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

The Incremental Cooperative Design of Preventive Healthcare Networks

This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe

Decision support system for vendor managed inventory supply chain:a case study

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs)

Relief distribution networks : a systematic review

In the last 20 years, Emergency Management has received increasing attention from the scientific community. Meanwhile, the study of relief distribution networks has become one of the most popular topics within the Emergency Management field. In fact, the number and variety of contributions devoted to the design or the management of relief distribution networks has exploded in the recent years, motivating the need for a structured and systematic analysis of the works on this specific topic. To this end, this paper presents a systematic review of contributions on relief distribution networks in response to disasters. Through a systematic and scientific methodology, it gathers and consolidates the published research works in a transparent and objective way. It pursues three goals. First, to conduct an up-to-date survey of the research in relief distribution networks focusing on the logistics aspects of the problem, which despite the number of previous reviews has been overlooked in the past. Second, to highlight the trends and the most promising challenges in the modeling and resolution approaches and, finally, to identify future research perspectives that need to be explored

Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock