A relax-and-fix with fix-and-optimize heuristic applied to multi-level lot-sizing problems

In this paper, we propose a simple but efficient heuristic that combines construction and improvement heuristic ideas to solve multi-level lot-sizing problems. A relax-and-fix heuristic is firstly used to build an initial solution, and this is further improved by applying a fix-and-optimize heuristic. We also introduce a novel way to define the mixed-integer subproblems solved by both heuristics. The efficiency of the approach is evaluated solving two different classes of multi-level lot-sizing problems: the multi-level capacitated lot-sizing problem with backlogging and the two-stage glass container production scheduling problem (TGCPSP). We present extensive computational results including four test sets of the Multi-item Lot-Sizing with Backlogging library, and real-world test problems defined for the TGCPSP, where we benchmark against state-of-the-art methods from the recent literature. The computational results show that our combined heuristic approach is very efficient and competitive, outperforming benchmark methods for most of the test problems

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Problemas de localização : solução por decomposição

Orientador: Hermano Medeiros Ferreira TavaresDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de CampinasResumo: O assunto deste trabalho é relativo aos problemas de localização. Numa rede onde circula um certo produto entre pontos que o produzem (ou estocam) e pontos que o consomem (ou demandam), o problema de localização procura encontrar locais para instalação de centros produtores (ou distribuidores) de modo a tornar mínimos os custos que intervem nesse sistema de distribuição. Entre outros, destacam-se os custos de transportar produto, o custo de construir e/ou operar o centro produtor e o custo de produzir o produto. Distinguem-se três partes no conteúdo deste trabalho. Na primeira, constituída pelo capitulo I , é feita uma revisão geral da literatura que se ocupa do problema de localização onde são discutidos modelos e técnicas do problema. Devida à grande proliferação de formulações e métodos para esse problema,, sua analise geral e feita sob uma classificação que procura agrupar problemas típicos. Numa segunda parte - capítulos II e III - é estudado um problema especifico de localização, o problema de localização estocástico (PLE) onde as demandas são consideradas variáveis aleatórias. Esse problema - um problema de programação mista não-linear - sendo encarado como a fusão do problema de localização de armazéns e o problema de transporte estocástico, sugere a aplicação de um método de decomposição, onde cada um desses problemas pode ser separadamente resolvidos, fugindo-se assim da não-convexidade imposta pelo problema global. A técnica de decomposição utilizada é a decomposição de Benders generalizada (DBG) e no capitulo III são relatadas experiências computacionais que atestam o bom comportamento do método numa serie de problemas. É enfatizada uma regra de escolha de multiplicadores que fornece um eficiente "corte de Benders". Por fim são apresentadas extensões ao PLE e discutido o modo de resolvê-las.Abstract: Not informed.MestradoDoutor em Engenharia Elétric

GRASP with evolutionary path-relinking for the capacitated arc routing problem

The Capacitated Arc Routing Problem (CARP) is a well-known NP-hard combinatorial optimization problem where, given an undirected graph, the objective is to find a minimum cost set of tours servicing a subset of required edges under vehicle capacity constraints. There are numerous applications for the CARP, such as street sweeping, garbage collection, mail delivery, school bus routing, and meter reading. A Greedy Randomized Adaptive Search Procedure (GRASP) with Path-Relinking (PR) is proposed and compared with other successful CARP metaheuristics. Some features of this GRASP with PR are (i) reactive parameter tuning, where the parameter value is stochastically selected biased in favor of those values which historically produced the best solutions in average; (ii) a statistical filter, which discard initial solutions if they are unlikely to improve the incumbent best solution; (iii) infeasible local search, where high-quality solutions, though infeasible, are used to explore the feasible/infeasible boundaries of the solution space; (iv) evolutionary PR, a recent trend where the pool of elite solutions is progressively improved by successive relinking of pairs of elite solutions. Computational tests were conducted using a set of 81 instances, and results reveal that the GRASP is very competitive, achieving the best overall deviation from lower bounds and the highest number of best solutions found. © 2011 Elsevier Ltd. All rights reserved

M.: The open capacitated arc routing problem

Abstract The Open Capacitated Arc Routing Problem (OCARP) is a new NP-hard combinatorial optimization problem where we must find a set of minimum cost paths which covers all required edges from an undirected graph under capacity constraints. This problem is based upon the Capacitated Arc Routing Problem (CARP) but differs from it since OCARP does not comply a depot and routes are not constrained to form cycles. An integer linear programming formulation followed by some usefull properties are given. The similarity between OCARP and other problems reported in literature has been exploited to derive practical (polynomial) reductions. Computational tests were conducted with an adapted path-scanning heuristic from literature applied to a set of 81 instances. Results show the first lower and upper bounds, where proven optimality was obtained to 19 instances

