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Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop. [ABSTRACT FROM AUTHOR]

Multi-factory production planning and scheduling problems have been increasingly studied by scholars recently due to market uncertainty, technological trends like Industry 4.0 and increasing collaboration. Geographically dispersed factories may provide cost-saving potential and increase efficiency while also being subjected to varying capabilities and restrictions such as capacity constraints and labour costs. Traditional approaches in production planning and scheduling focus on the allocation of demand to a single factory and obtain sequences of operations on machines in this factory. In the multi-factory or distributed setting, an additional task includes assigning orders to potential factories beforehand. Starting with the first case studies in the late 1990s, research has increasingly been devoted to this research field and has considered numerous variations of the problem. We review 128 articles on multi-factory production planning and scheduling problems in this contribution and classify the literature according to shop configuration, network structure, objectives, and solution methods. Bibliometric analysis and network analysis are utilised to generate new findings. Research opportunities identified include integration with other planning stages, an investigation of key real-life objectives such as due date compliance and examining dynamic characteristics in the context of Industry 4.0. Besides, empirical studies are necessary to gain new practical insights. [ABSTRACT FROM AUTHOR]

The industrial sector consumes half of the world delivered energy and is responsible for a third of carbon dioxide emissions which cause severe environmental pollution. The industry has to change its behaviour concerning the energy consumption. Since two-machine flow shop scheduling ( F 2 | p e r m | C m a x ) is one of the typical problems of the manufacturing industry, this paper aims to build an energy-cost-aware scheduling plan. This work tackles the joint optimisation of makespan and electricity cost in two-machine flow shop scheduling problem under electricity pricing. We enhance the financial aspect of the optimal solution of F 2 | p e r m | C m a x by minimising the electricity cost without increasing the makespan. Firstly, we show the contribution of the generation of several optimal equivalent solutions of F 2 | p e r m | C m a x . The optimal equivalent solutions have different electricity costs but present the same makespan. Then, we determine the optimal starting time of jobs on several equivalent optimal solutions to get the best production plan. Finally, the numerical tests show that our proposed approach improves the electricity cost significantly under optimal makespan. The results provide good solutions to managers and decision makers to achieve energy cost savings without sacrificing the productivity which can contribute to sustainable development of the manufacturing industry. [ABSTRACT FROM AUTHOR]

The traditional flexible job shop scheduling problem (FJSP) considers machine flexibility but not worker flexibility. Given the influence and potential of human factors in improving production efficiency and decreasing the cost in practical production systems, we propose a mathematical model of an extended FJSP with worker flexibility (FJSPW). A hybrid artificial bee colony algorithm (HABCA) is presented to solve the proposed FJSPW. For the HABCA, effective encoding, decoding, crossover and mutation operators are designed, and a new effective local search method is developed to improve the speed and exploitation ability of the algorithm. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the HABCA. Extensive computational experiments carried out to compare the HABCA with some well-performing algorithms from the literature confirm that the proposed HABCA is more effective than these algorithms, especially on large-scale FJSPW instances. [ABSTRACT FROM AUTHOR]

Job shop scheduling, as one of the classical scheduling problems, has been widely studied in literatures, and proved to be mostly NP-hard. Although it is extremely difficult to solve job shop scheduling with no-wait constraint to optimality, the two-machine no-wait job shop scheduling to minimise makespan could be solvable in polynomial time when each job has exactly two equal length operations (proportionate job shop). In the present paper, an extension is attempted by considering a proportionate flexible two-stage no-wait job shop scheduling problem with minimum makespan, and a set-covering formulation is put forward which contains a master problem and a pricing problem. To solve this problem, a column generation (CG)-based approach is implemented. In comparison, a mixed integer programming model is constructed and optimised by Cplex. A series of randomly generated numerical instances are calculated. And the testing result shows that the mixed integer model handled by Cplex can only solve small scale cases, while the proposed CG-based method can conquer larger size problems in acceptable time. [ABSTRACT FROM AUTHOR]

Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and the shop system, which not only limits the potential of energy-saving but also leads to a large deviation between the optimized result and the actual application. In practice, cutting-tool degradation during operation is inevitable, which will not only lead to the increase in actual machining power but also the resulting tool change operation will disrupt the rhythm of production scheduling. Therefore, to make the energy consumption calculation in scheduling optimization more consistent with the actual machining conditions and reduce the impact of tool degradation on the manufacturing shop, this paper constructs an integrated optimization model including a flexible job shop scheduling problem (FJSP), machining power prediction, tool life prediction and energy-saving strategy. First, an exponential function is formulated using actual cutting experiment data under certain machining conditions to express cutting-tool degradation. Utilizing this function, a reasonable cutting-tool change schedule is obtained. A hybrid energy-saving strategy that combines a cutting-tool change with machine tool turn-on/off schedules to reduce the difference between the simulated and actual machining power while optimizing the energy savings is then proposed. Second, a multi-objective optimization model was established to reduce the makespan, total machine tool load, number of times machine tools are turned on/off and cutting tools are changed, and the total energy consumption of the workshop and the fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the model. Finally, combined with the workshop production cost evaluation indicator, a practical FJSP example is presented to demonstrate the proposed optimization model. The prediction accuracy of the machining power is more than 93%. The hybrid energy-saving strategy can further reduce the energy consumption of the workshop by 4.44% and the production cost by 2.44% on the basis of saving 93.5% of non-processing energy consumption by the machine on/off energy-saving strategy. [ABSTRACT FROM AUTHOR]

