Simulation modelling and analysis of dispatching rules in an assembly job shop production system with machine breakdowns
06 Aug 2018-International Journal of Advanced Operations Management (Inderscience Publishers)-Vol. 10, Iss: 3, pp 234-251
TL;DR: The results indicate that earliest completion time rule performs better in comparison with the other rules investigated in this study for single-level structure with multiple objectives.
Abstract: This paper addresses the scheduling problem in assembly job shop systems with machine breakdowns. The present study intends to rank the dispatching rules that are applied in an assembly job shop production system. A simulation model of an assembly job shop is developed for the purpose of this ranking and eight dispatching rules from the literature are incorporated in the simulation model. The product structures considered in this study are single-level assembly structure, two-level assembly structures and three level assembly structures. The machines are subjected to non-availability due to of breakdowns. Five performance measures are considered for analysis. The performance of each dispatching rule for each performance measure is calculated individually. To identify which of the dispatching rule provides the optimum result when all the performance measures are equally important, grey relational analysis is adopted to rank the dispatching rules. The results indicate that earliest completion time rule performs better in comparison with the other rules investigated in this study for single-level structure with multiple objectives.
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TL;DR: A bi-objective batch scheduling model that minimizes the total energy consumption and the total completion time is developed, and the multi-objectives gray wolf optimizer (MOGWO) is employed as the solution to obtain the optimal schedule scheme.
Abstract: Energy-saving scheduling is a well-known issue in the manufacturing system. The flexibility of the workshop increases the difficulty of scheduling. In the workshop schedule, considering the collaborative optimization of multi-level structure product production and energy consumption has certain practical significance. The process sequence of parts and components should be consistent with the assembly sequence. Additionally, the non-production energy consumption (NPEC) (such as the energy consumption of workpiece handling, equipment standby, and workpiece conversion) generated by the auxiliary machining operations, which make up the majority of the total energy consumption, should not be ignored. A sub-batch priority is set according to the upper and lower coupling relationship in the product structure. A bi-objective batch scheduling model that minimizes the total energy consumption and the total completion time is developed, and the multi-objective gray wolf optimizer (MOGWO) is employed as the solution to obtain the optimal schedule scheme. A case study is performed to demonstrate the potential possibilities concerning NPEC in regard to reducing the total energy consumption and to show the effectiveness of the algorithm. Compared with the traditional optimization model, the joint optimization of NPEC and PEC can reduce the energy consumption of standby and handling by 9.95% and 22.28%, respectively.
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TL;DR: A discrete particle swarm optimization algorithm called DPSO is proposed to solve the two-stage assembly scheduling problem with respect to bicriteria of makespan and mean completion time where setup times are treated as separate from processing times.
Abstract: In this paper, a discrete particle swarm optimization (PSO) algorithm called DPSO is proposed to solve the two-stage assembly scheduling problem with respect to bicriteria of makespan and mean completion time where setup times are treated as separate from processing times In DPSO, the particle velocity representation is redefined, and particle movement is modified accordingly In order to refrain from the shortcoming of premature convergence, individual intensity is defined, which is used to control adaptive mutation of the particle, and mutation mode is decided by the individual fitness Furthermore, a randomized exchange neighborhood search is introduced to enhance the local search ability of the particle and increase the convergence speed Finally, the proposed algorithm is tested on different scale problems and compared with the proposed efficient algorithms in the literature recently The results show that DPSO is an effective and efficient for assembly scheduling problem
52 citations