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The paper considers the problems of scheduling n jobs that are released over time on a machine in order to optimize one or more objectives. The problems are dynamic single-machine scheduling problems (DSMSPs) with job release dates and needed to be solved urgently because they exist widely in practical production environment. Gene expression programming-based scheduling rules constructor (GEPSRC) was proposed to construct effective scheduling rules (SRs) for DSMSPs with job release dates automatically. In GEPSRC, Gene Expression Programming (GEP) worked as a heuristic search to search the space of SRs. Many experiments were conducted, and comparisons were made between GEPSRC and some previous methods. The results showed that GEPSRC achieved significant improvement. [ABSTRACT FROM AUTHOR]

This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment lead-time targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the request is not rejected, the rules prescribe how to assign the patient to an available slot. The intent of the scheduling rules is to maximize the utilization of the planned resource (i.e., the physician staff), or equivalently to maximize the number of patients that are admitted, while maintaining the service targets on the median, the 95th percentile, and the maximum appointment lead-times. We test the proposed scheduling rules with numerical experiments using real data from the chosen clinic of Tan Tock Seng hospital in Singapore. The results show the efficiency and the efficacy of the scheduling rules, in terms of the service-target satisfaction and the resource utilization. From the sensitivity analysis, we find that the performance of the proposed scheduling rules is fairly robust to the specification of the established lead-time targets. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mixed MTS/MTO policy to manage a single manufacturing facility producing two classes of end-products. A few end-products have high volume demands, whereas a fairly large number of end-products have low volume demands. In this situation, it is appealing to try to produce the high volume products according to an MTS policy and the low volume products according to an MTO policy. The purpose of this paper is to analyze and compare the impact of the choice of the scheduling policy on the overall performance of the system. We consider two policies: the classical FIFO policy and a priority policy (PR). The PR policy gives priority to production orders corresponding to low volume products over production orders corresponding to high volume products. Under some simple stochastic modeling assumptions, we develop analytical/numerical solutions to optimise each system. We then provide insights regarding this issue with the help of numerical examples. It appears that for some range of parameters, the PR rule can outperform the FIFO rule in the sense that, to achieve the same service level constraint, the corresponding cost under the PR rule is much lower. This situation is encountered when the low volume products can be managed with an MTO policy under the PR scheduling rule, while they have to be managed according to an MTS policy under the FIFO scheduling rule. We also derive some theoretical properties that support our empirical findings. [ABSTRACT FROM AUTHOR]

performance measures . The problem now is "how to evaluate these combinations" and "how to reflect the weighting of each performance measure in the system". Evaluation of scheduling rules is an inevitable task for any scheduler. Only a few evaluation procedures of the results obtained from computer simulation were found in the literature. In this paper, a framework for the evaluation of combinations of scheduling rules has been developed using fuzzy multi-criteria decision-making techniques, which are called MAW, Max–Min, and Max–Max. A simulation model is used to illustrate the proposed techniques. The results are compared with a simple approach for multi-criteria decision-making method, which is called SAW. Results show that MAW is the best technique for obtaining a high score in the analysis. [ABSTRACT FROM AUTHOR]

The increased use of flexible manufacturing systems to efficiently provide customers with diversified products has created a significant set of operational challenges for managers. Many issues concerning procedures and policies for the day-to-day operation of these systems still are unresolved. Previous studies in this area have concentrated on various problems by isolating or simplifying the systems under study. The primary objective of this study is to extend previous research by examining the effects of scheduling rules and routing flexibility on the performance of a constrained, random flexible manufacturing system (FMS). Other experimental factors considered are shop load, shop configuration, and system breakdowns. Within the bounds of this experiment, the results indicate that, in the presence of total routing flexibility, the effects of shop load, system breakdowns, and scheduling rules are significantly dampened. In particular, when total routing flexibility exists, the choice of scheduling rules is not critical. We also show that the behavior of scheduling rules in a more constrained FMS environment (i.e., where system breakdowns occur and material handling capability is limited) is consistent with the findings of previous research conducted under less constrained environments. Finally, results indicate that the shop configuration factor has little or no impact on a system's flow-time performance. [ABSTRACT FROM AUTHOR]

This paper investigates the Dynamic Flexible Job Shop Scheduling Problem (DFJSP), which is based on new job insertion, machine breakdowns, changes in processing time, and considering the state of Automated Guided Vehicles (AGVs). The objective is to minimize the maximum completion time and improve on-time completion rates. To address the continuous production status and learn the most suitable actions (scheduling rules) at each rescheduling point, a Dueling Double Deep Q Network (D3QN) is developed to solve this problem. To improve the quality of the model solutions, a MachineRank algorithm (MR) is proposed, and based on the MR algorithm, seven composite scheduling rules are introduced. These rules aim to select and execute the optimal operation each time an operation is completed or a new disturbance occurs. Additionally, eight general state features are proposed to represent the scheduling status at the rescheduling point. By using continuous state features as the input to the D3QN, state-action values (Q-values) for each scheduling rule can be obtained. Numerical experiments were conducted on a large number of instances with different production configurations, and the results demonstrated the superiority and generality of the D3QN compared to various composite rules, other advanced scheduling rules, and standard Q-learning agents. The effectiveness and rationality of the dynamic scheduling trigger rules were also validated. [ABSTRACT FROM AUTHOR]

