Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

An analysis of lower bound procedures for the bin packing problem

In this paper, we review LB2 and LB3, two lower bounds for the bin packing problem that were respectively introduced by Martello and Toth and by Labbé, Laporte and Mercure. We prove that LB3≥LB2. We also prove that the worst-case asymptotic performance ratio of LB3 is 34 and that this ratio cannot be improved. We study LB2, LB3 and three of their variants both analytically and computationally. In particular, we clarify the relationships between LB2″, the bound implemented by Martello and Toth in their well-known bin packing code, and both LB2 and LB3. Dans cet article, nous passons en revue LB2 et LB3, les deux bornes inférieures pour le problème de la mise en boîtes introduites par Martello et Toth, et par Labbé, Laporte et Mercure. Nous prouvons que LB3≥LB2. Nous prouvons également que le ratio de performance asymptotique dans le pire des cas de LB3 est égal à 34 et qu'il ne peut pas être amélioré. Nous étudions LB2 et LB3 et trois de leurs variantes, sur le plan analytique et sur le plan expérimental. En particulier, nous mettons au jour les relations entre LB2″, la borne utilisée par Martello et Toth dans leur programme bien connu de résolution du problème de la mise en boî tes et LB2 ainsi que LB3. Mots-Clés: Problème de la mise en boîtes; Bornes inférieures; Ratio de performance. © 2003 Elsevier Ltd. All rights reserved.SCOPUS: re.jinfo:eu-repo/semantics/publishe

Using simulated annealing to minimize the cost of centralized telecommunications networks

The network design problems studied in this paper are typically known in the Telecommunications literature as the Concentrator Location, Terminal Assignment and Terminal Layout Problems. These are versions of well-known Operations Research models such as Capacitated Location, Capacitated Assignment, Capacitated Minimum Spanning Tree, and Vehicle Routing. We describe two Simulated Annealing algorithms for solving them and analyze the results obtained through computational testing

Partial integration of frequency allocation within antenna positioning in GSM mobile networks

In this article we propose to partially integrate the antenna positioning (APP) and frequency allocation problems (FAP). The traditional wireless network design process examines these two major issues sequentially in order to avoid the very high complexity associated with the simultaneous resolution of the two problems. The proposed integration involves the introduction of interference protection guarantees within the APP. It is customary to define such guarantees in an intermediate step and to use them as input to FAP, in order to protect against interference in critical areas. The proposed approach consists of selecting these protections while solving the APP, allowing the optimization procedure to exploit the degrees of freedom that this would offer. Results on two real-life problem instances indicate a significant improvement in interference levels and resource utilization.OR in telecommunications Tabu search Antenna positioning Frequency allocation GSM

A Tabu-Search Heuristic for the Capacitated Lot-Sizing Problem with Set-up Carryover

This paper presents a tabu-search heuristic for the capacitated lot-sizing problem (CLSP) with set-up carryover. This production-planning problems allows multiple items to be produced within a time period, and setups for items to be carried over from one period to the next. Two interrelated decisions, sequencing and lot sizing, are present in this problem. Our tabu-search heuristic consists of five basic move types---three for the sequencing decisions and two for the lot-sizing decisions. We allow infeasible solutions to be generated at a penalty during the course of the search. We use several search strategies, such as dynamic tabu list, adaptive memory, and self-adjusting penalties, to strengthen our heuristic. We also propose a lower-bounding procedure to estimate the quality of our heuristic solution. We have also modified our heuristic to produce good solutions for the CLSP without set-up carryover. The computational study, conducted on a set of 540 test problems, indicates that on average our heuristic solutions are within 12% of a bound on optimality. In addition, for the set of test problems our results indicate an 8% reduction in total cost through set-up carryover.Lot Sizing, Tabu Search, Set-up Carryover