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Iterated local search and simulated annealing algorithms for the inventory routing problem

Aldair Álvarez(aldair***at***dep.ufscar.br)
Pedro Munari(munari***at***dep.ufscar.br)
Reinaldo Morabito(morabito***at***ufscar.br)

Abstract: This paper addresses the inventory routing problem (IRP), which consists of defining the customer visit schedule, the delivery quantities and the vehicle routing plan to meet the demands of a set of customers over a given time horizon. We consider the variant with a single item, a single supplier, multiple vehicles and a finite multi-period planning horizon. In addition, we address two different objective functions. The first minimizes the sum of the inventory and travel costs, whereas the second minimizes the logistic ratio, defined as the total travel cost divided by the total quantity delivered to customers. The second objective function, while more realistic in some logistics settings, poses a challenge for integer programming models and exact methods because of its nonlinearity. To our knowledge, no heuristic method has been proposed to address this objective in the IRP variant addressed in this paper. To solve this problem, we propose two effective metaheuristic algorithms based on Iterated Local Search (ILS) and Simulated Annealing (SA). Computational experiments show that these algorithms provide reasonably high quality solutions in relatively short running times, for both objective functions when applied to problem instances from the literature. Moreover, the algorithms produce new best solutions for some of these instances. Performance comparisons show that, when minimizing the total cost, ILS provides better solutions for instances with longer planning horizons, while SA performs better for shorter horizons. When minimizing the logistic ratio, SA shows slightly superior overall results than ILS.

Keywords: inventory routing problem; metaheuristics; iterated local search; simulated annealing; logistic ratio;

Category 1: Applications -- OR and Management Sciences (Transportation )

Category 2: Combinatorial Optimization (Meta Heuristics )

Category 3: Applications -- OR and Management Sciences (Production and Logistics )

Citation: Production Engineering Department, Federal University of São Carlos, Rod. Washington Luís - Km 235, 13565-905, São Carlos-SP, Brazil

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Entry Submitted: 01/11/2017
Entry Accepted: 01/11/2017
Entry Last Modified: 01/11/2017

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