Hybrid algorithms for Job shop Scheduling Problem with Lot streaming and A Parallel Assembly Stage

Abstract

In this paper, a Job shop scheduling problem with a parallel assembly stage and Lot Streaming (LS) is considered for the first time in both machining and assembly stages. Lot Streaming technique is a process of splitting jobs into smaller sub-jobs such that successive operations can be overlapped. Hence, to solve job shop scheduling problem with a parallel assembly stage and lot streaming, decision makers not only need to determine the processing sequences on machines in first stage, but also need to assign each product to a machine and determine the assembly sequences of the products in second stage and the sub-lot sizes of all jobs and products to minimize the makespan. At first, this problem is modeled as a mixed integer linear programming and GAMS software is applied to solve small problems. Since this problem is classified as NP-hard, four hybrid algorithms based on iterative procedures are suggested to solve the problem in medium and large dimensions. In order to verify the effectiveness of the proposed algorithms, a statistical analysis is used along with Relative Percentage Deviation (RPD) factor. Computational results revealed that the hybrid genetic and parallel simulated annealing algorithm (HGAPSA) and the hybrid genetic and parallel variable neighborhood search algorithm (HGAPVNS) perform better than the other proposed algorithms with respect to the objective function. Also, considering lot streaming for both stages instead of applying it only to the first stage leads to achieve better solutions. Finally, the HGAPSA algorithm is compared with a hybrid genetic algorithm (HGA). Experimental results showed that the HGAPSA outperforms the HGA in terms of solution quality.