Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Wei, Hongjing; Li, Shaobo; Jiang, Houmin; Hu, Jie; Hu, Jianjun

doi:10.3390/app8122621

Cited by 35 publications

(26 citation statements)

References 52 publications

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“…In which, the Individuals from the elite set are disturbed while the remaining population is reset. In 2018, Wei, et al [10] Proposed a Hybrid Genetic Simulated Annealing (HGSA) Algorithm. The initial solution was generated by combining NEH Heuristic, MinMax, and MME Algorithm, while the solution was optimized by using hormone regulation scheme in the SA algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

et al. 2021

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Being complex and combinatorial optimization problems, Permutation Flow Shop Scheduling Problems (PFSSP) are difficult to be solved optimally. PFSSP occurs in many manufacturing systems i.e. automobile industry, glass industry, paper industry, appliances industry, and pharmaceutical industry, and the generation of the best schedule is very important for these manufacturing systems. Evolution Strategy (ES) is a subclass of Evolutionary algorithms and in this paper, we propose an Improved Evolution Strategy to reduce the makespan of PFSSP. Two variants of the Improved Evolution Strategy are proposed namely ES5 and ES10. The initial solution is generated using the shortest processing time rule. In ES5, four offsprings are generated from one parent while in ES10, nine offsprings are generated from one parent. The selection pool consists of both the parents and offsprings. Quad swap mutation operator has been proposed to minimize computational time and for the maximum search of solution space. Also, a variable mutation rate is used for the fine-tuning of results, with the increasing number of iterations the mutation rate is reduced. The performances of both ES variants were tested on two test domains. First, it is applied to benchmark the PFSSP of Carlier and Reeves. Computational results are matched with other well-known techniques available in the literature, and the results show the effectiveness and robustness of the proposed techniques. Secondly, ES is applied to the real-life problem for the manufacturing of batteries to demonstrate its effectiveness. Data was taken from Pakistan Accumulator for NS30-40 Plates battery, the company is daily producing 1400 units of NS30-40 Plates battery. ES is applied to different batch sizes i.e. 35, 140, 1120 & 1400. Our results show that a Min %GAP of 1.25 is found using ES10. Hence the company can increase monthly 450 units of NS30 batteries using the ES10 algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

et al. 2021

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“…. , 600; the processing times p ij are defined in [1,49] and the setup times s i are defined in [1,10].…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

“…e mainly used metaheuristics are based on an improvement of an initial solution by research in its neighborhood by one of the disruption procedures of the current solution. In the literature, we find simulated annealing (SA) [2][3][4], the genetic algorithm (GA) [5][6][7][8], and the combined metaheuristics SA and GA [9,10] that are used to solve flow shop scheduling problem. We find also other resolution methods such as tabu search [11][12][13][14] and the greedy randomized adaptive search procedure [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Solving Permutation Flow Shop Scheduling Problem with Sequence-Independent Setup Time

Belabid

Aqil

Allali

2020

Journal of Applied Mathematics

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In this paper, we study the resolution of a permutation flow shop problem with sequence-independent setup time. The objective is to minimize the maximum of job completion time, also called the makespan. In this contribution, we propose three methods of resolution, a mixed-integer linear programming (MILP) model; two heuristics, the first based on Johnson’s rule and the second based on the NEH algorithm; and finally two metaheuristics, the iterative local search algorithm and the iterated greedy algorithm. A set of test problems is simulated numerically to validate the effectiveness of our resolution approaches. For relatively small-size problems, it has been revealed that the adapted NEH heuristic has the best performance than that of the Johnson-based heuristic. For the relatively medium and large problems, the comparative study between the two metaheuristics based on the exploration of the neighborhood shows that the iterated greedy algorithm records the best performances.

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“…Genetic simulated annealing algorithm is proposed in [41] and improves flowshop scheduling with a makespan criterion, but the improvements are not satisfied.…”

Section: Genetic Algorithms and Utilization To Pfsspmentioning

confidence: 99%

An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Yang

Chen

et al. 2020

Complexity

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Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively.

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Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Cited by 35 publications

References 52 publications

Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

Solving Permutation Flow Shop Scheduling Problem with Sequence-Independent Setup Time

An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

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