Search Results (215)

Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. Full article

This paper presents an advanced rescheduling approach that jointly applies two sets of objective functions within a novel multi-objective search algorithm and a production simulation of the manufacturing system. The role of the first set of objective functions is to optimize the performance of production systems, while the second newly proposed set of objective functions aims to minimize the intervention changes from the original schedule, thereby supporting schedule stability and smooth manufacturing processes. The combined use of these two objective sets is ensured by a flexible candidate-qualification method, which allows for priorities to be assigned to each objective function, offering precise control over the rescheduling process. The applicability of this approach is presented through an example of an extended flexible flow shop manufacturing system. A new test problem containing 16 objective functions has been developed. The effectiveness of the proposed new objective functions and rescheduling method is validated by a simulation model. The obtained numerical results are also presented in this paper. The aim of this study is not to compare different search algorithms but rather to demonstrate the beneficial impact of change-minimizing objective functions within a given search framework. Full article

Airports are facing significant challenges due to the increasing number of air travel passengers. After a significant downturn during the COVID-19 pandemic, airports are implementing measures to enhance security and improve their level of service in response to rising demand. However, the rising passenger volume has led to increased congestion and longer waiting times, undermining operational efficiency and passenger satisfaction. While most previous studies have focused on static modeling or infrastructure improvements, few have addressed the problem of dynamically allocating passengers in real-time. To tackle this issue, this study proposes a mathematical model with a dynamic rescheduling framework to balance the workload across multiple departure areas where security screening takes place, while minimizing the negative impact on passenger satisfaction resulting from increased walking distances. The proposed model strategically allocates departure areas for passengers in advance, utilizing data-based predictions. A mixed integer linear programming (MILP) model was developed and evaluated through discrete event simulation (DES). Real operational data provided by Incheon International Airport Corporation (IIAC) were used to validate the model. Comparative simulations against four baseline strategies demonstrated superior performance in balancing workload, reducing waiting passengers, and minimizing walking distances. In conclusion, the proposed model has the potential to enhance the efficiency of the security screening stage in the passenger departure process. Full article

Background: Pain is a prevalent issue among breast cancer patients and survivors, with a significant proportion receiving hydrocodone for pain management. However, the rescheduling of hydrocodone from Schedule III to Schedule II by the U.S. Drug Enforcement Administration (DEA) in October 2014 raised concerns about potential barriers to opioid access for cancer patients, particularly among vulnerable populations such as dually eligible Medicare–Medicaid beneficiaries and racial/ethnic minorities. Methods: We conducted a retrospective cohort study using Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data including 52,306 early-stage breast cancer patients from 2011 to 2019. We employed multivariable logistic regression models with model specification tests to stratify the subgroups and evaluate the differential effects of the policy change by Medicaid dual eligibility and race–ethnicity, while adjusting for other patient demographics, clinical characteristics, and cancer treatments. Results: The rescheduling of hydrocodone was associated with significantly different effects on prescription opioid use across subgroups, with the most pronounced reduction in hydrocodone prescription observed among dual-eligible racial/ethnic minority patients (adjusted odds ratio [AOR] = 0.57; 95% confidence interval [CI]: 0.44–0.74; p < 0.001). Non-dual-eligible patients experienced a smaller reduction in hydrocodone use (AOR = 0.84; 95% CI: 0.78–0.90; p < 0.001). Concurrently, non-hydrocodone opioid use significantly increased among non-dual-eligible non-Hispanic White patients (AOR = 1.29; 95% CI: 1.19–1.40; p < 0.001), suggesting a substitution effect, while smaller non-significant increases were observed among other subgroups. Conclusions: Hydrocodone rescheduling led to the greatest reduction in hydrocodone use among dual-eligible racial–ethnic minority patients. The corresponding increase in non-hydrocodone opioid use was limited to non-dual-eligible non-Hispanic White patients. These findings highlight the need for opioid policies that balance misuse prevention with equitable access to pain relief, particularly among underserved populations. Full article

Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article

Due to increased container traffic, the problems of congestion at terminal gates generate serious air pollution and decrease terminal efficiency. To address this issue, many terminals are implementing a truck appointment system (TAS) based on several concepts. Our work addresses gate congestion at a container terminal. A conceptual model was developed to identify system components and interactions, analyzing container flow from both static and dynamic perspectives. A truck appointment system (TAS) was modeled to optimize waiting times using a non-stationary approach. Compared to existing methods, our TAS introduces a more adaptive scheduling mechanism that dynamically adjusts to fluctuating truck arrivals, reducing peak congestion and improving resource utilization. Unlike traditional static appointment systems, our approach helps reduce truckers’ dissatisfaction caused by the deviation between the preferred time and the assigned one, leading to smoother operations. Various genetic algorithms were tested, with a hybrid genetic–tabu search approach yielding better results by improving solution stability and reducing computational time. The model was applied and adapted to the Port of Casablanca using real-world data. The results clearly highlight a significant potential to enhance sustainability, with an annual reduction of 785 tons of CO₂ emissions from a total of 1281 tons. Regarding trucker dissatisfaction, measured by the percentage of trucks rescheduled from their preferred times, only 7.8% of arrivals were affected. This improvement, coupled with a 62% decrease in the maximum queue length, further promotes efficient and sustainable operations. Full article