In this study, the dynamic joint scheduling problem for processing machines and transportation robots in a flexible job shop is investigated. The study aims to minimize the order completion time (makespan) of a job shop manufacturing system. Considering breakdowns, order insertion and battery charging maintenance of robots, an event-driven global rescheduling strategy is adopted. A novel memetic algorithm combining genetic algorithm and variable neighborhood search is designed to handle dynamic events and obtain a new scheduling plan. Finally, numerical experiments are conducted to test the effect of the improved operators. For successive multiple rescheduling, the effectiveness of the proposed algorithm is verified by comparing it with three other algorithms under dynamic events, and through statistical analysis, the results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In a manufacturing system, production control‐related decision‐making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation‐based virtual testbed that implements dynamic optimization, automatic information exchange, and decision‐making from the process‐level, system‐level, and factory‐level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python‐based numerical computation program, discrete‐event simulation software (Simul8), and an optimization solver (CPLEX) via a third‐party master program. The application of this simulation‐based virtual testbed is illustrated by a case study in a machining shop. [ABSTRACT FROM AUTHOR]

To realize the dynamic scheduling of complex workpiece processing in complex workpiece job shop, a hybrid dynamic scheduling method with Digital Twin and improved bacterial foraging algorithm (IBFOA) is proposed to minimize the maximum completion time and machine load. During the actual workshop processing, the flexible job shop scheduling problem (FJSP) is divided into two sub-problems: machine assignment and process sequencing. The initial scheduling scheme is completed using an IBFOA to construct a Digital Twin flexible job shop scheduling model. Digital Twin model is used to solve the impact of workshop emergencies. Based on typical benchmark cases and real data from a machine company's mould shop, the machining shop production scheduling experiments are conducted. The results show that the scheduling scheme using the IBFOA combined with the Digital Twin can optimize the system performance as a whole and effectively deal with the problem of extended production time caused by disruption. The algorithm can obtain the most satisfactory scheduling solution and the effectiveness of solving the multi-objective FJSP are verified. (Received in April 2022, accepted in June 2022. This paper was with the authors 3 weeks for 1 revision.) [ABSTRACT FROM AUTHOR]

We investigate the problem to schedule a set of precedence constrained jobs of unit size on an open-shop or on a flow-shop to minimize the makespan. The precedence constraints among the jobs are presented as a directed acyclic graph called the precedence graph. When the number of machines in the shop is part of the input, both problems are strongly NP-hard on general precedence graphs, but were proven polynomial-time solvable for some special precedence graphs such as intrees. In this paper, we characterize improved lower bounds on the minimum makespan in terms of the number of agreeable pairings among certain jobs and propose approximation algorithms based on a maximum matching among these jobs, so that every agreeable pair of jobs can be processed on different machines simultaneously. For open-shop with an arbitrary precedence graph and for flow-shop with a spine precedence graph, both achieved approximation ratios are 2 -- m, where m is the number of machines in the shop. [ABSTRACT FROM AUTHOR]

This paper focuses on the multi-objective energy-efficient flexible job shop scheduling problem with machine breakdowns. To mitigate the impact of machine breakdowns, a rescheduling strategy is implemented in the scheduling process. In addition to sequencing the operations, the current problem is to determine the appropriate allocation of the machine and the proper speed of the machine to minimize both makespan and total energy consumption simultaneously. A mixed integer linear programming model is established to describe the considered problem. With the aim of effectively solving this problem, a knowledge-driven two-stage memetic algorithm (KTMA) is proposed. In the first stage, a hybrid initialization strategy that combines three problem-specific heuristics is applied to generate a high-quality initial population. Then, a knowledge-driven variable neighborhood search approach is designed for quickly converging and fully exploiting the solution space of the KTMA. In the second stage, two energy-saving strategies are designed to further reduce the total energy consumption. Extensive experiments carried out to compare the KTMA with some well-known algorithms confirm that the proposed KTMA can efficiently solve this problem. • A two-stage evolution framework is proposed for EMBFJSP. • A rescheduling strategy is applied for machine breakdowns. • The population is initialized by three problem-specific heuristics. • Four knowledge-driven variable neighborhood search operators are proposed. • Two types of energy-saving strategies are designed. [ABSTRACT FROM AUTHOR]

