Abstract
This paper formulates a vehicle routing problem where constraints have been produced from a real world forestry commissioning dataset. In the problem, vehicles are required to fully load wood from forests and then deliver the wood to sawmills. The constraints include time windows and loading bay constraints at forests and sawmills. The loading bay constraints are examples of inter-route constraints that have not been studied in the literature as much as intra-route constraints. Inter-route constraints are constraints that cause dependencies between vehicles such that more than one vehicle is required to perform a task. Some locations have a lot of consignments at similar times, causing vehicles to queue for loading bays. The aim is to produce an optimal routing of consignments for vehicles such that the total time is minimised and there is as little queuing at forests and sawmills as possible. In this paper, the problem has been formulated into a vehicle routing problem with time windows and extra inter-route constraints. An ant colony optimisation heuristic is applied to the datasets and yields feasible solutions that appropriately use the loading bays. A number of methods of handling the inter-route constraints are also tested. It is shown that incorporating the delay times at loading bays into the ant’s visibility produces solutions with the best objective values.
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References
Donati, A.V., Montemanni, R., Casagrande, N., Rizzoli, A.E., Gambardella, L.M.: Time dependent vehicle routing problem with a multi ant colony system. European Journal of Operational Research 185(3), 1174–1191 (2008)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Management and Cybernetics. Part B, Cybernetics: A publication of the IEEE Systems, Management and Cybernetics Society 26(1), 29–41 (1996)
Drexl, M.: Synchronization in Vehicle Routing - A Survey of VRPs with Multiple Synchronization Constraints. Transportation Science 46(3), 1–58 (2011)
Epstein, R., Morales, R., Seron, J., Verso, P.T.R.A.: A Truck Scheduling System Improves Efficiency in the Forest Industries. Institute for Operations Research and the Management Sciences 1996(26), 1–12 (1996)
Epstein, R., Sero, J., Weintraub, A.: Use of OR Systems in the Chilean Forest Industries. Interfaces 29(1), 7–29 (1999)
Fisher, M.L., Jörnsten, K.O., Madsen, O.B.G.: Vehicle Routing with Time Windows: Two Optimization Algorithms. Operations Research 45(3), 488–492 (1997)
Hempsch, C., Irnich, S.: Vehicle routing problems with inter-tour resource constraints. In: The Vehicle Routing Problem: Latest Advances and New Challenges, pp. 421–444. Springer (2008)
Mazzeo, S., Loiseau, I.: An Ant Colony Algorithm for the Capacitated Vehicle Routing Problem. Electronic Notes in Discrete Mathematics 18, 181–186 (2004)
Reimann, M.: D-Ants: Savings Based Ants Divide and Conquer the Vehicle Routing Problem. Computers & Operations Research 31(4), 563–591 (2004)
Solomon, M.M.: Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Research 35(2), 254–265 (1987)
Solomon, M.M., Desrosiers, J.: Time window constrained routing and scheduling problems. Transportation Science 22(1), 1–13 (1988)
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Kent, E., Atkin, J.A.D., Qu, R. (2014). Vehicle Routing in a Forestry Commissioning Operation Using Ant Colony Optimisation. In: Dediu, AH., Lozano, M., MartĂn-Vide, C. (eds) Theory and Practice of Natural Computing. TPNC 2014. Lecture Notes in Computer Science, vol 8890. Springer, Cham. https://doi.org/10.1007/978-3-319-13749-0_9
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DOI: https://doi.org/10.1007/978-3-319-13749-0_9
Publisher Name: Springer, Cham
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