This study addresses a real-life multiship routing and scheduling application with inventory constraints that arises in pickup and delivery operations of different types of crude oil from various offshore oil rigs (platforms) to coastal terminals. Oil transportation largely results from the need to maintain inventories at each supply point (platform) between minimum and maximum levels, considering production rates in these operational points, and to meet demands of different oils in the terminals within the planning time horizon. Routing and scheduling of the available fleet aims to obtain solutions of minimum total costs, subject to various constraints such as the maximum volume of cargo carried on each ship, simultaneous cargo unloading in some terminals, conditions that rule ship docking in offshore platforms and terminal berths, among others. In this research, we modify and extend inventory constrained maritime routing and scheduling models to appropriately represent the problem of a case study at a Brazilian company and to solve small-to-moderate instances based on real data. We also present a matheuristic to deal with larger problem instances. Solution evaluation by company experts indicates that the model and this hybrid heuristic properly represent the problem and highlights the potential of their application in practice.
Palavras-chave: Roteirização e programação de veículos; Coleta e entrega; Transporte marítimo; Petróleo;
Relax-and-fix; Heurísticas baseadas em programação matemática.Abstract: This study analyzes a routing and scheduling problem of cabotage oil ships motivated by the actual operation of an oil company along the Brazilian coast. Maritime transportation costs from offshore platforms to coastal terminals are an important issue in the search for operational excellence in the oil industry, and the prospects for growth in oil exploration in Brazil have made operations more demanding for agile and effective decision support systems (DSS). This paper presents an optimization approach to deal with this problem consisting of a mixed integer linear (MIP) programming model and an MIP heuristic known as relax and fix. The problem is formulated as a pickup and delivery vessel routing with time windows and heterogeneous fleet which minimizes the costs of fuel consumption of ships and freight contracts. In addition to the usual routing constraints, it also considers specific restrictions of oil maritime transportation problems. Numerical experiments with this approach are presented for a set of real data of the company, confirming that the optimization method is able to find good solutions for moderate-size problem instances.
ResumoEsse artigo trata de um problema de programação da produção característico da indústria aeronáutica, envolvendo estruturas especiais de montagem chamadas gabaritos, compostas de diversos postos de trabalho em paralelo, na montagem de partes das aeronaves. Tarefas devem ser programadas para serem executadas nestes postos de trabalho de maneira a minimizar o makespan; porém, além das restrições usuais, como prazos de entrega das tarefas e precedências entre as tarefas, existem também restrições que impedem que duas tarefas possam ser executadas ao mesmo tempo em dois postos de trabalho adjacentes no gabarito. Com base no estudo de casos práticos de programação de gabaritos de montagem de uma empresa aeronáutica, propõe-se um modelo de programação linear inteira mista. As soluções geradas pelo modelo foram implementadas na prática com ganhos tanto na utilização dos gabaritos de montagem estudados quanto na utilização da mão de obra envolvida.
Palavras-chaveProgramação da produção. Gabaritos de montagem. Indústria aeronáutica. Programação linear inteira mista.
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.