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Made available in DSpace on 2020-12-12T01:25:10Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 The problem addressed in this paper focuses on the reduction of electrical energy costs for the operation of hydraulic pumps, which are used for catchment and distribution of water in water supply systems. As the electricity tariffs are different according to the time of day, it is necessary to decide when and how many pumps must be used to collect and distribute water, in each period of time, and, when there must be water transfer among tanks so that the demand of each consumer center is met without a lack of resources. A mixed integer programming (MIP) model is proposed and a competitive MIP-based heuristic is developed to solve this problem. Numerical tests in a case study carried out with the water supply system of a Brazilian city demonstrate the efficiency of the proposed mathematical model and solution method. Department of Applied Mathematics and Statistics University of São Paulo (USP), Av. Trabalhador São-carlense, 400 Department of Mathematics Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01 Department of Computer Science Universidade Federal da Grande Dourados, Rodovia Dourados/Itahum, Km 12 Department of Electrical and Computer Engineering University of São Paulo (USP), Av. Trabalhador São-carlense, 400 Department of Mathematics Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01

This paper presents a hybrid heuristic based on the bees algorithm combined with the fix-and-optimize heuristic to solve the multi-level capacitated lot-sizing problem. The bees algorithm can be used as a new method to determine the sequence in which to apply the partition in the fix-and-optimize approach. This new manner of choosing the partition adds diversity to the solution pool and yields different local optima solutions after some iterations. The bees-and-fix-and-optimize (BFO) algorithm attempts to avoid these local optima by performing random search in accordance with the concept of bees algorithm. The BFO has yielded good results for instances from the literature and, in most cases, the results are superior to the best results provided by approaches presented in recent literature. They show that this construction concept is advantageous and illustrate the efficiency of hybrid methods composed of matheuristics and metaheuristics. Furthermore, the BFO approach is a general-purpose heuristic that can be applied to solve other types of production planning problems.

We investigate the one-dimensional cutting-stock problem integrated with the lot-sizing problem in the context of paper industries. The production process in paper mill industries consists of producing raw materials characterized by rolls of paper and cutting them into smaller rolls according to customer requirements. Typically, both problems are dealt with in sequence, but if the decisions concerning the cutting patterns and the production of rolls are made together, it can result in better resource management. We investigate Dantzig–Wolfe decompositions and develop column generation techniques to obtain upper and lower bounds for the integrated problem. First, we analyze the classical column generation method for the cutting-stock problem embedded in the integrated problem. Second, we propose the machine decomposition that is compared with the classical period decomposition for the lot-sizing problem. The machine decomposition model and the period decomposition model provide the same lower bound, which is recognized as being better than the linear relaxation of the classical lot-sizing model. To obtain feasible solutions, a rounding heuristic is applied after the column generation method. In addition, we propose a method that combines an adaptive large neighborhood search and column generation method, which is performed on the machine decomposition model. We carried out computational experiments on instances from the literature and on instances adapted from real-world data. The rounding heuristic applied to the first column generation method and the adaptive large neighborhood search combined with the column generation method are efficient and competitive.

Disturbance Management is a major issue in process industries like the pulp and paper (P&P) industry. In this paper, a case study in an integrated P&P mill is examined. Production plans for the whole mill need not only to be optimized concerning company's indicators, but also to be robust so that disturbances can be avoided. We present a simulation-optimization approach that generates plans, correctly weighting their quality (regarding various indicators) and robustness. A discrete-event simulation model replicates the dynamics of implementation and adaptation of production plans in practice. The simulation model gives then feedback to optimization, in order to enhance the analytical model, which is thus able to generate robust plans.

