This Article Full Text (OnlineFirst PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Scopus Google Scholar Articles by Shen, Q. Articles by Zhou, M. Social Bookmarking                         What's this?

Multi-mode transportation planning of crude oil via Greedy Randomized Adaptive Search and Path Relinking

¹ Laboratoire d'optimisation des systéms industriels (LOSI), Institut Charles Delaunay, Université de Technologie de Troyes (UTT), Troyes Cedex, France
² Laboratory for Discrete Event Systems, Department of Electrical and Computer Engineering, New Jersey Institute of Technology Newark, NJ, USA

^* To whom correspondence should be addressed. E-mail: feng.chu{at}utt.fr.

	Abstract

The transportation of crude oil from production fields to refineries is a very important operation in the oil industry. In this paper, an inventory routing problem for crude oil transportation is studied, where the crude oil is transported from a central depot to a set of customers with dynamic demand using multiple transportation modes. Oil can be transported through marine routes, pipelines or a combination of the two modes. The marine transportation of crude oil is performed by a heterogeneous fleet of tankers with limited capacity owned by an oil distributor itself and/or the tankers of different types rented from a third party. Each transportation operation has a lead time and the storage capacity of oil at each customer is limited. The problem is to determine over a given planning horizon an optimal oil transportation plan that minimizes the total transportation and inventory costs subject to various constraints. The plan defines the number of tankers of each type to rent and the number of tankers of each type to dispatch on each route in each period. A mixed-integer programming model is established for the problem. Because of the high complexity and large size of the problem, the model is too complicated to be solved exactly. A metaheuristic method, the Greedy Randomized Adaptive Search Procedure (GRASP) enhanced by an intensification strategy based on Path Relinking is developed to find its near-optimal solutions. Numerical test results of the method demonstrate the effectiveness of the method.

First published on September 7, 2009
Transactions of the Institute of Measurement and Control 2009, doi:10.1177/0142331208100105


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?