A flow shop sequencing problem is one of the classical problems in the production scheduling. In a flow shop, a particular case of manufacturing process follows a fixed linear structure. The purpose of this paper is to find the minimum total processing time (makespan) of sequencing 'n' jobs on 'm' machines for a flow shop problem in a static workshop. The proposed approach is based on the slope of each job on its journey from the first to the last machine. This approach is compared with five well-known heuristics (Palmer, Gupta, CDS, Dannenbring, Hundal) and one more recent technique that is based on the harmonic triangle. The results obtained from this study for different sizes of 'n'x'm' flow shop sequencing problems ranging from 4x4 to 50x20 indicate that the proposed approach is efficient with an encouraging percentage of improvements compared with all other six heuristic techniques. [ABSTRACT FROM AUTHOR]

Accurate ORCT prediction was helpful to adjust production schedule and optimize manufacturing processes dynamically, which ensured timely orders delivery. ORCT affected by various production factors, including materials, equipment, works-in-process, et al. The related data possessed typical characteristics of large-scale, multi-dimensions and high-redundancy. Effective feature election might improve the prediction accuracy. On the basis of constructing candidate feature sets, a feature selection algorithm was proposed based on SOM-FWFCM algorithm. Firstly, the cluster centers of FWFCM algorithm were initialized by SOM network to reduce the reliance on initial cluster centers. Feature weights were calculated by mutual information to achieve feature clustering with guidance. Then, according to the cluster results, representational features were selected to build high-quality key feature subset. Finally, taking the production data of a machining shop as an example, the effectiveness of the proposed algorithm was verified by comparing with other four feature selection algorithms. [ABSTRACT FROM AUTHOR]

This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-based learning effect. However, the truncation function is very limited in the two-stage flow-shop assembly scheduling settings. To overcome this limitation, this study investigates a two-stage three-machine flow-shop assembly problem with a truncation learning function where the makespan criterion (completion of the last job) is minimized. Given that the proposed model is NP hard, dominance rules, lemmas and a lower bound are derived and applied to the branch-and-bound method. A dynamic differential evolution algorithm, a hybrid greedy iterated algorithm and a genetic algorithm are also proposed for searching approximate solutions. Results obtained from test experiments validate the performance of all the proposed algorithms. [ABSTRACT FROM AUTHOR]

Flexible Job Shop Scheduling is one of the most difficult optimization problems known. In addition, modern production planning and control strategies require continuous and process-parallel optimization of machine allocation and processing sequences. Therefore, this paper presents a new method for process parallel Flexible Job Shop Scheduling using the concept of quantum computing based optimization. A scientific benchmark and the application to a realistic use-case demonstrates the good performance and practicability of this new approach. A managerial insight shows how the approach for process parallel flexible job shop scheduling can be integrated in existing production planning and control IT-infrastructure. [ABSTRACT FROM AUTHOR]

The paper considers the three-machine open shop scheduling problem to minimize the makespan. In the model, each job consists of two operations, one of which is to be processed on the bottleneck machine, which is the same for all jobs. A new linear-time algorithm to find an optimal non-preemptive schedule is developed. The suggested algorithm considerably simplifies the only previously known method as it straightforwardly exploits the structure of the problem and its key components to yield an optimal solution. [ABSTRACT FROM AUTHOR]

Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource. [ABSTRACT FROM AUTHOR]

Most production scheduling problems, including standard flexible job shop scheduling problems, assume that machines are continuously available. However, in most cases, due to preventive maintenance activities, machines may not be available for a certain time. Meanwhile, in the entire workshop production process, the transportation process of workpieces cannot be ignored. Therefore, the impact of transportation on the production planning should be considered in the scheduling process. To consider both preventive maintenance and transportation processes in the flexible job shop scheduling problem, this paper proposes a flexible job shop scheduling problem considering preventive maintenance activities and transportation processes and establishes a multi-objective flexible job shop scheduling model optimizing the total energy consumption and total makespan. Furthermore, a multi-region division sampling strategy-based multi-objective optimization algorithm integrated with a genetic algorithm and a differential evolution algorithm (MDSS-MOGA-DE) is proposed to solve the model. In the proposed algorithm, a multi-region division sampling strategy and two evaluation functions are utilized to improve the diversity of solutions. In addition, this paper combines a genetic operation and a differential operation to further enhance the search ability of the algorithm. The validity of the algorithm is verified by a real case. The computational results reveal that the proposed model and algorithm obtain appropriate results and have the potential to be applied to other similar problems. [ABSTRACT FROM AUTHOR]

