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In this article, the due date setting problem consisting of due date quoting, order insertion and rescheduling with relevant sequence-dependent setups and a frozen zone is considered. A differential equation model is introduced. The differential equation can be solved analytically and requires low computing effort. Explicit expressions are gained concerning the earliest possible due date (due date quoting problem) and the latest possible insertion position (rescheduling problem) in order to meet the customer requested due date. The differential equation model is compared with an approach based on a recently introduced hybrid binomial inverse hypergeometric probability distribution for the due date quoting problem. The analytical results of the proposed model are very close to the expectation values (these cannot be explicitly calculated, and high computation effort is required) of the recently introduced approach. [ABSTRACT FROM AUTHOR]

In urban metro systems, unexpected events often occur to cause discordance in the planned timetable and delay in passenger travel. Therefore, this study focuses on the timetable rescheduling problem by adopting a stop-skipping strategy during the off-peak period. In addition, there are a number of existing optimization models that minimize the total passenger waiting time, which can cause fairness issues for a small group of passengers having longer waiting time at some stations. In this study, a rescheduling model is proposed to consider passenger waiting time fairness and train travel time cost. This model is tested with a randomly designed delay scenario of Nanjing Metro Line 2, which can be efficiently solved by a designed iterative algorithm based on decomposition method. Compared to the standard stop pattern, the result shows that the stop-skipping pattern can better alleviate the impact of sudden delays, while simultaneously ensuring the passenger waiting time fairness. [ABSTRACT FROM AUTHOR]

The current paper discusses the problem of the synchronization of public transit services through the rescheduling of their initial execution times. The objective of the analysis is the minimization of waiting time that occurs for passengers when they embark on journeys through a co-modal or a unimodal public transport network. Fundamental concepts of public transport and co-modality are discussed, and particular focus is given to the development of approaches that are practical and can be applied to the whole network and not just to some parts of it. Three instances of the problem of rescheduling are presented and further analyzed, and corresponding mixed integer-linear programming models are developed for their solution. The mathematical models aim to be flexible and applicable to a broad set of available datasets that have been homogenized according to the general transit feed specification. Parameters are used in the approaches in order to define the degree to which we want to change the existing way that services operate. Case studies are presented for Greek, Czech, and Slovak public transport networks, in which reductions from 1 to 20% in the overall waiting time of passengers are obtained. [ABSTRACT FROM AUTHOR]

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers' food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify behavioral factors that affect patient attendance in outpatient clinics, especially those related to rescheduling. Methods: A retrospective study was conducted on 20,386 appointment records for 6,252 patients in an outpatient specialty clinic at a women's and children's hospital in Singapore over 34 months (January 2012–October 2014). Multivariate logistic regression was used to analyze the influence of independent variables on appointment no-shows. Results: The average no-show rate of the study population was 28.87%. Patient historical behaviors were significantly associated with appointment attendance. In particular, a larger number of previous visits, more historical no-shows, more historical rescheduling events initiated by patients, and more reschedulings initiated by the clinic for the current appointment were positively associated with no-shows. Notably, the number of previous visits was found to exhibit a significantly diminishing marginal effect on no-show rates. Further analyses suggested that for appointments rescheduled by the clinic, subsequent rescheduling by patients was associated with a reduced risk of no-shows. Conclusions: No-shows were more common in patients who missed more historical appointments or had their appointments rescheduled more frequently. Encouraging patients to reschedule their own appointments could be an effective measure to reduce no-shows. However, the benefit of patient-rescheduling decreased if patients rescheduled appointments repeatedly. [ABSTRACT FROM AUTHOR]

