Abstract
A robust approach for solving a vehicle routing problem with time windows with uncertain service and travel times
Highlights
Since the pioneer paper of Dantzig and Ramser (1959) on the truck dispatching problem appeared at the end of the fifties of the last century, work in the field of the vehicle routing problem (VRP) has increased exponentially
The main purpose of this paper is to study the vehicle routing problem with hard time windows where the main challenges is to include both sources of uncertainties, namely the travel and the service time that can arise due to multiple causes
We propose an adaptive large neighborhood search (ALNS) heuristic to integrate into our approach in order to deal with robust vehicle routing problem with time window constraints (VRPTW)
Summary
Since the pioneer paper of Dantzig and Ramser (1959) on the truck dispatching problem appeared at the end of the fifties of the last century, work in the field of the vehicle routing problem (VRP) has increased exponentially. Errico et al (2016) formulated the VRPTW with stochastic service times as a set partitioning problem and solve it by exact branch-cut-and-price algorithms They elaborated efficient algorithms by choosing label components, developing lower and upper bounds on partial route reduced the cost to be used in the column generation step. Unlike this approach, robust optimization seeks to get good solutions for the VRPTW problem by only considering nominal values and deviations possible uncertain data. Toklu et al (2013) adapted their approach to solve the problem of VRPTW with uncertain travel times, whose objective was to minimize window time violation penalties by providing the decision makers a group of solutions found over several degrees of uncertainties considered.
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