Showing papers on "Job shop published in 2020"
TL;DR: This paper addresses the dynamic flexible job shop scheduling problem (DFJSP) under new job insertions aiming at minimizing the total tardiness and confirms both the superiority and generality of DQN compared to each composite rule, other well-known dispatching rules as well as the stand Q-learning-based agent.
170 citations
TL;DR: A dynamic greedy search strategy was developed to avoid blind searching in traditional strategy and weighted iteration update of the Q function, including the weighted mean of the maximum fuzzy earning, was designed to improve the speed and accuracy of the improved learning algorithm.
Abstract: Given the dynamic and uncertain production environment of job shops, a scheduling strategy with adaptive features must be developed to fit variational production factors. Therefore, a dynamic scheduling system model based on multi-agent technology, including machine, buffer, state, and job agents, was built. A weighted Q-learning algorithm based on clustering and dynamic search was used to determine the most suitable operation and to optimize production. To address the large state space problem caused by changes in the system state, four state features were extracted. The dimension of the system state was decreased through the clustering method. To reduce the error between the actual system states and clustering ones, the state difference degree was defined and integrated with the iteration formula of the Q function. To select the optimal state-action pair, improved search and iteration update strategies were proposed. Convergence analysis of the proposed algorithm and simulation experiments indicated that the proposed adaptive strategy is well adaptable and effective in different scheduling environments, and shows better performance in complex environments. The two contributions of this research are as follows: (1) a dynamic greedy search strategy was developed to avoid blind searching in traditional strategy. (2) Weighted iteration update of the Q function, including the weighted mean of the maximum fuzzy earning, was designed to improve the speed and accuracy of the improved learning algorithm.
56 citations
TL;DR: This work represents the first DL model for the JRT prediction at run time during production based on deep learning (DL) with production BD and shows the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in J RT prediction.
Abstract: Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of...
55 citations
TL;DR: The computational results reveal that the proposed PLMEAPS outperforms the other two algorithms both in solutions’ quality and convergence rate when solving FJSGSP-CT.
36 citations
TL;DR: This paper makes an investigation into minimizing the total tardiness, the total energy cost and the disruption to the original schedule in the job shop with new urgent arrival jobs with a dual heterogeneous island parallel genetic algorithm with the event driven strategy.
36 citations
TL;DR: A discrete event simulation approach integrated with the Flexible and Interactive Tradeoff (FITradeoff) compensatory method is considered to identify the best combination of due date assignment, order release and shop dispatching rules.
34 citations
TL;DR: It is shown that a self-learning agent can successfully manage time constraints with the agent performing better than the traditional benchmark, a time-constraint heuristic combining due date deviations and a classical first-in-first-out approach.
Abstract: Reinforcement learning (RL) offers promising opportunities to handle the ever-increasing complexity in managing modern production systems. We apply a Q-learning algorithm in combination with a process-based discrete-event simulation in order to train a self-learning, intelligent, and autonomous agent for the decision problem of order dispatching in a complex job shop with strict time constraints. For the first time, we combine RL in production control with strict time constraints. The simulation represents the characteristics of complex job shops typically found in semiconductor manufacturing. A real-world use case from a wafer fab is addressed with a developed and implemented framework. The performance of an RL approach and benchmark heuristics are compared. It is shown that RL can be successfully applied to manage order dispatching in a complex environment including time constraints. An RL-agent with a gain function rewarding the selection of the least critical order with respect to time-constraints beats heuristic rules strictly by picking the most critical lot first. Hence, this work demonstrates that a self-learning agent can successfully manage time constraints with the agent performing better than the traditional benchmark, a time-constraint heuristic combining due date deviations and a classical first-in-first-out approach.
34 citations
TL;DR: An energy-aware optimization model in which scheduling is integrated with layout in a single-level framework and a hybrid ant colony optimization and simulated annealing (ACO-SA) algorithm provides better performance than the other algorithms is proposed.
34 citations
TL;DR: A method combining an improved version of nondominated sorting genetic algorithm II and RMc was proposed to address the bi-objective FJSP to evaluate robustness.
