An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem

Deniz Aksen (Corresponding author), Onur Kaya, Sibel Salman, Özge Tüncel

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Scopus)
1 Downloads (Pure)

Abstract

We study a selective and periodic inventory routing problem (SPIRP) and develop an Adaptive Large Neighborhood Search (ALNS) algorithm for its solution. The problem concerns a biodiesel production facility collecting used vegetable oil from sources, such as restaurants, catering companies and hotels that produce waste vegetable oil in considerable amounts. The facility reuses the collected waste oil as raw material to produce biodiesel. It has to meet certain raw material requirements either from daily collection, or from its inventory, or by purchasing virgin oil. SPIRP involves decisions about which of the present source nodes to include in the collection program, and which periodic (weekly) routing schedule to repeat over an infinite planning horizon. The objective is to minimize the total collection, inventory and purchasing costs while meeting the raw material requirements and operational constraints. A single-commodity flow-based mixed integer linear programming (MILP) model was proposed for this problem in an earlier study. The model was solved with 25 source nodes on a 7-day cyclic planning horizon. In order to tackle larger instances, we develop an ALNS algorithm that is based on a rich neighborhood structure with 11 distinct moves tailored to this problem. We demonstrate the performance of the ALNS, and compare it with the MILP model on test instances containing up to 100 source nodes.
Original languageEnglish
Pages (from-to)413-426
Number of pages14
JournalEuropean Journal of Operational Research
Volume239
Issue number2
DOIs
Publication statusPublished - 2014
Externally publishedYes

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