Metaheuristic approaches to a vehicle scheduling problem in sugar beet transportation
- 50 Downloads
A variant of vehicle scheduling problem (VSP) arising from the sugar beet transportation in a sugar factory in Serbia is introduced. The objective of the considered VSP is to minimize the required transportation time under problem-specific constraints. The problem is formulated as a mixed integer linear program (MILP). Within the framework of commercial CPLEX solver the proposed MILP model was able to produce optimal solutions for small size problem instances. Therefore, two metaheuristic methods, variable neighborhood search (VNS) and greedy randomized adaptive search procedure (GRASP), are designed to solve problem instances of larger dimensions. The proposed GRASP and VNS are evaluated and compared against CPLEX and each other on the set of real-life and generated problem instances. Computational results show that VNS is superior method with respect to the solution quality, while GRASP is able to find high quality solutions within very short running times.
KeywordsOptimization in transport Vehicle scheduling problem Mixed integer linear programming Variable neighborhood search Greedy Randomized Adaptive Search Procedure
Mathematics Subject Classification90B06 68T20 90C11
The authors also state that the research conducted in this paper was partially supported by Serbian Ministry of Education, Science and Technological Development under the Grants Nos. 174010 and 174033.
- Bula G, Prodhon C, Gonzalez FA, Afsar H, Velasco N (2016) Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation. J Hazard Mater 324(part B):472–480Google Scholar
- Duarte A, Laguna M, Martí R (2018) Greedy randomized adaptive search procedures. In: Metaheuristics for business analytics. Springer, Cham, pp 57–83Google Scholar
- Pinedo M (2012) Scheduling theory, algorithms and systems, 4th edn. Springer, BerlinGoogle Scholar
- Resende M, Ribeiro C (2014) GRASP: Greedy randomized adaptive search procedures. In: Burke E, Kendall G (eds) Search methodologies—introductory tutorials in optimization and decision support systems. Springer, Berlin, pp 287–312Google Scholar