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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 focuses on essential aspects of production scheduling in a specific job shop environment, with the primary objective of optimizing operational efficiency. Through a detailed simulation conducted in the Lekin application, a Job Shop Scheduling (JSS) system developed by M. Pinedo, we introduced four distinct jobs, each characterized by specific parameters, such as processing times at machines and due dates. We explore various scheduling rules, including Earliest Due-Date (EDD), Longest Processing Time (LPT), Shortest Processing Time (SPT), First Come First Serve (FCFS), Critical Ratio (CR), and Minimum Slack (MS), assessing their impact on system efficiency and performance. Each job, with its unique requirements and constraints, adds complexity to the production process, and our goal is to identify optimal strategies for managing these challenges in a dynamic and complex environment. The detailed analysis of simulation results and the comparison between different scheduling rules will contribute to the development of efficient methodologies for optimizing production scheduling in a diverse and constantly changing context. The outcomes of this paper have the potential to make significant contributions to enhancing the performance of production scheduling systems, addressing the specific requirements of contemporary industries. [ABSTRACT FROM AUTHOR]

This paper focuses on essential aspects of production scheduling in a specific job shop environment, with the primary objective of optimizing operational efficiency. Through a detailed simulation conducted in the Lekin application, a Job Shop Scheduling (JSS) system developed by M. Pinedo, we introduced four distinct jobs, each characterized by specific parameters, such as processing times at machines and due dates. We explore various scheduling rules, including Earliest Due-Date (EDD), Longest Processing Time (LPT), Shortest Processing Time (SPT), First Come First Serve (FCFS), Critical Ratio (CR), and Minimum Slack (MS), assessing their impact on system efficiency and performance. Each job, with its unique requirements and constraints, adds complexity to the production process, and our goal is to identify optimal strategies for managing these challenges in a dynamic and complex environment. The detailed analysis of simulation results and the comparison between different scheduling rules will contribute to the development of efficient methodologies for optimizing production scheduling in a diverse and constantly changing context. The outcomes of this paper have the potential to make significant contributions to enhancing the performance of production scheduling systems, addressing the specific requirements of contemporary industries. [ABSTRACT FROM AUTHOR]

Traditional sheet metal processing enterprises are facing problems such as upgrading manufacturing systems, lower material utilization rate, and lower efficiency of CNC machining. In this paper, we design a flexible manufacturing production line for sheet metal processing of complex parts, use the DQN algorithm to optimize the scheduling of each part of the work in the production process of the production line, and optimize the adaptability of the DQN algorithm by using the action selection strategy and the reward function and so on. Then, the digital twin simulation system is constructed to simulate the process of a flexible manufacturing production line for sheet metal processing. The results show that the DQN algorithm can effectively assign the corresponding processing machines and processes to the workpieces in the production line, and the digital twin simulation system can also accurately simulate the actual situation of the production line. Meanwhile, it is found that the optimized production line can reach 87.52% of the production beat balance rate, which is 23.64% higher than the original production line balance rate, and it can improve the average utilization rate of the main operating units in the production line. The optimized production line proposed in this paper can effectively solve the practical problems in sheet metal production lines, improve the mobility of the production line and corporate efficiency, and provide new ideas for the lean production management of large-scale machinery manufacturing enterprises.

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To exploit the scheduling potential of cross-basin reservoir groups by employing runoff information,this paper proposes an improved reservoir scheduling diagram to retain simplicity and intuitiveness.Taking the Xiajiankou,Fengyan,and Nanpeng reservoirs in Banan District of Chongqing as an example,it adopts the POA algorithm to build a joint scheduling model of water diversion and water supply for the cross-basin reservoir groups by a simulation-optimization approach.Meanwhile,three scenarios of the current situation scheduling,optimized scheduling with conventional scheduling diagrams,and optimized scheduling with improved scheduling diagrams are set up to evaluate the water diversion and supply performance of reservoirs and analyze the influence of uncertainty runoff information on the scheduling performance.The results are as follows:① The improved scheduling diagram performs best in the long series scheduling,and it can increase the annual average water supply of the reservoir group by 6.87% and reduce the abandoned water by 87.58% compared with the current situation scheduling;② In a dry year,the method can reduce 486.3×104 m3 of water shortage and increase 477.6×104 m3 of water for power generation based on the current situation scheduling;③ When considering the runoff information uncertainty,the water supply of the improved scheduling diagram is 3.37% lower than the ideal case but is still 5.96% more than the conventional optimization.The results are conducive to making scientific decisions and improving the water supply effect.

