints (e.g., physical workload, rest breaks, and job rotation) and the solution techniques used, such as constraint programming and heuristic algorithms. Their survey highlights the various challenges of integrating ergonomic considerations into personnel scheduling and gives ideas for future research. Rinaldi et al. [43] presented an integrated approach combining mathematical programming and heuristic algorithms to minimize ergonomic risk in a manufacturing context. Their work demonstrates that by considering both ergonomic constraints and productivity goals, personnel scheduling can be optimized to improve worker safety while maintaining operational efficiency.

Summary of Findings

In summary, a significant body of research has been devoted to ergonomic personnel scheduling, with various methods proposed to address this issue. These methods include scheduling algorithms, job rotation, optimization models, and hybrid approaches. Studies [1,2,17,18] have consistently shown that integrating ergonomic considerations into personnel scheduling can lead to improved employee well-being and enhanced productivity. The literature suggests that adopting ergonomic personnel scheduling practices is critical for reducing injury risks and promoting a healthier work environment. The studies reviewed in this section highlight various approaches to personnel scheduling, ranging from scheduling algorithms and job rotation strategies to optimization models and hybrid approaches. While many studies focus on minimizing ergonomic risks and improving workforce efficiency, few integrate sustainability factors into personnel scheduling models.

Table 2. Literature summary table.

Study	Approach	Sector	Considered Factors	Contribution
Alakaş et al. [1]	Scheduling Algorithm	Manufacturing	Workload, Rest Breaks	Reduces MSD risks
Otto and Scholl [8]	Scheduling Algorithm	Healthcare	Task Duration, Safety	Ensures compliance with HSE standards
Asensio-Cuesta et al. [3]	Genetic Algorithm	Manufacturing	Employee Well-being, Productivity	Improves working conditions
Moussavi et al. [9]	Job Rotation	Healthcare	Repetitive Task Risk	Prevents injuries
Dewi and Septiana [4]	Optimization Model	Manufacturing	Ergonomic Risk, Workload Balance	Minimizes injury risks
Savino et al. [10]	Constraint Programming	Semiconductor Industry	Employee Safety, Efficiency	Reduces ergonomic strain
Hochdörffer et al. [11]	Linear Programming	Corporate Office	Workload Optimization	Improves scheduling efficiency
Mousavi et al. [13]	Hybrid Model	Manufacturing	Ergonomics, Costs	Balances safety and cost
Our Study	Goal Programming	Urban Transportation	Ergonomics (REBA), Sustainability (SDGs), Fairness	Holistic scheduling approach

provides a comparative summary of previous research, categorizing studies based on their approach, sector, considered factors, and key contributions. The table illustrates that most existing models have been applied in manufacturing and healthcare settings, with limited applications in urban transportation. Additionally, while some studies incorporate ergonomic constraints, they do not simultaneously address sustainability goals.

While many studies have focused on optimizing individual constraints like physical workload or rest breaks, there remains a gap in research that fully integrates both ergonomic and sustainability considerations into personnel scheduling, which this paper aims to address. These studies emphasize reducing environmental impact through optimized labor resource management. Despite these advancements, existing research rarely integrates both ergonomic safety and sustainability metrics into a unified scheduling framework. This study introduces a sustainability scoring system aligned with the United Nations Sustainable Development Goals (SDGs), ensuring that personnel scheduling decisions support corporate sustainability initiatives while maintaining employee well-being. This study contributes to urban transportation workforce management by integrating:

∘

Ergonomic risk assessments using REBA scores to prevent musculoskeletal disorders.

∘

Sustainability-based workforce scheduling aligned with SDGs.

∘

A goal programming approach that optimizes shift allocation while balancing operational efficiency, ergonomic safety, and sustainability goals.

By addressing these gaps, this research provides a holistic scheduling framework for urban transportation personnel management.

4. Problem Statement

Urban transportation workforce scheduling presents unique challenges due to high shift variability, physically demanding tasks, and the need for sustainability integration. Existing scheduling models primarily focus on cost efficiency and shift allocation, often neglecting ergonomic risk factors and sustainability goals.

Personnel scheduling in urban transportation systems faces several critical challenges that hinder optimal workforce management. One key issue is the ergonomic risks employees encounter, such as musculoskeletal disorders (MSDs) from prolonged sitting, repetitive movements, and heavy lifting, which traditional scheduling models fail to address by not prioritizing task allocation based on ergonomic safety. Additionally, sustainability integration remains overlooked, as workforce planning often disregards corporate sustainability goals like reducing environmental impact and enhancing employee well-being. Another concern is fair workload distribution, where employees burdened with high-risk tasks experience fatigue, absenteeism, and job dissatisfaction. Furthermore, the complexity of multi-objective scheduling poses a challenge, as conventional single-objective models focus narrowly on cost, efficiency, or fairness, neglecting holistic workforce optimization. These issues raise an important research question: How can personnel scheduling be optimized to simultaneously reduce ergonomic risks, enhance sustainability contributions, and maintain operational efficiency in urban transportation systems? Addressing this requires a comprehensive approach that balances employee health, environmental goals, and operational demands.

5. Methodology

This study proposes a multi-objective goal programming approach to optimize personnel scheduling by integrating ergonomic risk assessments (REBA scores) and sustainability metrics (SDG alignment).

The Rapid Entire Body Assessment (REBA) is a widely used ergonomic assessment tool designed to evaluate and quantify the risk of musculoskeletal disorders (MSDs) associated with specific job tasks [39]. It assesses various factors, including posture, force exertion, repetitive movements, duration, and other relevant ergonomic parameters. REBA provides a systematic approach to identify and mitigate ergonomic risks by assigning scores to different aspects of a task and combining them to obtain an overall risk score.

The integration of REBA with personnel scheduling allows organizations to proactively address ergonomic concerns, enhance employee health and well-being, reduce the risk of injuries, and improve overall productivity [1]. By assigning tasks in a way that balances ergonomic requirements and operational efficiency, organizations can create a healthier work environment, reduce absenteeism, and enhance job satisfaction among employees.

Goal programming is a versatile optimization technique used to address decision-making problems with multiple, often conflicting objectives. It extends linear programming by allowing decision-makers to set target goals for each objective and minimize deviations from these goals, assigning weights or priorities to reflect their relative importance. In personnel scheduling problems, goal programming is particularly valuable for balancing competing needs, such as minimizing labor costs, ensuring adequate staffing levels, and accommodating employee preferences [20].

5.1. Data Collection

This study was conducted in Company X, an urban transportation provider operating a 24/7 shift-based workforce system. The dataset includes 140 employees working in various departments such as operations, maintenance, public relations, cleaning services, and administration. Data sources included shift scheduling records encompassing work hours and job types, REBA scores for ergonomic risk assessment across various job roles, and sustainability scores reflecting contributions to initiatives aligned with the Sustainable Development Goals (SDGs). This comprehensive dataset provided the basis for analyzing and optimizing the company’s scheduling practices.

Table 3. Participant demographics.

Demographic Variable	Category	Proportion (%)
Total Sample Size	140 employees	100%
Age Distribution	18–30 years	35%
	31–45 years	45%
	46+ years	20%
Years of Work Experience	0–5 years	30%
	6–15 years	50%
	16+ years	20%
Job Roles	Operators (Drivers)	50%
	Maintenance Staff	20%
	Administrative Staff	15%
	Cleaning and Support Services	10%
	Public Relations	5%

presents the demographic characteristics of the surveyed employees, providing an overview of their age distribution, years of work experience, and job roles within the organization. The sample consists of 140 employees, categorized into different experience levels and job functions to reflect the workforce composition relevant to this study.

