Abstract
Different urban transportation flows (e.g., passenger journeys, freight distribution, and waste management) are conventionally separately handled by corresponding single-purpose vehicles (SVs). The multi-purpose vehicle (MV) is a novel vehicle concept that can enable the sequential sharing of different transportation flows by changing the so-called modules, thus theoretically improving the efficiency of urban transportation through the utilization of higher vehicles. In this study, a variant of the pick-up and delivery problem with time windows is established to describe the sequential sharing problem considering both MVs and SVs with features of multiple depots, partial recharging strategies, and fleet sizing. MVs can change their load modules to carry all item types that can also be carried by SVs. To solve the routing problem, an adaptive large neighborhood search (ALNS) algorithm is developed with new problem-specific heuristics. The proposed ALNS is tested on 15 small-size cases and evaluated using a commercial MIP solver. Results show that the proposed algorithm is time-efficient and able to generate robust and high-quality solutions. We investigate the performance of the ALNS algorithm by analyzing convergence and selection probabilities of the heuristic solution that destroy and repair operators. On 15 large-size instances, we compare results for pure SV, pure MV, and mixed fleets, showing that the introduction of MVs can allow smaller fleet sizes while approximately keeping the same total travel distance as for pure SVs.
Highlights
Different urban transportation flows are conventionally separately handled by corresponding single-purpose vehicles (SVs). e multi-purpose vehicle (MV) is a novel vehicle concept that can enable the sequential sharing of different transportation flows by changing the so-called modules, theoretically improving the efficiency of urban transportation through the utilization of higher vehicles
We aim to find the best vehicle configuration of the mixed fleet and routes of these vehicles in the network composed of customer points, recharging stations, change stations, and depots to achieve an overall minimum cost including the fixed cost of vehicles, the variable cost of the total travel distance, and the change cost at change stations. e problem can be regarded as a variant of the pick-up and delivery problem with time windows (PDPTWs) as defined in the literature review
We show that the proposed adaptive large neighborhood search (ALNS) can find solutions for small-scale cases with high qualities in an efficient time
Summary
One variant of the widely studied vehicle routing problem (VRP) is the PDPTW. In the PDPTW, a set of vehicle routes are determined so that a set of transportation requests are served at a minimum cost with the given vehicle fleet [10]. E PDPTW uses a heterogeneous vehicle fleet stationed at multiple depots to satisfy a set of predetermined transportation requests. Each request consists of a pick-up location, a delivery location, and a certain number of items that should be transported. E pick-up time and delivery time of a request should fall in the time windows of both locations. E dial-a-ride problem (DARP) is a variation of the PDPTW in which the transported items are people and focuses more on the quality of service and the convenience of passengers [11, 12]. We refer the interested reader to articles about PDPTW [13] and DARP [12, 14]
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