A neural network meta-model for identification of optimal combination of priority dispatching rules and makespan in a deterministic job shop scheduling problem

作者：Azadeh A^*; Shoja B Maleki; Moghaddam M; Asadzadeh S M; Akbari A

来源：International Journal of Advanced Manufacturing Technology, 2013, 67(5-8): 1549-1561.

DOI：10.1007/s00170-012-4589-y

• Priority dispatching rule
• Job shop scheduling
• Discrete event simulation
• Artificial neural network
• Meta-model
• Optimization

Selection of appropriate priority dispatching rules (PDRs) is a major concern in practical scheduling problems. Earlier research implies that using one PDR does not necessarily yield to an optimal schedule. Hence, this paper puts forward a novel approach based on discrete event simulation (DES) and artificial neural networks (ANNs) to decide on the optimal PDR for each machine from a set of rules so as to minimize the makespan in job shop scheduling problems. Non-identical PDRs are considered for each machine. Indeed, for a given number of machines, all permutations of PDRs are taken into account which could lead to nondeterministic polynomial-time hardness of the problem when the number of machines increases. To address this issue, DES and ANNs are employed as a meta-model. First, the problem is modeled and quite a number of feasible solutions are obtained from DES on its own. Afterward, a back-propagation neural network is developed in accordance with the results of DES to calculate the makespan based on all potential permutations of PDRs. The performance of the proposed approach is investigated through a set of test-bed problems.

• 出版日期2013-7