The methodological framework follows a structured approach based on prior workforce scheduling models [21]. The ergonomic risk assessments utilize the Rapid Entire Body Assessment (REBA) method [18,37], widely used for evaluating musculoskeletal risk exposure in high-risk work environments. The sustainability integration methodology follows previous studies that align workforce scheduling with United Nations Sustainable Development Goals (SDGs) [24].

A Flowchart for the Proposed Method is provided in Figure 1.

The sustainable personnel scheduling problem with ergonomic constraints involves scheduling employees to meet operational demands while promoting employee health and safety, ensuring work-life balance, and considering ergonomic constraints. The objective is to minimize the total risk of the schedule, subject to constraints on employee availability, task requirements, work hours, sustainability factors, and ergonomic constraints. The ergonomic constraints can include limits on the number of hours worked, rest breaks, and ergonomically safe working conditions.

5.2. Application Steps of REBA Method

Step 1: Observation of the task

While the task is being carried out: employee’s body posture, use of equipment, compatibility between the equipment used and the employee, the environment where the work is carried out, general environment, etc. Observation and, if possible, recording of tasks with photographs or video are carried out.

Step 2: Selecting the posture to be evaluated

After the tasks are evaluated, which stance will be evaluated for the task is chosen. When making a choice, the posture that has the highest number of working muscles or requires strength, has the longest repetition and duration, and has the most impact on the musculoskeletal system can be chosen. Selection can be made by considering the circumstances that include one or more of the mentioned situations.

Step 3: Scoring the postures

The movement patterns for the trunk, neck, legs, wrists, trunk movements, leg movements, and scores are calculated in this phase. If there is lateral bending and rotation in addition to the movement for the body and neck, +1 is added to the score of the movement. If there is flexion in the legs between 30–60°, +1 is added, if there is flexion greater than 60°, +2 is added.

Step 4: Processing points

In order to obtain calculated scores, REBA score of the posture is gathered by adding the Activity score.

Step 5: Determining Precautions

The risk level that REBA scores correspond to, the degree of risk and what should be done to prevent this risk are given in

Table 4. REBA score table.

Level	REBA Score	Risk	Necessity of Precaution
0	1	Negligible	Not necessary
1	2–3	Low	Maybe necessary
2	4–7	Middle	Necessary
3	8–10	High	Required in short time
4	11–15	Very high	Needed immediately

. According to this table, the risk level of a stance with a REBA score of 1 is 0 and no precautions are required. However, the risk level of a stance with a REBA score of 11–15 is 4 and precautions must be taken immediately.

5.3. Solution Approach

The paper proposes a solution approach based on goal programming to solve the sustainable personnel scheduling problem with ergonomic constraints. The paper describes how the model can be implemented using commercial optimization software and provide a detailed algorithm for solving the problem. The proposed solution approach can handle large-scale scheduling problems and can provide optimal or near-optimal solutions. The objective function of the proposed model is determined as minimizing the deviation values that will occur in these scores. By evaluating the three scores together in the created model, it provides ergonomic balance in the body posture of the personnel, allowing the load on all organs to be distributed more evenly.

The proposed method introduces a holistic personnel scheduling framework that simultaneously optimizes ergonomic safety, sustainability contributions, and operational efficiency. Unlike traditional scheduling approaches, which prioritize cost and shift allocation, this method integrates ergonomic risk assessments and sustainability factors into the decision-making process.

Key features are explained briefly after that point: The proposed method introduces several key innovations aimed at improving personnel scheduling in urban transportation by incorporating ergonomic safety, sustainability, and fairness into the scheduling process.

A crucial aspect of the model is the integration of ergonomics into scheduling. This study utilizes the Rapid Entire Body Assessment (REBA) method, a widely recognized ergonomic risk assessment tool. REBA scores are assigned to each job type based on factors such as physical workload, posture, force exertion, and task complexity. The scheduling algorithm is designed to minimize the assignment of high-risk tasks to employees, thereby reducing the likelihood of musculoskeletal disorders (MSDs). Additionally, job rotation strategies are embedded within the scheduling model to ensure a fair distribution of ergonomic risks among employees, preventing any worker from being consistently exposed to high-risk tasks.

Another key feature is sustainability-based workforce optimization. Unlike conventional scheduling models, this method incorporates sustainability considerations by introducing a sustainability scoring system aligned with the United Nations Sustainable Development Goals (SDGs). Employees are assigned a Sustainability Score (SS) based on their contributions to sustainability initiatives, such as energy conservation, waste reduction, and eco-friendly practices. The scheduling model strategically pairs employees with varying sustainability scores in teams to facilitate knowledge transfer and promote a culture of sustainability within the workforce. By integrating these considerations, the company can effectively reduce its carbon footprint while enhancing workforce management practices.

The model is structured as a multi-objective goal programming model, allowing for the simultaneous optimization of multiple conflicting objectives. The objective function is designed to minimize deviations from three key constraints: (i) ergonomic constraints by assigning employees to tasks with lower REBA scores, (ii) sustainability objectives by ensuring balanced sustainability contributions within teams, and (iii) operational constraints by maintaining shift coverage and employee availability. Unlike traditional single-objective models that focus solely on cost or productivity, this approach balances multiple priorities to create a fair, safe, and sustainable work schedule.

Adaptability for urban transportation scheduling is another significant advantage of the proposed method. Urban transportation presents unique challenges, including high shift variability (morning, evening, and night shifts), physically demanding tasks (e.g., vehicle operation and maintenance), and regulatory constraints such as work-hour limitations. The proposed method ensures optimal shift coverage while distributing workload equitably, preventing employees from being consistently placed in physically demanding roles. Moreover, fair rotation policies are implemented to prevent fatigue and excessive exposure to high-risk tasks, contributing to long-term workforce sustainability.

To enhance fairness and efficiency, the model also ensures fair and balanced workload distribution. High-risk tasks are evenly distributed among employees, ensuring that no individual is overburdened while others receive significantly lighter tasks. Furthermore, the model allows for equitable participation in sustainability initiatives, fostering a sense of shared responsibility. This balanced approach enhances employee well-being and job satisfaction and reduces absenteeism, ultimately leading to a more engaged and productive workforce. The proposed method and the traditional scheduling methods are compared, and the differences between them are given in

Table 5. Comparison table.

Feature	Traditional Scheduling	Proposed Method
Focus	Cost minimization and shift allocation	Ergonomic safety, sustainability, fairness
Ergonomic Considerations	Not included	REBA-based risk assessment
Sustainability Factors	Not considered	Sustainability scoring (aligned with SDGs)
Optimization Approach	Single objective	Multi-objective goal programming
Workload Distribution	May lead to unfair assignments	Balanced rotation of tasks and shifts
Suitability for Urban Transportation	Limited	Specifically designed for urban transportation workforce

below.

Benchmark Comparison with Existing Scheduling Methods

To validate the effectiveness of the proposed multi-objective personnel scheduling model, we compare its performance against two traditional scheduling approaches:

(i)

Rule-Based Scheduling (Standard shift assignment without optimization).

(ii)

Single-Objective Optimization (Scheduling models that optimize either cost efficiency or ergonomic risk but not both).

Evaluation Metrics:

∘

Reduction in high-risk task assignments (Ergonomic Risk—REBA Scores).

∘

Improvement in sustainability balance (Sustainability Score Standard Deviation).

∘

Workload fairness (Deviation in work hours assigned per employee).

Benchmarking results are given in

Table 6. Benchmark Results.

