Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic

Surendra Reddy Kancharla and Gitakrishnan Ramadurai ()
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Surendra Reddy Kancharla: Indian Institute of Technology Madras
Gitakrishnan Ramadurai: Indian Institute of Technology Madras

Networks and Spatial Economics, 2019, vol. 19, issue 3, No 13, 969-1005

Abstract: Abstract We formulate the two-echelon routing problem considering multiple depots and heterogeneous fleets. Our study (a) presents a Mixed Integer Linear Programming (MILP) formulation with load-dependent fuel minimization objective, (b) uses driving cycles to represent speed variations along a path, (c) allows the vehicles to return to any depot/satellite, and (d) conserves the total number of vehicles at each depot/satellite. We call the problem a Multi-Depot Two-Echelon Fuel Minimizing Routing Problem (MD2E-FMRP). Prior studies assumed there is a fixed number of vehicles available at each satellite/depot, whereas we allow different number of vehicles of each vehicle type at each satellite and depot. Our formulation relaxes several unrealistic assumptions in existing two-echelon formulations and hence has greater practical application. Despite the relaxation of constraints, the running time of our model is comparable to existing formulations. Gurobi optimizer is used to find a better upper bound for up to 56 node instances within a given time limit of 10,000s. We also propose an Adaptive Large Neighborhood Search (ALNS) based heuristic solution technique that outperformed Gurobi in all the tested instances of MD2E-FMRP. We observe an average saving of 13.11% in fuel consumption by minimizing fuel consumed instead of minimizing distance. In general, adapting heterogeneous fleets results in fuel savings and consequently lower emissions compared to using a homogeneous fleet.

Keywords: Multi-depot; Heterogeneous fleet; Adaptive large neighborhood search; Fuel consumption; Vehicle routing problem; Mixed integer linear programming (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11067-018-9437-7

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