Aiming at the optimization problem of minimizing the maximum completion time in job shop scheduling, a deep reinforcement learning optimization algorithm is proposed. First, a deep reinforcement learning scheduling environment is built based on the disjunctive graph model, and three channels of state characteristics are established. The action space consists of 20 designed combination scheduling rules. The reward function is designed based on the proportional relationship between the total work of the scheduled operation and the current maximum completion time. The deep convolutional neural network is used to construct action network and target network, and the state features are used as inputs to output the Q value of each action. Then, the action is selected by using the action validity exploration and exploitation strategy. Finally, the immediate reward is calculated and the scheduling environment is updated. Experiments are carried out using benchmark instances to verify the algorithm. The results show that it can balance solution quality and computation time effectively, and the trained agent has good generalization ability to the scheduling problem in the non-zero initial state. [ABSTRACT FROM AUTHOR]

This paper deals with the development of a microcomputer-based simulation model of a random flexible manufacturing system aimed at solving scheduling problems. The model is written in SLAM II and can be used interactively. It offers five alternative scheduling rules, but other rules could be incorporated if required. The selection process is demonstrated through an example, and an experimental design is conducted to evaluate the effect of changes in the levels of various resources on system performance. [ABSTRACT FROM AUTHOR]

As the internet and digital technologies revolutionize the healthcare sector, the development of smart hospitals is pivotal for improving medical service efficiency. This research presents a mathematical model for the outpatient scheduling problem and introduces the Graph-based Two-Agent Deep Reinforcement Learning algorithm (GTARS-DRL) to address the escalating demand for medical services and the scarcity of resources. The GTARS-DRL leverages graph neural networks for feature extraction of patient medical activities and applies the PDQPN algorithm to train agents for optimizing patient sequencing and service desk allocation, effectively minimizing wait times. Our experiments indicate that GTARS-DRL outperforms traditional scheduling rules in solution quality and matches the performance of Genetic Algorithms (GA), with a notable advantage in computational efficiency, boasting an ACPU of 0.762 s and an ARPT of 0.327 s. The algorithm's efficacy is further supported by its successful application to larger-scale instances, showcasing its potential for real-world use. This study contributes a novel approach to outpatient scheduling in smart hospitals, providing theoretical backing and practical direction for the optimization of medical resource allocation, and is poised to significantly enhance service efficiency and patient satisfaction. [ABSTRACT FROM AUTHOR]

This paper presents a real-time fuzzy expert system to scheduling parts for a flexible manufacturing system (FMS). First, some vagueness and uncertainties in scheduling rules are indicated and then a fuzzy-logic approach is proposed to improve the system performance by considering multiple performance measures. This approach focuses on characteristics of the system’s status, instead of parts, to assign priorities to the parts waiting to be processed. Secondly, a simulation model is developed and it has shown that the proposed fuzzy logic-based decision making process keeps all performance measures at a good level. The proposed approach provides a promising alternative framework in solving scheduling problems in FMSs, in contrast to traditional rules, by making use of intelligent tools. [ABSTRACT FROM AUTHOR]

There is no denying smart manufacturing is a critical step in responding to a new round of energy crises and promoting the high-quality development of the manufacturing industry, among which, the construction of intelligent production lines is the key link. In the practical production of complex aerospace components, production scheduling optimization plays an important role in achieving cost savings and energy reduction for a range of existing problems, such as cumbersome process design, difficult real-time scheduling adjustment, inefficient quality data testing, and complex interrelationships of state-type data. In this work, the optimization of scheduling objective, the selection of scheduling method, and the construction of scheduling management control system are the pointcuts to review the recent development of production scheduling optimization, systematically. The research on the more practical implications of multi-objective production scheduling optimization has shown that efficiency and energy consumption are the primary priorities of scheduling objectives. Scheduling rules and heuristic algorithms are the crucial research methods. Intelligent information technologies are an effective means to decrease the complexity of scheduling. Meanwhile, building intelligent management control systems for production scheduling is of great significance for the transformation and upgrading of production from digitalization to intelligence. [ABSTRACT FROM AUTHOR]

One of the critical issues in healthcare management is the operating room (OR) scheduling problem. Solutions to this problem consider surgery durations and allocate elective surgeries to OR sessions in order to create surgical lists of high quality. Determining the quality of a surgical list is a key undertaking within OR scheduling and is the focus of this research. Currently, probability- and/or expectation-based measures of surgical lists are used instead of statistical distributions of surgery lists to measure quality. The use of multiple measures, e.g., a combination of expectation and probability to assess a surgical list, complicates OR scheduling, so we introduce a new single measure – the OR scheduling metric – for evaluating surgical lists before their realisations, i.e., for use within OR scheduling. We apply the OR scheduling metric to an actual elective dataset and use simulation to demonstrate its use, including customised scheduling rules. We recommend the adoption of a benchmarked OR scheduling metric by the elective surgical services in hospitals with expected practical benefits in the long run, i.e., simpler OR scheduling and more desirable room utilisation, to be similar to that observed in our simulations. [ABSTRACT FROM AUTHOR]

Server works in discrete time, and is equipped with a given sequence of per-date capacities. It has to accommodate a set of agents with unit jobs, arriving at different dates. It can process a job in several installments, however no monetary transfers are allowed. Server is given jobs' birth dates and it only knows that agents want their jobs done as soon as possible, but not agents' complete preferences over delays (thus, this is the model with ordinal input). We investigate how scheduling rules, coming from both assignment and queueing literature, fare in this setting. The tension between fairness and incentive compatibility, inherent to the assignment models, disappears on this domain, as both Serial and Random Priority assignment rules become strategy-proof and non-envious. This is also true for Uniform rule; but First Come First Serve or First Come Last Serve rules are not strategy-proof and generate envy. [ABSTRACT FROM AUTHOR]

