Comparison of GA-Shift Neighbourhood Mutation and GA-Pairs Exchange Mutation with Multi Cut Point Crossover in Solving RICH-VRP

This research was focused on a heterogeneous fleet of passenger ships to solve multi depot by using genetic algorithm (GA) to solve combinatorial problem i.e. vehicle routing problem (VRP). The objective of this study is to compare the roulette wheel selection, multi cut point crossover, and shift neighbourhood mutation with roulette wheel selection, multi cut point crossover, and pairs exchange mutation to minimize the sum of the fuel consumption travelled, the cost for violations of the ship draft and sea depth, and penalty cost for violations of the load factor; maximize number port of call; and maximize load factor. Problem solving in this study is how to generate feasible route combinations for rich VRP that meets all the requirements with optimum solution. Route generated by roulette wheel selection, multi cut point crossover, and shift neighbourhood mutation could decrease fuel consumption about 19.4350% compared to roulette wheel selection, multi cut point crossover, and pairs exchange mutation about 18.6738%.