An Evolutionary Algorithm Based Hyper-heuristic for the Job-Shop Scheduling Problem with No-Wait Constraint

Chaurasia, Sachchida Nand; Sundar, Shyam; Jung, Donghwi; Lee, Ho Min

doi:10.1007/978-981-13-0761-4_25

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…As Bozejko & Makuchowski ( 2011) used for NWJSSP a methodology of automatic genetic algorithm parameters adjustment with a makespan criterion. Sachchida et al (2018) developed an evolutionary algorithm with guided mutation (EA/G) based hyper-heuristic which employs an evolutionary algorithm to explore the search space and a generic guided mutation, multi-insert points and multi-swap. Bürgy & Gröflin (2012) proposed a highly efficient algorithm based on a compact formulation of the NWJS problem and a characterization of all feasible insertions as the stable sets in a derived comparability graph.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

BOUGLOULA

2023

Management and Production Engineering Review

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This work is interested to optimize the job shop scheduling problem with a no wait constraint. This constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. The no wait job shop scheduling problem is a combinatorial optimization problem. Therefore, the study presented here is focused on solving this problem by proposing strategy for making Jaya algorithm applicable for handling optimization of this type of problems and to find processing sequence that minimizes the makespan (Cmax). Several benchmarks are used to analyze the performance of this algorithm compared to the best-known solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

BOUGLOULA

2023

Management and Production Engineering Review

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“…Comparative results show that the proposed algorithm can find better solutions in a reasonable time than other comparative algorithms. Chaurasia et al [17] conducted a study entitled "providing an evolutionary algorithm based on the ideological phenomenon for the job-shop scheduling problem with no-wait constraint." In this study, an evolutionary algorithm with guided mutation (EA/G)-based hyper-heuristic for solving the job-shop problem with no-wait constraint (JSPNW) is provided.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm

olya

Shayannia

Movahedi

2022

Discrete Dynamics in Nature and Society

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Nowadays, the major and substantial challenge and constraint of organizations, specifically in developing countries, are not the lack of raw materials, energy, or even technology but cases that are directly or indirectly associated with human resource management. The most significant problems that organizations face include dissatisfaction, lack of motivation, inefficiency of human resources, high amount of job turnovers, and low productivity. Today’s organizations and institutions should realize true perfection and health create the conditions for survival, order, excellence, and growth. In this research, considering the high importance of these issues, the problems of scheduling and allocation of manpower in a real place are solved. To this end, the metaheuristic Tabu search algorithm is used with the aim of minimizing the duration of activity and the presence of all manpower. For this purpose, to achieve the objectives of the research, three different scenarios related to the required manpower and the minimum-maximum presence of manpower according to whether they are on leave or not are used.

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“…Hyper-heuristics have also been used for tackling JSSPs [76]. For example, Chaurasia et al implemented an Evolutionary Algorithm with guided mutation as HH for solving the JSSP, assuming no waiting time between activities for each job [77]. The authors claimed that this approach surmounted other well-known methodologies in 80% of their experiments.…”

Section: ) Job Shop Schedulingmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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An Evolutionary Algorithm Based Hyper-heuristic for the Job-Shop Scheduling Problem with No-Wait Constraint

Cited by 6 publications

References 17 publications

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

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