Rolling is a well-established forming process employed in many industrial sectors. Although highly optimized, process disruptions can still lead to undesired final mechanical properties. This paper demonstrates advances in pass schedule design based on reinforcement learning and analytical rolling models to guarantee sound product quality. Integrating an established physical strengthening model into an analytical rolling model allows tracking the microstructure evolution throughout the process, and furthermore the prediction of the yield strength and ultimate tensile strength of the rolled sheet. The trained reinforcement learning algorithm Deep Deterministic Policy Gradient (DDPG) automatically proposes pass schedules by drawing upon established scheduling rules combined with novel rule sets to maximize the final mechanical properties. The designed pass schedule is trialed using a laboratory rolling mill while the predicted properties are confirmed using micrographs and materials testing. Due to its fast calculation time, prospectively this technique can be extended to also account for significant process disruptions such as longer inter-pass times by adapting the pass schedule online to still reach the desired mechanical properties and avoid scrapping of the material. [ABSTRACT FROM AUTHOR]

With the development of collaborative edge computing (CEC), the manufacturing market is gradually moving toward large-scale, multi-scenario, and dynamic directions. The existing scheduling strategies based on machine learning or deep learning are only applicable to specific scenarios, which is difficult to meet the requirements of dynamic real-time scheduling in multiple scenarios. The proposed digital twin technology provides a new solution for real-time scheduling of multiple scenarios. In this paper, a digital twin-oriented multi-scene real-time scheduler (GNN-RL) is proposed. This scheduler converts task sequences into node trees and sets up two learning layers. The first layer is an online learning representation layer, which uses GNN to learn node features of embedded structures in real time to boost large instances without additional training. The second layer is the online learning policy layer, which introduces imitation learning mappings into optimal scheduling behavior policies adapted to multiple scenarios. Finally, our approach is validated in several scenarios in 3D digital twin factories, such as computationally intensive, delay-sensitive, and task-urgent scenarios. Since the scheduler proposed in this paper learns general features of the embedding graph rather than instance-specific features, it has good generality and scalability, with good generalization and scalability, outperforming other scheduling rules and schedulers on various benchmarks. [ABSTRACT FROM AUTHOR]

Order acceptance and scheduling is an interesting scheduling problem when scheduling and acceptance decisions need to be handled simultaneously. The complexity of the problem causes difficulty for many solution methods. In this paper, we proposed a learning and optimizing system to deal with the order acceptance and scheduling problem with a single-machine and dependent setup times. The aim of this system is to combine the advantages of the hyper-heuristic for learning useful scheduling rules and the meta-heuristic for further refining the solutions from the obtained rules. The experiments show that the proposed system is very effective as compared to other heuristics proposed in the literature. The analyses also show the benefits of scheduling rules obtained by the hyper-heuristic, especially for large-scale problem instances. [ABSTRACT FROM AUTHOR]

This study proposes a method to combine the improved gene expression programming (IGEP) and buffer-local scheduling to solve the optimization problem of flexible job shop production scheduling with buffer capacity limitation (BLFJSP). The IGEP algorithm was first used for the overall optimization and later combined with the flexible job shop production mode to design a multi-gene chromosome coding method to realize the overall optimization of the production schedule using the genetic biological evolution function. Furthermore, when the algorithm fell into a local extremum, the Jaccard similarity coefficient method was used to filter the approximate chromosomes, and the historical excellent chromosome backtracking method was used to increase the diversity of the population. Lastly, we designed the local scheduling rules of the job in and out of the buffer area. Based on the IGEP algorithm and considering the influence of buffer capacity limitation on the scheduling scheme, the final production scheduling scheme was generated by dual optimization methods aiming at minimizing blocking time and minimizing maximum completion time. Based on the actual production data of an assembly manufacturing company, multiple sets of simulation programs were designed to validate the IGEP algorithm for solving the optimization problem of BLFJSP. [ABSTRACT FROM AUTHOR]

This paper presents the salient aspects of a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence-dependent. A discrete event simulation model of the job shop system is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model. Five new setup-oriented scheduling rules are proposed and implemented. Simulation experiments were conducted under various experimental conditions characterized by factors such as shop load, setup time ratios, and due date tightness. The results indicate that setup-oriented rules provide better performance than ordinary rules. The difference in performance between these two groups of rules increases with the increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures. [ABSTRACT FROM AUTHOR]

The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method, such as self-regulation and self-learning capabilities. While traditional scheduling methods cannot meet these needs due to their rigidity. Self-learning is an inherent ability of reinforcement learning (RL) algorithm inhered from its continuous learning and trial-and-error characteristics. Self-regulation of scheduling could be enabled by the emerging digital twin (DT) technology because of its virtual-real mapping and mutual control characteristics. This paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm, which was called explicit exploration and asynchronous update proximal policy optimization algorithm (E2APPO). Firstly, the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops, strengthening the self-regulation of the scheduling model. Secondly, an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling model. Lastly, the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms, such as well-known scheduling rules and genetic algorithms, as well as other existing scheduling methods based on reinforcement learning. The comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy. [ABSTRACT FROM AUTHOR]

The flexible job shop scheduling problem (FJSP) is a classic NP-hard problem, and the quality of its scheduling solution directly affects the operational efficiency of the manufacturing system. However, the traditional scheduling algorithms suffer from poor generalization when solving FJSP; there are problems such as long computational time and dimensional disasters, especially as the scale of FJSP increases. Therefore, an effective deep actor-critic reinforcement learning (DACRL) method is proposed for solving FJSP. Firstly, the FJSP is modeled as a multi-agent Markov decision process (MMDP), the state space, action space, and reward function in the MMDP are designed. Secondly, a DACRL model is constructed to solve FJSP. The actor network is responsible for choosing the most suitable scheduling rule in different states, while the critic network is responsible for outputting the value function of the actions and providing feedback to the actor network to better adjust the scheduling strategy. Finally, the proposed DACRL method is validated on benchmark FJSP instances of different scales. The experimental results show that the proposed method significantly outperforms the heuristic scheduling rules and double deep Q-network (DDQN) in terms of solution quality. Compared with the meta-heuristic algorithms and the self-learning genetic algorithm (SLGA), the proposed method has higher solution efficiency with the same solution quality. [ABSTRACT FROM AUTHOR]

