Search Results (272)

Economic growth, especially in emerging economies, has altered the composition of international tourism. It is therefore essential to possess the skills necessary to understand the influence of culture on human behaviour, thereby enabling an appropriate response to the traveller. This research aims to develop a tool for identifying openness, flexibility, awareness, and intercultural preparedness. It focuses on the metacognitive and cognitive aspects of cultural intelligence that shape the development of empathy in customer service staff in hotels in Quintana Roo. The variables were validated and incorporated into a quantitative study using multivariate analysis and inferential statistics. A sample of 77 questionnaires was analysed using simple random sampling under a proportional design. Multiple Correspondence Analysis (MCA) was employed as a discriminatory technique to identify the most significant independent variables. These were subsequently entered as regressors into ordinal logistic regression (OLR), along with age and work experience, in order to estimate the probabilities associated with each level of the dependent variable. The results indicated that age had minimal influence on the metacognitive and cognitive variables, whereas years of experience among tourism staff exerted a significant effect. Full article

The port logistics service innovation (PLSI) is closely associated with cross-border e-commerce (CBEC) enterprise performance, given that the port, as the spatial carrier and the joint point of goods, information, customs house affairs, etc., is essentially a key node of the CBEC logistics chain. However, the influence mechanism of PLSI on CBEC enterprise performance has still not yet been elaborated by consensus. To fill this gap, this study aims to figure out the effect mechanism integrating the probe into two variables (i.e., information interaction and environmental upgrade) in a moderated mediation model. Specifically, this study collects questionnaire survey data of logistics enterprises and CBEC enterprises in the Ningbo-Zhoushan Port of China by the Bootstrap method in the software SPSS 26.0. The results show the following: (1) PLSI can positively affect the CBEC enterprise performance; (2) information interaction plays an intermediary role between PLSI and CBEC enterprise performance; and (3) environmental upgrade can not only positively regulate the relationship between information interaction and CBEC enterprise performance, but also enhance the mediating role of information interaction with a moderated intermediary effect. Full article

This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios. Full article

Automated parcel lockers (APLs) are transforming urban last-mile delivery by reducing failed distributions, decoupling delivery from recipient availability, optimizing carrier routes, reducing carbon foot-print and mitigating traffic congestion. The paper investigates the optimal design of APLs systems under stochastic demand and operational constraints, formulating the problem as a resource allocation optimization with service-level guarantees. We proposed a data-driven discrete-event simulation (DES) model implemented in ARENA to (i) determine optimal locker configurations that ensure customer satisfaction under stochastic parcel arrivals and dwell times, (ii) examine utilization patterns and spatial allocation to enhance system operational efficiency, and (iii) characterize inventory dynamics of undelivered parcels and evaluate system resilience. The results show that the configuration of locker types significantly influences the system’s ability to maintain high customers service levels. While flexibility in locker allocation helps manage excess demand in some configurations, it may also create resource competition among parcel types. The heterogeneity of locker utilization gradients underscores that optimal APLs configurations must balance locker units with their size-dependent functional interdependencies. The Dickey–Fuller GLS test further validates that postponed parcels exhibit stationary inventory dynamics, ensuring scalability for logistics operators. As a theoretical contribution, the paper demonstrates how DES combined with time-series econometrics can address APLs capacity planning in city logistics. For practitioners, the study provides a decision-support framework for locker sizing, emphasizing cost–service trade-offs. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article

Recycling plastic waste has emerged as one of the most pressing environmental challenges of the 21st century. One of the biggest challenges in polyethylene terephthalate (PET) recycling is the requirement to return bottles in their original, undeformed state. This necessitates storing large volumes of waste and takes up substantial space. Therefore, this paper seeks to address this issue and introduces a novel AIoT-based infrastructure that integrates the PET Bottle Identification Algorithm (PBIA), which can accurately recognize bottles regardless of color or condition and distinguish them from other waste. A detailed study of Azure Custom Vision services for PET bottle identification is conducted, evaluating its object recognition capabilities and overall performance within an intelligent waste management framework. A key contribution of this work is the development of the Algorithm for Citizens’ Trust Level by Recycling (ACTLR), which assigns trust levels to individuals based on their recycling behavior. This paper also details the development of a cost-effective prototype of the AIoT system, demonstrating its low-cost feasibility for real-world implementation, using the Asus Tinker Board as the primary hardware. The software application is designed to monitor the collection process across multiple recycling points, offering Microsoft Azure cloud-hosted data and insights. The experimental results demonstrate the feasibility of integrating this prototype on a large scale at minimal cost. Moreover, the algorithm integrates the allocation points for proper recycling and penalizes fraudulent activities. This innovation has the potential to streamline the recycling process, reduce logistical burdens, and significantly improve public participation by making it more convenient to store and return used plastic bottles. Full article