Background: Codeine was rescheduled in Australia to prescription only in February 2018. Initial studies reported an increase in population level paracetamol and ibuprofen sales following codeine upscheduling. However, to date no study has been able to investigate changes in non-opioid analgesic use at the individual patient level to determine if sales data reflect actual consumption patterns. Aim: To address this gap, we aimed to determine the impact of codeine rescheduling on non-opioid analgesic use in people who regularly used over-the-counter codeine, primarily for pain, prior to the rescheduling change. Method: We conducted a prospective cohort study with 260 participants who reported regular over-the-counter codeine consumption at cohort entry. Surveys were completed at baseline (November 2017, 3 months before rescheduling) and at 1 month (February 2018), 4 months (June 2018), and 12 months (February 2019), following rescheduling. The primary outcomes were mean daily doses of non-opioid analgesics, captured through a 7 day medication diary. Results: The mean daily paracetamol dose decreased from 1754.4 mg (95% CI 1300.5–2208.3) at baseline to 1023.8 mg (95% CI 808.5–1239.1) at the final time-point (+ 12 months) (p =.009). The mean daily ibuprofen dose decreased from 305.1mg (95% CI 217.9–392.4) at baseline to 161.2 mg (95% CI 98.5–224.0) 12 months after rescheduling (p =.03). No significant change in doses of other medications remained was found. Conclusion: In people who regularly consumed over-the-counter codeine, doses of non-opioid analgesics either reduced or remained stable following codeine rescheduling, suggesting concerns of medication substitution or overuse following the change were not realised. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusion Registration We aimed to determine the impact of codeine rescheduling on prescribing of codeine and other opioids, with a focus on demographic and diagnoses associated with codeine prescribing before and after rescheduling of codeine to prescription‐only in February 2018.We used interrupted time series analysis (February 2016‐February 2020) and probit regression to examine prescribing of codeine and other opioids according to primary care data from 464 general practice clinics in Victoria, Australia.The rate of codeine prescribing increased in the month following rescheduling (additional 76 people/10000, 95% confidence interval [CI] 49‐103), then declined to baseline rates (slope −2.02, 95% CI 3.79, −0.25). Prescribing of other opioids did not change. Post rescheduling, females were more likely to receive codeine prescriptions compared to males (β = 0.094, 95% CI 0.08‐0.108) and those aged 70‐79 years were more likely to receive codeine compared to those aged <30 years. Those residing in the least disadvantaged areas had a greater probability of being prescribed codeine than those in more disadvantaged areas after rescheduling (β = 0.154, 95% CI 0.129‐0.179). A documented mental health diagnosis (β = 0.067, 95% CI 0.052‐0.082) or migraine diagnosis (β = 0.057, 95% CI 0.037‐0.078) was associated with increased likelihood of receiving a codeine prescription after rescheduling compared to before in contrast to those without such a diagnosis.Codeine rescheduling did not result in a sustained increase in codeine prescribing nor a change in the prescribing of other opioids. Patient factors associated with increased codeine prescribing after compared to before rescheduling included female sex, older age, migraine diagnosis and comorbid mental health conditions.EU PAS Register (EUPAS43218). [ABSTRACT FROM AUTHOR]

With the increasing uncertainty and complexity of industrial production, dynamic events are inevitably happened during the production process. This paper deals with the transportation time constrained dynamic flexible job shop scheduling problem with machine breakdown, new jobs arrival, jobs cancellation, and processing time change of operations. Considering the actual production situation, a makespan rescheduling model based on transportation time is established. A Monte‐Carlo tree search based algorithm is proposed to solve the dynamic flexible job shop scheduling problem such that the makespan is minimized. In order to improve the accuracy of the developed algorithm and the quality of the obtained scheduling schemes, subtree keeping policy, multi‐branching simulation, reinforcement learning, ε−$$ \varepsilon - $$greedy strategy, and expectation‐best evaluation method are employed. Moreover, a subtree pruning policy is embedded to improve the search efficiency of the developed algorithm. Simulation experiments are conducted on a series of instances of different sizes to compare the developed approach with the GA‐based scheduling approach and the Monte‐Carlo tree search based approach. The simulation results indicate that the developed approach is superior to the existing scheduling approaches in terms of the qualities of the obtained solutions. [ABSTRACT FROM AUTHOR]

Cloud architecture and its operations interest both general consumers and researchers. Google, as a technology giant, offers cloud services globally. This paper analyzes the Google cluster usage trace, focusing on three key aspects: task execution times, rescheduling frequency, and the relationship between task priority and rescheduling. Firstly, we examine how memory and processor performance impact task execution times across different machines. Next, we investigate how the number of task constraints influences rescheduling frequency and overall environmental efficiency. Furthermore, we analyze how task priority affects rescheduling and explore its correlation with task constraints. The results reveal that doubling the memory size can accelerate tasks by a factor of nine and that 90% of rescheduling is associated with tasks having less than seven constraints. We aim to enhance data center performance by identifying bottlenecks in the Google Cluster Dataset and providing recommendations for all cloud service providers. Our key findings indicate that memory plays a more significant role than the processor, and tasks with higher constraints have a less pronounced impact on rescheduling than anticipated. [ABSTRACT FROM AUTHOR]

Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required to reduce scrap. In this study, the multi-objective flexible job shop scheduling problem (MO-FJSP) that considers energy efficiency is discussed. The created model aims to minimize the energy consumption, total machine workload and makespan. In this study, new job arrivals are considered as dynamic events. Another dynamic event added to the model is the addition of rework processes between operations to reduce the scrap rate when a scrap decision is made during the production stages. The enhanced NSGA II algorithm was applied to solve this problem. The enhanced NSGA II algorithm was applied to test instances and its performance was compared using some of the multi-objective performance indicators. These experimental results prove the effectiveness of the proposed solution method.

