Combining vehicle routing and packing for optimal delivery schedules of water tanks

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 multi-objective 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.

[1]  Maria Grazia Speranza,et al.  Exact solutions to the double travelling salesman problem with multiple stacks , 2010, Networks.

[2]  Manuel Iori,et al.  Routing problems with loading constraints , 2010 .

[3]  Josef Stoer,et al.  Numerische Mathematik 1 , 1989 .

[4]  G. Clarke,et al.  Scheduling of Vehicles from a Central Depot to a Number of Delivery Points , 1964 .

[5]  Giselher Pankratz,et al.  A Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows , 2005, OR Spectr..

[6]  Jean-Yves Potvin,et al.  Vehicle Routing , 2009, Encyclopedia of Optimization.

[7]  Henry C. W. Lau,et al.  A hybrid genetic algorithm for the multi-depot vehicle routing problem , 2008, Eng. Appl. Artif. Intell..

[8]  Maria Grazia Speranza,et al.  The periodic vehicle routing problem with intermediate facilities , 2002, Eur. J. Oper. Res..

[9]  William J. Cook,et al.  The Traveling Salesman Problem: A Computational Study , 2007 .

[10]  R. Ackoff The Future of Operational Research is Past , 1979 .

[11]  G. Laporte,et al.  Transportation Demand , 2019, Energy: Supply and Demand.

[12]  Christian Prins,et al.  A simple and effective evolutionary algorithm for the vehicle routing problem , 2004, Comput. Oper. Res..

[13]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[14]  Clifford A. Shaffer,et al.  A practical introduction to data structures and algorithm analysis prentice hall , 1996 .

[15]  Jean-Yves Potvin,et al.  Genetic Algorithms for the Traveling Salesman Problem , 2005 .

[16]  Guido Perboli,et al.  EVE-OPT: a hybrid algorithm for the capacitated vehicle routing problem , 2008, Math. Methods Oper. Res..

[17]  Gerhard Wäscher,et al.  An improved typology of cutting and packing problems , 2007, Eur. J. Oper. Res..

[18]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[19]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[20]  Limin Fu,et al.  FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data , 2007, BMC Bioinformatics.