Fleet design optimisation from historical data using constraint programming and large neighbourhood search
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We present an original approach to compute efficient mid-term fleet configurations, at the request of a Queensland-based long-haul trucking carrier. Our approach considers one year’s worth of demand data, and employs a constraint programming (CP) model and an adaptive large neighbourhood search (LNS) scheme to solve the underlying multi-day multi-commodity split delivery capacitated vehicle routing problem. Our solver is able to provide the decision maker with a set of Pareto-equivalent fleet setups trading off fleet efficiency against the likelihood of requiring on-hire vehicles and drivers. Moreover, the same solver can be used to solve the daily loading and routing problem. We carry out an extensive experimental analysis, comparing our approach with an equivalent mixed integer programming (MIP) formulation, and we show that our approach is a sound methodology to provide decision support for the mid- and short-term decisions of a long-haul carrier.
KeywordsVehicle routing problem Fleet size and mix Large neighbourhood search Mixed integer programming Constraint programming Pre-processing
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- 1.Archetti, C., & Speranza, M. (2008). The split delivery vehicle routing problem: a survey. In Golden, B., Raghavan, S., & Wasil, E. (Eds.) The vehicle routing problem: latest advances and new challenges, operations research/computer science interfaces, (Vol. 43 pp. 103–122). US: Springer.Google Scholar
- 3.Birattari, M., Yuan, Z., Balaprakash, P., & Stützle, T. (2010). F-Race and iterated F-Race: an overview. In Experimental methods for the analysis of optimization algorithms (pp. 311–336). Springer.Google Scholar
- 5.Crainic, T.G. (2003). Long-haul freight transportation. In Hall, R.W. (Ed.) Handbook of transportation science, international series in operations research & management science, (Vol. 56 pp. 451–516). US: Springer.Google Scholar
- 7.Di Gaspero, L., Rendl, A., & Urli, T. (2015). Balancing bike sharing systems with constraint programming. Constraints, 1–31.Google Scholar
- 8.Di Gaspero, L., & Urli, T. (2014). A cp/lns approach for multi-day homecare scheduling problems. In Hybrid metaheuristics (pp. 1–15). Springer.Google Scholar
- 11.Gurobi Optimization, I. (2015). Gurobi optimizer reference manual. http://www.gurobi.com.
- 12.Kilby, P., & Shaw, P. (2006). Vehicle routing. Handbook of Constraint Programming, 799–834.Google Scholar
- 14.Pisinger, D., & Ropke, S. (2010). Large neighborhood search. In Handbook of metaheuristics (pp. 399–419). Springer.Google Scholar
- 16.Schulte, C., Tack, G., & Lagerkvist, M.Z. (2015). Modeling and programming with gecode. In Schulte, C., Tack, G., & Lagerkvist, M.Z (Eds.) Modeling and programming with gecode.Google Scholar
- 17.Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. In Maher, M. J., & Puget, J.F. (Eds.) CP’98: the 4th international conference on principles and practice of constraint programming, 1998, proceedings of, lecture notes in computer science, (Vol. 1520 pp. 417–431): Springer.Google Scholar
- 18.Toth, P., & Vigo, D. (2002). The vehicle routing problem. Society for Industrial and Applied Mathematics.Google Scholar
- 19.Urli, T. (2013). json2run: a tool for experiment design & analysis. arXiv preprint arXiv:1305.1112.