A hybrid variable neighborhood search algorithm for solving the limited-buffer permutation flow shop scheduling problem with the makespan criterion

2014 ◽  
Vol 52 ◽  
pp. 260-268 ◽  
Author(s):  
Ghasem Moslehi ◽  
Danial Khorasanian
Keyword(s):  
Variable Neighborhood Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Hybrid Variable ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Neighborhood Search Algorithm ◽  
Makespan Criterion

scholarly journals Using Non-dominated Sorting Particle Swarm Optimization Algorithm II for Bi-objective Flow Shop Scheduling Problems

2021 ◽  
pp. 275-288
Author(s):  
Hanan Ali Chachan ◽  
Faez Hassan Ali
Keyword(s):  
Hybrid Algorithm ◽  
Variable Neighborhood Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

A hybrid particulate swarm optimization (hybrid) combination of an optimization algorithm of the particle swarm and a variable neighborhood search algorithm is proposed for the multi-objective permutation flow shop scheduling problem (PFSP) with the smallest cumulative completion time and the smallest total flow time. Algorithm for hybrid particulate swarm optimization (HPSO) is applied to maintain a fair combination of centralized search with decentralized search. The Nawaz-Enscore-Ham )NEH) heuristic algorithm in this hybrid algorithm is used to initialize populations in order to improve the efficiency of the initial solution. The method design is based on ascending order (ranked-order-value, ROV), applying the continuous PSO algorithm to the PFSP, introducing the external archive set storage Pareto solution, and using a hybrid strategy that combines strong dominance and aggregation distance to ensure the distribution of the solution set. We adopted the Sigma method and the roulette method, based on the aggregation distance, to select the global optimal solution. A variable neighborhood search algorithm was proposed to further search the Pareto solution in the external set. The suggested hybrid algorithm was used to solve the Taillard test set and equate the test results with the SPEA2 algorithm to check the scheduling algorithm’s efficacy.

scholarly journals An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 112
Author(s):  
Mehrdad Amirghasemi
Keyword(s):  
Variable Neighborhood Search ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Stochastic Algorithm ◽  
High Quality ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Machine Problem

This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a seed using a recursive application of the extended two-machine problem. In this method, the jobs are recursively decomposed into two separate groups, and, for each group, an optimal permutation is calculated based on the extended two-machine problem. Then the overall permutation, which is obtained by integrating the sub-solutions, is improved through the application of a variable neighborhood search technique. The same as the first technique, this one is also based on the decomposition paradigm and can find an optimal arrangement for a subset of jobs. In the employed large neighborhood search, the concept of the critical path has been used to help the decomposition process avoid unfruitful computation and arrange only promising contiguous parts of the permutation. In this fashion, the algorithm leaves those parts of the permutation which already have high-quality arrangements and concentrates on modifying other parts. The results of computational experiments on the benchmark instances indicate the procedure works effectively, demonstrating that solutions, in a very short distance of the best-known solutions, are calculated within seconds on a typical personal computer. In terms of the required running time to reach a high-quality solution, the procedure outperforms some well-known metaheuristic algorithms in the literature.

A Variable Neighborhood Search Algorithm to Solve the Flow Shop-Scheduling Problem Through Blocking

2020 ◽  
pp. 63-73
Author(s):  
Dalel Bouzidi
Keyword(s):  
Flow Shop ◽  
Search Algorithm ◽  
Optimization Criterion ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Total Delay ◽  
Shop Scheduling ◽  
Neighborhood Search Algorithm

In this chapter, we deal with the flow shop-scheduling problem through blocking known to be difficult, where there is no space and a task remains blocked on a machine until the next machine is available. For this reason, we propose the heuristic approach to minimize the delay (tardiness) as an optimization criterion. This chapter proposes a VNS-based heuristic the solutions of which are compared to those of the metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). We have developed a heuristic-based VNS to get better solutions in a reasonable time. Finally, comparisons with optimal solutions for small problems have shown that all versions can give good results; it would be interesting to extend the ideas presented in this document to the blocking flow shop at minimizing the total delay.

scholarly journals A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

2019 ◽  
Vol 9 (7) ◽  
pp. 1353 ◽  
Author(s):  
Ko-Wei Huang ◽  
Abba Girsang ◽  
Ze-Xue Wu ◽  
Yu-Wei Chuang
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

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