To solve the flexible job shop dynamic scheduling problem with fuzzy quality control time, which is more realistic, this paper established two rescheduling models for machine breakdown and urgent order insertion with the objective of minimizing completion time. Two models were for three failure scenarios: emergency order insertion, machine failure during no-load operation, and machine failure during work-piece processing. To solve this model, this paper designed a novel genetic-neighborhood search algorithm that integrated cellular-automata-based neighborhood search and random restart hill-climbing. Aiming at the characteristics of dual sub-decisions involving order sequencing and machine selection, this paper designed a two-layer coding scheme including process code and machine code, and designed corresponding genetic operations such as crossover and mutation for them. After that, this paper applied the genetic operations into the framework of cellular-automata-based neighborhood search to enhance the algorithm's global search capability, and integrated the random restart hill-climbing algorithm based on key operations to improve the algorithms local development capability. Experiments tested 10 flexible job shop scheduling instances and one flexible job shop scheduling instance with fuzzy quality control time to verify the effectiveness of the proposed algorithm and models. In addition, the test results under different failure scenarios show the proposed method outperforms the backward-based scheduling strategy used in practice. [ABSTRACT FROM AUTHOR]

With the increasing level of manufacturing and the rapid development of the market economy, enterprises need to improve the level of service to meet the individual needs of customers. Among them, urgent order is a common problem faced by order-oriented enterprises, but it brings additional profits to enterprises and also causes damage to the stability of production. To address this challenge, this paper takes the hybrid flow shop as the research object, considers the equipment switching strategy, AGV transportation device and the carrying capacity of the shop buffer, establishes a mathematical model with the optimization objectives of minimizing the total energy consumption, minimizing the maximum completion time and minimizing the production cost, and proposes the Improved Red-billed Blue Magpie Optimizer (IRBMO) based on the complete re-scheduling strategy for solving the problem. In this algorithm, according to the characteristics of the hybrid flow shop, a strategy approach based on workpiece-equipment two-layer encoding and decoding is adopted, and a Logistic Chaos Mapping backward learning strategy is proposed to generate the initial population; an adaptive cross-variance operator is introduced to balance the algorithm's search capability. For the emergency order insertion scenario, a comparison test with real workshop data is carried out through the extension of the algorithm, which verifies the feasibility of the IRBMO algorithm in complex combinatorial optimization problems and the superiority of the solving effect. It provides new ideas for the green and efficient production scheduling technology of manufacturing enterprises, and has practical application value for promoting the sustainable development of the production system. [ABSTRACT FROM AUTHOR]

This paper addresses timetable disruptions in metro systems and their impact on passenger service quality. To mitigate these effects, the study introduces a time-space network-based traffic management approach with a two-layer network. The first layer focuses on timetable rescheduling, using an integer program for modeling and a Lagrangian relaxation-based heuristic for solution. The second layer addresses rolling stock duty rescheduling, employing a greedy heuristic for train set duties. The proposed approach is applied to a real-world problem presented in the INFORMS railway application competition (2022). Results demonstrate that, with topological vertex orders, the approach achieves schedule amendments within 3–6 minutes for various disruption scenarios. This rapid response highlights the method's efficacy and advantage in real-time applications, showcasing its potential for practical implementation in metro systems. [ABSTRACT FROM AUTHOR]

Copyright of Bait Al-Mashura Journal is the property of Bait Al-Mashura Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the flow shop rescheduling literature, many papers consider unlimited buffer capacities between successive machines. In real fact, these capacities may be limited, or no store may exist. Thus, a blocking situation is inducted. Diverse types of blocking constraints are studied in the flow shop scheduling problems. However, in dynamic environments, only few papers deal with these kinds of constraints. The aim of this paper is to investigate a problem of rescheduling the jobs in a flowshop environment and mixed blocking as a constraint, considering simultaneously schedule efficiency and stability as a performance measure, and job arrival as a disruption. An iterative methodology based on the predictive–reactive strategy is implemented for dealing with this rescheduling problem. The problem has first been modeled as a Mixed Integer Linear Programing (MILP) model. Experimental results show that the MILP resolution is only possible for small-sized instances. Hence, inspired by NEH algorithm, we proposed four heuristics for solving large-sized instances of this problem. Eventually, we discussed the performance of the proposed heuristics for different blocking situations, both in terms of solution efficiency and resolution time. [ABSTRACT FROM AUTHOR]

