A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

Wu, Zigao; Yu, Shuai; Li, Tiancheng

doi:10.3390/math7060529

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Recent studies indicated the effects of robustness in the real time scenario of machine breakdowns. Robustness in the multi-objective optimization with the inclusion of machine breakdowns along with makespan leads to the development of meta-models which, when solved through MOEA, indicates a better convergence and diversity in Pareto solution sets as compared to the slack-based surrogate measures [120,121].…”

Section: Robustness In Job Shop and Industry 40mentioning

confidence: 99%

A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

et al. 2021

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Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation.

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Section: Robustness In Job Shop and Industry 40mentioning

confidence: 99%

A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

et al. 2021

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“…If it is impossible to determine probability distributions for all random job processing times, other approaches have to be used [11,[22][23][24][25]. In the approach of seeking a robust schedule [22,[26][27][28], a decision-maker looks for a schedule that hedges against the worst-case possible scenario.…”

Section: Approaches To Scheduling Problems With Different Forms Of Unmentioning

confidence: 99%

Schedule Execution for Two-Machine Job-Shop to Minimize Makespan with Uncertain Processing Times

2020

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This study addresses a two-machine job-shop scheduling problem with fixed lower and upper bounds on the job processing times. An exact value of the job duration remains unknown until completing the job. The objective is to minimize a schedule length (makespan). It is investigated how to best execute a schedule, if the job processing time may be equal to any real number from the given (closed) interval. Scheduling decisions consist of the off-line phase and the on-line phase of scheduling. Using the fixed lower and upper bounds on the job processing times available at the off-line phase, a scheduler may determine a minimal dominant set of schedules (minimal DS), which is based on the proven sufficient conditions for a schedule dominance. The DS optimally covers all possible realizations of the uncertain (interval) processing times, i.e., for each feasible scenario, there exists at least one optimal schedule in the minimal DS. The DS enables a scheduler to make the on-line scheduling decision, if a local information on completing some jobs becomes known. The stability approach enables a scheduler to choose optimal schedules for most feasible scenarios. The on-line scheduling algorithms have been developed with the asymptotic complexity O(n2) for n given jobs. The computational experiment shows the effectiveness of these algorithms.

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“…Dynamic scheduling with simulation models can be a way of tackling the problem, as implemented by Turker et al [2]. Evolutionary algorithm approaches are well known, very efficient, and still currently being investigated and developed for the job-shop problem [3], as well as for flexible job-shop scheduling problems [4]. Moreover, the technical constraints and technologies applied in production systems generate different types of blocking situations.…”

Section: Introduction and Targeted Contributionmentioning

confidence: 99%

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

Sauvey

Trabelsi²,

Sauer

2020

Mathematics

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In this paper, we consider a job-shop scheduling problem with mixed blocking constraints. Contrary to most previous studies, where no blocking or only one type of blocking constraint was used among successive operations, we assume that, generally, we may address several different blocking constraints in the same scheduling problem depending on the intermediate storage among machines, the characteristics of the machines, the technical constraints, and even the jobs. Our objective was to schedule a set of jobs to minimize the makespan. Thus, we propose, for the first time, a mathematical model of the job-shop problem taking into account the general case of mixed blocking constraints, and the results were obtained using Mosel Xpress software. Then, after explaining why and how groups of jobs have to be processed, a blocking constraint conflict-free warranted evaluation function is proposed and tested with the particle swarm optimization and genetic algorithm methods. The results prove that we obtained a near-optimal solution to this problem in a very short time.

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A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

Cited by 8 publications

References 44 publications

A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

Schedule Execution for Two-Machine Job-Shop to Minimize Makespan with Uncertain Processing Times

Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems

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