A Hybridized Approach for Solving Group Shop Problems (GSP)

Abstract

In recognition of the industriesa?? need for single and robust algorithm for the various classes of shop scheduling problems, the thesis addresses the application of meta-heuristics approaches to tackle a generalized formulation of shop scheduling problem known as the Group Shop Problem (GSP) by developing a hybridized two-phase approach. The proposed algorithm incorporates features of simulated annealing and variable neighborhood search to diversify its search in the initial phase. Additionally, the algorithm adopts the use of tabu-lists from Tabu Search throughout to prevent cyclical search from arising and implements backtrack memory to limit the search to only promising regions of the search space. To evaluate its performance, the algorithm has been subjected to extensive computational experiments using a set of benchmark problems for comparison with other known approaches for solving GSP. The empirical results show that the proposed algorithm does produce solutions of comparable quality but with shorter processing time.