Multiobjective service restoration in electric distribution networks using a local search based heuristic

Contingency situations may cause emergency states in distribution systems; these states are defined as the interruption of power supply. Such situations should be avoided whenever possible in order to maintain certain quality limits related to frequency and duration of interruptions. The main objective of service restoration is to minimize the number of consumers affected by the fault, by transferring them to energized support feeders. Electrical and operational conditions, such as radial network configuration, equipment and voltage drop limits, must be respected. This paper presents a new multiobjective local search based heuristic for the restoration of service which considers the minimization of two conflicting criteria: the load not supplied and the number of switching operations involved. Computational experiments with three network systems have shown the flexibility and effectiveness of the proposed method.

Meta-heurística para programação da produção com tempos de preparação dependentes da seqüência Metaheuristic for scheduling with dependent setup times

Este trabalho considera o problema de programação da produção, em uma máquina, de um conjunto de ordens de produção que podem ser agrupadas em famílias, sendo que os tempos de preparação entre essas famílias são dependentes da seqüência em que são executadas. Propõe-se um procedimento aproximado, baseado na meta-heurística de Busca Tabu, para a resolução deste problema. A função objetivo considera uma ponderação envolvendo os custos de preparação de máquina, uma penalidade por atraso em relação à data de entrega das ordens e o custo de estoque. O desempenho do método proposto é avaliado, computacionalmente, frente a três diferentes situações. 1) análise empírica de desempenho da heurística, em função dos parâmetros do problema; 2) comparação entre a heurística e regras de despacho tradicionais EDD e SPT; 3) emprego da heurística para a resolução de um problema prático real.This article focuses on the one machine scheduling problem where jobs can be grouped in classes with the same machine setups. The setup times between classes are sequence dependent. An approximation method based on Tabu Search metaheuristic is proposed. The objetive is to minimize the weighted sum of setup costs, tardiness and inventory holding costs. The performance of the heuristic is evaluated through three sets of computational tests: 1) empirical performance analysis of the heuristic with different data sets; 2) comparison between the heuristic and the well known dispatching rules EDD and SPT; 3) application of the heuristic for solving a real life scheduling problem

Metaheuristic for scheduling with dependent setup times

This article focuses on the one machine scheduling problem where jobs can be grouped in classes with the same machine setups. The setup times between classes are sequence dependent. An approximation method based on Tabu Search metaheuristic is proposed. The objetive is to minimize the weighted sum of setup costs, tardiness and inventory holding costs. The performance of the heuristic is evaluated through three sets of computational tests: 1) empirical performance analysis of the heuristic with different data sets; 2) comparison between the heuristic and the well known dispatching rules EDD and SPT; 3) application of the heuristic for solving a real life scheduling problem.Este trabalho considera o problema de programação da produção, em uma máquina, de um conjunto de ordens de produção que podem ser agrupadas em famílias, sendo que os tempos de preparação entre essas famílias são dependentes da seqüência em que são executadas. Propõe-se um procedimento aproximado, baseado na meta-heurística de Busca Tabu, para a resolução deste problema. A função objetivo considera uma ponderação envolvendo os custos de preparação de máquina, uma penalidade por atraso em relação à data de entrega das ordens e o custo de estoque. O desempenho do método proposto é avaliado, computacionalmente, frente a três diferentes situações. 1) análise empírica de desempenho da heurística, em função dos parâmetros do problema; 2) comparação entre a heurística e regras de despacho tradicionais EDD e SPT; 3) emprego da heurística para a resolução de um problema prático real.228243Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Evaluating genetic algorithms with different population structures on a lot sizing and scheduling problem

This paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated. Copyright 2008 ACM

A parallel memetic algorithm applied to the total tardiness machine scheduling problem

This work proposes a parallel memetic algorithm applied to the total tardiness single machine scheduling problem. Classical models of parallel evolutionary algorithms and the general structure of memetic algorithms are discussed. The classical model of global parallel genetic algorithm was used to model the global parallel memetic analogue where the parallelization is only applied to the individual optimization phase of the algorithm. Computational tests show the efficiency of the parallel approach when compared to the sequential version. A set of eight instances, with sizes ranging from 56 up to 323 jobs and with known optimal solutions, is used for the comparisons