This paper addresses an extension of the flexible job shop scheduling problem by considering that jobs need to be moved around the shop-floor by a set of vehicles. Thus, this problem involves assigning each production operation to one of the alternative machines, finding the sequence of operations for each machine, assigning each transport task to one of the vehicles, and finding the sequence of transport tasks for each vehicle, simultaneously. Transportation is usually neglected in the literature and when considered, an unlimited number of vehicles is, typically, assumed. Here, we propose the first mixed integer linear programming model for this problem and show its efficiency at solving small-sized instances to optimality. In addition, and due to the NP-hard nature of the problem, we propose a local search based heuristic that the computational experiments show to be effective, efficient, and robust. [ABSTRACT FROM AUTHOR]

This paper introduces the P3D-QAP method, which extends the prioritization and machine assignment functions of pseudo periodical priority dispatching (P3D) to postreplenishment production dealing with parallel substitute machines, upper and lower limits of inventories, and no-setup-periods (i.e., operation shifts), in addition to asymmetric setup time. Here, in the past, the P3D method has been used in semiconductor assembly production systems where several hundred product mixes are produced by simultaneously using multi-type shared resources. In an evaluation using the actual production data of an automobile parts manufacturer, P3D-QAP is compared with the extended service disciplines of EDD and a goal-chasing method, as well as the company's basic rule and results. Performance measures include three points of view: due date, resource utilization, and makespan aspects. The P3D-QAP method analyzes and much improves the main issues of due date and makespan measures, while resource utilization is maintained at a high level. These effects are believed to be due to the extended mechanism and consideration of the supply-demand dynamics in the production process. [ABSTRACT FROM AUTHOR]

A decentralized two-machine job-shop system is considered, where each machine minimizes its own flow-time objective. Analyzing the system as a non-cooperative game, we investigate the Decentralization Cost (DC), the ratio in terms of the system flow-time between the best Nash equilibrium and the centralized solution. Settings generating significant inefficiency are identified and discussed. We provide bounds on the maximal DC, and prove they are tight for two-job problems. For larger problems, we use a cross entropy meta-heuristic that searches for DC maximizing job durations. This supports the tightness of the proposed bounds for a flow-shop. Additionally, for a flow-shop, a simple, scheduling-based mechanism is proposed, which always generates efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective mathematical model for a flexible job shop scheduling problem (FJSSP) with fuzzy processing times, which is solved by a hybrid intelligent algorithm (HIA). This problem contains a combination of a classical job shop problem with parallel machines (JSPM) to provide flexibility in the production route. Despite the previous studies, the number of parallel machines is not pre-specified in this paper. This constraint with other ones (e.g., sequence-dependent setup times, reentrant workflows, and fuzzy variables) makes the given problem more complex. To solve such a multi-objective JSPM, Pareto-based optimization algorithms based on multi-objective meta-heuristics and multi-criteria decision making (MCDM) methods are utilized. Then, different comparison metrics (e.g., quality, mean ideal distance, and rate of achievement simultaneously) are used. Also, this paper includes two major phases to provide a new model of the FJSSP and introduce a new proposed HIA for solving the presented model, respectively. This algorithm is a hybrid genetic algorithm with the SAW/TOPSIS method, namely HGASAW/HGATOPSIS. The comparative results indicate that HGASAW and HGATOPSIS outperform the non-dominated sorting genetic algorithm (NSGA-II) to tackle the fuzzy multi-objective JSPM. [ABSTRACT FROM AUTHOR]

Currently, energy saving is increasingly important. During the production procedure, energy saving can be achieved if the operational method and machine infrastructure are improved, but it also increases the complexity of flow-shop scheduling. Actually, as one of the data mining technologies, Grey Wolf Optimization Algorithm is widely applied to various mathematical problems in engineering. However, due to the immaturity of this algorithm, it still has some defects. Therefore, we propose an improved multiobjective model based on Grey Wolf Optimization Algorithm related to Kalman filter and reinforcement learning operator, where Kalman filter is introduced to make the solution set closer to the Pareto optimal front end. By means of reinforcement learning operator, the convergence speed and solving ability of the algorithm can be improved. After testing six benchmark functions, the results show that it is better than that of the original algorithm and other comparison algorithms in terms of search accuracy and solution set diversity. The improved multiobjective model based on Grey Wolf Optimization Algorithm proposed in this paper is conducive to solving energy saving problems in flow-shop scheduling problem, and it is of great practical value in engineering and management. [ABSTRACT FROM AUTHOR]