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results. [ABSTRACT FROM AUTHOR]

In this study, we address a production scheduling problem. The scheduling problem is encountered in a job-shop production type. The production system is discrete and dynamic system in which jobs arrive continually. We introduce a simulation model (SM) to identify several situations such as machine failures, changing due dates in which scheduling rules (SRs) should be selected independently. Three SRs, i.e. the earliest due date rule (EDD), the shortest processing time first rule (SPT) and the first in first out rule (FIFO), are incorporated in a SM. A simulated annealing heuristic (SA) based simulation-optimisation approach is proposed to identify the unknown schedules in the dynamical system. In the numerical analysis, the performance of SRs and SA are compared using the simulation experiments. The objective functions minimising the mean flowtime and the mean tardiness are examined with varying levels of shop utilization and due date tightness. As an overall result, we observe that the proposed SA heuristic outperforms EDD and FIFO, the well-known SPT rule provides the best results. However, SA heuristic achieves very close results to the SPT and offers a reasonable computational burden in time-critical applications.

Bu çalışmada, bir üretim çizelgeleme problem ele alınmaktadır. Bu çizelgeleme problemine atölye tipi bir üretim tipinde karşılaşılmaktadır. Üretim sistemi sürekli iş gelişlerinin söz konusu olduğu kesikli dinamik sistemdir. Çizelgeleme kurallarının birbirinden bağımsız şekilde kullanılmasının gerektiği makine bozulmaları ve değişen teslim süreleri gibi bazı durumların değerlendirilmesi için bir simülasyon modeli sunulmaktadır. En erken teslim süresi, en kısa işlem süresi ve ilk giren ilk çıkar kuralı olmak üzere üç çizelgeleme kuralı bu simülasyon modeline dahil edilmiştir. Dinamik sistemdeki belirsiz çizelgeleri ortaya koymak için tavlama benzetimi sezgiseli tabanlı bir simülasyon optimizasyonu yöntemi önerilmektedir. Sayısal analizlerde çizelgeleme kurallarının ve önerilen tavlama benzetimi sezgiselinin performansları simülasyon deneyleri kullanılarak kıyaslanmıştır. Ortalama akış süresini ve ortalama gecikme süresini en küçükleyen amaç fonksiyonları farklı seviyelerdeki atölye kullanım oranı ve teslim süresi durumlarında incelenmiştir. Genel bir sonuç olarak, önerilen tavlama benzetimi sezgiselinin en erken teslim zamanı ve ilk giren ilk çıkar kurallarından daha iyi sonuç verdiği, en kısa işlem süresi kuralının en iyi sonuçları sağladığı gözlenmiştir. Fakat tavlama benzetimi sezgiseli en kısa işlem süresi kuralına çok yakın sonuçlara erişmektedir ve çözüm zamanının kritik olduğu uygulamalarda kabul edilebilir bir hesaplama yükü getirmektedir.

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 have been conducted under experimental conditions characterised by different setup time ratios. The simulation results are analysed using statistical significance tests. 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 increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures. Multiple linear regression based metamodels have been developed for the best performing scheduling rules. [ABSTRACT FROM AUTHOR]

In this study, we address a production scheduling problem. The scheduling problem is encountered in a job-shop production type. The production system is discrete and dynamic system in which jobs arrive continually. We introduce a simulation model (SM) to identify several situations such as machine failures, changing due dates in which scheduling rules (SRs) should be selected independently. Three SRs, i.e. the earliest due date rule (EDD), the shortest processing time first rule (SPT) and the first in first out rule (FIFO), are incorporated in a SM. A simulated annealing heuristic (SA) based simulation-optimisation approach is proposed to identify the unknown schedules in the dynamical system. In the numerical analysis, the performance of SRs and SA are compared using the simulation experiments. The objective functions minimising the mean flowtime and the mean tardiness are examined with varying levels of shop utilization and due date tightness. As an overall result, we observe that the proposed SA heuristic outperforms EDD and FIFO, the well-known SPT rule provides the best results. However, SA heuristic achieves very close results to the SPT and offers a reasonable computational burden in time-critical applications. [ABSTRACT FROM AUTHOR]

For solving a NP-hard crane scheduling problem with spatio-temporal constraints in steelmakingcontinuous casting process, a mathematical programming model was built to minimize the total waiting time of tasks, the total transportation time of tasks and the difference among the transportation time of cranes by considering full ladles and empty ladles as transportation tasks. The operation characters of cranes were concluded as such three rules, including the matching rule designed for choosing crane according to the relationship between the position of crane and the transportation task in the crane selection process, the updating rule designed for describing the evolution of state and position of cranes in the running process, the priority rule of transportation task according to the category and estimated transportation time of task designed for choosing collision avoidance crane in the collision resolution process. An improved genetic algorithm was used to solve the model. To validate this algorithm, experiments were conducted by using the production data in the casting span of a steel plant. By comparing with the results of tabu search algorithm, the feasibility and efficiency of improved genetic algorithm was verified, which provides a useful tool for crane scheduling in actual production. [ABSTRACT FROM AUTHOR]