Metric	Rule-Based Scheduling	Single-Objective Scheduling	Proposed Model	Improvement Over Rule-Based (%)
High-Risk Task Assignments	32.5%	24.3%	18.2%	↓ 44.0%
Average REBA Score per Employee	6.7	5.4	4.3	↓ 35.8%
Sustainability Score Balance (Standard Deviation)	1.95	1.42	0.89	↓ 54.4%
Workload Imbalance (Hours Variance)	5.4 h	3.2 h	2.1 h	↓ 61.1%
Employee Satisfaction (Survey Score: 1–10)	6.1	7.2	8.4	↑ 37.7%

.

The benchmark comparison reveals significant improvements when utilizing the proposed multi-objective model compared to traditional rule-based scheduling. Notably, the model achieves a 44% reduction in high-risk task assignments, demonstrating a substantial decrease in ergonomic risks. Furthermore, sustainability balance improves by 54.4%, indicating a more evenly distributed commitment to Sustainable Development Goals (SDGs) across the workforce. Workload balance sees a 61.1% enhancement, effectively minimizing overburdening and fatigue among employees. Consequently, employee satisfaction rises by 37.7%, reflecting the positive impact of fair scheduling and ergonomic safety improvements.

This benchmark analysis underscores the efficacy of the proposed multi-objective model in outperforming traditional scheduling methods by simultaneously balancing ergonomic risk, sustainability objectives, and workload fairness. Unlike prior methods that typically focus solely on cost or ergonomic safety, this model integrates both sustainability and ergonomic constraints within a real-world urban transportation context. This holistic approach not only enhances operational efficiency but also significantly improves employee well-being and organizational sustainability.

6. Case Study

Company X is a leading urban transportation service provider committed to delivering efficient, eco-friendly, and employee-centric transit solutions. With a fleet and a focus on sustainability, the company seeks to revolutionize urban transportation while prioritizing the well-being of its dedicated personnel. The company operates 24/7 and has a high demand for transportation services. Therefore, it requires its employees to work in 3 different shifts per day. The first shift starts at 6:00 AM and ends at 2:00 PM, the second shift starts at 2:00 PM and ends at 10:00 PM, and the third shift starts at 10:00 PM and ends at 6:00 AM. The company has a total of 140 employees who work in various departments such as operating, maintenance, public relations, cleaning service, and administration. Due to the high workload, the company needs to ensure that its employees are working efficiently and effectively to maintain a high level of service level.

The company is known for its commitment to sustainability and now aims to further improve the well-being of its transportation personnel through an innovative Ergonomic and Sustainable Urban Transportation (ESUT) Model. There are some challenges in this case like: Ergonomic Strain (Transportation staff often face physical strain due to extended working hours and repetitive tasks), Operational Inefficiencies (Inefficient routes and schedules lead to increased operational costs and idle time for vehicles), and Environmental Impact (The company aims to reduce its carbon footprint by optimizing operations and promoting sustainable practices).

The company is also concerned about the health and safety of its employees. Therefore, it has implemented ergonomic and sustainable policies to ensure that its employees are working in a safe and comfortable environment. This includes providing ergonomic workstations, training on proper lifting techniques, and regular breaks to prevent repetitive strain injuries. The company needs to create a work schedule for its employees that ensures that all shifts are covered, and that each employee has an same opportunity to work in each shift. The company also needs to ensure that the workload is evenly distributed among the employees and that the ergonomic and sustainable policies are followed.

To create the work schedule, the company does not use any scheduling software that takes into account the number of employees, their skills and qualifications, and the required workload for each shift. The manual scheduling study also does not consider the factor in the ergonomic and sustainable policies to ensure that the work schedule is safe and comfortable for the employees.

Overall, the goal of the company is to maintain a high level of productivity while ensuring the health and safety of its employees. By implementing effective scheduling strategies and following ergonomic and sustainable policies, the company can achieve its goals and create a positive work environment for its employees.

Since ergonomic constraints were used in this study, the number of personnel required to perform a task was taken into account, taking into account the processes of the company. Ergonomic conditions are evaluated according to factors such as musculoskeletal disorders, difficulties of tasks, environmental conditions, and factors that cause psychological strain. Considering these conditions in the scheduling of the personnel, the personnel are assigned to the tasks and shifts. Ergonomic evaluation of each task was made and scores resulting from the REBA method were determined.

The number of personnel required for each shift is as follows: 28 personnel for Shift 1, 18 personnel for Shift 2, and 10 personnel for Shift 3. Twelve different job types are given in the study. All these types of jobs have different difficulty levels. The study was carried out in four stages. In the first step, the current problem was determined. At this stage, it was determined that the working principles of the unit responsible for transportation in a company should be rearranged considering the ergonomic risk conditions. Later, data collection on this problem started. All data are taken from the operation and human resources departments. This paper illustrates the company’s commitment to creating a workplace where personnel thrive, and operations align with sustainability goals. The innovative ESUT Model aims to set a new standard in the industry.

6.1. Ergonomic Conditions of the Company

All personnel can be assigned to these 12 different job types (J1 to J12). It is aimed to make this job change rotational and fair because the ergonomic difficulty levels of these different jobs are different. All these jobs include auxiliary services in urban transportation and all personnel mentioned are capable of performing these jobs.

The Rapid Entire Body Assessment (REBA) is a method used to evaluate the ergonomic risks associated with various tasks performed by personnel. It involves assessing different body regions and assigning scores based on posture, force, and other ergonomic factors.

The ergonomic risk levels of different job types have been assessed using the REBA method, which has been widely used in prior studies to evaluate musculoskeletal risks in industrial and service sectors [37,38]. The REBA scores for each job type have been adapted from established ergonomic research [39,40] along with modifications based on real-world urban transportation tasks.

At this point, the risk status of each job has been determined. According to the risk situation determined in Table 4, a REBA score was calculated. Personnel scheduling will be carried out with this perspective. The details of this ergonomic risk score are given in

Table 7. REBA scores for each job.

Job ID	Required Personnel	Posture Score	Force Score	Other Factors	Body Region Scores	REBA Score
J1	2	2	3	1	Upper: 2, Lower: 1	4
J2	1	3	2	2	Upper: 3, Lower: 2	5
J3	1	1	1	1	Upper: 1, Lower: 1	3
J4	1	2	4	2	Upper: 2, Lower: 2	6
J5	1	3	2	1	Upper: 2, Lower: 1	5
J6	1	2	3	2	Upper: 3, Lower: 2	6
J7	1	1	1	1	Upper: 1, Lower: 1	3
J8	2	3	4	2	Upper: 3, Lower: 2	7
J9	2	2	2	1	Upper: 2, Lower: 1	4
J10	2	3	3	2	Upper: 3, Lower: 2	6
J11	2	2	4	1	Upper: 3, Lower: 1	6
J12	2	3	3	2	Upper: 2, Lower: 1	6

.

6.2. Sustainability Conditions of the Company

The company for which the scheduling study was carried out has already adopted the realization of 6 goals that are considered important for the business among the 17 goals within the scope of the Sustainable Development Goals (SDG) that are declared by United Nations [44] and has taken measures to achieve these goals and raised the awareness of its employees. These six goals that are important for the business are:

∘

Good health and well-being;

∘

Industry, Innovation, and Infrastructure;

∘

Sustainable Cities and Communities;

∘

Responsible Consumption and Production;

∘

Decent Work and Economic Growth;

∘

Partnerships for the Goals;

Sustainability scores are assigned to employees based on their engagement in sustainability-related activities within the organization. The scoring system (1 to 3) is designed to ensure fairness, objectivity, and alignment with corporate sustainability goals (SDGs) (1 being the lowest and 3 being the highest).

Each employee’s sustainability score (S_i) is determined based on the following weighted criteria (

Table 8. Sustainability score calculation.

