An improved heuristic for no-wait flow shop to minimize makespan

Ye, Honghan; Li, Wei; Abedini, Amin

doi:10.1016/j.jmsy.2017.04.007

Cited by 26 publications

(8 citation statements)

References 26 publications

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“…This paper measures the solution quality of the algorithm using Relative Percentage [11]. In order to evaluate the performance of the proposed algorithm, BBA-VNS was compared with the latest algorithms including ADT and AIT [12]. An experiment that follows the principle of a single module is designed to clarify the contribution of BBA and VNS to the performance of BBA-VNS.The calculation results of the example are shown in the following table, in which the bold value represents the optimal solution in the comparison algorithm.…”

Section: The Experiments and Comparisonsmentioning

confidence: 99%

A building block inspired algorithm with variable neighborhood search for no-wait flow shop scheduling problems

An²,

Yan³

2022

International Conference on Intelligent Manufacturing and Industrial Automation (CIMIA 2022)

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The no-wait flow shop scheduling problem (NWFSP) has been achieved a wide variety of researches by scholars as a typical NP-hard problem. In this paper, a construction block inspired variable neighborhood search heuristic algorithm (BBA-VNS) is proposed for solving NWFSP with the goal of maximum completion time. The core idea of BBA is to seek out the best match until all jobs are arranged in the process set. In the BBA, waiting time matrix and matching matrix between two jobs are constructed to obtain an approximate optimal scheduling sequence. In addition, the variable neighborhood search mechanism (VNS) is used to further improve the solution quality which based on block neighborhood structure and insert neighborhood structure. The results on a large-scale benchmark indicate that BBA-VNS is better than the most advanced heuristic algorithm.

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Section: The Experiments and Comparisonsmentioning

confidence: 99%

A building block inspired algorithm with variable neighborhood search for no-wait flow shop scheduling problems

An²,

Yan³

2022

International Conference on Intelligent Manufacturing and Industrial Automation (CIMIA 2022)

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“…Ye et al [17] proposed a heuristic that was named "average departure time" to minimize makespan. Ye et al [18] introduced an average idle time heuristic to minimize makespan in nowait flow shop production. An initial sequence algorithm was proposed in which current and future idle times were taken into consideration.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

Keskin

Engin

2021

SN Appl. Sci.

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This paper addresses the m-machine no-wait Flow Shop Scheduling with Setup Times (NW-FSSWST). Two performance measures: total flow time and makespan are considered. The objective is to find a sequence that minimizing total flow time ($$\sum C_{j}$$ ∑ C j ) and makespan ($$C_{j}$$ C j ) simultaneously. A Hybrid Genetic Local and Global Search Algorithm (HGLGSA) is proposed to solve the NW-FSSWST for two performance criteria. The hybrid genetic algorithm is constructed by insert-search and self-repair algorithm with self-repair function. The proposed HGLGSA is tested on 192 benchmark problems of NW-FSSWST in the literature. A full factorial experimental design is made for determined the best parameter sets that improve the performance of the proposed algorithm. The computational results are compared with the benchmark solutions from the literature. The experimental results demonstrate the effectiveness and efficiency of the proposed HGLGSA for solving NW-FSSWST.

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“…Besides, there are improvement heuristics proposed in the literature to solve PFSSP (e.g., Suliman [29]; Chen, Tzeng [30]; Ye, Li [31]). These heuristics improved some of the already developed heuristics by considering specific knowledge of some problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Evolution Strategy Hybridization With Simulated Annealing for Permutation Flow Shop Scheduling Problems

et al. 2021

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Flow Shop Scheduling Problem (FSSP) has significant application in the industry, and therefore it has been extensively addressed in the literature using different optimization techniques. Current research investigates Permutation Flow Shop Scheduling Problem (PFSSP) to minimize makespan using Hybrid Evolution Strategy (HESSA). Initially, a global search of the solution space is performed using an Improved Evolution Strategy (IES), then the solution is improved by utilizing local search abilities of Simulated Annealing (SA). IES thoroughly exploits the solution space using the reproduction operator, in which four offsprings are generated from one parent. A double swap mutation is used to guide the search to more promising areas in less computational time. The mutation rate is also varied for the fine-tuning of results. The best solution of the IES acts as a seed for SA, which further improved the results by exploring better neighborhood solutions. In SA, insertion mutation is used, and the cooling parameter and acceptancerejection criteria induce randomness in the algorithm. The proposed HESSA algorithm is tested on wellknown NP-hard benchmark problems of Taillard (120 instances), and the performance of the proposed algorithm is compared with the famous techniques available in the literature. Experimental results indicate that the proposed HESSA algorithm finds 54 Upper bounds for Taillard instances, while 38 results are further improved for the Taillard instances.

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An improved heuristic for no-wait flow shop to minimize makespan

Cited by 26 publications

References 26 publications

A building block inspired algorithm with variable neighborhood search for no-wait flow shop scheduling problems

A building block inspired algorithm with variable neighborhood search for no-wait flow shop scheduling problems

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

An Improved Evolution Strategy Hybridization With Simulated Annealing for Permutation Flow Shop Scheduling Problems

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