Abstract: In modern manufacturing systems, a flexible job-shop schedule problem (FJSP) with random machine breakdown has been widely studied. Two objectives, namely makespan and robustness, were simultaneously considered in this study. Maximizing the workload and float time of each operation and the machine breakdowns, one surrogate measure named RMc was developed via an extreme learning machine (ELM) to evaluate robustness. Specifically, this measure determines the impact of float time on the robustness by the probability of machine breakdown and the location of float time. Simultaneously, the impact was automatically adjusted by the ELM. Then, a method combining an improved version of nondominated sorting genetic algorithm II and RMc was proposed to address the bi-objective FJSP. Computational results on the benchmarks show that RMc accurately evaluates the robustness of the schedules with a small amount of computation cost.
32 citations
TL;DR: A novel dual-resource constrained flexible job-shop problem (DRCFJSP) whose performance considers simultaneously makespan and due date-oriented criteria, where eligibility and processing time are both dependent on worker expertise is states.
Abstract: Current struggles for customer satisfaction in make-to-order companies focus on product customization and on-time delivery For better management of demand-mix variability, production activities are typically configured as flexible job shops The advent of information technology and process automatization has given rise to very specific training requirement for workers, which indeed turns production scheduling into a dual-resource constrained problem This paper states a novel dual-resource constrained flexible job-shop problem (DRCFJSP) whose performance considers simultaneously makespan and due date-oriented criteria, where eligibility and processing time are both dependent on worker expertise Our research comes from an automobile collision repair shop with re-scheduling needs to react to real-time events like due date changes, delay in arrival, changes in job processing time and rush jobs We have developed constructive iterated greedy procedures that performs efficiently on the large-scale bi-objective DRCFJSP arisen (good schedules in < 5 s), hence providing planners with the required responsiveness in their scheduling of repairing orders and allocation of workers at the different work centres In addition, computational experiments were conducted on a test bed of smaller DRCFJSP instances generated for benchmarking purposes Off-the-shelf resolution for an 80% of the medium-sized instances is not fruitful after 9000 s
32 citations
TL;DR: Two of the most widely used and considered best performing periodic order release models out of both streams are compared: the LUMS (rule based) and the clearing function model (optimisation based).
Abstract: An important goal of Production Planning and Control systems is to achieve short and predictable flow times, especially where high flexibility in meeting customer demand is required, while maintain...
TL;DR: The population-based iterated greedy (PBIG) algorithm for the sequencing subproblem is proposed and significantly outperforms several state-of-the-art metaheuristics, and it could be applicable to practical production environment.
TL;DR: This study aims to present the forms of scrutiny of multi-objective FJSSPs and various heuristic techniques used to solve problems in the last decade to allow the reader to select specific methods and follow the guidelines set forth in their future research.
Abstract: Flexible job shop scheduling problem is a relaxation of the job shop scheduling problem and is one of the well-known combinatorial optimization problems that has wide applications in the industrial fields such as production management, supply chain, transport systems, manufacturing systems. In recent years, many researches have been carried out with different approaches—ranging from mathematical models to heuristic methods—to solve multi objective flexible job shop scheduling problems (FJSSP). This study aims to present the forms of scrutiny of multi-objective FJSSPs and various heuristic techniques used to solve problems in the last decade. This review will allow the reader to select specific methods and follow the guidelines set forth in their future research.
TL;DR: A multi-period production planning based real-time scheduling (MPPRS) approach for the IoT-enabled low-carbon flexible job shop (LFJS) is presented in this study and can achieve better results than the existing dynamic scheduling methods.
TL;DR: This paper takes workers into account and considers the effects of their learning abilities on the processing time and energy consumption of a new low carbon flexible job shop scheduling problem considering worker learning (LFJSP-WL).
Abstract: Green low carbon flexible job shop problems have been extensively studied in recent decades, while most of them ignore the influence of workers. In this paper, we take workers into account and consider the effects of their learning abilities on the processing time and energy consumption. And then a new low carbon flexible job shop scheduling problem considering worker learning (LFJSP-WL) is investigated. To reduce carbon emission (CE), a novel CE assessment of machines is presented which combines the production scheduling strategies based on worker learning. A memetic algorithm (MA) is tailored to solve the LFJSP-WL with objectives of minimizing the makespan, total CE and total cost of workers. In LFJSP-WL, a three-layer chromosome encoding method is adopted and several approaches considering the problem characteristics are designed in population initialization, crossover and mutation. Besides, four effective neighborhood structures are developed to enhance the exploitation and exploration capacities, and the elite pool strategy is presented to reserve elite solutions along each iteration. The Taguchi method of DOE is used to obtain the best combination of the key parameters used in MA. Computational experiments conducted show that the MA is able to easily obtain better solutions for most of the tested 22 challenging problem instances compared to two other well-known algorithms, demonstrating its superior performance for the proposed LFJSP-WL.