To effectively solve the problem of flexible job shop scheduling, the distribution of processing procedures under various factors is comprehensively considered. For the purpose of reducing production costs and processing time, the reasonable flexible shop scheduling method is established. By establishing a mathematical model for flexible job shop scheduling, the constraint conditions such as machine allocation and process sequencing in production are constructed as a model. The proposed ant colony-genetic hybrid model is used to solve the problem, to obtain a better solution. The results show that compared with the calculation results of genetic algorithm, improved genetic algorithm, ant colony algorithm, and improved ant colony algorithm, the maximum completion time obtained by ant colony-genetic algorithm is 110s, 17s, 7s, and 14s, respectively. The results are less than the maximum completion time achieved by other algorithms. Also, compared with other algorithms, the ant colony-genetic algorithm has the highest execution efficiency, and it can converge to the optimal solution in a lower number of iterations. Therefore, the proposed ant colony-genetic hybrid algorithm has high feasibility and effectiveness in dealing with flexible shop operations. [ABSTRACT FROM AUTHOR]

This paper considers two different due date assignment and sequencing problems in single machine where the processing times of jobs are random variables. The first problem is to minimise the maximum due date so that all jobs are stochastically on time. It is shown that sequencing the jobs in decreasing service level (DSL) order optimally solves the problem. The results are then extended for two special cases of flow shop problem. The other problem is to minimise a total cost function which is a linear combination of three penalties: penalty on job earliness, penalty on job tardiness, and penalty associated with long due date assignment. The assignment of a common due date and distinct due dates are investigated for this problem. It is shown that the optimal sequence for the case of common due date is V-shaped. [ABSTRACT FROM PUBLISHER]

In this paper, the no-wait two-stage hybrid flow shop scheduling problem, under makespan minimization is addressed. A special attention will also be given to solutions with no-idle time as such solution limits material waste. We compare the performance of two metaheuristics, that are Tabu search (TS) and Genetic Algorithm (GA). For both approaches, when two solutions have the same makespan, the one with zero or smaller idle time is privileged. [ABSTRACT FROM AUTHOR]

The conventional production scheduling problem has mainly emphasized the time-related metrics, such as makespan, machine workload and tardiness/earliness, and so on. With the advent of the sustainable manufacturing, the green scheduling problem has been received more and more attention from scholars and researchers. In this paper, we investigate a green flexible job shop scheduling problem (GFJSP) with the consideration of environmental factors. To formulate the GFJSP problem, a mathematical model is first established to minimize the amount of total energy-consumption. To solve the model, a kind of improved African buffalo optimization (IABO) algorithm is proposed based on the characteristics of the problem. In the proposed IABO, a two-vector solution representation method is first designed, and a population initialization method is adopted to generate the initial solutions with certain quality and diversity. Based on the original ABO, several improvement strategies are introduced to enhance the performance of the algorithm, i.e., the modified individual learning mechanism and the aging-based re-initializaiton mechanism. In addition, in order to adapt our algorithm to the scheduling problem, a discrete individual updating method is developed to ensure the algorithm search directly in a discrete domain. Finally, a number of experiments have been conducted to test the performance of the proposed IABO algorithm. The simulation data demonstrate the effectiveness of the proposed IABO for the considered GFJSP. [ABSTRACT FROM AUTHOR]

A suitable sequencing and dispatching of jobs on machines is very much essential to improve the performance of any industry. Sequencing is the prioritizing of a set of jobs in a queue based on some decision rule to determine the order in which they will be processed. This paper aims at detailed study and analysis of hybrid rule selection to reduce lead time, waiting time and to increase machine utilization and throughput. In this study four static and eight hybrid dynamic rules were considered and prioritization of jobs is done using TOPSIS algorithm. The hybrid rules are obtained by combining certain dynamic rules. The data includes 47 jobs and 17 machines with different monthly order quantities. Discrete event simulation tool is used for the study and validation of actual data of a manufacturing plant. The results shows that the proposed hybrid dispatching rule, which is a combination of Earliest Creation time (ECT), Shortest waiting time (SWT) and Most work remaining time (MWRT) are very effective in minimizing lead time and maximizing throughput. The another hybrid dispatching rule, which is a combination of Longest Waiting Time (LWT), ECT and MWRT are effective in maximizing the machine utilization. [ABSTRACT FROM AUTHOR]

This work seeks to study one of the most complex and important issues in production scheduling research: flexible job shop systems. These systems are extremely important for industry, which uses the make-to-order strategy and seeks mix and volume flexibility. The model system will use agents within discrete-event simulation models, generating a Hybrid Simulation model. The agent will sequence the production orders at the beginning of the process and re-sequence them, when necessary, in order to achieve a multi-objective optimization. For this, the agent will bring together two different logics that have opposing goals. This work consists of the comparison of the results of three scheduling methods: firstly, with the sequence of arrival; secondly, with the agent using one sequencing logic; and, finally, using the same logic, but with adjustments in the sequence during the batch production, seeking to improve the negative points generated by the logic. It also stresses that this schedule ensures that the Manager Agent reduces makespan and increases machine utilization while increasing its interference in the model. This is a quantitative study, using the modeling and simulation method and following an empirical model. [ABSTRACT FROM AUTHOR]