Criterion	Description	Weight (%)
Participation in company-led sustainability programs	Engagement in corporate environmental and social responsibility (CSR) initiatives (e.g., waste reduction, energy conservation).	30%
Adherence to sustainable workplace practices	Consistent compliance with sustainability-related workplace policies (e.g., paperless work, eco-friendly commuting).	25%
Training and certifications in sustainability	Completion of internal or external training on sustainability topics.	20%
Team-based sustainability contributions	Active involvement in workplace teams focused on green initiatives.	15%
Voluntary environmental efforts	Independent actions outside the workplace (e.g., personal energy conservation, community sustainability efforts).	10%

):

The final score is calculated as:

S_i = 0.3P + 0.25A + 0.2T + 0.15C + 0.1V

To comprehensively assess employee contributions to sustainability, a multi-faceted evaluation framework was employed. This framework considered five key dimensions: P, representing active participation in sustainability programs; A, reflecting adherence to workplace sustainability practices and protocols; T, indicating the attainment of relevant training and certifications; C, measuring team-based contributions towards sustainability goals; and V, acknowledging voluntary efforts beyond regular job responsibilities. By evaluating employees across these dimensions, a holistic understanding of their engagement and impact on sustainability initiatives was achieved. The total score is then mapped to the 1–3 scale.

The final weighted sustainability score, derived from a comprehensive evaluation of employee contributions, was categorized into three tiers to reflect varying levels of engagement. Scores above 80% were designated as “High”, indicating substantial contributions and were assigned a sustainability score of 3. Scores ranging from 50% to 79% were classified as “Medium”, signifying moderate engagement, and received a score of 2. Scores below 50% were labeled “Low”, representing limited contributions, and were assigned a score of 1. This tiered system provided a clear and concise method for assessing and comparing employee sustainability performance (

Table 9. Explanation of the sustainability score.

Point	Assigned Sustainability Score
3 (High)	The personnel who have a score above 80%
2 (Medium)	The personnel who have a score between 50%–79%
1 (Low)	The personnel who have a score below 50%

).

The scores of each staff member are given in

Table 10. Sustainability scores (SS) of each personnel.

P.N.	S.S.	P.N.	S.S.	P.N.	S.S.	P.N.	S.S.	P.N.	S.S.	P.N.	S.S.	P.N.	S.S.
1	1	21	3	41	2	61	1	81	2	101	1	121	1
2	2	22	1	42	1	62	3	82	1	102	1	122	1
3	3	23	1	43	1	63	3	83	3	103	2	123	3
4	1	24	1	44	3	64	2	84	1	104	3	124	1
5	1	25	3	45	1	65	1	85	3	105	3	125	1
6	1	26	1	46	3	66	1	86	2	106	1	126	2
7	1	27	1	47	1	67	1	87	3	107	3	127	1
8	2	28	3	48	3	68	1	88	1	108	1	128	3
9	3	29	2	49	3	69	2	89	1	109	2	129	3
10	3	30	1	50	2	70	3	90	1	110	3	130	2
11	1	31	3	51	1	71	1	91	3	111	3	131	3
12	1	32	1	52	3	72	1	92	2	112	2	132	1
13	2	33	3	53	3	73	2	93	3	113	1	133	1
14	3	34	3	54	1	74	3	94	1	114	1	134	1
15	3	35	3	55	1	75	1	95	1	115	1	135	3
16	2	36	2	56	2	76	1	96	1	116	3	136	1
17	1	37	1	57	1	77	3	97	3	117	3	137	3
18	3	38	1	58	3	78	3	98	3	118	2	138	1
19	3	39	3	59	3	79	3	99	1	119	2	139	3
20	1	40	3	60	1	80	1	100	3	120	1	140	2

P.N. = Personnel Number, S.S. = Sustainability Score.

. These scores are included in the mathematical model.

To ensure the validity and fairness of sustainability scores, a rigorous evaluation process is implemented. Employee sustainability data are collected quarterly through a combination of HR surveys, team assessments, and participation records, providing a comprehensive view of employee engagement. HR teams meticulously verify participation through event logs, certifications, and automated tracking systems, ensuring data accuracy. To eliminate bias, all employees are guaranteed equal access to sustainability initiatives, and anonymous scoring review panels are utilized to evaluate sustainability engagement. Furthermore, a grievance mechanism is in place, allowing employees to appeal their scores if they perceive unfair evaluation. This multi-layered approach to data collection and evaluation promotes transparency and equity, fostering trust in the scoring system.

The integration of sustainability scores into personnel scheduling is justified by its potential to drive positive organizational change. By incorporating sustainability scores into the scheduling model, the company actively encourages workplace engagement in sustainability initiatives, promoting a culture of environmental responsibility. This integration also facilitates knowledge-sharing, as employees with high sustainability awareness are strategically positioned to mentor and guide their colleagues. Moreover, aligning workforce planning with corporate sustainability objectives, such as the SDGs, ensures that the company’s operational practices reflect its broader environmental commitments. The validation framework, which emphasizes data-driven, structured, and fair evaluation, guarantees that sustainability scores are not arbitrary, but rather a reliable measure of employee contributions. This integration ultimately contributes to a more sustainable and socially responsible workforce.

In the second stage, personnel scheduling studies in the literature were examined first. During the review phase, examples of large-scale scheduling problems in the literature were identified and analyzed. As a result of the literature review, a problem solution of this size has not been encountered in scheduling studies in the production and service sector. Especially in most shift scheduling studies, application results are not given. At this point, our study reveals its difference from other studies. When the solution methods are examined, it is inferred that the goal programming technique is a suitable method for the solution of this problem. Considering all these, it is thought that personnel scheduling studies are a new area waiting to be explored.

In the third stage, the analysis of the result obtained after the development of the mathematical model and the solution of the developed mathematical model is made. Goal programming method is used as a solution technique, a solution was made with the Gurobi Optimization tool [45], and the results were examined. In the fourth step, the model was verified. As a result, a monthly schedule for personnel was obtained.

One of the most important features that distinguishes this study from other studies is that this scoring is considered and taken into account. Thus, efforts are intended to be made to assign the less-contributing personnel to a shift with the more-contributing personnel. Through this approach, the goal is to enhance the institutional trust in and contribution for enhancing sustainable development goals.

In total, the main purpose of this study is to create a schedule that will minimize the risk value in assigning personnel to duties and considering the sustainability factors. In doing so, two different indicators were taken into account. A value was obtained by averaging the REBA score and the sustainability score. We used some assumptions for the mathematical model. The assumptions of the problem are as follows:

• All personnel have an equal workforce.
• Personnel will not take annual leave within a 30-day period.
• There is no absence in any of the personnel at the beginning of the study.
• The execution times of the tasks are predetermined.
• The number of personnel required to perform each task is determined by taking into account the processes within the job.
• Ergonomic evaluation of each task and the scores were calculated was made with the REBA method.

6.3. Labor Law and Regulatory Considerations in Personnel Scheduling

Implementing an ergonomically optimized and sustainability-driven personnel scheduling model must comply with national labor laws, union agreements, and workplace safety regulations. This section addresses key legal concerns and how the proposed model ensures compliance [46,47].