TL;DR: An energy-conscious optimization method which updates the jobs and machine plan status when dynamic events occur is proposed and shows that the proposed method is effective at adjusting the schedule in response to dynamic events.
TL;DR: Two different constraint programming formulations are proposed for the first time for a flexible job shop scheduling problem with transbots, significantly outperforming all other benchmark approaches in the literature and proving optimality of the well-known benchmark instances.
Abstract: This paper studies a simultaneous scheduling of production and material transfer in a flexible job shop environment. The simultaneous scheduling approach has been recently adopted by a robotic mobile fulfillment system, wherein transbots pick up jobs and deliver to pick-stations for processing, which requires a simultaneous scheduling of jobs, transbots, and stations. Two different constraint programming formulations are proposed for the first time for a flexible job shop scheduling problem with transbots, significantly outperforming all other benchmark approaches in the literature and proving optimality of the well-known benchmark instances.
14 Dec 2020
TL;DR: In this article, two DQN agents are trained with a discrete-event simulation model of the problem, where one agent is responsible for the selection of operation sequences, while the other allocates jobs to machines.
Abstract: The following paper presents the application of Deep Q-Networks (DQN) for solving a flexible job shop problem with integrated process planning. DQN is a deep reinforcement learning algorithm, which aims to train an agent to perform a specific task. In particular, we train two DQN agents in connection with a discrete-event simulation model of the problem, where one agent is responsible for the selection of operation sequences, while the other allocates jobs to machines. We compare the performance of DQN with the GRASP metaheuristic. After less than one hour of training, DQN generates schedules providing a lower makespan and total tardiness as the GRASP algorithm. Our first investigations reveal that DQN seems to generalize the training data to other problem cases. Once trained, the prediction and evaluation of new production schedules requires less than 0.2 seconds.
08 Jul 2020
TL;DR: The proposed evolutionary framework and knowledge transfer mechanism for genetic programming to train scheduling heuristics for different tasks simultaneously can dramatically reduce the training time for solving multiple dynamic flexible job shop tasks.
Abstract: Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for job shop scheduling. However, the environments of job shops vary in configurations, and the scheduling heuristic for each job shop is normally trained independently, which leads to low efficiency for solving multiple job shop scheduling problems. This paper introduces the idea of multitasking to genetic programming to improve the efficiency of solving multiple dynamic flexible job shop scheduling problems with scheduling heuristics. It is realised by the proposed evolutionary framework and knowledge transfer mechanism for genetic programming to train scheduling heuristics for different tasks simultaneously. The results show that the proposed algorithm can dramatically reduce the training time for solving multiple dynamic flexible job shop tasks.
TL;DR: The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms.
Abstract: The purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced.,As the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes.,Numerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms.,Scheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper.,This paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.
TL;DR: GA_X outperforms benchmark algorithms, and Φ s (the proposed incomplete chromosome representation) has considerable merit, which highlights a promising direction in developing “incomplete solution representation schemes” when solving complex space-search problems with genetic or other metaheuristic algorithms.
TL;DR: A new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment is proposed and shown that the proposed approach is efficient and competitive.
Abstract: This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The objectives considered in scheduling jobs and transportation tasks in a flexible job shop manufacturing system include makespan, robot travel distance, time difference with due date and critical waiting time. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.
01 Feb 2020
TL;DR: This paper is presents a Comprehensive Survey on Tabu Search Algorithms (TSA), which focuses on main characteristics of TSA and its behaviour.
Abstract: This paper is presents a Comprehensive Survey on Tabu Search Algorithms (TSA). TSA is a meta heuristics kind of algorithm which works on global optimal solution for a given problem such as vehicle routing problem (VRP), open vehicle routing problem (OVRP), multi-trip vehicle routing and scheduling problem (MTVRSP), container loading problem (CLP) and job shop problem, etc. in this paper focuses on main characteristics of TSA and its behaviour.
TL;DR: 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: 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 sched...
TL;DR: This study solved the concurrent layout and scheduling problem by using a local neighborhood search algorithm (LNSA), which used a random local neighborhood, where neighbors were produced by simple operators commonly used in metaheuristics for scheduling problems.