The production system of a wafer fabrication factory is a very complicated process. Job scheduling in a wafer fabrication factory is a very difficult task. To solve this problem, two intelligent scheduling rules are proposed in this study. The intelligent scheduling rules are modified from the well-known fluctuation smoothing rules with some innovative treatments. To evaluate the effectiveness of the proposed methodology, production simulation was also applied in this study. According to experimental results, the proposed methodology outperformed some existing approaches by reducing the average cycle time and cycle time standard deviation, the most important objectives of job scheduling in a wafer fabrication factory. [ABSTRACT FROM AUTHOR]

Operation theatre is one of the most significant assets in a hospital as the greatest source of revenue as well as the largest cost unit. This paper focuses on surgery scheduling optimization, which is one of the most crucial tasks in operation theatre management. A combined scheduling policy composed of three simple scheduling rules is proposed to optimize the performance of scheduling operation theatre. Based on the real-life scenarios, a simulation-based model about surgery scheduling system is built. With two optimization objectives, the response surface method is adopted to search for the optimal weight of simple rules in a combined scheduling policy in the model. Moreover, the weights configuration can be revised to cope with dispatching dynamics according to real-time change at the operation theatre. Finally, performance comparison between the proposed combined scheduling policy and tabu search algorithm indicates that the combined scheduling policy is capable of sequencing surgery appointments more efficiently. [ABSTRACT FROM AUTHOR]

In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multi-objective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop. [ABSTRACT FROM AUTHOR]

This study analyzes two decision levels in appointment system design in the context of clinics that face seasonal demand for scheduled and walk-in patients. The macro-level problem addresses access rules dealing with capacity allocation decisions in terms of how many slots to reserve for walk-ins and scheduled patients given fixed daily capacity for the clinic session. The micro-level problem addresses scheduling rules determining the specific time slots for scheduled arrivals. A fully-integrated simulation model is developed where daily demand actualized at the macro level becomes an input to the micro model that simulates the in-clinic dynamics, such as the arrivals of walk-ins and scheduled patients, as well as stochastic service times. The proposed integrated approach is shown to improve decision-making by considering patient lead times (i.e., indirect wait), direct wait times, and clinic overtime as relevant measures of performance. The traditional methods for evaluating appointment system performance are extended to incorporate multiple trade-offs. This allows combining both direct wait and indirect wait that are generally addressed separately due to time scale differences (minutes vs. days). The results confirm the benefits of addressing both decision levels in appointment system design simultaneously. We investigate how environmental factors affect the performance and the choice of appointment systems. The most critical environmental factors emerge as the demand load, seasonality level, and percentage of walk-ins, listed in the decreasing order of importance. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) have been the target of a significant body of recent real-time research, but research is often hampered by the "black box" nature of GPU hardware and software. Now that one GPU manufacturer, AMD, has embraced an open-source software stack, one may expect an increased amount of real-time research to use AMD GPUs. Reality, however, is more complicated. Without understanding where internal details may differ, researchers have no basis for assuming that observations made using NVIDIA GPUs will continue to hold for AMD GPUs. Additionally, the openness of AMD's software does not mean that their scheduling behavior is obvious, especially due to sparse, scattered documentation. In this paper, we gather the disparate pieces of documentation into a single coherent source that provides an end-to-end description of how compute work is scheduled on AMD GPUs. In doing so, we start with a concrete demonstration of how incorrect management triggers extreme worst-case behavior in shared AMD GPUs. Subsequently, we explain the internal scheduling rules for AMD GPUs, how they led to the "worst practices," and how to correctly manage some of the most performance-critical factors in AMD GPU sharing. [ABSTRACT FROM AUTHOR]

In this paper, we study the construction of a cyclic appointment schedule in an outpatient department. In particular, we determine the capacity distribution between elective and urgent patients and the scheduling of the time slots reserved for these patients such that the operational waiting times of elective and urgent patients are minimised. The proposed solution methodology devises a Pareto set of cyclic appointment schedules based on these waiting times with different capacity allocations for urgent patients. An approximation of the Pareto set of non-dominated schedules is obtained using a multi-objective archived simulated annealing heuristic. To accurately validate the cyclic appointment schedules, we incorporate operational decision-making via scheduling individual patients. To this end, we simulate operational variability, i.e., patient arrivals, no-show behaviour, punctuality and scan durations, based on real-life input data. The patients are assigned one-by-one using an online scheduling rule. Computational experiments are conducted with a real-life case study. We compare different appointment scheduling rules and discuss the impact of the capacity distribution between elective and urgent patients and the timing of urgent slots in the cyclic appointment schedules. The results show that the distribution of capacity between patient types, the timing of urgent slots and appointment rules all have significant impacts on patient waiting times. Appointment waiting times improve when urgent slots are spread equally over and throughout the days considered and when the Bailey–Welch rule is used to schedule patients. Trade-offs between elective and urgent waiting times resulting from different capacity distributions or slot timing are exemplified via a Pareto front. The proposed method outperforms relevant single-pass methodologies, and we demonstrate that its performance is strengthened thanks to the integrated optimisation of strategic, tactical and operational decisions. [ABSTRACT FROM AUTHOR]

