DISTRIBUTED COMPUTER SYSTEM DESIGN FOR LARGE DECENTRALIZED ORGANIZATIONS

This paper deals with the issue of designing distributed computer systems for large decentralized organizations. Specific problems addressed within this context include: determining the number of computer installations, choosing the sites that will receive these installations, deciding on the sizes of computers at different sites, configuring databases, allocating databases to computer installations and assigning users to computer installations. A class of decentralized organizations is identified for which decisions regarding the database configuration and the allocation of databases among processors can be effectively merged with decisions regarding the assignment of user nodes to processors. For these organizations, the design problem is formulated as an integer programming model. A brief outline of an effective solution procedure is provided and potential uses of the model as a design tool is demonstrated through a number of experiments

PSROUTE: AN INTERACTIVE DESIGN TOOL FOR SELECTING ROUTES IN DATA COMMUNICATION NETWORKS

This paper describes a prototype interactive design tool, PSROUTE, that can be used by designers of data communication networks for selecting primary and secondary routes for every pair of communicating nodes in a network. The objective in selecting routes is to minimize the mean delay faced by messages. The problem was previously modeled as a mathematical programming problem (Pirkul and Narasimhan 1987). PSROUTE allows the user to interactively specify various network and traffic parameters and observe the effects on the solution. The solution can be examined in detail so that the designer can modify the parameters based on his judgment and then use PSROUTE to repeatedly execute the solution procedure until a satisfactory solution is reached

Liner Shipping Hub Network Design in a Competitive Environment

International audienc

Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems

The 0–1 multidimensional knapsack problem (MKP) arises in many fields of optimization and is NP-hard. Several exact as well as heuristic methods exist. Recently, an artificial fish swarm algorithm has been developed in continuous global optimization. The algorithm uses a population of points in space to represent the position of fish in the school. In this paper, a binary version of the artificial fish swarm algorithm is proposed for solving the 0–1 MKP. In the proposed method, a point is represented by a binary string of 0/1 bits. Each bit of a trial point is generated by copying the corresponding bit from the current point or from some other specified point, with equal probability. Occasionally, some randomly chosen bits of a selected point are changed from 0 to 1, or 1 to 0, with an user defined probability. The infeasible solutions are made feasible by a decoding algorithm. A simple heuristic add_item is implemented to each feasible point aiming to improve the quality of that solution. A periodic reinitialization of the population greatly improves the quality of the solutions obtained by the algorithm. The proposed method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method gives a competitive performance when solving this kind of problems.Fundação para a Ciência e a Tecnologia (FCT

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.The authors wish to thank three anonymous referees for their comments and valuable suggestions to improve the paper. The first author acknowledges Ciˆencia 2007 of FCT (Foundation for Science and Technology) Portugal for the fellowship grant C2007-UMINHO-ALGORITMI-04. Financial support from FEDER COMPETE (Operational Programme Thematic Factors of Competitiveness) and FCT under project FCOMP-01-0124-FEDER-022674 is also acknowledged

Modelando o projeto logístico de uma indústria multicommodity

Resumo: Neste trabalho apresentamos duas diferentes formulações matemáticas para o Projeto de Rede da Cadeia de Suprimentos de uma empresa responsável por produção e distribuição multicommodity. O investimento em uma nova fábrica exigiu a readequação do projeto logístico da empresa, implicando na reestruturação dos fluxos de matérias-primas e produtos acabados, assim como a abertura de novos CD. A cadeia de suprimentos foi modelada utilizando-se uma formulação em programação inteira linear mista na qual as facilidades são representadas pelos nós e os links, pelos arcos. As implementações computacionais foram realizadas em OPL e os resultados obtidos utilizando-se o solver CPLEX©. Para validar os modelos implementados, uma série de experimentos computacionais foi realizada. Para viabilizar a aplicação dos modelos ao problema da empresa, estudos para identificar as demandas de mercado (market shares) e os custos de transporte foram incorporados ao trabalho. A aplicação dos modelos apoiou várias decisões referentes ao projeto inicial da empresa, realizadas pela equipe de projeto