Maria Albareda-Sambola

Heuristic and lower bound for a stochastic location-routing problem

In this article a stochastic location-routing problem is defined and cast as a two-stage model. In a first stage the set of plants and a family of routes are determined; in a second stage a recourse action is applied to adapt these routes to the actual set of customers to visit, once they are known. A two-phase heuristic is developed. An initial feasible solution is built by solving a sequence of subproblems, and an improvement phase is then applied. A lower bound based on bounding separately different parts of the cost of any feasible solution is also developed. Computational results are reported.

The multi-period incremental service facility location problem

In this paper we introduce the multi-period incremental service facility location problem where the goal is to set a number of new facilities over a finite time horizon so as to cover dynamically the demand of a given set of customers. We prove that the coefficient matrix of the allocation subproblem that results when fixing the set of facilities to open is totally unimodular. This allows to solve efficiently the Lagrangean problem that relaxes constraints requiring customers to be assigned to open facilities. We propose a solution approach that provides both lower and upper bounds by combining subgradient optimization to solve a Lagrangean dual with an ad hoc heuristic that uses information from the Lagrangean subproblem to generate feasible solutions. Numerical results obtained in the computational experiments show that the obtained solutions are very good. In general, we get very small percent gaps between upper and lower bounds with little computation effort.

The dynamic multiperiod vehicle routing problem with probabilistic information

Abstract This paper introduces the Dynamic Multiperiod Vehicle Routing Problem with Probabilistic Information, an extension of the Dynamic Multiperiod Vehicle Routing Problem in which, at each time period, the set of customers requiring a service in later time periods is unknown, but its probability distribution is available. Requests for service must be satisfied within a given time window that comprises several time periods of the planning horizon. We propose an adaptive service policy that aims at estimating the best time period to serve each request within its associated time window in order to reduce distribution costs. The effectiveness of this policy is compared with that of two alternative basic policies through a series of computational experiments.

Multiperiod location-routing with decoupled time scales

This paper focuses on a multiperiod discrete facility location problem where transportation costs are considered together with location costs to design the operating facility pattern along a time horizon. The problem captures the difference in the scope of the location and routing decisions by considering different scales within the time horizon. Thus, solutions to this problem reflect the stability of locational decisions along time. The high complexity of this problem makes it impossible to be solved in practice with commercial software. For this reason, an approximation based on replacing vehicle routes by spanning trees is proposed, and its capability for providing good quality solutions is assessed in a series of computational experiments.

VARIABLE NEIGHBORHOOD SEARCH FOR ORDER BATCHING IN A WAREHOUSE

In this paper we address the problem of batching orders in a warehouse, with the objective of minimizing the total travel time. Order batching is an NP-hard optimization problem that is very difficult to solve exactly in practice. Thus, most implemented solutions are based on elementary heuristic methods that perform a relatively limited exploration of the solution space. As an alternative, we propose a heuristic based on variable neighborhood search, where the emphasis is placed on performing an intensive exploration of the most promising regions of the solution space. Simulations are conducted to study the performance of the method with different warehouse configurations, and an exhaustive comparative analysis, which considers all the best known heuristics, is carried out. The results obtained show that the proposed heuristic is competitive and that it provides a suitable method which can be used in practice. Additionally, since the performance of the algorithms depends heavily on factors such as storage policy, routing strategies, or the structure of the orders, we have developed an ANOVA in order to consider the effect of all the above factors on the different methods tested.

The capacity and distance constrained plant location problem

This article introduces a new problem called the Capacity and Distance Constrained Plant Location Problem. It is an extension of the discrete capacitated plant location problem, where the customers assigned to each plant have to be packed in groups that will be served by one vehicle each. The constraints include two types of capacity. On the one hand plants are capacitated, and the demands of the customers are indivisible. On the other hand, the total distance traveled by each vehicle to serve its assigned customers in round trips plant-customer-plant is also limited. The paper addresses different modeling aspects of the problem. It describes a tabu search algorithm for its solution. Extensive computational tests indicate that the proposed heuristic consistently yields optimal or near-optimal solutions.

Fix-and-Relax-Coordination for a multi-period location-allocation problem under uncertainty

A multi-period discrete facility location problem is introduced for a risk neutral strategy with uncertainty in the costs and some of the requirements along the planning horizon. A compact 0-1 formulation for the Deterministic Equivalent Model of the problem under two alternative strategies for the location decisions is presented. Furthermore, a new algorithmic matheuristic, Fix-and-Relax-Coordination, is introduced. This solution scheme is based on a specialization of the Branch-and-Fix Coordination methodology, which exploits the Nonanticipativity Constraints and uses the Twin Node Family concept. The results of an extensive computational experience allow to compare the alternative modeling strategies and assess the effectiveness of the proposed approach versus the plain use of a state-of-the-art MIP solver.

A biased random-key genetic algorithm for the capacitated minimum spanning tree problem

This paper focuses on the capacitated minimum spanning tree (CMST) problem. Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals, the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost. The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-key genetic algorithm (BRKGA) is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem. This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectiveness of the approach: Seven new best-known solutions are presented for the set of benchmark instances used in the experiments.

Lagrangean Duals and Exact Solution to the Capacitated p-Center Problem

In this work, we address the capacitated p-center problem (CpCP). We study two auxiliary problems, discuss their relation to CpCP, and analyze the lower bounds obtained with two different Lagrangean duals based on each of these auxiliary problems. We also compare two different strategies for solving exactly CpCP, based on binary search and sequential search, respectively. Various data sets from the literature have been used for evaluating the performance of the proposed algorithms.

Location-Routing and Location-Arc Routing

This chapter overviews the most relevant contributions on location-routing problems. Although there exist many different models where location and routing decisions must be made in an integrated way, the chapter focuses on the so-called classical location-routing problems without entering into the details of other related problems that might be included in the location-routing area from a more general point of view. Reflecting the imbalance in the existing literature and available approaches, the case of problems with node routing is treated in detail throughout the chapter, while results concerning arc routing problems are concentrated in a single section.