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system is heavily dependent on the optimality of its scheduling plan. In this study, we present a reactive scheduling approach based on the S-graph framework. The proposed method is specifically designed to handle the arrival of new jobs and generate schedules with the shortest makespan, i.e., the minimum total completion time. Whenever a new order is received, the method dynamically adjusts the production plan through rescheduling. Three distinct scheduling policies are identified that determine which tasks require scheduling or rescheduling and which tasks should remain unchanged in their schedules. To evaluate the effectiveness of the algorithm, we solve several examples from the literature and analyze the results. The findings demonstrate the efficiency and efficacy of the proposed approach. The ability to accommodate new job arrivals and generate schedules with a minimized makespan highlights the practical relevance and benefits of the S-graph-based reactive scheduling method. [ABSTRACT FROM AUTHOR]

In flight test engineering, the flight test duration (FTD) affects the aircraft's delivery node and directly impacts costs. In the actual flight test process, the environmental status updates frequently, and various uncertain events are often encountered, which affect the flight test progress and project implementation. Therefore, when scheduling flight test tasks, rescheduling should be taken into account. This paper proposes a predictive-reactive strategy based on a deep reinforcement learning approach to solve the flight test task scheduling problem with consideration of aircraft grounding. In the predictive stage, a constructive heuristic algorithm is designed to generate an initial schedule. The rescheduling problem is solved by the appropriate rescheduling method that aims to optimize the FTD deviation, task reallocation, and workload cost simultaneously. The problem is modeled as a Markov decision process, including the well-designed state features, rewards, and actions based on different rescheduling methods. The policy is trained by the proximal policy optimization algorithm. At last, numerical results are provided to demonstrate the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

We study a rescheduling problem in paced aircraft assembly lines with specific resource and precedence constraints. We develop an efficient model based on constraint programming to provide rapidly rescheduling solutions to managers. We propose original optimisation criteria and show that their use outperforms the classic criteria in terms of solution time and quality. We validate our approach through experiments run on industrial problem instances of large scale and derive useful managerial insights from this experimental study. [ABSTRACT FROM AUTHOR]

Abstract In flight test engineering, the flight test duration (FTD) affects the aircraft’s delivery node and directly impacts costs. In the actual flight test process, the environmental status updates frequently, and various uncertain events are often encountered, which affect the flight test progress and project implementation. Therefore, when scheduling flight test tasks, rescheduling should be taken into account. This paper proposes a predictive-reactive strategy based on a deep reinforcement learning approach to solve the flight test task scheduling problem with consideration of aircraft grounding. In the predictive stage, a constructive heuristic algorithm is designed to generate an initial schedule. The rescheduling problem is solved by the appropriate rescheduling method that aims to optimize the FTD deviation, task reallocation, and workload cost simultaneously. The problem is modeled as a Markov decision process, including the well-designed state features, rewards, and actions based on different rescheduling methods. The policy is trained by the proximal policy optimization algorithm. At last, numerical results are provided to demonstrate the effectiveness and superiority of the proposed approach.

The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges. [ABSTRACT FROM AUTHOR]

As the realities of production and operation in green and intelligent workshops become more variable, the adverse risks arising from disruptions to modernized workshop energy consumption schedules and customer churn caused by dynamic events are increasing. In order to solve those problems, we take the intelligent hybrid flow shop as the research subject, use buffer capacity and automated guided vehicles (AGVs) transport devices as resource constraints, construct a multi-objective rescheduling model that considers both energy consumption and customer satisfaction. According to the model characteristics, an improved lion swarm optimization algorithm (ILSO) is designed to solve the above model. To improve the initial solution quality and global search capability of the algorithm, ILSO is improved by combining the reverse learning initialization strategy of Logistic chaotic mapping with the tabu search strategy. The results of experiments on the proposed algorithm with different sizes of arithmetic cases and real cases in the workshop indicate that ILSO can effectively solve the bi-objective rescheduling problem oriented to inserting orders, and the proposed model can provide green dynamic scheduling solutions for manufacturing enterprises to achieve the purpose of transformation to green intelligent manufacturing.

This paper addresses the real-time Railway Traffic Management Problem (rtRTMP), which involves adjusting train timetables during perturbations. Perturbations in railway networks often lead to significant delays, necessitating strategies to minimize their propagation. An important objective of traffic management is to facilitate passenger transfers through connecting trains, which may become difficult when traffic is disturbed. Pursuing this objective, the paper focuses on mitigating train delays by reducing connection times during transfers without compromising connections. To achieve this, we extend an existing Mixed-Integer Linear Programming (MILP) formulation for the rtRTMP by introducing two alternative enhancements. Moreover, we pursue the same delay mitigation by extending an Ant Colony Optimization algorithm for the Train Routing Selection Problem (TRSP): this problem reduces the number of alternative routes to be considered for trains, making rtRTMP instances tractable. We assess the efficiency of the proposed enhancements in reducing the total train delay while preserving passenger connections in multiple instances representing traffic in the Lille-Flandres station control area, located in the north of France. The results demonstrate that the integration of these enhancements, in both the TRSP and the rtRTMP, results in a significant reduction in delay propagation.