All multi-criteria decision making (MCDM) techniques used with discrete event simulation (DES) in previous literature in the analysis of healthcare systems assume that performance measures are independent, while in fact many are correlated. In this study, correlations between performance measures are taken into consideration using modified TOPSIS. A framework is developed in which DES is used to model multiple experiments and factor levels to estimate various performance measures. Design of experiments is used to study the effects of factors and their interactions. Experiments are input to modified TOPSIS as alternatives to be compared based on multiple correlated performance measures. A case study is presented at Educational Dentistry Clinics in Jordan. This study was designed to improve overall clinic performance. Results show that when using appropriate appointment scheduling rules and orthodontist working schedules, and exploiting the resources effectively, the best scenario obtained shows an effective decrease in orthodontic patient waiting times and length of stay (LOS), and primary diagnosis patients LOS by 29.44%, 37.74% and 7.12%, respectively. It also shows an increase in orthodontist and receptionist utilizations, and number of patients served by 11.22%, 65.82% and 69.39%, respectively. The results obtained from modified TOPSIS were more accurate as compared to traditional TOPSIS. As a future work, other appointment rules can be tested and other MCDM techniques that take criteria correlations into consideration can be used to rank the experiments such as the analytic network process. [ABSTRACT FROM AUTHOR]

Effective energy-aware scheduling is paramount for current and future multiprocessor real-time systems, being Dynamic Power Management (DPM) one of the employed techniques. In this paper we extend the Reduction to Uniprocessor (RUN) algorithm making it DPM-compliant. RUN is an optimal multiprocessor real-time scheduling for periodic implicit-deadline tasks and it is known to generate low overhead in terms of preemptions and migrations. It is based on an off-line reduction of the target multiprocessor system into one or more uniprocessor systems. On-line scheduling decisions for the latter is then efficiently translated back to the original system. The developed approach in this paper, called Dynamic Slack Filling (DSF-RUN), extends RUN in two aspects. First, the RUN reduction process is adapted to take into account slack entities, properly defined to represent processor spare capacity for DPM management. Second, on-line scheduling rules of RUN are modified so as to generate long time periods in the schedule by dynamically inserting slacks. Our approach has the same off-line complexity of the original RUN algorithm and takes either quadratic or linear on-line complexity in the number of tasks, depending on the strategy used to compute the maximum allowed idle periods. Results obtained with simulations show that our approach consumes less than 10% more energy than an idealized optimum DPM strategy. [ABSTRACT FROM AUTHOR]

A robust optimal scheduling method driven by multi-objects is proposed for the collaborative optimization problem between dynamic scheduling, preventive maintenance of equipment, and robustness of scheduling schemes in a complex manufacturing system. Firstly, the equipment maintenance task is mapped to the process level, and composite dispatching rules with weight parameters are designed, which flexibly consider equipment maintenance and system processing status. Secondly, the performance-driven ideology is analyzed through two models based on the IWOA-MLP algorithm. Thirdly, the feedback mechanism ideology facilitates adaptive closed-loop optimizations. Finally, a series of experiments were carried out on the simulation platform of a semiconductor manufacturing enterprise in Shanghai. The experimental results show that the proposed robust optimal scheduling system can effectively deal with mixed uncertainty, improve production performances, and maintain highly robust measures. [ABSTRACT FROM AUTHOR]

This paper addresses the crew scheduling problem for a mass rapid transit (MRT) system. The problem is to find a minimum number of duties to cover all tasks while satisfying all the hard and soft scheduling rules. Such rules are complicated in real-world operations and difficult to follow through optimization methods alone. In this paper, we propose a constraint programming (CP)-based approach to solve the problem. The approach involves a CP model for duty generation, a set covering problem model for duty optimization, and alternative ways to identify the final solution in different situations. We applied the proposed CP-based approach to solve a case problem for the Taipei MRT. Case application results using real-world data showed that our approach is capable of reducing the number of daily duties from 58 to 55 and achieving a 5.2 % savings in labor costs. We also incorporated the soft rule considerations into the CP model in order to generate alternative optimum solutions that would improve the workload balance. The coefficient of variation of the work time distribution improves significantly, falling from 21 % to approximately 5 %. Given the CP model's comprehensive coverage of various scheduling rules, our proposed approach and models would also be applicable to other MRT systems. [ABSTRACT FROM AUTHOR]

In the spirit of Little's law $$L=\lambda W$$ and its extension $$H=\lambda G$$ we use sample-path analysis to give a general conservation law. For queueing models the law relates the asymptotic average workload in the system to the conditional asymptotic average sojourn time and service times distribution function. This law generalizes previously obtained conservation laws for both single- and multi-server systems, and anticipating and non-anticipating scheduling disciplines. Applications to single- and multi-class queueing and other systems that illustrate the versatility of this law are given. In particular, we show that, for anticipative and non-anticipative scheduling rules, the unconditional delay in a queue is related to the covariance of service times and queueing delays. [ABSTRACT FROM AUTHOR]

This paper presents the salient aspects of developing simulation-based metamodels for scheduling a typical flexible manufacturing system (FMS) operating in a tool-sharing environment. A discrete-event simulation model of the FMS is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model for part scheduling decision. The performance measures considered for analysis are mean flow time, mean tardiness, and percentage of tardy parts. Simulation experiments have been carried out for various scenarios arising out of the settings of the mean interarrival time of parts for processing in the system and due-date factor. The simulation results are used to develop regression-based metamodels. These metamodels have been subjected to systematic analysis. The metamodels are found to offer a good prediction of the performance of FMS within the domain of their definition. [ABSTRACT FROM AUTHOR]

