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Chapter 5. Conclusions and further research
This research proposes two population-based metaheuristics based on the local best solution, B-HLBS and A-HLBS, for the permutation flow shop scheduling problem (PFSP-makespan). The computational results in Chapter 4 have shown that A-HLBS is an effective heuristic for PFSP-makespan. It dominates all the promising population-based metaheuristics related to ACO, PSO and GA (M-MMAS, PACO, PSOvns, HGA_RMA, and NEGAvns). However, our results demonstrate that the operation of A-HLBS can be further improved. Our analyses illustrate that the performance of HLBS is highly influenced by the three major factors: the trace-model, the filter strategy, and the jump strategy. With proper selection of x, y and q0 of the trace-model and application of different filter strategy and jump strategy, A-HLBS significantly dominates B-HLBS. Therefore, further studies on the interaction of these three factors are worthwhile. For instance, the path relinking method (Glover, 1996) can be applied to the solutions in the populations generated by J-Startegy1 and J-Strategy2 to produce new initial solutions. Since the path relinking has been proved to be effective for generating promising solutions for PFSP-makespan (Nowicki and Smutnicki, 1996), it is believed that the method is able to produce effective initial solutions and improve the performance of HLBS. In addition, since the flow shop problem is a special case of the job shop problem, the proposed heuristic can also be applied towards job shop problems.
It is important to note that although computation time needed in a PC is a major termination criterion used to compare the performance of most of the metaheuristics developed for PFSP–makespan, this criterion is inappropriate because the computation time using a PC is affected by several factors of the PC such as the level of CPU, the size of memory and the operating system. It is very difficult to find equal
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computing-power machines when comparing the performance of different metaheuristics. In addition, the coding skill of the computer program will also significantly affect the performance of the metaheuristics, given computation time as the termination criterion, because it will affect the number of solutions searched in a limited computation time. Therefore, it is believed that the number of solutions searched using a metaheuristic could be a more appropriate metric to evaluate its effectiveness. Assuming analysis under this new criterion, the effectiveness of A-HLBS may be comparable or even better than the current optimal metaheuristics.
Therefore in order to more accurately assess the effectiveness of metaheuristics, future studies considering this criterion is warranted.
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