The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges.

Electrical Multiple Units (EMUs) need to undergo services like repairing, cleaning and wheel turning according to a train shunting and service scheduling (SSS) plan at the high-speed railway depot. Disturbances in the timetable at the station and overtime services affect the original SSS plan. Therefore, frequent modifications to the original SSS plan and rescheduling of some EMUs are required to recover the feasibility of the plan. This paper considers a rescheduling method for integrating train shunting, service scheduling and routing at EMU depots with various layouts. In this paper, the movement process of EMUs at depots is represented with the help of a two-layer time-space network. A heuristic algorithm for rescheduling is designed. Case studies based on a real-world depot show that the heuristic can repair the original SSS plan in 180 seconds while ensuring that the new SSS plan is as close as possible to the original SSS plan.

We consider a single machine rescheduling problem where a set of original jobs have been scheduled to minimize the total weighted completion time. However, before formal processing begins, a new set of jobs arrives and creates a disruption. The decision maker can reject a subset of the new jobs by paying certain rejection penalties, and reschedule the original and the remaining new jobs without excessively disrupting the original schedule, which is measured by the maximum completion time deviation for any original jobs between the original and adjusted schedules. The objective is to minimize the sum of total weighted completion time of the original jobs and the accepted new jobs in the adjusted schedule, the weighted maximum completion time deviation and the total rejection cost. We first provide a dynamic programming-based exact algorithm running in pseudo-polynomial time, and then propose a fully polynomial time approximation scheme. Given the NP-hardness for the studied problem, our result is the best possible from the approximation algorithm perspective. In addition, we conduct extensive computational experiments to evaluate the performance of our proposed methods, and provide some managerial insights on their applicability under different situations. [ABSTRACT FROM AUTHOR]

As the only largest source of revenue and cost in a hospital, the operation room (OR) scheduling problem is a hot research topic. Nonetheless, an integrated model is the missing key to managing and improving the efficiency of ORs. This paper presents a fully integrated model regarding three concepts: meditating elective patients and emergency patients together, considering ORs and downstream units, and proposing hierarchical weekly, daily, and rescheduling models. Due to the inherent randomness in emergency patient arrival, a random forest machine learning model and geographical information systems are used to obtain the emergency patient surgery duration and arrival time, respectively. According to the machine learning model in weekly and daily scheduling, initially, fixed capacity is reserved for emergency patients. When an emergency patient arrives, the surgery starts if a reserved OR is available. Otherwise, the first available OR will be dedicated to the patient due to an emergency patient's higher priority than an elective patient. In this case, it is needed to reschedule the OT schedule for the remaining patient. Moreover, the three-phase model guarantees that an emergency patient assigns to an OR within a specific time limit. To solve the models, genetic algorithm and particle swarm optimization are developed and compared. In addition, a real-world case study is undertaken at a hospital. The results of comparing the proposed approach to the hospital's current scheduling show that the three-phase model had a considerable positive effect on the ORs schedule. [ABSTRACT FROM AUTHOR]

To improve the productivity, the resource utilization and reduce the production cost of flexible job shops, this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching. Firstly, a mathematical model is established to minimize the maximum completion time. Secondly, an improved two-layer optimization algorithm is designed: the outer layer algorithm uses an improved PSO (Particle Swarm Optimization) to solve the workpiece batching problem, and the inner layer algorithm uses an improved GA (Genetic Algorithm) to solve the dual-resource scheduling problem. Then, a rescheduling method is designed to solve the task disturbance problem, represented by machine failures, occurring in the workshop production process. Finally, the superiority and effectiveness of the improved two-layer optimization algorithm are verified by two typical cases. The case results show that the improved twolayer optimization algorithm increases the average productivity by 7.44% compared to the ordinary two-layer optimization algorithm. By setting the different numbers of AGVs (Automated Guided Vehicles) and analyzing the impact on the production cycle of the whole order, this paper uses two indicators, the maximum completion time decreasing rate and the average AGV load time, to obtain the optimal number of AGVs, which saves the cost of production while ensuring the production efficiency. This research combines the solved problem with the real production process, which improves the productivity and reduces the production cost of the flexible job shop, and provides new ideas for the subsequent research. [ABSTRACT FROM AUTHOR]