Background: The Vehicle Routing Problem with Time Windows (VRPTW) has been extensively researched due to its applicability across various real-world domains, including logistics, healthcare, and distribution systems. With the global elderly population projected to continue increasing, the demand for home healthcare (HHC) services is also on the rise. This work focuses on a specific application within an HHC system, aiming to minimize the total completion time for a fleet of vehicles delivering healthcare services to patients at home. Methods: We propose a mathematical model for the VRPTW, targeting a reduction in both customer and server waiting times on each route and seeking to decrease the total completion time. Our proposed algorithm employs a tabu search to narrow the search space, leveraging a greedy algorithm to establish the tabu list. Results: Our experimental results, based on Solomon’s benchmark datasets, demonstrate that the proposed algorithms achieve optimal solutions, particularly in minimizing total completion time compared to traditional methods, in a case study involving 400 customers where vehicles’ hours are restricted to align with caregivers’ average daily working hours. Conclusions: Our algorithm resulted in a 59% reduction in the number of vehicles required compared to the most recent algorithms, which combine k-mean clustering and local search. Full article

The rapid growth of e-commerce and surges in shipment volumes have increased the pressure on transport systems, requiring innovations in collaborative logistics where consumers participate in dual roles as receivers and deliverers. However, existing research often addresses these roles in isolation, overlooking the flexibility with which users switch between them. Moreover, the literature has focused predominantly on monetary value in paid crowdsourced or social value in free social delivery, without fully exploring how users perceive value across both models. Addressing these gaps, this study profiles users of collaborative logistics services from both receiver and deliverer perspectives and examines their motivations in paid and unpaid delivery contexts. Based on survey data from 493 participants in Singapore, cluster analysis identified four distinct user segments: hesitators, potential customers, active users, and loyal advocates. The findings indicate that user preferences differ by role, with functional value prioritised in paid delivery and social value more prominent in free models. Free models attract a higher proportion of favourable users, highlighting the significance of non-monetary incentives. This study contributes to the literature by offering an integrated perspective on user roles and value perceptions and provides practical insights for developing more inclusive, community-oriented last-mile logistics solutions. Full article

The proliferation of digital platforms and interactive technologies has transformed the way service providers engage with their customers, particularly in emerging economies, where digital inclusion is an ongoing process. This study explores the relationship between omnichannel strategies and customer satisfaction, conceptualized here as a proxy for Quality of Experience (QoE), within a smart service station located in a digitally underserved region. Grounded in customer journey theory and the expectancy–disconfirmation paradigm, the study investigates how data integration, digital payment systems, and logistical flexibility—key components of intelligent e-service systems—influence user perceptions and satisfaction. Based on a correlational design with a non-probabilistic sample of 108 customers, the findings reveal a moderate association between overall omnichannel integration and satisfaction (ρ = 0.555, p < 0.01). However, a multiple regression analysis indicates that no individual dimension significantly predicts satisfaction (adjusted R² = 0.002). These results suggest that while users value system integration and interaction flexibility, no single technical feature drives satisfaction independently. The study contributes to the growing field of intelligent human-centric service systems by contextualizing QoE and digital inclusion within emerging markets and by emphasizing the importance of perceptual factors in ICT-enabled environments. Full article

The sharing economy is an inevitable trend in cold chain logistics. Most cold chain logistics enterprises are small and operate independently, with limited collaboration. Joint distribution is key to integrating cold chain logistics and the sharing economy. It aims to share logistics resources, provide collective customer service, and optimize distribution routes. However, existing studies have overlooked uncertainty factors in joint distribution optimization. To address this, we propose the Cold Chain Logistics Joint Distribution Vehicle Routing Problem with Time-Varying Network (CCLJDVRP-TVN). This model integrates traffic congestion uncertainty and constructs a time-varying network to reflect real-world conditions. The solution combines simulated annealing strategies with genetic algorithms. It also uses the entropy mechanism to optimize uncertainties, improving global search performance. The method was applied to optimize vehicle routing for three cold chain logistics companies in Beijing. The results show a reduction in logistics costs by 18.3%, carbon emissions by 15.8%, and fleet size by 12.5%. It also effectively addresses the impact of congestion and uncertainty on distribution. This study offers valuable theoretical support for optimizing joint distribution and managing uncertainties in cold chain logistics. Full article

