scholarly journals A Wale Optimization Algorithm for Distributed Flow Shop with Batch Delivery

2021 ◽  
Author(s):  
Qinghua Li ◽  
Junqing Li ◽  
Xinjie Zhang ◽  
Biao Zhang
Keyword(s):  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Neighborhood Structure ◽  
Shop Scheduling ◽  
Batch Delivery ◽  
Search Heuristics ◽  
Annealing Algorithm

Abstract In this study, a distributed flow shop scheduling problem with batch delivery constraints is investigated. The objective is to minimize the makespan and energy consumptions simultaneously. To this end, a hybrid algorithm combining the wale optimization algorithm (WOA) with local search heuristics is developed. In the proposed algorithm, each solution is represented by three vectors, namely a job scheduling sequence vector, batch assignment vector, and a factory assignment vector. Then, an efficient neighborhood structure is applied in the proposed algorithm to enhance search abilities. Furthermore, the simulated annealing algorithm and clustering method are embedded to improve the global search abilities of the algorithm. Finally, 30 instances are generated based on realistic application to test the performance of the algorithm. After detailed comparisons with three efficient algorithms, i.e., ABC-Y, ICA-K, and IWOA NS , the superiority of the proposed algorithm is verified.

scholarly journals Flexible Flow Shop Scheduling Method with Public Buffer

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 681
Author(s):  
Han ◽  
Han ◽  
Lin ◽  
Dong ◽  
Shi
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Flexible Flow Shop ◽  
The Public ◽  
Shop Scheduling ◽  
Annealing Algorithm ◽  
Flexible Flow Shop Scheduling ◽  
Local Scheduling

Actual manufacturing enterprises usually solve the production blockage problem by increasing the public buffer. However, the increase of the public buffer makes the flexible flow shop scheduling rather challenging. In order to solve the flexible flow shop scheduling problem with public buffer (FFSP–PB), this study proposes a novel method combining the simulated annealing algorithm-based Hopfield neural network algorithm (SAA–HNN) and local scheduling rules. The SAA–HNN algorithm is used as the global optimization method, and constructs the energy function of FFSP–PB to apply its asymptotically stable characteristic. Due to the limitations, such as small search range and high probability of falling into local extremum, this algorithm introduces the simulated annealing algorithm idea such that the algorithm is able to accept poor fitness solution and further expand its search scope during asymptotic convergence. In the process of local scheduling, considering the transferring time of workpieces moving into and out of public buffer and the manufacturing state of workpieces in the production process, this study designed serval local scheduling rules to control the moving process of the workpieces between the public buffer and the limited buffer between the stages. These local scheduling rules can also be used to reduce the production blockage and improve the efficiency of the workpiece transfer. Evaluated by the groups of simulation schemes with the actual production data of one bus manufacturing enterprise, the proposed method outperforms other methods in terms of searching efficiency and optimization target.

An Improved Heuristic Algorithm for a Hybrid Flow-Shop Scheduling

2013 ◽  
Vol 333-335 ◽  
pp. 1414-1417
Author(s):  
Min Dai ◽  
Dun Bing Tang ◽  
Kun Zheng ◽  
Qi Xiang Cai
Keyword(s):  
Parallel Machines ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Shop Scheduling ◽  
Hormone Modulation ◽  
Hybrid Flow Shop Scheduling ◽  
Annealing Algorithm

This paper presents an improved metaheuristic algorithm to minimize the makespan in a hybrid flow-shop scheduling (HFS) with non-identical parallel machines. First, a mathematical model for an HFS problem is introduced. Second, an improved simulated annealing algorithm (ISAA) which is inspired from a hormone modulation mechanism is presented to retrofit speed and accuracy of the algorithm. Finally, the computer simulation demonstrates the good quality of the proposed procedure, and it outperforms several other algorithms.

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