Abstract: The concurrent layout and scheduling problem is an extension of the well-known job-shop scheduling problem with transport delays, in which, in addition to the decisions taken in the classic problem, the location of machines must be selected from a set of possible sites. The aim of this study was to solve this problem by using a local neighborhood search algorithm (LNSA). This algorithm used a random local neighborhood, where neighbors were produced by simple operators commonly used in metaheuristics for scheduling problems. The solution coding used in the LNSA enabled all the calculated solutions to be valid schedules for the problem. The findings of the study were compared with those obtained by Ranjbar, who proposed the benchmark problems. Our method obtained a minor average relative percentage compared to the best-known results. Furthermore, it achieved a smaller makespan in the problems with higher computational complexity, which may aid companies that require customized manufacturing.
TL;DR: This paper establishes an experimental platform for automated guided vehicles (AGVs) with a six-wheel dual-drive mechanical structure, and designs a multi-AGV scheduling system for unmanned factories that can be applied widely in manufacturing factories to replace traditional manual handling and conveyor belt transmission, reduce labour cost, and improve production efficiency.
Abstract: Considering the actual needs of the job-shop, this paper establishes an experimental platform for automated guided vehicles (AGVs) with a six-wheel dual-drive mechanical structure, and designs a multi-AGV scheduling system for unmanned factories. The scheduling system works in the following steps: Firstly, the deviation of each AGV from the magnetic strip is calculated based on the data of the magnetic sensor, and the speeds of left and right drive wheels are adjusted based on the deviation, keeping the AGV moving stably on the magnetic strip. Next, the A* algorithm is called to plan a collision-free and efficient path. Finally, the conflict points and AGV priorities are determined by comparing the paths of different AGVs, and the paths are planned again if head-on collision may happen on the conflict points. In this way, multiple AGVs can operate coordinately on the same map. The proposed multi-AGV scheduling system was proved feasible through simulation and experiments. Our system can be applied widely in manufacturing factories to replace traditional manual handling and conveyor belt transmission, reduce labour cost, and improve production efficiency. (Received in September 2019, accepted in January 2020. This paper was with the authors 1 month for 1 revision.)
01 Nov 2020
TL;DR: In this paper, a multi-objective problem is solved using an evolutionary algorithm based on the NSGA-II procedure, where the decoding operator incorporates a new heuristic procedure in order to improve the solutions' energy consumption.
Abstract: A growing concern about the environmental impact of manufacturing processes and in particular the associated energy consumption has recently driven some researchers within the scheduling community to consider energy costs in addition to more traditional performance-related measures, such as satisfaction of due-date commitments. Recent research is also devoted to narrowing the gap between real-world applications and academic problems by handling uncertainty in some input data. In this paper, we address the job shop scheduling problem, a well-known hard problem with many applications, using fuzzy sets to model uncertainty in processing times and with the target of finding solutions that perform well with respect to both due-date fulfilment and energy efficiency. The resulting multi-objective problem is solved using an evolutionary algorithm based on the NSGA-II procedure, where the decoding operator incorporates a new heuristic procedure in order to improve the solutions’ energy consumption. This heuristic is based on a theoretical analysis of the changes in energy consumption when a solution is subject to slight changes, referred to as local right shifts. The experimental results support the theoretical study and show the potential of the proposal.
TL;DR: The Migrating Birds Optimization algorithm is improved so that it can be applied to the Batch Splitting Scheduling Problem of Flexible Job-Shop and the effectiveness of the three proposed stages is confirmed and the proposed algorithm is proven to effectively decrease the makespan.
TL;DR: An easy‐to‐perform approximate algorithm for minimizing the makespan in a flexible two‐center job shop with one‐unit‐time operations in the first center and k‐ unit‐time Operations in the second center is proposed and has the absolute worst‐case error bound of k − 1, and thus for k = 1 it is optimal.
Abstract: Job shop scheduling with a bank of machines in parallel is important from both theoretical and practical points of view. Herein we focus on the scheduling problem of minimizing the makespan in a flexible two‐center job shop. The first center consists of one machine and the second has k parallel machines. An easy‐to‐perform approximate algorithm for minimizing the makespan with one‐unit‐time operations in the first center and k‐unit‐time operations in the second center is proposed. The algorithm has the absolute worst‐case error bound of k − 1, and thus for k = 1 it is optimal. Importantly, it runs in linear time and its error bound is independent of the number of jobs to be processed. Moreover, the algorithm can be modified to give an optimal schedule for k = 2.
TL;DR: Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales.
Abstract: The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time betwee...