Adherence to legal constraints is paramount in workforce scheduling, particularly in sectors like urban transportation where labor regulations are stringent. Firstly, the proposed model strictly enforces shift limits and maximum working hours, ensuring compliance with national laws and directives such as the European Union (EU) Working Time Directive, which limits work to 48 h per week. The model guarantees that no employee exceeds 40–48 h per week, provides adequate rest periods of at least 11 h between shifts (following EU standards), and evenly distributes overtime to prevent undue burden on specific individuals. Secondly, workplace safety and ergonomic compliance are integrated through the model’s prevention of high-risk assignments based on REBA scores, aligning with ergonomic safety guidelines set by regulatory bodies like OSHA (Occupational Safety and Health Administration, USA) and EU-OSHA (European Agency for Safety and Health at Work) [46,47]. This includes provisions for periodic ergonomic risk assessments and training, as mandated by labor protection laws. Thirdly, fairness and non-discriminatory scheduling are addressed by ensuring equal distribution of shifts, including night and weekend duties, to avoid discriminatory practices prohibited by Equal Employment Opportunity (EEO) laws [46,47]. Employees are also granted the ability to request schedule adjustments, with the system automatically balancing shift preferences when feasible. Lastly, the model accommodates union agreements and collective bargaining rules, which are prevalent in sectors like urban transportation. It can be customized to integrate union-mandated rest breaks, work-hour caps for senior employees, and shift preferences for employees with medical conditions or family needs, ensuring that schedules align with negotiated work conditions.

The scheduling optimization model prioritizes ensuring legal compliance through a series of embedded mechanisms. To adhere to maximum work hours per week, the model incorporates hard constraints that prevent excessive scheduling. Minimum rest periods are enforced by preventing the assignment of shifts with insufficient break times between work periods. Fair shift distribution is achieved through automated shift rotation, eliminating bias and preventing the overburdening of specific employees. Overtime regulations are addressed by evenly distributing overtime assignments, thus avoiding overwork violations. Furthermore, ergonomic risk prevention is integrated by utilizing REBA scores to ensure that no employee is repeatedly assigned high-risk tasks.

The model’s legal adaptability is a crucial feature, allowing it to be tailored to different jurisdictions by modifying constraints based on country-specific labor laws. Future iterations of the model will include real-time compliance checks, providing HR managers with immediate alerts if a generated schedule violates legal work-hour regulations. This proactive approach ensures continuous adherence to legal standards and mitigates the risk of non-compliance.

6.4. Mathematical Model

This paper presents a multi-objective mixed-integer linear programming model for the sustainable personnel scheduling problem with ergonomic constraints. The model is formulated to balance ergonomic safety, sustainability objectives, and operational efficiency while satisfying constraints on employee availability, task requirements, work hours, and workload distribution fairness. The decision variables include employee-task assignments, ergonomic risk levels (REBA scores), and sustainability scores, ensuring an equitable and efficient scheduling framework. Instead of solely minimizing costs, the objective function simultaneously optimizes ergonomic safety, sustainability alignment, and fair shift allocation, providing a holistic approach to workforce scheduling:

The Mathematical Model of the Problem

The sustainable personnel scheduling problem with ergonomic constraints is formulated as a goal programming model that simultaneously optimizes:

∘

Ergonomic risk minimization (reducing REBA scores in shift assignments).

∘

Sustainability-based workforce balancing (ensuring SDG-aligned employee contributions). For that reason, the model of the study is called sustainable in this paper [24].

∘

Operational efficiency (maintaining shift coverage and workload balance).

i: Personnel index, i = 1, 2…, e,

(1)

j: Day index, j = 1, 2…, m,

(2)

k: Shift index, k = 1, 2…, n,

(3)

l: Goal index, l = 1, 2…, z,

(4)

c: Job index, v = 1, 2…, y,

(5)

e: Personnel number, e = 140,

(6)

m: Day number, m = 30

(7)

n: Shift number, n = 3

(8)

z: Goal number, z = 3,

(9)

v: Job number, v = 12,

(10)

t_i: Ergonomic risk level of each personnel, i = 1, 2…, e,

(11)

s_i: Sustainability score of each personnel, i = 1, 2…, e,

(12)

$$d_{ljk}^{+} \mathrm{p}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{d}\mathrm{e}\mathrm{v}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{v}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{g}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{l} \mathrm{o}\mathrm{n} \mathrm{d}\mathrm{a}\mathrm{y} \mathrm{j} \mathrm{i}\mathrm{n} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{s}\mathrm{h}\mathrm{i}\mathrm{f}\mathrm{t} \mathrm{k},\mathrm{l}=1,2\dots ,\mathrm{z}; \mathrm{j}=1,2\dots ,\mathrm{m}; \mathrm{k}=1,2\dots ,\mathrm{n},$$

(13)

$$d_{ljk }^{-}:\mathrm{n}\mathrm{e}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{d}\mathrm{e}\mathrm{v}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{v}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{g}\mathrm{o}\mathrm{a}\mathrm{l} \mathrm{l} \mathrm{o}\mathrm{n} \mathrm{d}\mathrm{a}\mathrm{y} \mathrm{j} \mathrm{i}\mathrm{n} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{s}\mathrm{h}\mathrm{i}\mathrm{f}\mathrm{t} \mathrm{k},\mathrm{l}=1,2\dots ,\mathrm{z}; \mathrm{j}=1,2\dots ,\mathrm{m}; \mathrm{k}=1,2\dots ,\mathrm{n},$$

(14)

$${\mathit{X}}_{\mathit{i}\mathit{j}\mathit{k}\mathit{c}}=\left\{\begin{array}{cc}1,\hfill & if personnel i is assigned to day j of shift k to the job c \hfill \\ 0,\hfill & otherwise\hfill \end{array}\right. \mathrm{i}=1,2\dots ,\mathrm{e}; \mathrm{j}=1,2\dots ,\mathrm{m}; \mathrm{k}=1,2\dots ,\mathrm{n}; \mathrm{c}=1,2\dots ,12$$

(15)

$${\mathit{h}}_{\mathit{i}\mathit{j}}=\left\{\begin{array}{cc}1,\hfill & if personnel i is on leave on day j\hfill \\ 0,\hfill & otherwise\hfill \end{array}\right. \mathrm{i}=1,2,\dots ,\mathrm{e}; \mathrm{j}=1,2,\dots ,\mathrm{m}; \mathrm{k}=1,2,\dots ,\mathrm{n},$$

(16)

$${\sum }_{j=1}^{30}{X}_{ij1c}\ge 28, \mathrm{i}=1,2,3\dots ,\mathrm{e}; \mathrm{c}=1,2\dots ,12,$$

(17)

$${\sum }_{j=1}^{30}{X}_{ij2c}\ge 18, \mathrm{i}=1,2,3\dots ,\mathrm{e}; \mathrm{c}=1,2\dots ,12,$$

(18)

$${\sum }_{j=1}^{30}{X}_{ij3c}\ge 10, \mathrm{i}=1,2,3\dots ,\mathrm{e}; \mathrm{c}=1,2\dots ,12,$$

(19)

$$X_{ij3c}+X_{i(j+1)1c}+X_{i(j+1)2c} \le 1, \mathrm{i}=1,2,3\dots ,\mathrm{e}; \mathrm{j}=1,2\dots ,29; \mathrm{c}=1,2\dots ,12,$$

(20)

$$X_{ij2c}+X_{i(j+1)1c} \le 1,\mathrm{i}=1,2,3\dots ,\mathrm{e}; \mathrm{j}=1,2\dots ,29; \mathrm{c}=1,2\dots ,12,$$

(21)

$${\sum }_{i=1}^{24}{h}_{ij}+h_{i(j+1)}+h_{i(j+2)}+h_{i(j+3)}+h_{i(j+4)}+h_{i(j+5)}+h_{i(j+6)}>=1, \mathrm{i}=1,2\dots ,\mathrm{e},$$

(22)

$${\sum }_{k=1}^3{X}_{ijkc}+h_{ij}=1, \mathrm{i}=1,2,3\dots ,\mathrm{e};\mathrm{j}=1,2\dots ,\mathrm{m}; \mathrm{c}=1,2\dots ,12,$$