This paper addresses the lot-sizing and scheduling problem of pulp and paper mills involving multiple paper machines. The underlying multi-stage integrated production process considers the following critical units: continuous digester, intermediate stocks of pulp and liquor, multiple paper machines and a recovery line to treat by-products. This work presents a mixed integer programming (MIP) model to represent the problem, as well as a solution approach based on a customized genetic algorithm (GA) with an embedded residual linear programming model. Some GA tools are explored, including literature and new operators, a novel diversification process and other features. In particular, the diversification process uses a new allele frequency measure to change between diversification and intensification procedures. Computational results show the effectiveness of the method to solve relatively large instances of the single paper machine problem when compared to other single paper machine solution methods found in the literature. For multiple paper machine settings, in most runs the GA solutions are better than those obtained for the MIP model using an optimization software. This paper addresses the lot-sizing and scheduling problem of pulp and paper mills involving multiple paper machines. The underlying multi-stage integrated production process considers the following critical units: continuous digester, intermediate stocks of pulp and liquor, multiple paper machines and a recovery line to treat by-products. This work presents a mixed integer programming (MIP) model to represent the problem, as well as a solution approach based on a customized genetic algorithm (GA) with an embedded residual linear programming model. Some GA tools are explored, including literature and new operators, a novel diversification process and other features. In particular, the diversification process uses a new allele frequency measure to change between diversification and intensification procedures. Computational results show the effectiveness of the method to solve relatively large instances of the single paper machine problem when compared to other single paper machine solution methods found in the literature. For multiple paper machine settings, in most runs the GA solutions are better than those obtained for the MIP model using an optimization software.

O problema de dimensionamento de lotes determina um plano de produção que apoia às tomadas de decisões, a médio prazo, em meios industriais. Este plano de produção indica as quantidades de cada item que devem ser produzidas em cada período do horizonte de planejamento, de acordo com um objetivo dado e satisfazendo a demanda dos clientes. Diversos métodos de solução foram propostas na literatura, considerando a dificuldade de solução de algumas classes de problemas e a necessidade de métodos que gerem soluções de alta qualidade em um tempo computacional adequado. Neste trabalho, abordamos heurísticas baseadas na formulação matemática (LP-and-fix, relax-and-fix e fix-and-optimize), uma metaheurística (algoritmo de abelhas) e dois métodos híbridos, utilizados na solução de dois problemas distintos de dimensionamento de lotes multiestá- gio com limitação de capacidade. Consideramos também, a utilização de três formulações da literatura, para verificar a influência de cada uma sobre as abordagens de solução verificadas. Os resultados computacionais demonstraram que os métodos baseados na formulação matemática do problema se mostraram eficientes, mas limitados normalmente a ótimos locais, enquanto os métodos híbridos puderam superar estes ótimos locais, utilizando conceitos da metaheurística algoritmo de abelhas para isto. Além disso, pudemos verificar a influência de uma formulação \"forte\" sobre as soluções geradas pelas abordagens de solução, demonstrando que métodos baseados em relaxação linear conseguem obter maiores vantagens deste tipo de formulação, mas outras abordagens podem ou não obter estas vantagens, dependendo do problema abordado The lot-sizing problem determines a production plan, which supports the decision making, in the medium term, at the industrial environment. This production plan indicates the amounts of each item to be produced in each period of the planning horizon, according to a given objective and satisfying customer\'s demand. Diverse solution methods have been proposed in the literature, considering the difficulty of solving some problem classes and the need of methods to generate solutions quickly. In this work, we develop matheuristics (LP-and-fix, relax-and-fix and fix-and-optimize), one metaheuristic (bees algorithm) and two hybrid methods, used to solve two different multilevel capacitated lot-sizing problems. We also consider the use of three different formulations of the literature to verify the influence of each one on the solutions approaches. The computational results show that the matheuristics proved to be efficient, but usually limited to local optima, while the hybrid methods could escape from these local optima, using concepts of bees algorithm to do this. Additionally, we test the effect of a tight formulation on the solutions approaches, demonstrating that LP-based heuristics can obtain further advantages from this type of formulation, but other approaches can take these advantages, depending on the problem addressed