Combining vehicle routing and packing for optimal delivery schedules of water tanks
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Combining vehicle routing and packing for optimal delivery schedules of water tanks Jacob Stolka,*, Isaac Manna, Arvind Mohaisa and Zbigniew Michalewicza,b,c,d a SolveIT Software Pty Ltd, Level 1, 99 Frome Street, Adelaide, SA 5000, Australia. E-mails: [email protected]; [email protected]; [email protected] b School of Computer Science, University of Adelaide, Adelaide, SA 5005, Australia. c Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, Warsaw 01-248, Poland. d Polish-Japanese Institute of Information Technology, ul. Koszykowa 86, Warsaw 02-008, Poland. E-mail: [email protected]
*Corresponding author.
Abstract
This article describes a decision-support system that was developed in 2011 and is currently in production use. The purpose of the system is to assist planners in constructing delivery schedules of water tanks to often remote areas in Australia. A delivery schedule consists of a number of delivery trips by trucks. An optimal delivery schedule minimises cost to deliver a given total sales value of delivered products. To construct an optimal delivery schedule, trucks need to be optimally packed with water tanks and accessories to be delivered to a set of delivery locations. This packing problem, which involves many packing and loading constraints, is intertwined with the transport problem of minimising distance travelled by road. Such a decision-support system that optimises multi-component operational problems is of great importance for an organisation; it supports what-if analysis for operational and strategic decisions and trade-off analysis to handle multiobjective optimisation problems; it is capable of handling and analysing variances; it is easy to modify – constraints, business rules, and various assumptions can be re-configured by a client. Construction of such decision-support systems requires the use of heuristic methods rather than linear/integer programming. OR Insight (2013) 26, 167–190. doi:10.1057/ori.2013.1; published online 13 March 2013 & 2013 Operational Research Society Ltd 0953-5543 OR Insight www.palgrave-journals.com/ori/
Vol. 26, 3, 167–190
Stolk et al
Keywords: optimisation; clustering; vehicle routing; packing; shortest path Received 15 May 2012; accepted 31 January 2013 after one revision
Impact Statement The original system described in this article shows new possibilities of hybridising and adapting algorithms from various areas to solve real-world problems, by: K
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addressing optimisation problems in the context of operations of a commercial company; solving these optimisation problems by combining and adapting various algorithms from fields of data clustering, geographic information systems, shortest path finding, vehicle routing, as well as packing and cutting.
Introduction Large-scale business decision problems consist of interconnected components, but need to be solved to achieve optimal results for a business as a whole. Even if we know exact algorithms for solving sub-problems, it remains an open ques
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