To advance energy conservation and emissions reduction in urban logistics systems, this study focuses on the green vehicle routing problems with time windows (GVRPTWs), which remains underexplored in balancing environmental and service quality objectives. We propose a comprehensive multi-objective optimization framework that addresses this gap by simultaneously minimizing total distribution costs and carbon emissions while maximizing customer satisfaction, quantified based on the vehicle’s arrival time at the customer location. The rationale for adopting this tri-objective formulation lies in its ability to reflect real-world trade-offs between economic efficiency, environmental performance, and service level, which are often considered in isolation in previous studies. To tackle this complex problem, we develop an improved Non-Dominated Sorting Genetic Algorithm III (NSGA-III) that incorporates three key enhancements: (1) an integer-encoded initialization method to enhance solution feasibility, (2) a refined selection strategy utilizing crowding distance to maintain population diversity, and (3) an embedded 2-opt local search operator to prevent premature convergence and avoid local optima. Comprehensive validation experiments using Solomon’s benchmark instances and a real-world case demonstrate that the presented algorithm consistently outperforms several state-of-the-art multi-objective optimization methods across key performance metrics. These results highlight the effectiveness and practical relevance of our approach in advancing energy-efficient, low-emission, and customer-centric urban logistics systems. Full article

This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they are needed most. It also shows how precise forecasting is not just about saving costs, it is about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike. Full article

This study addresses the optimization of the Vehicle Routing Problem (VRP) with prioritized customers by introducing and comparing two advanced solution approaches: a metaheuristic-based hyperheuristic framework and a Variational Autoencoder (VAE)-based hyperheuristic. The VRP with prioritized customers introduces additional complexity by requiring efficient routing while ensuring high-priority customers receive service within strict constraints. To tackle this challenge, the proposed metaheuristic-based hyperheuristic dynamically selects and adapts low-level heuristics using Simulated Annealing (SA) and Ant Colony Optimization (ACO), enhancing search efficiency and solution quality. In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. Through extensive computational experiments on benchmark VRP instances, our results reveal that both approaches significantly enhance routing efficiency, with the VAE-based method demonstrating superior generalization across varying problem structures. Specifically, the VAE-based approach reduces total travel costs by an average of $8\%$ and improves customer priority satisfaction by $95\%$ compared to traditional hyperheuristic methods. Moreover, a comparative analysis with recent state-of-the-art algorithms highlights the competitive performance of our approaches in balancing computational efficiency and solution quality. These findings underscore the potential of integrating metaheuristics with machine learning in complex routing problems and provide valuable insights for real-world logistics and transportation planning. Future research will explore the generalization of these methodologies to dynamic and large-scale routing scenarios. Full article

With the rapid development of e-commerce, the importance of logistics distribution is becoming increasingly prominent. In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. In the process of last-mile delivery, flexible delivery locations and variable delivery times are effective means to improve customer satisfaction. Therefore, this paper introduces a Vehicle Routing Problem with flexible time windows and delivery locations, considering customer satisfaction (VRP-CS), which considers customer satisfaction by using prospect theory from two aspects: the flexibility of delivery time and delivery locations. This VRP-CS is formally modeled as a bi-objective optimization problem, which is an NP-hard problem. To solve this problem, a Multi-Strategy Adaptive Large Neighborhood Search (MSALNS) method is proposed. Operators guided by strategies such as backtracking and correlation are introduced to create different neighborhoods for ALNS, thereby enriching search diversity. In addition, an acceptance criterion inspired by simulated annealing is designed to balance exploration and exploitation, helping the algorithm avoid being trapped in local optima. Extensive numerical experiments on generated benchmark instances demonstrate the effectiveness of the VRP-CS model and the efficiency of the proposed MSALNS algorithm. The experiment results on the generated benchmark instances show that the total cost of the VRP-CS is reduced by an average of 14.22% when optional delivery locations are utilized compared to scenarios with single delivery locations. Full article

Postal services have re-emerged across numerous emerging economies worldwide as essential logistics providers, harnessing their vast coverage and dependability in the face of expanding e-commerce platforms and technological innovations. This study investigates India Post, one of the largest postal networks globally, to determine the key logistics service parameters prioritized by customers in southern India. Quantitative data obtained from 255 India Post end-users were evaluated using the logistics service quality (LSQ) scale, assessing eight dimensions including information quality, timeliness, ordering procedure, order accuracy, order condition, personal contact quality, order discrepancy handling, and order release quantities. The Analytical Hierarchy Process (AHP) ranked these elements, while Quality Function Deployment (QFD) bridged customer expectations with service improvements. The findings highlight the need to improve sorting and distribution processes to meet customer demands for timely, high-quality delivery. By refining logistics efficiency, this study provides suggestions and recommendations for boosting satisfaction and profitability, shedding light on service-led economic advancement for postal services in emerging economies. These insights equip postal service providers to cultivate loyalty and maintain competitiveness within the dynamic logistics landscape. Full article