This paper investigates a three-competing group scheduling problem on serial-batching machines considering the setup time of groups and batches, as well as the job-dependent deteriorating effect, where the setup time of groups and batches depends on their own corresponding start time and deterioration rate, and the jobs' actual processing time depends on their starting time and deterioration rate. In this problem, the jobs in three groups are processed competitively, and the jobs belonging to the same group are processed together on a machine, meanwhile, the jobs from each group are divided into batches. The objective is to minimize the makespan of one group with deterioration effect under the constraint of satisfying the threshold value of another group. Some key structural properties are proposed under the relevant demonstration, and a decision flow chart of scheduling rules is constructed based on these structural properties. Then, an effective improved differential evolution (DE) search algorithm combining the shaking operation from variable neighborhood search is developed to solve the studied scheduling problem. Computational experiments are conducted to evaluate the performance of proposed improved DE algorithm and some other well-known algorithms. The experimental results show that the proposed algorithm is more effective and stable than compared algorithms. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing industry, traditional scheduling rules are not conducive to improving production capacity to autonomously adjust based on real-time status. To fill this gap, this study proposes a dynamic dispatching rule based on self-organization (DDRSO) to autogenerate optimal scheduling scheme through mechanisms of interaction, coordination and competition. Besides, an extended DDRSO is proposed to further consider hot lots and transient dynamic bottlenecks. Both DDRSO and E-DDRSO are designed from three aspects: role definition of self-organization units, negotiation mechanism among self-organization units, and decision methods. This research adopts a benchmark industrial manufacturing system to illustrate the availability of the proposed approach. Compared with heuristic dispatching strategies, DDRSO achieves improvement on MOV, TH and ODR by 4.9%, 9.06% and 20.23%, respectively. Meanwhile, E-DDRSO performs better than DDRSO under all workload levels. In addition, compared with a flexible dispatching method BPSO-SVM, E-DDRSO also obtain better performances, especially improvement on CT by 16.51%. [ABSTRACT FROM AUTHOR]

This paper investigates a competitive two-agent parallel-batching scheduling problem with aging effect on parallel machines. The objective is to minimize the makespan of agent A with the constraint that the makespan of agent B is no more than a given threshold. Some key structural properties are first identified in two different cases, and based on these structural properties a novel decision tree of scheduling rules is constructed and a heuristic algorithm is designed. Then, an effective hybrid BF-VNS algorithm combining Bacterial Foraging (BF) with variable neighborhood search (VNS) is developed to tackle the studied problem. Computational experiments are conducted to evaluate the performance of the proposed hybrid algorithm and some other well-known algorithms. The experimental results indicate that the hybrid BF-VNS algorithm performs quite better than the compared algorithms. [ABSTRACT FROM AUTHOR]

This study presents the development of a multi-criteria control methodology for flexible manufacturing systems (FMSs). The control methodology is based on a two-tier decision making mechanism. The first tier is designed to select a dominant decision criterion and a relevant scheduling rule set using a rule-based algorithm. In the second tier, using a look-ahead multi-pass simulation, a scheduling rule that best advances the selected criterion is determined. The decision making mechanism was integrated with the shop floor control module that comprises a real-time simulation model at the top control level and RapidCIM methodology at the low equipment control level. A factorial experiment was designed to analyze and evaluate the two-tier decision making mechanism and the effects that the main design parameters have on the system’s performance. Next, the proposed control methodology was compared to a selected group of scheduling rules/policies using DEA. The results demonstrated the superiority of the suggested control methodology as well as its capacity to cope with a fast changing environment. [ABSTRACT FROM AUTHOR]

Cloud computing has a powerful capability to handle a large number of tasks. However, this capability comes with significant energy requirements. It is critical to overcome the challenge of minimizing energy consumption within cloud service platforms without compromising service quality. In this paper, we propose a heuristic energy-saving scheduling algorithm, called Real-time Multi-workflow Energy-efficient Scheduling (RMES), which aims to minimize the total energy consumption in a container-based cloud. RMES schedules tasks in the most parallelized way to improve the resource utilization of the running machines in the cluster, thus reducing the time of the global process and saving energy. This paper also considers the affinity constraints between containers and machines, and RMES has the ability to satisfy the resource quantity and performance requirements of containers during the scheduling process. We introduce a re-scheduling mechanism that automatically adjusts the scheduling decisions of remaining tasks to account for the dynamic system states over time. The results show that RMES outperforms other scheduling algorithms in energy consumption and success rate. In the higher arrival rate scenario, the proposed algorithm saves energy consumption by more than 19.42%. The RMES approach can enhance the reliability and efficiency of scheduling systems. [ABSTRACT FROM AUTHOR]