(23)

$${\sum }_{k=1}^n{X}_{ijkc}\le 1, \mathrm{i}=1,2,3\dots ,\mathrm{e}; \mathrm{j}=1,2\dots ,\mathrm{m}; \mathrm{c}=1,2\dots ,12,$$

(24)

$${\sum }_{i=1}^{140}{({ t}_i\ast X}_{ijkc})-{d1}_{jk}^{+}+{d1}_{jk}^{-}\le {\sum }_{i=1}^{140}{ (3\ast X}_{ijkc}), \mathrm{j}=1,2,3\dots ,\mathrm{m};\mathrm{k}=1,2\dots ,\mathrm{n}; \mathrm{c}=1,2\dots ,12,$$

(25)

$${\sum }_{i=1}^{140}{{ (s}_i\ast X}_{ijkc})-{d2}_{jk}^{+}+{d2}_{jk}^{-}\ge 23, \mathrm{j}=1,2,3\dots ,\mathrm{m};\mathrm{k}=1,2\dots ,\mathrm{n}; \mathrm{c}=1,2\dots ,12,$$

(26)

$${\sum }_{i=1}^{140}{\sum }_{j=1}^{30}{\sum }_{k=1}^3{{\sum }_{c=1}^{12}X}_{ijkc}-{d3}_{ij}^{+}+{d3}_{ij}^{-}=23, \mathrm{i}=1,2,3\dots ,\mathrm{e}; \mathrm{j}=1,2\dots ,\mathrm{m}; \mathrm{k}=1,2\dots ,\mathrm{n}; \mathrm{c}=1,2\dots ,12,$$

(27)

$$MinZ={\sum }_{j=1}^m{\sum }_{k=1}^n{ (d3}_{jk}^{-}-{d3}_{jk}^{+})+\left({d1}_{jk}^{-}-{d1}_{jk}^{+}\right)+{\sum }_{i=1}^e{\sum }_{j=1}^m\left({d2}_{ij}^{-}-{d2}_{ij}^{+}\right)$$

(28)

Equations (1)–(14) give information about parameter. Equations (15) and (16) are our decision variables. Equations (17)–(19) are about number of personnel needed for each shift and day which must be met. Equation (20) is about personnel working night shifts on any given day and area because it must be met, who should not work the morning and evening shifts of the next day. Equation (21) is for personnel working the evening shift on any day and in the field, who should not work the morning shift of the next day. Equation (22) defines that personnel should not work more than six consecutive days. Equation (23) is about each personnel who should not work on a day off. Equation (24) defines that only one shift per day should be assigned to each personnel. Equations (25)–(27) are goal constraints. Our objective function is given in Equation (28).

Once this model is formulated, it was solved using an optimization solver Gurobi (Intel Corporation, Santa Clara, CA, USA) to find an optimal schedule that meets all constraints and minimizes the ergonomic risk of the schedule.

7. Result and Discussion

In the solution of the model, a computer with “Intel (R) Core (TM) i7-6700 CPU@2.60 GH” processor, 16 GB of memory and Windows 11 operating system features were used. The proposed model was solved in Gurobi.

7.1. Computational Complexity, Solver Efficiency, and Scalability

The proposed goal programming model is formulated as a Mixed-Integer Linear Programming (MILP) problem, which is known to be NP-hard (non-deterministic polynomial-time hard). This means that as the number of employees, tasks, and constraints increase, the computational complexity grows exponentially. Thus, for large workforce sizes, solving the MILP model exactly can become computationally expensive, requiring an efficient optimization solver.

The proposed model was implemented and solved using Gurobi Optimization Solver 12.0, a state-of-the-art tool for handling large-scale MILP problems. The computational results are as follows (

Table 11. Performance of Gurobi.

Workforce Size (n)	Decision Variables	Constraints	Solver Time (s)	Optimality Gap
50 employees	~7200	~2500	4.2 s	0.00% (Exact Solution)
100 employees	~14,400	~5000	12.6 s	0.00% (Exact Solution)
140 employees (Our Study)	~20,160	~7000	22.4 s	0.00% (Exact Solution)
250 employees	~36,000	~12,500	65.3 s	0.42% (Near Optimal)
500 employees	~72,000	~25,000	180.7 s	1.05% (Near Optimal)

):

The analysis of solver performance in the personnel scheduling model reveals several key insights that highlight both its strengths and limitations across different problem sizes. For small to medium-sized problems, involving up to 140 employees, the Gurobi solver demonstrates impressive efficiency, consistently finding an optimal solution within 30 s, making it well-suited for organizations of this scale. However, as the workforce size grows beyond 250 employees, the solver’s performance begins to show constraints, requiring longer computational times to reach near-optimal solutions with a gap of less than 1.1%, indicating that while the solutions remain highly effective, the increased complexity introduces additional processing demands. The most significant challenge emerges with very large-scale problems, such as those involving 500 employees, where computational time spikes dramatically to over 180 s. This substantial increase in processing time underscores scalability challenges, suggesting that for large-scale implementations, the current approach may benefit from the integration of heuristics or decomposition methods to improve efficiency and manage the computational burden effectively, ensuring the model remains practical and viable for real-world applications across diverse organizational sizes.

Scalability considerations and future improvements for the personnel scheduling model are critical to addressing its limitations and enhancing its applicability, particularly for large-scale and dynamic environments. For workforce sizes exceeding 500 employees, decomposition methods such as Benders Decomposition or Column Generation offer a promising solution by breaking down the complex scheduling problem into smaller, more manageable subproblems. This approach can significantly improve solver performance, reducing computational time and enabling the model to handle larger-scale scheduling challenges effectively. Additionally, in real-world scenarios where dynamic changes—such as last-minute shift cancellations or unexpected operational demands—frequently occur, the current model’s static nature poses a limitation. Future work could explore heuristic approaches, including metaheuristic algorithms like Genetic Algorithms or Tabu Search, to enable faster, real-time scheduling decisions that adapt quickly to such changes, ensuring operational continuity and efficiency. Furthermore, to enhance scalability and address the increased computational demands of large-scale scheduling problems, future implementations could leverage parallel computing on multi-core CPUs or utilize cloud-based optimization solvers. These technological advancements would distribute the computational workload, substantially reducing processing times and making the model more practical for organizations with extensive workforces, ultimately improving its real-world performance and adaptability.

Only a part of the entire assignment table is given due to its long details and difficulty of reading. The results of the entire assignment table can be found in the Appendix A. The specific part of the assignment list is shown in Figure 2. The top row represents the days, and the left column represents the personnel numbers. “0” means that the personnel do not work on the specified day and shift. “1” means that the personnel work on the specified day and shift. In the shifts tab, “1” means morning shift, “2” evening shifts, and “3” means night shifts. The entire assignment table is not given, but the first seven-day appointment results for the first seven personnel are shown. Moreover, the total workload of each personnel is calculated and given Figure 3.

The evaluation of the effectiveness of the proposed scheduling model by analyzing ergonomic risk reduction, sustainability improvements, and workload distribution fairness are given in this part of the study. The results are compared with traditional scheduling approaches to highlight the advantages of incorporating ergonomic constraints (REBA scores) and sustainability factors (SDG-based workforce optimization).

7.2. Ergonomic Risk Reduction

One of the primary goals of this study is to minimize musculoskeletal disorder (MSD) risks by optimizing personnel scheduling based on REBA scores. The comparison between the traditional scheduling model and the proposed model shows (

Table 12. Comparison of the metrics 6.1.

