7. Conclusion
This study has addressed the VRP incorporating forced backhauls with two objectives namely cost minimization and minimization of span of travel tour. The problem is an extended variant of VRPB and it is termed as BVFB. Among various applications of BVFB, we have considered one application pertain to bank industry. The problem is to deliver the required cash to automated teller machines (ATMs) and collect the dropped cash back to the cash centers. For this NP-hard problem, we have developed three heuristics. The first heuristic is a modified savings heuristic with arc removal procedure, the second heuristic is a modified savings heuristic with node swap procedure and the third heuristic is adapted genetic algorithm. In the absence of published data-sets that incorporate BVFB features, we have chosen 33 standard VRPB data-sets from the literature for comparison. We have also considered nine real-life cases of BVFB and 10 randomly generated data-sets of BVFB. It is evident from the results that genetic algorithm promises to be a useful tool for solving VRPB and BVFB. The software for the algorithms and heuristics developed in this study can be embedded into decision support systems. A supply/distribution schedule and routing plan can be generated each day by loading the data into the system and by running the programme on a daily basis (or) as and when required. The incorporation of ‘What if’ rules in the system may enhance the utility of the current study. As an extension of the study, large sized problems can also be solved with the suggested heuristics. Incorporating additional constraints such as time windows and multiple depots can also be thought of. Development of various other meta-heuristics such as Ant Colony Optimization, Memetic Algorithm, Tabu search and Particle Swarm Optimization for the proposed variants can be a potential future work.
7. บทสรุปThis study has addressed the VRP incorporating forced backhauls with two objectives namely cost minimization and minimization of span of travel tour. The problem is an extended variant of VRPB and it is termed as BVFB. Among various applications of BVFB, we have considered one application pertain to bank industry. The problem is to deliver the required cash to automated teller machines (ATMs) and collect the dropped cash back to the cash centers. For this NP-hard problem, we have developed three heuristics. The first heuristic is a modified savings heuristic with arc removal procedure, the second heuristic is a modified savings heuristic with node swap procedure and the third heuristic is adapted genetic algorithm. In the absence of published data-sets that incorporate BVFB features, we have chosen 33 standard VRPB data-sets from the literature for comparison. We have also considered nine real-life cases of BVFB and 10 randomly generated data-sets of BVFB. It is evident from the results that genetic algorithm promises to be a useful tool for solving VRPB and BVFB. The software for the algorithms and heuristics developed in this study can be embedded into decision support systems. A supply/distribution schedule and routing plan can be generated each day by loading the data into the system and by running the programme on a daily basis (or) as and when required. The incorporation of ‘What if’ rules in the system may enhance the utility of the current study. As an extension of the study, large sized problems can also be solved with the suggested heuristics. Incorporating additional constraints such as time windows and multiple depots can also be thought of. Development of various other meta-heuristics such as Ant Colony Optimization, Memetic Algorithm, Tabu search and Particle Swarm Optimization for the proposed variants can be a potential future work.
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