Patrick Soriano

Balancing assembly lines with tabu search

Balancing assembly lines is a crucial task for manufacturing companies in order to improve productivity and minimize production costs. Despite some progress in exact methods to solve large scale problems, softwares implementing simple heuristics are still the most commonly used tools in industry. Some metaheuristics have also been proposed and shown to improve on classical heuristics but, to our knowledge, no computational experiments have been performed on real industrial applications to clearly assess their performance as well as their flexibility. Here we present a new tabu search algorithm and discuss its differences with respect to those in the literature. We then evaluate its performance on the Type I assembly line balancing problem. Finally, we test our algorithm on a real industrial data set involving 162 tasks, 264 precedence constraints, and where the assembly is carried out on a sequential line with workstations located on both sides of the conveyor, with two possible conveyor heights and no re-positioning of the product. We discuss the flexibility of the metaheuristic and its ability to solve real industrial cases. � 2004 Elsevier B.V. All rights reserved.

Solving arc routing problems with turn penalties

In several arc routing problems, it is necessary to take turn penalties into account when designing a solution. Traditionally, this is done through a transformation of the arc routing problem into an equivalent vertex routing problem. In this paper it is shown that a more direct approach, not resorting to such a transformation, may be more efficient.

Design and Dimensioning of Survivable SDH/Sonet Networks

Basically, a telecommunications network may be represented as a graph where the edges correspond to transmission cables carrying information (signals) between nodes that represent users. Associated to each pair of users there is an integer valued traffic demand which must flow through this network. Building a network in which all customers are directly connected to one another is clearly not a realistic solution due to the huge amount of cables needed to convey all the demands. Consequently, individual signals are grouped into larger aggregate signals, also called frames, and high capacity transmission equipments may then be used to transport demands more efficiently. In technical language, this grouping operation is called multiplexing and the opposite operation which consists in extracting initial information from a high capacity frame is called demultiplexing.

Diversification strategies in local search for a nonbifurcated network loading problem

Abstract This paper examines a variant of the network loading problem, a network design problem found in the telecommunications industry. In this problem, facilities of fixed capacity must be installed on the edges of an undirected network to carry the flow from a central vertex to a set of demand vertices. The objective is to minimize the total installation costs. In this work, the nonbifurcated version of the problem is considered, where the demand at any given vertex must be satisfied through a single path. The proposed heuristics alternate between a construction phase and a local search phase. Each new construction phase, except the first one, is part of a diversification strategy aimed at providing a new starting point for the following local search phase. Different diversification strategies are tested and compared on large-scale instances with up to 500 vertices.

Multi-period capacity expansion for a local access telecommunications network

In this paper, we examine a multi-period capacity expansion problem for a local access telecommunications network with a tree topology. Capacity expansion is realized through the installation of concentrators at the nodes and cables on the links of the network. Clearly, the installation of concentrators reduces the need for additional cables, and conversely. The goal is to find the least cost alternative to satisfy the demand. A heuristic approach is proposed to solve this problem, where local installation decisions at each node are propagated in the network. This information is then used to adjust prices that guide the decision process from one iteration to the next, until a fixed point is reached. Numerical results are reported on problem instances based on different cost and demand structures. � 2005 Elsevier B.V. All rights reserved.

Covering a graph with cycles

Abstract In several areas of engineering and telecommunications, it is necessary to determine a least cost cover of a graph by means of cycles. We propose a highly efficient yet simple heuristic for this difficult problem. On test problems it consistently produces optimal or near optimal solutions. This article describes a lower bounding procedure and heuristics for the Cycle Cover Problem which consists of determining a least cost cover of an undirected graph with simple cycles. Applications of this problem arise in network design and in telecommunications. Computational results demonstrate the quality of the proposed heuristics. On 100 vertex graphs, the best of these consistently produces optimal or quasi-optimal solutions.

An Optimal Strategy for the Constrained Cycle Cover Problem

A common telecommunications network design strategy, in order to protect it against failures, is to achieve a decomposition into a set of rings covering the network. We propose a hybrid algorithm for the corresponding Constrained Cycle Cover Problem which draws from constraint programming and graph algorithms. Optimal solutions are obtained efficiently on networks of realistic size.

Minimization of the Wood Density Variation in Pulp and Paper Production

Abstract Kraft pulp, the raw material in paper manufacturing, is obtained by exposing pieces of wood to a cooking process. The parameters of this process (temperatures, times, chemicals, etc.) depend strongly on the density of the different woods in the cooker and have an influence on the quality of the pulp that is obtained. In order to optimize both the production process and the pulp quality, one wishes to cook together woods having similar densities. However, given that the harvest areas contain limited quantities of trees and that wood densities vary significantly from one area to another, deciding how harvested wood from different areas should be mixed for processing is a significant operational decision in pulp production planning. This situation is modelled as a scheduling problem where one has to decide which harvested area goes to each available processing units in order to minimize the variance of wood densities within each cooker for each period of the planning horizon. We also present an approximated solution approach based on the formulation proposed. Some results are reported and the efficiency of the method is discussed.