scholarly journals Two-machine flow shop with synchronized periodic maintenance

2019 ◽  
Vol 53 (1) ◽  
pp. 351-365 ◽  
Author(s):  
Issam Krimi ◽  
Rachid Benmansour ◽  
Saïd Hanafi ◽  
Nizar Elhachemi
Keyword(s):  
Variable Neighborhood Search ◽  
Flow Shop ◽  
Programming Model ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Shop Scheduling ◽  
Availability Constraints ◽  
Periodic Maintenance ◽  
One Machine

In the literature, some works deal with the two-machine flow shop scheduling problem under availability constraints. Most of them consider those constraints only for one machine at a time and also with limited unavailability periods. In this work, we were interested by the unlimited periodic and synchronized maintenance applied on both machines. The problem is NP-hard. We proposed a mixed integer programming model and a variable neighborhood search for solving large instances in order to minimize the makespan. Computational experiments show the efficiency of the proposed methods.

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.

scholarly journals A DE-LS Metaheuristic Algorithm for Hybrid Flow-Shop Scheduling Problem considering Multiple Requirements of Customers

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yingjia Sun ◽  
Xin Qi
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Programming Model ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

In this paper, we address a hybrid flow-shop scheduling problem with the objective of minimizing the makespan and the cost of delay. The concerned problem considers the diversity of the customers’ requirements, which influences the procedures of the productions and increases the complexity of the problem. The features of the problem are inspired by the real-world situations, and the problem is formulated as a mixed-integer programming model in the paper. In order to tackle the concerned problem, a hybrid metaheuristic algorithm with Differential Evolution (DE) and Local Search (LS) (denoted by DE-LS) has been proposed in the paper. The differential evolution is a state-of-the-art metaheuristic algorithm which can solve complex optimization problem in an efficient way and has been applied in many fields, especially in flow-shop scheduling problem. Moreover, the study not only combines the DE and LS, but also modifies the mutation process and provides the novel initialization process and correction strategy of the approach. The proposed DE-LS has been compared with four variants of algorithms in order to justify the improvements of the proposed algorithm. Experimental results show that the superiority and robustness of the proposed algorithm have been verified.

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