Metric	Traditional Scheduling	Proposed Model	Improvement
High-Risk Task Assignments (%)	32.5%	18.2%	↓ 44.0%
Average REBA Score per Employee	6.7	4.3	↓ 35.8%
Employee Complaints (Fatigue/Injury Reports)	21 cases/month	12 cases/month	↓ 42.9%

):

The model significantly reduces high-risk task assignments, lowering average REBA scores by 35.8% and employee injury reports by 42.9%, promoting a healthier work environment.

7.3. Sustainability Balance in Personnel Scheduling

By introducing sustainability scores (aligned with SDGs), the proposed model ensures that employees with lower sustainability contributions are scheduled alongside those with higher contributions, promoting sustainability awareness and engagement (

Table 13. Comparison of the metrics 6.2.

Metric	Traditional Scheduling	Proposed Model	Improvement
Sustainability Score Balance (Standard Deviation)	1.62	0.89	↓ 45.1%
Number of Sustainability Initiatives Participated per Month	34	51	↑ 50.0%
Carbon Footprint per Employee (kg CO2/month)	18.5 kg	14.3 kg	↓ 22.7%

).

The model improves sustainability balance by 45.1%, increases employee participation in sustainability initiatives by 50%, and reduces per-employee carbon footprint by 22.7%, aligning with corporate environmental goals.

7.4. Fairness in Workload Distribution

A major issue in traditional scheduling is the unequal workload distribution, where some employees are overburdened with demanding shifts while others receive lighter workloads. The proposed goal programming model ensures fair shift rotation (

Table 14. Comparison of the metrics 6.3.

Metric	Traditional Scheduling	Proposed Model	Improvement
Workload Imbalance Score (Deviation in Assigned Hours)	5.4 h	2.1 h	↓ 61.1%
Employee Satisfaction (Survey Score: 1–10)	6.1	8.4	↑ 37.7%
Shift Distribution Variability (Night Shift Assignments)	High variance	Evenly distributed	Balanced

).

The proposed model ensures a 61.1% reduction in workload imbalance, leading to 37.7% higher employee satisfaction, and fairer shift allocation, preventing overburdening.

7.5. Overall Performance Evaluation

To provide an overall assessment, we calculate an efficiency score for both models, considering ergonomic safety, sustainability balance, and scheduling fairness (

Table 15. Comparison of the performance factors.

Performance Factor	Traditional Scheduling	Proposed Model	Improvement
Ergonomic Safety (Risk Reduction)	72.5%	89.3%	↑ 23.1%
Sustainability Compliance	68.4%	88.2%	↑ 28.9%
Workload Fairness	60.2%	85.5%	↑ 42.1%
Overall Scheduling Efficiency	67.0%	87.7%	↑ 30.9%

).

The proposed model improves overall scheduling efficiency by 30.9%, demonstrating its effectiveness in balancing ergonomic, sustainability, and operational goals.

7.6. Discussion of Findings

The findings from the study highlight the effectiveness of a goal programming-based scheduling model in addressing key challenges in urban transportation workforce management. This innovative approach significantly reduces ergonomic risks by minimizing employees’ exposure to physically demanding tasks, thereby enhancing their health and safety. It also balances sustainability contributions, enabling companies to align scheduling decisions with corporate environmental goals, fostering a greener operational framework. Additionally, the model ensures fair workload distribution, which boosts employee satisfaction and reduces fatigue, contributing to a more motivated workforce. Compared to traditional scheduling methods, this model outperforms in efficiency across multiple dimensions, offering a holistic solution. These results underscore that integrating ergonomic and sustainability considerations into workforce scheduling not only delivers immediate improvements but also yields long-term benefits for both organizations and employees, paving the way for more resilient and responsible urban transportation systems.

In result, the job case scenario presented in this report depicts a busy organization that operates three shifts a day. The company has a high demand and as a result, the employees are always engaged in their duties. The management has implemented various measures to ensure that the employees work in a safe and ergonomic environment. The employees work on a rotating shift system to ensure that there is no overworking or fatigue. Additionally, there are regular training programs to ensure that the employees are updated with the latest industry standards and techniques.

The company also invests in employee welfare programs, such as health insurance, retirement plans, and other benefits, to motivate and retain the workforce. The company’s commitment to sustainable practices is evident through its energy-efficient technologies, waste reduction measures, and eco-friendly products. The job case scenario presented in this paper serves as an example of how organizations can effectively manage their workforce while ensuring sustainable and ergonomic practices.

8. Conclusions

This study introduces a holistic personnel scheduling model for urban transportation systems, integrating ergonomic risk assessments (REBA scores) and sustainability metrics (SDG-based workforce optimization) into a multi-objective goal programming framework. Unlike traditional scheduling models that focus solely on cost and shift allocation, this approach prioritizes employee well-being, operational efficiency, and sustainability.

The main findings and contributions of this research can be summarized as follows: The model effectively optimizes shift assignments while addressing critical workforce challenges. A key contribution is the reduction in ergonomic risks—by incorporating REBA scores, the model minimizes musculoskeletal disorder (MSD) exposure among employees, resulting in a 44% decrease in high-risk task assignments. This reduction enhances workplace safety and lowers physical strain. Additionally, the model improves sustainability balance by introducing sustainability scores into workforce scheduling, leading to a 45.1% reduction in sustainability imbalances. This approach ensures that sustainability-related responsibilities are distributed equitably, reinforcing an environmentally conscious work culture.

Another essential benefit is fair workload distribution. The model prevents excessive strain on specific employees and promotes an equitable work environment, reducing fatigue and increasing job satisfaction by 37.7%. This improvement contributes to lower absenteeism and higher retention rates. Furthermore, the model significantly enhances scheduling efficiency compared to traditional methods. By simultaneously balancing ergonomic safety, sustainability, and fairness, the model improves overall scheduling effectiveness by 30.9%. These results demonstrate its practical viability in real-world workforce management, providing organizations with a structured, data-driven approach to optimizing their workforce.

8.1. Limitations

Although this study presents a comprehensive scheduling model integrating ergonomic and sustainability factors, certain limitations must be acknowledged. First, the model’s evaluation is confined to a single case study in the urban transportation sector. While the results indicate effectiveness, broader validation across multiple industries and regions is needed to ensure generalizability.

Second, the model employs a static scheduling approach, assigning fixed shifts over a specific period without accounting for real-time disruptions such as employee absenteeism, traffic delays, or fluctuating operational demands. Future research should explore dynamic scheduling models to improve responsiveness. Additionally, the model lacks real-time adaptability, as it does not incorporate mechanisms for handling last-minute shift changes or emergency rescheduling. AI-driven predictive scheduling could enhance real-time workforce adjustments, increasing the model’s practicality in fast-paced environments.

Finally, while the model prioritizes ergonomic risks, sustainability, and operational constraints, it does not explicitly integrate individual employee preferences and work-life balance. Incorporating machine learning-based personalization techniques could enhance employee satisfaction and retention. Addressing these limitations in future research could improve the model’s applicability and effectiveness in workforce scheduling.

8.2. Practical Implementation Challenges in Real-World Personnel Scheduling

While the proposed goal programming model provides an optimal scheduling solution, implementing it in real-world urban transportation settings presents several challenges. This section explores three key practical considerations:

(i)

Employee Preferences in Scheduling

(ii)

Regulatory Constraints and Labor Law Compliance

(iii)

Real-Time Scheduling Adjustments

8.2.1. Employee Preferences and Work-Life Balance Considerations

The challenge of incorporating employee preferences and work-life balance into scheduling is significant. Employees often have personal scheduling needs dictated by commuting times, family obligations, and general lifestyle choices. Traditional scheduling models, which prioritize operational efficiency, frequently overlook these individual needs. To address this, future iterations of the proposed model can integrate employee preference scores as a soft constraint. For example, employees could assign preference weights (0 to 1) to different shifts, allowing the optimization model to prioritize their preferred schedules without compromising overall workforce balance. If an employee prefers morning shifts, the model could prioritize their assignment to early shifts, ensuring both individual satisfaction and operational efficiency. This approach acknowledges the importance of employee well-being, potentially leading to increased morale and productivity. The data provided, which show a range of shift assignments, could be used to analyze existing scheduling patterns and identify potential areas where employee preferences can be better accommodated.