Production scheduling is an important research topic widely studied during past few decades. However, many manufacturers still fail to successfully deploy scheduling algorithms and systems, even though information and communication technologies can be used to collect and process data associated with production scheduling under modern smart manufacturing environment. The primary problem is that many scheduling algorithms and systems did not consider diverse variety of scheduling requirements of real production systems. Especially, production schedulers in small make-to-order manufacturers have much trouble in utilizing such algorithms and systems. In order to address this issue, this paper aims to propose a functional architecture of production scheduling system for small make-to-order manufactures under smart manufacturing environment and develop a flexible scheduling algorithm for this system. For illustration, the proposed system and algorithm are applied to a two-machine flow shop scheduling problem, and it is expected that this paper will provide a meaningful insight into the user experiences of production scheduling systems. [ABSTRACT FROM AUTHOR]

In recent years, confronted with serious global warming and rapid exhaustion of non-renewable resources, green manufacturing has become an increasingly important theme in the world. As a significant way to achieve the purpose of green manufacturing, the energy-efficient scheduling has been intensively studied by both academia and industry due to its ability to keep a compromise between production efficiency and environmental impacts. To this end, this study investigates the multi-objective flexible job shop scheduling problem (MOFJSP) with variable processing speeds aiming at minimizing the makespan and total energy consumption simultaneously. An elaborately-designed multi-objective grey wolf optimization (MOGWO) algorithm is proposed to address this issue. Specifically, a three-vector representation corresponding to three sub-problems including machine assignment, speed assignment and operation sequence is utilized for chromosome encoding. A new decoding method (N D M) is presented to obtain active schedules and reach a trade-off between two conflicting criteria. In consideration of the multi-objective problem nature, two Pareto-based mechanisms are developed to determine the leader wolves and the lowest (worst) wolves so that the hierarchy of a wolf pack can be constructed. Finally, to avoid premature convergence and maintain population diversity, a new position updating mechanism (N P U M), which integrates information from both the leader wolves and the lowest wolves based on a comprehensive point of view, is developed to guide the other wolves in the searching space. Extensive numerical experiments on 35 different scale benchmarks have not only verified the effectiveness of N D M and N P U M but also demonstrated that the proposed MOGWO is more effective than well-known multi-objective evolutionary algorithms such as NSGA-II and SPEA-II. • A multi-objective grey wolf optimization (MOGWO) algorithm is proposed for the MOFJSP with variable processing speeds to minimize makespan and total energy consumption. • Two Pareto-based mechanisms are presented to determine the leader wolves and the lowest wolves. • A new decoding method (NDM) is developed to obtain active schedules as well as reach a trade-off between makespan and total energy consumption. • A new position updating mechanism (NPUM) integrating information from both the leader wolves and the lowest wolves is designed to guide the searching process. • Extensive numerical experiments on 35 benchmarks have confirmed the effectiveness of the MOGWO. [ABSTRACT FROM AUTHOR]

Production scheduling problems are typically combinational optimization problems named bases on the processing routes of jobs on different machines. In this paper, the flexible job shop scheduling problem aimed to minimize the maximum completion times of operations or makespan is considered. To solve such an NP-hard problem, variable neighborhood search (VNS) based on genetic algorithm is proposed to enhance the search ability and to balance the intensification and diversification. VNS algorithm has shown excellent capability of local search with systematic neighborhood search structures. External library is improved to save the optimal or near optimal solutions during the iterative process, and when the objective value of the optimal solutions are the same, the scheduling Gantt charts need to be considered. To evaluate the performance of our proposed algorithm, benchmark instances in different sizes are optimized. Consequently, the computational results and comparisons illustrate that the proposed algorithm is efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

In the flexible job shop (FJS), waiting for product processing, idle state of machining machine, product transportation, waste disposal, etc. all will significantly increase the production energy consumption and processing cost. The scheduling optimization of the shop processing routes can greatly decrease the energy consumption, reduce environmental pollution, and increase product processing efficiency and economic efficiency. Based on the traditional FJS scheduling, firstly this paper innovatively considers the impact of production energy consumption on shop scheduling, and studies the direct energy consumption and indirect energy consumption characteristics of flexible job shops. Then, taking the production energy consumption and processing time as objective functions, it establishes an FJS scheduling optimization model of multi-process planning routes considering energy consumption. Finally, the improved simulated annealing algorithm was used to solve this optimization model, and the simulation examples were taken to verify the model. The verification results showed that the model constructed in this paper mostly considers the processing route, transportation equipment and processing machine with lower energy consumption, adopts the scheduling method combining the multi-process route and product batch scheduling, and uses the independent processing method for different batches of products, so that the optimized scheduling plan can effectively reduce the production energy consumption and product processing time in the shops. The research findings can provide a new idea for the optimization of flexible job shop scheduling. [ABSTRACT FROM AUTHOR]

