An effective heuristic for no-wait flow shop production to minimize makespan

Ye, Honghan; Li, Wei; Miao, Enming

doi:10.1016/j.jmsy.2016.05.001

Cited by 23 publications

(9 citation statements)

References 21 publications

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“…Lin and Ying [16] designed a no-wait flow shop as an asymmetric traveling salesman problem and suggested two metaheuristics algorithms to solve it for minimizing the makespan criterion. 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.…”

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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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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“…Miltenburg and Sinnamon [11], for example, used a heuristic technique to solve a mixed model with just-in-time production. Ye et al [17] investigated a complex flow shop problem and constructed a heuristic algorithm to solve it. Perlman et al [13] investigated a production system with two stations, using queuing theory and numerical analysis.…”

Section: Introductionmentioning

confidence: 99%

An FPTAS for just-in-time scheduling of a flow shop manufacturing process with different service quality levels

Elalouf

2021

RAIRO-Oper. Res.

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This paper addresses the problem of identifying a profit-maximizing schedule for jobs in a just-in-time production line, where the decision maker can adjust each job's processing time, and thereby its quality level and the profit that can be gained from it. The system comprises two sequential machines with a transition period between them and a setup time after completion of each job on the second machine. We first construct an exact algorithm that maximizes the profit. Since this problem is NP-hard, we construct a fully polynomial time approximation scheme (FPTAS) to address it and evaluate its computational complexity.

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“…Many remarkable heuristic and metaheuristic algorithms have been proposed in the literature to optimize NWFSSP with the minimization of makespan, total flow time, and other objectives. Aldowaisan and Allahverdi [6], Laha and Chakraborty [7], Ye et al [8], and Nailwal et al [9] proposed heuristic and constructive heuristic algorithms to solve NWFSSP with the makespan criterion. In recent years, meta-heuristics, such as genetic algorithm (GA) by Seng et al [10], differential evolution (DE) algorithm by Qian et al [11], and variable neighborhood search (VNS) by Komaki and Malakooti [12], have been studied to solve the aforementioned problem.…”

Section: Introductionmentioning

confidence: 99%

An Improved Discrete Migrating Birds Optimization Algorithm for the No-Wait Flow Shop Scheduling Problem

Zhang

Zhou

2020

IEEE Access

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An improved discrete migrating birds optimization (IDMBO) algorithm is presented in this work to solve the no-wait flow shop scheduling problem (NWFSSP) with makespan criterion. In the algorithm, all of the solutions in population are treated as birds aligned in a V formation named the leader and followers. To guarantee the quality and the diversity of initial population, the leader is provided by the standard deviation heuristic (SDH) algorithm, and the rest (the followers) are generated randomly. Given that IDMBO is a neighborhood-based search algorithm, the quality of algorithm depends heavily on the neighborhood structures, where the two variants of the hybrid multi-neighborhood strategy, which are multiply neighborhood structures embedded in variable neighborhood search (VNS) in different forms, are performed to generate the neighborhood solutions for leader and followers, respectively. Furthermore, the population reset mechanism is performed after a given number iterations without improving the current solution. The local search (LS) method can further ameliorate the quality of solutions. The computational study is conducted to analyze the efficiency of IDMBO algorithm on benchmarks designed by Reeve and Taillard. And the comparison results are shown that the presented algorithm is superior to several existing algorithms for NWFFSP. INDEX TERMS Improved discrete migrating birds optimization algorithm, no-wait flow shop scheduling problem, hybrid multi-neighborhood strategy, population reset mechanism.

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An effective heuristic for no-wait flow shop production to minimize makespan

Cited by 23 publications

References 21 publications

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

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

An FPTAS for just-in-time scheduling of a flow shop manufacturing process with different service quality levels

An Improved Discrete Migrating Birds Optimization Algorithm for the No-Wait Flow Shop Scheduling Problem

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