In high-speed railway networks, multiple spatiotemporal correlated disruptions often cause passenger trip failures and delay propagation. Conventional single-disruption rescheduling strategies struggle to resolve such cross-line conflicts, necessitating an integrated, passenger-centric rescheduling framework for multiple correlated disruptions. This paper proposes a mixed-integer linear programming (MILP) model to minimize total passenger delay time and trip failures under scenarios involving disruptions that are geographically dispersed but operationally interconnected. Two rescheduling mechanisms are introduced: a stepwise rescheduling method, which iteratively applies single-disruption models to optimize local problems, and an integrated rescheduling method, which simultaneously considers the global impact of all disruptions. Case studies on a real-world China’s high-speed railway network (29 stations, 42 trains, and 36,193 passenger trips) demonstrate that the proposed integrated rescheduling method reduces total passenger delays by 13% and trip failures by 67% within a 300 s computational threshold. By systematically coordinating spatiotemporal interdependencies among disruptions, this approach enhances network accessibility and service quality while ensuring operational safety, providing theoretical foundations for intelligent railway rescheduling. Full article

Background/Objectives: Operating rooms represent valuable and pivotal units of any hospital. Therefore, their management affects healthcare service delivery through rescheduling, staff shortage/overtime, cost inefficiency, and patient dissatisfaction, among others. To optimize scheduling, we aim to assess preoperative evaluation criteria that influence the prediction of surgery duration for gastric cancer (GC) patients. In GC, radical surgery with curative intent is the ideal treatment. Nevertheless, the intervention sometimes must be palliative if the patient’s status and tumor staging prove too advanced. Methods: A 6-year retrospective cohort study was performed in a tertiary care hospital, including all cases diagnosed with GC (ICD-10 code C16), confirmed through histopathology, and undergoing surgical treatment (N = 108). Results: The results of our study confirm male predominance (63.89%) among GC surgery candidates while bringing new perspectives on patient evaluation criteria and choice of surgical intervention (curative—Group 1, palliative—Group 2). Surgery duration, including anesthesiology (175.19 [95% CI (157.60–192.77)] min), shows a direct correlation with the number of lymph nodes dissected (Surgical duration [min] = 10.67 × No. of lymph nodes removed − 32.25). Interestingly, the aggressiveness of the tumor based on histological grade (highly differentiated being generally less aggressive than poorly differentiated) shows differential correlation with surgery duration among curative and palliative surgery candidates. Similarly, TNM staging indicates the need for a longer surgical duration (pTNM stage IIA, IIB, and IIIA) for curative interventions in patients with less advanced stages, as opposed to shorter surgery duration for palliative interventions (pTNM stage IIIC and IV). Conclusions: The study quantitatively presents the resources needed for the optimal surgical treatment of different groups of GC patients, as the disease coding systems in use regard the treatment of each pathology as “standard” in terms of patient management. The results obtained are anchored in the global perspectives of surgical outcomes and aim to improve the management of operating room scheduling, staff, and resources. Full article

Although dynamic, energy-efficient container-supply hybrid flow shops have attracted increasing attention, most existing research overlooks how transportation within container production affects makespan, resilience, and sustainability. To bridge this gap, we frame a resilient, energy-efficient container-supply hybrid flow shop (TDEHFSP) scheduling model that utilizes vehicle transportation to maximize operational efficiency. To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). The algorithm integrates a time gap left-shift scheduling strategy with a machine on–off control mechanism to construct an energy-saving optimization framework. Additionally, a predictive–reactive dynamic rescheduling model is introduced to address unexpected task disturbances. To validate the algorithm’s effectiveness, 36 benchmark test cases with varying scales are designed for horizontal comparison. Results show that the proposed Q-MGCOA outperforms benchmarks on convergence, diversity, and supply-chain resilience while lowering energy utilization. Moreover, it achieves about an 8% reduction in energy consumption compared to traditional algorithms. These findings reveal actionable insights for next-generation intelligent, low-carbon container production. Full article

Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction. Full article

Designing an efficient task offloading system is essential in the Industrial Internet of Things (IIoT). Owing to the limited computational capability of IIoT devices, offloading tasks to edge servers enhances computational efficiency. When an edge server is overloaded, it may experience interruptions, preventing it from serving local devices. Existing studies mainly address interruptions by rerouting, rescheduling, or implementing reactive strategies to mitigate their impact. In this study, we introduce an interruption-aware proactive task offloading framework for IIoT. We develop a load-based interruption model in which the probability of server interruption is formulated as an exponential function of the total computational load, which provides a more realistic estimation of service availability. This framework employs Multi-Agent Advantage Actor–Critic (MAA2C)—a simple yet efficient approach that enables decentralized decision-making while handling large action spaces and maintaining coordination through the centralized critic to make adaptive offloading decisions, taking into account edge availability, resource limitations, device cooperation, and interruptions. Experimental results show that our approach effectively reduces the average total service delay by optimizing the tradeoff between system delay and availability in IIoT networks. Additionally, we investigate the impact of various system parameters on performance under an interruptible edge task offloading scenario, providing valuable insights into how these parameters influence the overall system behavior and efficiency. Full article

Background/Objectives: The low-resource, minority and underserved populations (LRMU) that carry the highest risk of oral cancer (OC) experience many barriers to early detection and treatment, resulting in disproportionately poor outcomes. One major barrier to better outcomes is poor compliance with specialist referral for diagnosis and treatment. The goal of this prospective study was to compare specialist referral compliance for Telehealth vs. in-person visits in LRMU individuals screening positive for increased OC risk. Methods: Forty subjects who had screened positive for oral potentially malignant lesions (OPMLs) were recruited from community clinics. The subjects indicated whether they would prefer an in-person or Telehealth specialist visit. They were offered assistance with all aspects of the visit, and then tracked over 3 months for referral compliance. A novel, very low-cost, simple Telehealth platform located within the community clinic was used for the remote specialist visits. Results: In the Telehealth group, 16/24 subjects attended their first scheduled remote specialist visit; 4/24 attended rescheduled visits within 3 months, and 4/24 did not comply at all. All attendees and specialists were able to complete the remote visits in full. Of the 7/16 subjects who completed in-person visits, 3/16 attended their first scheduled visit, and 4/16 complied within 3 months; 9/16 subjects did not comply at all with specialist referral. Significantly more individuals complied with Telehealth specialist referral at 1 month (p = 0.0006) and after 3 months (p = 0.0154). Conclusions: This novel Telehealth platform may improve compliance with specialist referral in low-resource individuals with OPMLs. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article

An integrated solution that considers the shortening of scheduling and the planning of resource integration was conceived. The proposed method allocates the resources and the execution mode costs effectively in order to minimize the project duration and the cost of construction activities. Costs are managed based on the management of the costs already in place for people and those costs involved in the modes of execution of the project, trying to decrease the cost as much as possible. The proposed method is used in order to achieve the maximum potential and minimum costs during a project, including direct costs, indirect costs, and delay penalties. Furthermore, it finds a balance between the costs of acquiring and releasing human resources. The most interesting aspect of the proposed method is that it suggests addressing problems with resource planning and project scheduling simultaneously under uncertainty. FS theory is used to model project activity duration and cost uncertainty in the method. In addition, the above approach involves a genetic algorithm (GA) for schedule optimization. The optimization method utilizes a GA as an optimization approach to identify a set of non-dominated solutions. In this paper, we discuss how string-based multi-object optimization can be solved with ES using the elitist non-dominated sorting genetic algorithm (NSGA-II). The method is implemented in Python (v3.12.9), the computer programming language, as a standalone automated computational tool for schedule optimization in order to subsequently reschedule. Full article

This study discusses the issue of train rescheduling in high-speed railways (HSR) when unexpected interruptions occur. These interruptions can lead to delays, cancellations, and disruptions to passenger travel. An optimization model for train rescheduling under uncertain-duration interruptions is proposed. The model aims to minimize both the decline in passenger service quality and the total operating cost, thereby achieving sustainable rescheduling. Then, a hybrid optimization algorithm combining rolling horizon optimization with a deep-learning-embedded NSGA-II algorithm is introduced to solve this multi-objective problem. This hybrid algorithm combines the advantages of each single algorithm, significantly improving computational efficiency and solution quality, particularly in large-scale scenarios. Furthermore, a case study on the Beijing–Shanghai high-speed railway shows the effectiveness of the model and algorithm. The optimization rates are 16.27% for service quality and 15.58% for operational costs in the small-scale experiment. Compared to other single algorithms or algorithm combinations, the hybrid algorithm enhances computational efficiency by 26.21%, 15.73%, and 25.13%. Comparative analysis shows that the hybrid algorithm outperforms traditional methods in both optimization quality and computational efficiency, contributing to enhanced overall operational efficiency of the railway system and optimized resource utilization. The Pareto front analysis provides decision makers with a range of scheduling alternatives, offering flexibility in balancing service quality and cost. In conclusion, the proposed approach is highly applicable in real-world railway operations, especially under complex and uncertain conditions, as it not only reduces operational costs but also aligns railway operations with broader sustainability goals. Full article