Copyright of Ingeniería (0121-750X) is the property of Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Steelmaking-refining-Continuous Casting (SCC) is a bottleneck in the iron and steel production operation. In order to enhance production efficiency, SCC scheduling is employed to find an optimal schedule. Unfortunately, dynamic events such as charge start-time delay may occur in a real-world SCC process, which will invalidate the optimal SCC schedule, i.e., making the schedule not optimal or inexecutable. To cope with such a situation, SCC rescheduling is significant for generating a new optimal schedule. This paper proposes a mathematical model of the SCC rescheduling problem considering charge start-time delay, and further presents an Elite solutions and Tabu assisted Variable Neighbourhood Descent (ETVND) method to tackle the problem. The main framework of the ETVND method is Variable Neighbourhood Descent (VND). In the ETVND method, three Tabu based neighbourhood structures are elaborately designed. Moreover, three distinguished features are incorporated, i.e., an elite solutions based exploration strategy, two-layer local search based on the Fruit fly Optimization Algorithm, and multi-type perturbation. The first two features are devised to enhance the intensification abilities while the third is devised to improve the diversification abilities. Experimental results have demonstrated the effectiveness of the ETVND method by comparing with several algorithms in the literature. Further comparison experiments have validated the efficiency of the Tabu based neighbourhood structures and specially devised strategies. [ABSTRACT FROM AUTHOR]

The Central Bank of Malaysia ordered all banks to give an automatic six months moratorium or loan or financing deferment starting April, 1 2020 to their customers during the Covid-19 pandemic since many customers had difficulties paying their monthly financing obligations to banks. Thus, the objective of this paper is to explore the bankers' perspectives on Shariah and operational issues arising in Islamic banks in implementing moratorium which involves restructuring and rescheduling financing facilities during Covid-19 pandemic. At the same time, the methods these bankers used to manage the issues are also highlighted in this paper. A qualitative analysis is applied in this paper where the researchers conducted interview sessions with the Head of Shariah Department from an Islamic banking window and another from the Development Financial Institution which offers Islamic banking products and services. This study's findings show that there are certain perspectives from bankers in dealing with Shariah issues regarding restructuring and rescheduling during moratorium such as the issue of qalb aldayn (debt restructuring), capitalization of accrued profit in the financing facilities as well as the operational issues related to the system and people. It can be deduced then that all Islamic banks were doing extremely well in their steadfast endeavors towards offering remedies to the issues faced for the sake of the overall improvement of Islamic banks in the implementation of restructuring and rescheduling of financing facilities as outlined by guidelines or Resolutions issued by the Shariah Advisory Council of Bank Negara Malaysia. [ABSTRACT FROM AUTHOR]

We address a rescheduling problem of unrelated parallel machines with job-dependent setup times where a machine breakdown is known in advance. Typical rescheduling methods usually re-assign or re-sequence jobs from a given schedule after machines break down. Recently, machine breakdowns can be forecasted with high accuracy before their actual occurrences from IoT sensors or artificial intelligence methods. We therefore define a new rescheduling problem in which jobs are re-assigned before machine breakdowns occur, and propose a mathematical programming model with three objective measures, makespan, stability and penalty cost. We then develop a simulated annealing (SA) algorithm combined with a fuzzy logic controller for adjusting the parameters in SA. We demonstrate the performance of the proposed algorithm with extensive experiments. [ABSTRACT FROM AUTHOR]

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system is heavily dependent on the optimality of its scheduling plan. In this study, we present a reactive scheduling approach based on the S-graph framework. The proposed method is specifically designed to handle the arrival of new jobs and generate schedules with the shortest makespan, i.e., the minimum total completion time. Whenever a new order is received, the method dynamically adjusts the production plan through rescheduling. Three distinct scheduling policies are identified that determine which tasks require scheduling or rescheduling and which tasks should remain unchanged in their schedules. To evaluate the effectiveness of the algorithm, we solve several examples from the literature and analyze the results. The findings demonstrate the efficiency and efficacy of the proposed approach. The ability to accommodate new job arrivals and generate schedules with a minimized makespan highlights the practical relevance and benefits of the S-graph-based reactive scheduling method.

