An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

Huang, Jiang-Ping; Pan, Quan-Ke; Gao, Liang

doi:10.1016/j.swevo.2020.100742

Cited by 102 publications

(36 citation statements)

References 45 publications

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“…Instead of applying a simple simulated annealing criterion to the IG algorithm [40] , Lin et al [34] used an acceptance criterion with a settling temperature value and included the number of elements to be removed in the destruction step as a variable. Ruben et al [7] employed an improved IG algorithm to optimize the makespan of DPFSP. Jing et al [41] adopted an improved IG algorithm to solve the DPFSP with windows.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (6) describes the start time and completion time of a job processing. Constraint (7) represents that the start time must be equal or greater than the completion time of two adjacent jobs on a certain machine. Constraint (8) describes the constraints between the start time and completion time of the job, including preparation time.…”

Section: Sdst-dpfsp Problemmentioning

confidence: 99%

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Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Han

Chen

et al. 2021

Complex Syst. Model. Simul.

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To meet the multi-cooperation production demand of enterprises, the distributed permutation flow shop scheduling problem (DPFSP) has become the frontier research in the field of manufacturing systems. In this paper, we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequencedependent setup times. To solve DPFSPs, significant developments of some metaheuristic algorithms are necessary. In this context, a simple and effective improved iterated greedy (NIG) algorithm is proposed to minimize makespan in DPFSPs. According to the features of DPFSPs, a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed algorithm. We compare the proposed algorithm with state-of-the-art algorithms, including the iterative greedy algorithm (2019), iterative greedy proposed by Ruiz and Pan (2019), discrete differential evolution algorithm (2018), discrete artificial bee colony (2018), and artificial chemical reaction optimization (2017). Simulation results show that NIG outperforms the compared algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Sdst-dpfsp Problemmentioning

confidence: 99%

Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Han

Chen

et al. 2021

Complex Syst. Model. Simul.

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“…Constraints ( 9)-( 12) define the start time of each job in the considered flowshop. Constraints ( 13)- (15) define the relationship between x ir and y ijr for sequencedependent setup times. Constraints ( 16) and ( 17) compute the completion time and tardiness of jobs.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…A two-machine flowshop scheduling problem with sequence-dependent setup times (SDSTs) to minimize the makespan is very similar to the typical travel salesman problem (TSP) [4], and dynamic programming methods [5,6] and branch and bound algorithms [7,8] have been proposed to obtain optimal solutions for the problem. However, since finding optimal solutions of the flowshop problem with SDST requires considerably long computation times, recent studies have focused on development of metaheuristic algorithms: genetic algorithms [9][10][11], variable neighborhood search algorithm [12], migrating bird optimization algorithm [13], discrete artificial bee colony optimization [14], iterated greedy algorithm [15][16][17], and local search based heuristic algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm for a Two-Machine Flowshop with a Limited Waiting Time Constraint and Sequence-Dependent Setup Times

Lee

2020

Mathematical Problems in Engineering

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This study considers a two-machine flowshop with a limited waiting time constraint between the two machines and sequence-dependent setup times on the second machine. These characteristics are motivated from semiconductor manufacturing systems. The objective of this scheduling problem is to minimize the total tardiness. In this study, a mixed-integer linear programming formulation was provided to define the problem mathematically and used to find optimal solutions using a mathematical programming solver, CPLEX. As CPLEX required a significantly long computation time because this problem is known to be NP-complete, a genetic algorithm was proposed to solve the problem within a short computation time. Computational experiments were performed to evaluate the performance of the proposed algorithm and the suggested GA outperformed the other heuristics considered in the study.

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“…In contrast, meta-heuristics have been used to solve COPs because this method can reasonably find a feasible solution within an acceptable timeframe [2][3][4] . The Swarm Intelligence (SI) algorithms, which are used to address continuous optimization problems or COPs, are widely studied and effective to address large-scale problems [5,6] . The SI algorithms are inspired by the laws of human intelligence and social or natural phenomena in biological groups.…”

mentioning

confidence: 99%

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

Zhao

Cao

et al. 2021

Complex Syst. Model. Simul.

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A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level heuristics. The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic. In this study, a Cooperative Multi-Stage Hyper-Heuristic (CMS-HH) algorithm is proposed to address certain combinatorial optimization problems. In the CMS-HH, a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution. In the search phase, an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution. In addition, a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time. The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems, including Boolean satisfiability problems, one-dimensional packing problems, permutation flow-shop scheduling problems, personnel scheduling problems, traveling salesman problems, and vehicle routing problems. The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.

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An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

Cited by 102 publications

References 45 publications

Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

A Genetic Algorithm for a Two-Machine Flowshop with a Limited Waiting Time Constraint and Sequence-Dependent Setup Times

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

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