This paper presents a new integer linear programming (ILP) model to schedule flexible job shop, discrete parts manufacturing industries that operate on a make-to-order basis. The model considers groups of parallel homogeneous machines, limited intermediate buffers and negligible set-up effects. Orders consist of a number of discrete units to be produced and follow one of a given number of processing routes. The model allows re-circulation to take place, an important issue in practice that has received scant treatment in the scheduling literature. Good solution times were obtained using commercial mixed-integer linear programming (MILP) software to solve realistic examples of flexible job shops to optimality. This supports the claim that recent advances in computational power and MILP solution algorithms are making this approach competitive with others traditionally applied in job shop scheduling. [ABSTRACT FROM AUTHOR]

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Distributed scheduling problems are among the most investigated research topics in the fields of Operational Research, and represents one of the greatest challenges faced by industrialists and researchers today. The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories and with determining the sequence of operations on each machine in distributed manufacturing environments. The objective is to minimize the global makespan over all the factories. Since the problem is NP-hard to solve, one option to cope with this intractability is to use an approximation algorithm that guarantees near-optimal solutions quickly. Ant based algorithm has proved to be very effective and efficient in numerous scheduling problems, such as permutation flow shop scheduling, flexible job shop scheduling problems and network scheduling, etc. This paper proposes a hybrid ant colony algorithm combined with local search to solve the Distributed Job shop Scheduling Problem. A novel dynamic assignment rule of jobs to factories is also proposed. Furthermore, the Taguchi method for robust design is adopted for finding the optimum combination of parameters of the ant-based algorithm. To validate the performance of the proposed algorithm, intensive experiments are carried out on 480 large instances derived from well-known classical job-shop scheduling benchmarks. Also, we show that our algorithm can process up to 10 factories. The results prove the efficiency of the proposed algorithm in comparison with others. [ABSTRACT FROM AUTHOR]

Aiming at the disturbance event of single machine sudden failure in the initial job scheduling of flexible job shop, the dissatisfaction of customers, enterprises and labor workers is quantified using the unascertained theory, and a scheduling interference management model considering the characteristics of three parties is constructed. The NSGA-II algorithm is improved using the strategy of close relative crossover and mutation, and the efficient solution to the flexible job shop scheduling problem is realized. The example shows that the interference management model proposed in this paper can better reduce disturbance of the disturbance events compared with rescheduling, AOR rescheduling and full right shift scheduling, which can restore the normal operation of the processing and manufacturing system to realize the coordination of different stakeholders. [ABSTRACT FROM AUTHOR]

The objectives of this research were to develop the sequence production for multiple machine-multiple job, and to reduce schedule production time in a case study problem using Visual Basic for Application (VBA) on Microsoft Excel. Generally, sequencing problems occur frequently and are important issues for manufacturing processes in industrial processes It is difficult to find the best method to solve these problems, since there are many variables that affect the manufacturing processes, such as processing time, queue time and idle time. This research studied especially manufacturing on machine including five jobs and four machines flow shop. The operating system for this analysis assumes that each machine can handle one job at a time, all machines are ready to perform and a machine will be available for the next job only when the previous job has been completed. Based on the information used in the study and results of analytical algorithm by using VBA on Microsoft Excel, it was found the best method to solve problems in sequence production with case studies, and presented the minimize makespan of 34 minutes. Finally, the results obtained from the VBA program are accurate and can be evaluated quickly in about 12 minutes of processing time. [ABSTRACT FROM AUTHOR]

In this paper, a cost-based due date assignment methodology is proposed. The method uses two pre-specified parameters: an estimate of the job flow time and the probability of the job being tardy. An optimization model is presented to find the best tardiness probability of the job based on a total cost function involving the work-in-process inventory and the tardiness costs. Using the results of the correlation analysis performed on a wide set of shop, job and job's route related variables, ten models are constructed to estimate the flow time of a job in the shop. The models are then compared based on the simplicity of the model and the standard deviation of the estimate errors. One of the models is recommended and tested to check if it can be used in different shop scenarios. As a result, it is observed that the model performs satisfactorily in all the shop conditions considered in this study. [ABSTRACT FROM AUTHOR]

