Fleet dimensioning and scheduling in the Brazilian ethanol industry: a fuzzy logic approach

This work solves a real-world multi-depot vehicle routing problem (MDVRP) with a homogeneous fleet and capacitated depots. A pipeline company wants to establish a vehicle policy in order to own part of its fleet and serve its customers for a period of one year. The company also wants to know the schedule of the visits for collecting ethanol from 261 producers and taking it to their three terminals located in Brazil. This problem presents uncertain demand, since weather conditions impact the final crop and uncertain depot capacity. Due to the vagueness of managers’ speech, this problem also presents uncertain travel time. In this paper, fuzzy logic is used to model uncertainty and vagueness and to split the initial instance into smaller ones. Besides solving a real-world problem with fuzzy demand, fuzzy depot capacity and fuzzy travel time, this paper contributes with a decision making tool that reports different solutions for different uncertainty levels.Este trabalho resolve um problema de roteamento de veículos multi-depósito do mundo real (MDVRP) com frota homogênea e depósitos capacitados. Uma empresa de pipeline deseja estabelecer uma política de veículos para possuir parte de sua frota e atender seus clientes por um período de um ano. A empresa também quer saber o agendamento das visitas para coleta de etanol de 261 produtores e retirada para seus três terminais localizados no Brasil. Este problema apresenta incertezas de demanda, já que as condições climáticas impactam a safra final e depósito de capacidade incerta. Devido à imprecisão do discurso dos gerentes, este problema também apresenta tempo de viagem incerto. Neste artigo, a lógica fuzzy é usada para modelar a incerteza e vagueza e dividir a instância inicial em outras menores. Além de resolver um problema do mundo real com demanda difusa, capacidade de depósito difusa e tempo de viagens difusas, este artigo contribui com uma ferramenta de tomada de decisão que relata diferentes soluções para diferentes níveis de incerteza

Solving the bi-objective Robust Vehicle Routing Problem with uncertain costs and demands

In this paper, a bi-objective Vehicle Routing Problem (bi-RVRP) with uncertainty in both demands and travel times is studied by means of robust optimization. Uncertain demands per customer are modeled by a discrete set of scenarios representing the deviations from an expected demand, while uncertain travel times are independent from customer demands. Then, traffic records are considered to get discrete scenarios to each arc of the transportation network. Here, the bi-RVRP aims at minimizing the worst total cost of traversed arcs and minimizing the maximum total unmet demand over all scenarios. As far as we know, this is the first study for the bi-RVRP which finds practical applications in urban transportation, e.g., serving small retail stores. To solve the problem, different variations of solution approaches, coupled with a local search procedure are proposed: the Multiobjective Evolutionary Algorithm (MOEA) and the Non-dominated Sorting Genetic Algorithm (NSGAII). Different metrics are used to measure the algorithmic performance, the convergence, as well as the diversity of solutions for the different methods