The container terminal is a key node in global trade and logistics, where trucks connect quay cranes, storage yards, and vessels. Optimizing truck scheduling is crucial for enhancing port efficiency by addressing issues such as low truck utilization, excessive quay crane waiting times, and extended equipment completion times. This paper develops a container terminal simulation model based on graph theory, with the objective of minimizing the maximum completion time of terminal equipment. A collaborative scheduling algorithm for truck fleets, based on Deep Double Q-Networks (DDQN), is proposed. The algorithm designs five heuristic rules as the action space and refines state features and reward functions to optimize scheduling effectively. Experimental results indicate that this algorithm consistently identifies optimal scheduling strategies, outperforming both the five heuristic rules and the Deep Q-Network (DQN) algorithm. It significantly reduces quay crane waiting times and equipment completion times, improves truck utilization, and enhances overall container terminal efficiency. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things has led to the explosive growth of smart devices connected to the internet, generating vast amounts of data. However, this growth has brought forth a set of challenges, including limited bandwidth, slow response times, and elevated security concerns, particularly within conventional cloud computing models. To tackle the demand for faster and more efficient data processing, a concept known as edge clouds has emerged. In contrast to traditional cloud networks, edge networks are physically closer to end-users. This geographic proximity empowers them to handle certain user processing needs without the necessity of transferring data to a central cloud layer. This approach not only alleviates the load on the central cloud but also provides users connected to the edge with faster and higher-quality services. Nevertheless, edge networks surrounded by inherent limitations, including constraints on processing resources, storage capacity, and communication bandwidth. User-required services are organized into chains of functions, forming specific sequences that serve as requests within the edge network. Given these constraints, it becomes vital to establish these service chains with minimal delay while efficiently utilizing the available edge resources. This entails scheduling virtual functions to run on edge resources in a manner that expedites resource release and reduces the overall response time for incoming requests. This research presents an innovative approach grounded in Deep Q-learning for Service Function Chain management. It selects the most suitable nodes on which the Virtual Network Functions have already been deployed and are ready for chaining into service function chains (SFCs). Then to achieve an optimum Quality of Experience an scheduling mechanism will be used to reorder the functions within the chains to achieve the shortest total completion time of chains. Going beyond traditional SFC chaining, our study explores simultaneous scheduling for multiple SFCs, setting it apart from prior efforts. Significantly, this research pioneers the use of Deep Q-Learning for joint resolution of SFC chaining and scheduling of multiple SFCs, offering a comprehensive analysis in comparison with two distinct scenarios; focusing solely on chaining or concentrating exclusively on scheduling. Employing a wide set of synthetic SFC requests, evaluation results shows an average of 58% reduction in total completion time of SFCs, 43% reduction in request rejection ratio, and almost 23% better resource utilization which is crucial in edge computing. [ABSTRACT FROM AUTHOR]

The flexible job shop scheduling problem (FJSP) holds significant importance in both theoretical research and practical applications. Given the complexity and diversity of FJSP, improving the generalization and quality of scheduling methods has become a hot topic of interest in both industry and academia. To address this, this paper proposes a Preference-Based Mask-PPO (PBMP) algorithm, which leverages the strengths of preference learning and invalid action masking to optimize FJSP solutions. First, a reward predictor based on preference learning is designed to model reward prediction by comparing random fragments, eliminating the need for complex reward function design. Second, a novel intelligent switching mechanism is introduced, where proximal policy optimization (PPO) is employed to enhance exploration during sampling, and masked proximal policy optimization (Mask-PPO) refines the action space during training, significantly improving efficiency and solution quality. Furthermore, the Pearson correlation coefficient (PCC) is used to evaluate the performance of the preference model. Finally, comparative experiments on FJSP benchmark instances of varying sizes demonstrate that PBMP outperforms traditional scheduling strategies such as dispatching rules, OR-Tools, and other deep reinforcement learning (DRL) algorithms, achieving superior scheduling policies and faster convergence. Even with increasing instance sizes, preference learning proves to be an effective reward mechanism in reinforcement learning for FJSP. The ablation study further highlights the advantages of each key component in the PBMP algorithm across performance metrics. [ABSTRACT FROM AUTHOR]

The concept of green scheduling, which deals with the environmental impact of the scheduling process, is becoming increasingly important due to growing environmental concerns. Most green scheduling problem variants focus on modelling the energy consumption during the execution of the schedule. However, the dynamic unrelated machines environment is rarely considered, mainly because it is difficult to manually design simple heuristics, called dispatching rules (DRs), which are suitable for solving dynamic, non-standard scheduling problems. Using hyperheuristics, especially genetic programming (GP), alleviates the problem since it enables the automatic design of new DRs. In this study, we apply GP to automatically design DRs for solving the green scheduling problem in the unrelated machines environment under dynamic conditions. The total energy consumed during the system execution is optimised along with two standard scheduling criteria. The three most commonly investigated green scheduling problem variants from the literature are selected, and GP is adapted to generate appropriate DRs for each. The experiments show that GP-generated DRs efficiently solve the problem under dynamic conditions, providing a trade-off between optimising standard and energy-related criteria. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0 and intelligent manufacturing, AGVs are widely used in flexible job shop resource transportation, which sharply increases the uncertainty and complexity of the scheduling process. For this reason, an improved double Deep Q Network (DDQN) real-time scheduling method is proposed for the Flexible Job Shop Scheduling Problem with Automated Guided Vehicle (FJSP-AGV) to minimize the makespan. Firstly, the optimization model of the FJSP-AGV is established, and the corresponding constraints and the objective function are defined. Then, the FJSP-AGV is converted into a Markov Decision Process (MDP), in which the state space, action space, and reward function are defined in detail. Next, an improved DDQN is proposed to generate the optimal scheduling policy considering AGV. Finally, the computational experiments are conducted based on data from public benchmarks and the real-world flexible job shop, and the results demonstrate the accuracy and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the development of a simulation software using the features of visual interactive computer graphics for analysing the dynamic scheduling of a flexible manufacturing system (FMS) to cater to the need of fluctuating markets. While carrying out this work, the main concepts of dynamic modelling, computer simulation and visual interactive graphics have been used for studying the reduction in backlogs of workorders. In addition, queue length, waiting time and the capacity utilisation of the manufacturing system are also considered. The arrival pattern of components is fitted to a suitable distribution pattern based on the workorder position. The effects of despatching rules such as SPT, LPT, EDD, SIO, SLACK, SLACK/LRO on scheduling are analysed using the simulator to imitate the real life operation of FMS. Visual interactive computer graphics enables the user to find out machine utilisation levels, queue length of the components, throughput and comparison between the application of various despatching rules. Since dynamic queue records are maintained, it is possible to establish the various components waiting in the respective work centres and their attributes like lateness, slack, remaining operations etc. This software is very useful in enabling the user to select the appropriate despatching rule for the system. The software has been developed in the 'C' language for IBM/PC compatible systems. [ABSTRACT FROM AUTHOR]