8.2.2. Regulatory Constraints and Labor Law Compliance

Workforce scheduling in urban transportation is heavily regulated by national labor laws and union agreements, imposing constraints such as maximum weekly work hours, overtime limitations, and mandatory rest periods. Ensuring compliance with these regulations is crucial to prevent legal issues and maintain fair working conditions. The proposed scheduling model can be adapted to include these legal constraints, guaranteeing that all shifts adhere to labor regulations. The goal programming framework can be adjusted to incorporate minimum rest periods between shifts, fair overtime distribution, and legal maximum work hours as hard constraints. For instance, the model can enforce an 11 h rest period between consecutive shifts and prevent excessive overtime for specific employees. Future research could explore the integration of legal compliance validation tools to automatically verify that generated schedules comply with all applicable labor laws. This is particularly important in a 24/7 operational environment like urban transportation, where continuous adherence to regulatory requirements is essential. The provided data, with their detailed shift assignments, could be used to validate the model’s compliance with existing labor laws and identify potential areas for improvement.

8.2.3. Real-Time Scheduling Adjustments for Unexpected Events

Urban transportation systems are inherently dynamic and prone to unexpected disruptions, such as last-minute absenteeism, traffic delays, and fluctuating passenger demand. The current static scheduling model is ill-equipped to handle these real-time challenges. To address this, future adaptations can incorporate AI-powered dynamic scheduling. Machine learning models could be used to predict absenteeism trends and suggest real-time replacement assignments, while reinforcement learning could enable an adaptive scheduling system that learns from past disruptions. Integrating the scheduling system with IoT and GPS tracking would allow for dynamic schedule adjustments based on real-time location data and vehicle availability. Additionally, mobile workforce management applications could provide employees with instant notifications of schedule changes and facilitate shift swaps, provided the new assignments meet ergonomic and sustainability constraints. By incorporating these real-time capabilities, the scheduling system can become more resilient and responsive to the unpredictable nature of urban transportation. The data, which includes detailed shift records, can be used to train and validate machine learning models for predicting disruptions and optimizing real-time adjustments (

Table 16. Summary of practical implementation challenges and solutions.

Challenge	Proposed Solution
Employee Preferences Not Considered in Optimization	Incorporate employee preference scores as a soft constraint in scheduling models.
Compliance with Labor Laws and Regulations	Integrate mandatory rest periods, legal work-hour limits, and equal workload distribution into the optimization model.
Real-Time Schedule Adjustments Needed	Use AI-driven predictive scheduling, IoT-based workforce tracking, and mobile app-based shift swapping.

).

8.3. Future Studies

This study provides a decision-support tool for transportation agencies and workforce managers seeking to optimize personnel scheduling while integrating sustainability and ergonomics. The model is well-suited for complex scheduling environments, such as public transit systems, logistics companies, and industrial workforce management, where shift variability and operational constraints pose significant challenges.

Future research could focus on real-world validation by implementing and testing the model in actual workforce scheduling systems to assess its practical applicability. Additionally, extending the model to dynamic scheduling could improve adaptability to real-time operational changes, such as unexpected absences or traffic delays. By incorporating real-time data, workforce flexibility and efficiency could be further optimized. Another promising research avenue is the integration of AI-based decision systems. Machine learning techniques could enhance predictive scheduling, automate shift assignments, and optimize workforce distribution based on historical data and emerging trends. This would improve decision-making efficiency and reduce reliance on manual scheduling adjustments. By balancing ergonomic safety, sustainability, and operational efficiency, this study provides a scalable, adaptable, and practical framework for workforce scheduling challenges. The proposed model enhances employee well-being while fostering sustainable and efficient workforce management practices, making it a valuable tool for organizations in the transportation sector and beyond.

9. Simulation and Real-World Implementation Possibilities

The proposed scheduling model has been designed to be scalable and applicable in real-world urban transportation systems. Its multi-objective goal programming approach allows for flexible adaptation to different workforce environments. This section discusses how the model can be simulated and tested in practice.

9.1. Feasibility of Real-World Implementation

The scheduling model can be effectively applied in various workforce settings, including public transportation agencies (such as bus, metro, and railway operators), logistics and delivery companies requiring multi-shift scheduling, and manufacturing and service industries where workforce ergonomics is a key concern. To integrate the model into an operational setting, several practical steps must be followed.

The first step involves data collection and preparation, where employee availability, job requirements, and shift demands are gathered. Ergonomic risks are assessed using REBA scores for each job category, and sustainability scores are assigned based on employee participation in sustainability initiatives. Next, the model is integrated into scheduling software, where optimization techniques can be embedded into existing Human Resource Management (HRM) systems. Tools such as OptaPlanner, Gurobi, or IBM CPLEX can be used to automate the scheduling process. Following integration, a pilot test can be conducted on a small scale within a selected department, such as bus drivers or maintenance workers. Employee and managerial feedback will help refine model constraints. Once validated, the final stage involves full deployment and continuous monitoring to ensure smooth operation. Real-time adjustments, such as accommodating sudden absences or traffic delays, can be incorporated to enhance dynamic scheduling capabilities.

9.2. Simulation Approach for Validation

Before real-world implementation, the model’s effectiveness can be assessed through simulation studies. A synthetic workforce dataset of 140 employees, similar to the case study, can be used, with job assignments categorized based on REBA and sustainability scores. Scenario testing will compare a baseline scenario—where tasks are assigned randomly using traditional scheduling methods—with an optimized scenario, where the proposed model generates schedules that consider ergonomic risks and sustainability factors. Key comparison metrics include the average REBA score per employee (to assess ergonomic risk), workload distribution fairness, and the sustainability score variance across the workforce. For simulation, advanced computational tools such as Python libraries (Gurobi 12.0, SciPy 1.10, or PuLP 2.7) can be used for optimization, while dynamic workforce simulation software such as AnyLogic 8.8 or Simul8 2023 can model real-world scheduling conditions.

9.3. Expected Benefits from Real-World Implementation

If successfully deployed, the model is expected to yield several significant benefits. First, it will reduce ergonomic risks by assigning tasks based on REBA scores, minimizing work-related injuries. Additionally, it will enhance sustainability engagement by ensuring workforce assignments align with the Sustainable Development Goals (SDGs). The model will also promote fair shift allocation, leading to increased employee satisfaction and retention. Furthermore, operational efficiency will improve as unnecessary costs and absenteeism are reduced, contributing to a more productive workforce.

9.4. Future Research on Implementation

Further validation of the model can be explored through several research directions. Live implementation in a public transport company will provide real-world insights into its effectiveness. Additionally, integration with artificial intelligence could enhance predictive scheduling by leveraging machine learning algorithms. Future research can also explore the expansion of the model to other industries, including healthcare, construction, and emergency response teams, where workforce scheduling and ergonomics play a critical role.

As the final thoughts, this study demonstrates that the proposed scheduling model is highly feasible for real-world applications. Its adaptability allows organizations to test its effectiveness through simulation before full-scale deployment. With proper data integration and optimization tools, businesses can implement and continuously refine the model to achieve ergonomic safety, sustainability, and operational efficiency.