(518h) Solving the Robust Vehicle Routing Problem Via Adaptive Memory Programming | AIChE

(518h) Solving the Robust Vehicle Routing Problem Via Adaptive Memory Programming


Gounaris, C. E. - Presenter, Princeton University
Repoussis, P. P., Stevens Institute of Technology
Wiesemann, W., Imperial College London
Tarantilis, C. D., Athens University of Economics and Business
Floudas, C. A., Princeton University

Vehicle Routing constitutes one of the key decision areas in supply chain optimization.  We address the Capacitated Vehicle Routing Problem (CVRP) [1, 2], which seeks the minimum cost depot-returning routes for a fleet of capacitated vehicles to serve a set of geographically scattered customers.  We consider uncertainty in the customer demands, and we aim at identifying vehicle routes that are robust-feasible, i.e., routes that are feasible for any possible realization of the demand vector within a prespecified uncertainty set [3, 4, 5].

Earlier efforts to address demand uncertainty in the CVRP [6, 7] have focused on problems where each customer demand can attain its worst-case individually, independent of the other customers.  Problems of this type can be solved via deterministic CVRP methods, but their solutions may be very conservative.  A recent contribution [8] addressed the more general case where the vector of all customer demands lies in a prespecified (typically non-rectangular) uncertainty set.  Four families of robust CVRP formulations that do not exhibit any over-conservatism beyond the specification of the uncertainty set were presented, and a branch-and-cut procedure was developed that can solve instances with up to 50 customers to guaranteed optimality.

In this presentation, we propose an Adaptive Memory Programming (AMP) metaheuristic to push the envelope in terms of size of instances that can be addressed.  AMP is a general-purpose metaheuristic framework that has been successfully applied to a range of difficult combinatorial optimization problems in the vehicle routing domain [9, 10, 11, 12].  Our framework incorporates novel methods to identify, select and combine elite solution components, and employs a sophisticated Tabu Search algorithm to conduct search in both the robust-feasible and robust-infeasible solution spaces.  Two broad classes of polyhedral uncertainty sets for which robust-feasibility can be checked efficiently are considered.  Results on benchmark data sets involving up to 483 customers are reported, including new best solutions for a total of 123 instances.

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