Context: Job sequencing has been approached from a static perspective, without considering the occurrence of unexpected events that might require modifying the schedule, thereby affecting its performance measures. Method: This paper presents the development and application of a genetic algorithm to the Job Shop Rescheduling Problem (JSRP), a reprogramming of the traditional Job Shop Scheduling Problem. This novel approach seeks to repair the schedule in such a way that theoretical models accurately represent real manufacturing environments. Results: The experiments designed to validate the algorithm aim to apply five classes of disruptions that could impact the schedule, evaluating two performance measures. This experiment was concurrently conducted with a genetic algorithm from the literature in order to facilitate the comparison of results. It was observed that the proposed approach outperforms the genetic algorithm 65% of the time, and it provides better stability measures 98% of the time. Conclusions: The proposed algorithm showed favorable outcomes when tested with well-known benchmark instances of the Job Shop Scheduling Problem, and the possibility of enhancing the tool's performance through simulation studies remains open.

In project scheduling, the durations of activities are often uncertain. Delays may cause a massive disorganization if a large number of activities must be rescheduled. In "The Anchor-Robust Project Scheduling Problem," Bendotti, Chrétienne, Fouilhoux, and Pass-Lanneau propose a novel criterion for solution stability in project scheduling under processing times uncertainty. They define anchored jobs as jobs whose starting times can be guaranteed in a baseline schedule. Finding a project schedule with bounded makespan and a max-weight set of anchors is shown to be an NP-hard robust two-stage problem. Taking advantage of the combinatorial structure of project scheduling and budgeted uncertainty, the authors obtain a compact MIP formulation for the problem. Numerical results show that the obtained MIP outperforms standard techniques from the literature. They also showcase the practical interest of anchored jobs in project scheduling. In project scheduling with uncertain processing times, the decision maker often needs to compute a baseline schedule in advance while guaranteeing that some jobs will not be rescheduled later. Standard robust approaches either produce a schedule with a very large makespan or offer no guarantee on starting times of the jobs. The concept of anchor-robustness is introduced as a middle ground between these approaches. A subset of jobs is said to be anchored if the starting times of its jobs in the baseline schedule can be guaranteed. The Anchor-Robust Project Scheduling Problem (AnchRobPSP) is proposed as a robust two-stage problem to find a baseline schedule of bounded makespan and a max-weight subset of anchored jobs. AnchRobPSP is considered for several uncertainty sets, such as box or budgeted uncertainty sets. Dedicated graph models are presented. In particular, the existence of a compact mixed integer programming reformulation for budgeted uncertainty is proven. AnchRobPSP is shown to be NP-hard even for budgeted uncertainty. Polynomial and pseudopolynomial algorithms are devised for box uncertainty and special cases of budgeted uncertainty. According to numerical results, the proposed approaches solve large-scale instances and outperform classical affine decisions rules for AnchRobPSP. Insights on the price of anchor-robustness are given based on computations. [ABSTRACT FROM AUTHOR]

This paper extends our previous algorithm, termed as Particle Swarm Optimization with Distributed Acceleration Constants (PSODAC), which has been introduced for mitigating congestion based on rescheduling in a deregulated environment. The proposed variant adopts a sequential hybridization of the PSODAC principle with Differential Evolution, which is a well-known evolutionary algorithm. The variant in this paper is termed as Sequentially Hybridized Differential Evolution with Particle Swarm Optimization (SH-DEPSO). The experimental investigations are carried out in the IEEE 14 bus system in two scenarios namely, single point congestion and multipoint congestion. Firstly, the performance investigation is carried out on mitigating the congestion using cost analysis, stability analysis, complexity analysis, and strategy analysis. Secondly, the characteristics of the algorithm are observed by performing convergence analysis and investigating the quality of the solution dynamics. The studies demonstrate the competing performance of SH-DEPSO over PSODAC and the traditional PSO. [ABSTRACT FROM AUTHOR]

Rescheduling is the guarantee to maintain the reliable operation of production system process. In production system, the original scheduling scheme cannot be carried out when machine breaks down. It is necessary to transfer the production tasks in the failure cycle and replan the production path to ensure that the production tasks are completed on time and maintain the stability of production system. To address this issue, in this paper, we studied the event-driven rescheduling policy in dynamic environment, and established the usage rules of right-shift rescheduling and complete rescheduling based on the type of interference events. And then, we proposed the rescheduling decision method based on genetic algorithm for solving flexible job shop scheduling problem with machine fault interference. In addition, we extended the "mk" series of instances by introducing the machine fault interference information. The solution data show that the complete rescheduling method can respond effectively to the rescheduling of flexible job shop scheduling problem with machine failure interference. [ABSTRACT FROM AUTHOR]

Abstract Railway operations are highly susceptible to delays and disruptions caused by various factors, such as technical issues, operational inefficiencies, and unforeseen events. To counter these delays and ensure efficient railway operations during real-time management, several rescheduling approaches can be implemented. Among these approaches, passenger-oriented rescheduling considers train rescheduling while taking passenger data into account, as opposed to operation-oriented rescheduling. This paper provides an overview of the former group of approaches. Particular focus is put on different ways passenger data is exploited to optimize rescheduling and on the measures, the approaches can decide on. The rescheduling measures typically considered vary from decisions on maintaining transfers, canceling trains, adding emergency trains, changing routes and orders of trains, skipping or adding stops at stations, short-turning trains, applying speed control, and modifying rolling stock compositions. In this regard, the paper presents a comprehensive analysis of real-time rescheduling approaches adopted in both the conventional railway and urban rail transit and points out possible directions for further research in the field.