This paper describes a broad-based simulation study of the performance of two-stage group scheduling heuristics in a job shop cell. The objective of this study was to examine the direct and interactive effects of a variety of shop factors on the performance of the best, previously reported, group scheduling heuristics. A set of traditional single-stage scheduling heuristics were examined as well. Shop factors considered include: setup to runtime ratio, cell load level and variability of interarrival times. An assumption common to group scheduling research which provides for an equal division of the part family into subfamilies is also examined. This is accomplished through the creation of an alternative scenario where the majority of the parts are assigned to one subfamily, i.e. one subfamily dominates the part family population. The effects of set up to runtime ratio and cell load have been examined in previous group scheduling research, but not in conjunction with the inter-arrival time variability factor. Further, no study has examined the impact of subfamily dominance on group scheduling heuristics in a full-scale simulation study. The results indicate that performance comparable to that of the two-stage heuristics can be obtained with the easily implementable single-stage heuristics when factors which lessen the impact of setup times are in place. In particular, the tardiness performance of two-stage scheduling heuristics deteriorates when subfamily dominance is in effect while the single-stage heuristics exhibit dramatic improvements in tardiness performance. Low setup to runtime ratio, shop load, and less variable inter-arrivals all induce dramatic performance gains across all measures among the single-stage heuristics, while yielding only marginal improvement in the performance of the two-stage heuristics. As a result, in many instances when combinations of these factors are in effect, the single-stage heuristics yield similar performance to the... [ABSTRACT FROM AUTHOR]

This paper describes a heuristic procedure for sequencing the n job m machine statice flow shop. Basically, the procedure is performed in two overall steps. In the first step, each of the n jobs is tested as a potential immediate follower to each of the other jobs. In effect, this step of the procedure asks the question, how well does a particular job fit in terms of job blocking or machine idleness if it were to follow some other job? An overall figure of merit, or cost Cip is determined for each job j as a follower to another job i. Six different heuristies are presented for determining sets of cip values. Using these values of Cip, the second step then heuristically develops a job sequence by solving the travelling salesman problem. The paper also presents computational experience with the algorithm for a variety of randomly generated test problems (up to 50 jobs and. 50 machines in size), and compares its performance with other published heuristic techniques. [ABSTRACT FROM AUTHOR]

A maintenance planning and scheduling system often resembles that of a 'job shop' that is, the orders are one of a kind, and is characterized by having to schedule N orders through M or less tasks. The orders are of two types, e.g., (a) emergency—have to be done now, and (b) non-emergency—can be delayed until later. In this type of 'job shop' the schedule becomes immediately out of date as soon as an emergency order is received. Consequently non-emergency orders are continually moved back in the schedule and forecasted completion dates are not met. Further if the orders entering the system exceed the normal available capacity, the backlog will continue to increase causing more disruption of schedules. The research, which is based on a large petrochemical plant, will deal with the above problems by (a) applying dynamic decision rules for day-to-day scheduling to ensure completion dates are met, (b) a method for controlling backlog, and (c) forecasting future load, and completion dates for orders. The results of the simulation experiments applied to the machine shop will be discussed. [ABSTRACT FROM AUTHOR]

In this article, a competitive two-agent scheduling problem in a twomachine open shop is studied. The objective is to minimize the weighted sum of the makespans of two competitive agents. A complexity proof is presented for minimizing the weighted combination of the makespan of each agent if the weight α belonging to agent B is arbitrary. Furthermore, two pseudo-polynomial-time algorithms using the largest alternate processing time (LAPT) rule are presented. Finally, two approximation algorithms are presented if the weight is equal to one. Additionally, another approximation algorithm is presented if the weight is larger than one. [ABSTRACT FROM AUTHOR]

• Robust optimization method with dynamic events is proposed. • Reusability of the system and repeatability of the processing tasks are considered. • A dynamic event response strategy (DERS) is proposed. • A particle swarm arithmetic optimization (PSAO) is used to solve the problem. • The results show that the method can effectively improve the robustness performance. This paper focuses on the production scheduling problem of flexible job shops. In the production process of flexible job shop, there are dynamic events such as machine breakdowns or new job arrivals, which will interfere with the implementation of scheduling scheme and reduce the stability of the system. In response to this problem, this paper proposes a new robust optimization method that considers dynamic events and designs two indicators for evaluating the robustness of the system, namely the reusability of the system and the reproducibility of processing tasks. Two indicators are used to evaluate the comprehensive reusability of the system jointly. In the process of system rescheduling, this paper proposes a dynamic event response strategy (DERS) considering the comprehensive reusability of the system and establishes a multi-objective optimization model considering the total energy consumption, the makespan, and the comprehensive reusability of the system. In order to solve the model efficiently and obtain the optimal Pareto frontier, a multi-objective particle swarm arithmetic optimization (PSAO) is proposed in this paper. Finally, this paper designs experiments based on standard data cases, solves them based on this model and compares them with other algorithms. The final results show that in the flexible job-shop scheduling process, this method can effectively adjust the scheduling plan to respond to dynamic events to achieve stable scheduling in an uncertain environment. [ABSTRACT FROM AUTHOR]