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Adaptive large neighborhood search for the periodic capacitated arc routing problem with inventory constraints
Author(s) -
RiquelmeRodríguez JuanPablo,
Langevin André,
Gamache Michel
Publication year - 2014
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/net.21562
Subject(s) - routing (electronic design automation) , truck , traverse , computer science , mathematical optimization , arc routing , vehicle routing problem , operations research , mathematics , engineering , automotive engineering , computer network , geography , geodesy
This article describes the problem in which the edges of a network represent customers, and a quantity of material is delivered to them so that each one achieves a desired inventory level while finding the lowest‐cost route of delivery. Routing and inventory decisions are made at the same time. An example of an application of this problem is dust suppression in open‐pit mines. A fleet of trucks spray water along the roads of a mine. Humidity increases the effectiveness of dust‐particle retention. Because the level of humidity decreases, replenishment is done periodically. Other examples of applications include dust suppression in forest roads and plants watering in street medians and sidewalks. We develop a mathematical model that combines two objectives: An inventory objective that minimizes the penalty for the lack of humidity and a routing objective that minimizes watering and traversing costs. Due to the complexity of the mathematical model, we developed an adaptive large neighborhood search algorithm that combines several destroy and repair operators dynamically. © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 64(2), 125–139 2014