Healthcare providers can benefit from adding less costly capacity to their existing resources in order to satisfy demand while maintaining the quality of patient care. The addition of mid-level service providers (MLSPs) such as physician assistants or nurse practitioners that carry out portions of patient care provides a viable alternative for adding physician capacity. This research considers the circumstances under which adding an MLSP to a single-physician outpatient office becomes the best strategy for the clinic, and determines how scheduling policies from the widely-researched single-stage environment should be adjusted for a multi-stage environment. Compared to a single-stage system where a physician completes all portions of the service, we show that adding an MLSP can reduce patient waiting time, patient flow time, and physician service time with patients. This, in turn, can enable the clinic to see more patients and/or free up physician time for other tasks. Appointment scheduling rules are developed for a multi-stage outpatient service system using a simulation optimization approach. Performance measures focus on the patient experience and clinic operation before and during each stage of service. [ABSTRACT FROM AUTHOR]

With ever-increasing concerns about climate change, and in the context of diminishing fossil energy resources, energy consumption has become a subject of great importance. In this framework, our study is concerned with the reduction of the energy consumed by a manufacturing system composed of several unrelated parallel machines. We investigate the problem of scheduling a set of n deteriorating jobs on m parallel unrelated machines subject to a deterioration effect, where the processing time of each job is a linear deterioration function of the starting time. The objective is to determine the best scheduling of the jobs, minimizing the makespan and the total energy consumption. We implement a nondominated sorting-based multiobjective algorithm (NSGA-II)-based approaches with a given scheduling rules to solve the developed mathematical model of the considered problem. The performance of the proposed approaches are compared with an exact algorithm and evaluated through computational experiments. Then, we give a multiobjective decision-making method based on the TOPSIS (technique for order of preference by similarity to ideal solution) technique to determine the best solution from the Pareto front according to the decision-maker's preferences. [ABSTRACT FROM AUTHOR]

This paper presents two hyper-heuristic approaches for solving a facility location problem called reliable p-median problem with at facility service (RpMF). In RpMF, service is provided to customers at the facility locations and it is closely related to the p-median problem. p-median problem is concerned with locating p-facilities while minimizing the total distance traveled by the customers to the corresponding nearest facilities and it is an N P -hard problem. But according to the p-median problem, it doesn’t consider the possibility of facility failures. On the other hand, RpMF assumes that facilities can fail and the customers assigned to that facility do not know about the facility failure till they reach the facility for service. So, the customers have to travel from failed facilities to other functioning facilities to receive service. RpMF deals with locating p facilities to minimize the cost of serving the customers while considering facility failures. We have proposed two multi-start hyper-heuristic based approaches that are based on greedy and random selection mechanisms to solve the RpMF. The solutions obtained through hyper-heuristics are improved further via a local search. The two proposed hyper-heuristic approaches are evaluated on 405 RpMF benchmark instances from the literature. Experimental results prove the effectiveness of the proposed approaches in comparison to the state-of-the-art approaches available in literature for the RpMF. [ABSTRACT FROM AUTHOR]

Demand-Driven Material Requirements Planning (DDMRP) was designed to improve supply chain performance in complex and uncertain environments. Literature on the topic suggests that production replenishment orders should be dispatched for execution based on the buffers' penetration ratio of the products ordered, which is a measure of protection against stock depletion. However, the actual performance impact of this dispatching rule remains largely unknown as is the impact of different lot transfer policies. A simulation analysis was carried out to compare the performance of the lowest net flow position, the highest buffer penetration ratio, earliest operation due date and first-come first-served rules under synchronized and unsynchronized lot transfer policies. Results of our study show that the choice of dispatching rules is contingent on the setting of top-of-yellow and top-of-green, which determine the re-order quantity, and on the demand mix of products. The earliest operation due date rule shows great potential to outperform the rule typically applied in a DDMRP context specifically for a high demand mix. These findings provide important insights for improving industrial practice and for guiding future research on DDMRP. [ABSTRACT FROM AUTHOR]

This paper proposes a novel evolutionary status guided hyper-heuristic algorithm named ES-HHA for continuous optimization. A representative hyper-heuristic algorithm consists of two components: the low-level component and the high-level component. In the low-level component, to balance the exploitation and exploration during optimization, we design an exploitative operator pool and an explorative operator pool as low-level heuristics (LLHs), where the former is constructed using local search based operators, and the latter consists of various mutation operators from differential evolution (DE). In the high-level component, we design a probabilistic selection function based on the fitness distance correlation (FDC) and the population diversity (PD). Since these two metrics can reflect the complexity of the fitness landscape and the status of the evolutionary swarm, the integration of these two metrics is expected to determine the sequence of heuristics automatically and intelligently. To evaluate the performance of our proposal, we implement comprehensive numerical experiments on CEC2014, CEC2022, and eight engineering optimization tasks. A total of 14 famous optimization approaches are adopted as competitors. Furthermore, the ablation experiment is conducted to evaluate the high-level component independently, while the sensitivity experiment contributes to determining the optimal hyperparameter setting. The experimental results and statistical analysis show that ES-HHA is competitive, and the evolutionary status guided probabilistic selection function can determine the optimization intelligently. [ABSTRACT FROM AUTHOR]