A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem

• Published in: International journal of production research Vol. 54; no. 18; pp. 5554 - 5566
• Main Authors: Zheng, Xiao-long, Wang, Ling
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 16.09.2016; Taylor & Francis LLC
• Subjects: Algorithms; dual-resource constrained flexible job shop; fruit fly optimisation algorithm; Fruits; Job shop scheduling; Job shops; knowledge-guided search; Mathematical models; Operators; Optimization; Production scheduling; Resource scheduling; Searching; smell-based search
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2016.1170226

Different from the classical job shop scheduling, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) should deal with job sequence, machine assignment and worker assignment all together. In this paper, a knowledge-guided fruit fly optimisation algorithm (KGFOA) with a new encoding scheme is proposed to solve the DRCFJSP with makespan minimisation criterion. In the KGFOA, two types of permutation-based search operators are used to perform the smell-based search for job sequence and resource (machine and worker) assignment, respectively. To enhance the search capability, a knowledge-guided search stage is incorporated into the KGFOA with two new search operators particularly designed for adjusting the operation sequence and the resource assignment, respectively. Due to the combination of the knowledge-guided search and the smell-based search, global exploration and local exploitation can be balanced. Besides, the effect of parameter setting of the KGFOA is investigated and numerical tests are carried out using two sets of instances. The comparative results show that the KGFOA is more effective than the existing algorithms in solving the DRCFJSP.