Optimization models for fair horizontal collaboration in demand-responsive transportation. (July 2022) - Angelelli, E.; Morandi, V.; Speranza, M.G.
Abstract: The advances in information and communication technology are changing the way people move. Companies that offer demand-responsive transportation services have the opportunity to reduce their costs and increase their revenues through collaboration, while at the same time reducing the environmental impact of their operations. In this paper, we consider the case of companies, offering a shared taxi service, that are involved in horizontal collaboration, and present mixed integer programming models for the optimization of their routes that embed constraints aimed at controlling the workload exchange of each company with others. These constraints bound the workload exchange in terms of traveled time and/or served customers to be less than thresholds agreed in advance by the companies. We also present an efficient heuristic algorithm and show the effectiveness of the heuristic and the benefits of the collaboration. Highlights: Fair collaboration among companies involved in dial-a-ride-services. Balance in both workload exchange and customers' visibility. Adaptive large neighborhood search for the collaboration among companies.