A vascular surgery clinic in an academic center faced many delays for patients in gaining access to an appointment. In January 2014, wait times from referral to patient appointment were estimated to be 3 to 4 weeks. A 6-month pilot project was conducted from January 2014 through June 2014 with the aim of scheduling 50% of new patients within 7 days of referral. The clinic manager, medical director, clinic staff, and clerical team worked in partnership to initiate a series of interventions to reduce the backlog of patients, implement scheduling guidelines, and use nurse practitioners to improve practice efficiencies. By June 2014, the vascular surgery clinic achieved the goals of increased accommodation of new patients and a positive trend patient satisfaction comments as surveyed by Press Ganey. Performance outcomes in both areas were sustained for 6 consecutive months. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a proposed methodology to select optimal scheduling rule for robotic flexible assembly cells based fuzzy decision support system. Six common scheduling rules are considered, and three independent objectives, namely, the minimising of makespan (Cmax), total tardiness (TD) and percentage of tardy jobs (%T) are measured. A fuzzy decision support system is implemented using the Matlab fuzzy toolbox. The final results demonstrate the effectiveness of the proposed methodology to select optimal scheduling rule in a multi-product assembly environment. [ABSTRACT FROM AUTHOR]

The inter-basin water transfer operation belongs to the conventional water transfer planning operation mode, and the real-time information is not considered in the operation. To solve this problem, the decision tree method in data mining is used combining the current reservoir forecast information, underlaying surface water storage condition, perennial reservoir running situation and other data with the reservoir managers' actual operation experiences, and then, the inter-basin water transfer real-time operation rules can be realized. Research process has three steps. Firstly, initial reservoir water level, actual rainfall, GFS forecasting rainfall, soil moisture, diversion water quantity, etc. are selected to compose the reservoir operation data set. Secondly, the inter-basin water transfer operation decision tree is extracted by using data mining technology. Finally, the operation decision tree is tested and the inter-basin water transfer real-time operation rules are obtained. The actual example results show that using decision tree inter-basin water transfer operation rules in the reservoir operating can increase the water resource efficiency and the reservoir comprehensive benefits. This research result has some reference value for the further study and application of the inter-basin water transfer real-time operation. [ABSTRACT FROM AUTHOR]

We consider the transport of containers through a fleet of ships. Each ship has a capacity constraint limiting the total number of containers it can carry and each ship visits a given set of ports following a predetermined route. Each container has a release date at its origination port, and a due date at its destination port. A container has a size 1 or size 2; size 1 represents a 1 TEU (20-foot equivalent unit) and size 2 represents 2 TEUs. The delivery time of a container is defined as the time when the ship that carries the container arrives at its destination port. We consider the problem of minimizing the maximum tardiness over all containers. We consider three scenarios with regard to the routes of the ships, namely, the ships having (i) identical, (ii) nested, and (iii) arbitrary routes. For each scenario, we consider different settings for origination ports, release dates, sizes of containers, and number of ports; we determine the computational complexity of various cases. We also provide a simple heuristic for some cases, with its worst case analysis. Finally, we discuss the relationship of our problems with other scheduling problems that are known to be open. [ABSTRACT FROM AUTHOR]

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 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]

In recent years, growing quantities of end-of-life electronics have increased the amount of attention devoted to product recovery. Research on end-of-life electronics returns has primarily focused on manual disassembly operations. In this paper, we focus on the scheduling problem for a facility with staging, manual disassembly operations, and bulk recycling. In bulk recycling, shredding or grinding reduces the size of the material fragments while magnetic, eddy current or other density separation techniques separate the material fragments. Unlike production, there are often no due dates in materials recovery processing. Recyclers can sell the recovered materials to material commodity buyers at any time. However, recyclers wait to accumulate a shipment of material to reduce transportation costs and meet minimum sales quantities. Another important difference between production and recycling is that manufacturers purchase raw materials while recyclers may be paid to receive products. When due dates do not apply to scheduling products for materials recycling and product receipts generate revenue for recycling services, we propose two new metrics: the staging space turnover and the shipment fill time. We use our metrics to analyze new scheduling rules for disassembly and bulk recycling and to evaluate their performance. Using discrete-event simulation models, we test our scheduling rules on seven product families, where product families are defined based on material composition and separation operations. Of the rules we test, the disassembly scheduling rule which ranks product families based on the ratio of product size to disassembly time (SDT) most quickly empties the staging space. Shipment fill time is less sensitive to our scheduling rules. Our results illustrate how a recycler can reduce incoming product inventory with a new scheduling rule. [ABSTRACT FROM PUBLISHER]

Although the problem of scheduling dynamic job shops is well studied, setup and changeover times are often assumed to be negligibly small and therefore ignored. In cases where the product mix changes occur frequently, setup and changeover times are of critical importance. This paper applies some known results from the study of multi-class single-server queues with setup and changeover times to develop an approximation for evaluating the performance of job shops. It is found that the product mix, setup and changeover times, and scheduling rules affect the performance significantly, in particular at high levels of machine utilisation. This approach could be used to determine the required level of flexibility of machines and to choose an appropriate scheduling policy such that production rates remain within acceptable limits for foreseeable changes in the product mix. [ABSTRACT FROM AUTHOR]