One-way car-sharing systems are transportation systems that allow customers to rent cars at stations scattered around the city, use them for a short journey, and return them at any station. The maximum customers' satisfaction problem concerns the task of assigning the cars, initially located at given stations, to maximize the number of satisfied customers. We consider the problem with two stations where each customer has exactly two demands in opposite directions between both stations, and a customer is satisfied only if both their demands are fulfilled. For solving this problem, we propose mixed-integer programming (MIP) models and matheuristics based on local search. We created a benchmark of instances used to test the exact and heuristic approaches. Additionally, we proposed a preprocessing procedure to reduce the size of the instance. Our MIP models can solve to optimality 85% of the proposed instances with 1000 customers in 10 minutes, with an average gap smaller than 0.1% for all these instances. For larger instances (2500 and 5000 customers), except for some particular cases, they presented an average gap smaller than 0.8%. Also, our localbased matheuristics presented small average gaps which are better than the MIP models in some larger instances.
Neste artigo, consideramos as generalizações dos problemas k-median e k-center, conhecidas, respectivamente, por leasing k-median (LKM) e leasing k-center (LKC). Apresentamos formulações de programação linear inteira e uma heurı́stica baseada na meta-heurı́stica BRKGA. Comparamos as soluções geradas pela heurı́stica com as soluções geradas pelo resolvedor GUROBI aplicado às formulações em programação linear, estabelecendo um prazo de 10 minutos de execução para ambos. Para as instâncias pequenas testadas, os custos das soluções heurı́sticas foram próximos aos custos ótimos (GAP médio ≤ 8%). Para instâncias maiores testadas a heurı́stica gerou soluções superiores às do resolvedor, em pelo menos metade dos testes.
We introduce the electric vehicle sharing problem (EVSP), a problem that arises from the planning and operation of electric car-sharing systems which allow one-way rental of vehicles. The problem aims at finding the maximum total daily rental time in which customers' demands are assigned to the existing fleet. In addition, either all of the customer's demands are completely fulfilled or the customer does not use the system at all. We show that the EVSP is NP-hard, and we provide four mixed-integer linear programming formulations based on space-time network flow models, along with some theoretical results. We perform a comprehensive computational study of the behavior of the proposed formulations using two benchmark sets, one of which is based on real-world data from an electric car-sharing system located in Fortaleza, Brazil. The results show that our best formulation is effective in solving instances where each customer has only one demand. In general, we were able to optimally solve at least 55% of the instances within the time limit of one hour.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.