A Discrete Group Search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown

• Published in: Mathematical problems in engineering Vol. 2014; no. 1
• Main Authors: Cui, Zhe, Gu, Xingsheng
• Format: Journal Article
• Language: English
• Published: New York Hindawi Publishing Corporation 01.01.2014; John Wiley & Sons, Inc
• Subjects: Algorithms; Breakdown; Evolution; Evolutionary computation; Genetic algorithms; Heuristic; Mathematical analysis; Mathematical models; Mathematical problems; Neural networks; Optimization algorithms; Phases; Production scheduling; Scheduling; Searching
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2014/621393

The scheduling problems have been discussed in the literature extensively under the assumption that the machines are permanently available without any breakdown. However, in the real manufacturing environments, the machines could be unavailable inevitably for many reasons. In this paper, the authors introduce the hybrid flowshop scheduling problem with random breakdown (RBHFS) together with a discrete group search optimizer algorithm (DGSO). In particular, two different working cases, preempt-resume case, and preempt-repeat case are considered under random breakdown. The proposed DGSO algorithm adopts the vector representation and several discrete operators, such as insert, swap, differential evolution, destruction, and construction in the producers, scroungers, and rangers phases. In addition, an orthogonal test is applied to configure the adjustable parameters in the DGSO algorithm. The computational results in both cases indicate that the proposed algorithm significantly improves the performances compared with other high performing algorithms in the literature.