A Re-constructed Meta-Heuristic Algorithm for Robust Fleet Size and Mix Vehicle Routing Problem with Time Windows under Uncertain Demands

Abstract

Recent work of the fleet size and mix vehicle routing problem with time windows mostly assumes that the input variables are deterministic. Practice in the real world, however, faces considerable uncertainty in the data. But recent research studies lack emphasis on this uncertainty. This paper focuses to contribute to a new challenging study by considering the customer demand as uncertain. This characteristic increases the difficulty for solving. The meta-heuristic algorithms are developed consisting a modification of a genetic algorithm and an adaptation of a greedy search hybridized with inter-route neighborhood search methods. Because this paper relates to uncertain customer demands, decision making is performed using the robust approach based on worst case scenarios. The final results are evaluated by using the extra cost and the unmet demand against the deterministic approach to balance the decision making.