A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem

Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker’s preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.

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