Scheduling can be defined as the allocation of available resources over time while optimising a set of criteria like early completion time of task, holding inventory, etc. The complexity of the scheduling problem, already known to be high, increases if dynamic events and disruptions are considered. In addition, in production and logistics, designers of scheduling systems must consider sustainability-related expectations. This paper presents an energy-efficient scheduling and rescheduling method (named Green Rescheduling Method, GRM). GRM aims at the solving of the dynamic scheduling problem under the condition of a certain level of routing flexibility enabling the reassignment of tasks to new resources. The key performance indicators integrated into the proposed GRM are effectiveness and efficiency-oriented. Applications concern the domains of production and logistics. In order to assess the proposed approach, experimentations have been made and results illustrate the applicability of GRM to build efficient and effective scheduling and rescheduling both for flexible manufacturing systems and inventory distribution systems in a physical internet network. A mathematical formulation for flexible job shop problem with energy consumption is also proposed using mixed Integer programming to evaluate the performance of the predictive part of GRM. [ABSTRACT FROM AUTHOR]

With the development of cloud-native technologies, Kubernetes becomes the standard of fact for container scheduling. Kubernetes provides service discovery and scheduling of containers, load balancing, service self-healing, elastic scaling, storage volumes, etc. Although Kubernetes is mature with advanced features, it does not consider reducing the cost in Kubernetes clouds using the factor of communication frequent between pods while scheduling pods, nor does it have a rescheduling mechanism to save cost. Hence, we propose a cost-efficient scheduling algorithm and a rescheduling algorithm to reduce the cost of communication-intensive and periodically changing web applications deployed in Kubernetes, respectively. Network communication-intensive pods are scheduled to the same node by the scheduling algorithm based on Improved Beetle Antennae Search. According to the changing pod intimacy relationship, the rescheduling algorithm is completed through the replacement of new and old pods to reduce the cost. In addition, this paper evaluates the proposed algorithms in terms of cost and performance on a Kubernetes cloud. The result shows that the cost consumption of Kubernetes clusters in cloud environment is reduced by 20.97% on average compared with the default Kubernetes framework. [ABSTRACT FROM AUTHOR]

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An aero-engine has to be assembled twice or more to satisfy stringent technical standards. Substandard engines are required to re-enter for additional reassembly, which may trigger rework disruption. Work groups with flexible skills are responsible for the operations and can be reassigned when rescheduling triggers. An integrated problem of rescheduling of engines and reconfiguration of work groups with rework disruption is formulated here. A heuristic is proposed to solve the problem. In self-reconfiguration optimization, work groups are reassigned in accordance with the workload balance criterion without violating the assembly skills constraints. In rescheduling optimization, locally optimal sequencing propositions of operations are derived and applied to generate a feasible schedule. Three neighborhood structures are defined, and a variable neighborhood search is implemented to optimize the assignment and the schedule of operations on parallel work groups. The effectiveness of the proposed algorithm is tested by several numerical experiments. The results show that the proposed algorithm promises apparently lower total cost and more desirable assignment of work groups by scheduling. [ABSTRACT FROM AUTHOR]

Rescheduling can help to improve the quality of a schedule with respect to an initially given sequence. In this paper, we consider the possibility of rescheduling jobs arriving for processing at a single machine under the following limitations: (a) jobs can only be moved toward the end of the schedule and not toward the front, and (b) when a job is taken out of the sequence, it is put on a buffer of limited capacity before being reinserted in its new position closer to the end of the sequence. The buffer is organized as a stack with a last-in/first-out policy. As an objective function, we consider the minimization of the weighted number of late jobs. For this NP-hard problem, we first provide two different integer linear programming (ILP) formulations. Furthermore, we develop a branch-and-bound algorithm with a branching rule based on the movement of jobs. Then a new pseudo-polynomial dynamic programming algorithm is presented which utilizes dominance criteria and an efficient handling of states. Our computational experiments with up to 100 jobs show that this algorithm performs remarkably well and can be seen as the current method of choice. [ABSTRACT FROM AUTHOR]