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Submitted by Pedro Rochavetz de Lara Andrade (pedro.rochavetz@gmail.com) on 2021-10-07T12:31:03Z No. of bitstreams: 1 Tese Pedro.pdf: 3230272 bytes, checksum: fdb4ce75a11b36e071e74437482d62ea (MD5) Approved for entry into archive by Lucilene Cordeiro da Silva Messias null (lubiblio@bauru.unesp.br) on 2021-10-07T14:50:22Z (GMT) No. of bitstreams: 1 andrade-prl_dr_bauru.pdf: 3230272 bytes, checksum: fdb4ce75a11b36e071e74437482d62ea (MD5) Made available in DSpace on 2021-10-07T14:50:23Z (GMT). No. of bitstreams: 1 andrade-prl_dr_bauru.pdf: 3230272 bytes, checksum: fdb4ce75a11b36e071e74437482d62ea (MD5) Previous issue date: 2021-08-25 Nesta tese, o Problema de Corte de Estoque é estudado, considerando três abordagens ambientadas em uma indústria de molas automotivas. As duas primeiras abordagens tratam da otimização do processo de corte unidimensional de barras, minimizando perdas de material e custos de estoque em Problemas Integrados de Dimensionamento de Lotes e Corte de Estoque. Foram propostos dois modelos matemáticos e respectivos métodos de solução baseados em geração de colunas. O primeiro deles trata de decisões a curto prazo, considerando máquinas paralelas e questões relativas aos itens e produtos finais. O segundo modelo está focado em questões a médio prazo, considerando a compra de objetos como uma das variáveis de decisão, além de demanda, limites e custos de estoque de objetos, itens e produtos finais. A terceira abordagem trata da alocação de itens ao forno de têmpera como um Problema de Corte de Estoque. O modelo matemático proposto baseia-se em uma formulação de fluxo em arcos. Resultados com dados reais mostram que os três modelos matemáticos obtiveram, em tempo computacional viável, soluções significativamente superiores em comparação à prática da empresa. Testes com instâncias aleatórias foram realizados, permitindo uma análise da influência de diversos parâmetros destes problemas. Em geral, instâncias pequenas com itens grandes apresentam melhores resultados, com gaps e tempos computacionais reduzidos. In this thesis, the Cutting Stock Problem is studied, considering three approaches set in an automotive spring industry. The first two approaches deal with the optimization of the process of one-dimensional bar cutting, minimizing material losses and inventory costs in Integrated Lot Sizing and Cutting Stock Problems. Two mathematical models and their respective solution methods based on column generation were proposed. The first refers to short term decisions, considering parallel machines and matters relative to the items and final products. The second model focuses on the medium term, considering the purchase of objects as one of the decision variables, besides demand, inventory costs and limits for objects, items, and final products. The third approach considers assigning items to the hardening furnace as a Cutting Stock Problem. The proposed mathematical model is based on an arc flow formulation. Results with real data show that the three mathematical models obtained significantly superior solutions in viable computational time, in comparison to the company's practice. Tests with random instances were performed allowing for an analysis of the influence of several parameters of these problems. Generally, small instances with large items present better results, with reduced gaps and computational times.

In the production process for wooden furniture, the raw material costs account for more than 50% of furniture costs, and the utilization rate of raw materials depends mainly on the layout scheme. Therefore, a reasonable layout is an important measure to reduce furniture costs. This paper investigates the solid wood board cutting stock problem (CSP) and establishes an optimization model, with the goal of the highest possible utilization rate for original boards. An ant colony-immune genetic algorithm (AC-IGA) is designed to solve this model. The solutions of the ant colony algorithm are used as the initial population of the immune genetic algorithm, and the optimal solution is obtained using the immune genetic algorithm after multiple iterations are transformed into the accumulation of global pheromones, which improves the search ability and ensures the solution quality. The layout process of the solid wood board is abstracted into the construction process of the solution. At the same time, in order to prevent premature convergence, several improved methods, such as a global pheromone hybrid update and adaptive crossover probability, are proposed. Comparative experiments are designed to verify the feasibility and effectiveness of the AC-IGA, and the experimental results show that the AC-IGA has better solution precision and global search ability compared with the ant colony algorithm (ACA), genetic algorithm (GA), grey wolf optimizer (GWO), and polar bear optimization (PBO). The utilization rate increased by more than 2.308%, which provides effective theoretical and methodological support for furniture enterprises to improve economic benefits.

Made available in DSpace on 2021-06-25T10:15:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-03-01 In this paper, a manufacturer of automotive springs is studied in order to reduce inventory costs and losses in the steel bar cutting process. For that, a mathematical model is proposed considering parallel machines and operational constraints, besides the demand, inventory costs and limits for items and final products. This one-dimensional integrated lot sizing and cutting stock problem is solved by a price-and-branch approach, using column generation and a method for obtaining integer solutions. Results using real data show that the proposed method achieved, in reasonable computational time, considerably better solutions than current practice at the company. Furthermore, focused on managerial insights, tests with random data were performed to analyze the influence that different parameters have in the solution, as well as the overall performance of the method. Paraná Federal University of Technology (UTFPR), Avenida dos Pioneiros, 3131, Jardim Morumbi, Londrina-PR São Paulo State University (UNESP) Avenida Eng. Luís Edmundo Carrijo Coube, 14-01, Vargem Limpa, Bauru-SP São Paulo State University (UNESP) Rua Cristóvão Colombo, 2265, Jardim Nazareth, São José do Rio Preto-SP São Paulo State University (UNESP) Avenida Eng. Luís Edmundo Carrijo Coube, 14-01, Vargem Limpa, Bauru-SP São Paulo State University (UNESP) Rua Cristóvão Colombo, 2265, Jardim Nazareth, São José do Rio Preto-SP

RESUMO Neste artigo apresentamos um estudo sobre o Problema de Corte de Estoque com Sobras Aproveitáveis (PCESA) unidimensional. O Problema de Corte de Estoque (PCE) consiste em cortar um conjunto de objetos, disponíveis em estoque, para produzir um conjunto de itens em quantidades e tamanhos especificados. O PCESA é um PCE no qual nem todas as sobras de um processo de corte são consideradas perdas, ou seja, quando uma sobra for maior que um determinado comprimento, ela retorna ao estoque para ser usada nos processos de corte subsequentes. Assim, o objetivo do problema abordado é otimizar os planos de corte a serem aplicados em objetos padronizados e objetos não padronizados (sobras de cortes anteriores); atender a demanda conhecida de itens menores, de forma a minimizar a perda de material, controlando a quantidade de retalhos gerados. Para resolver este problema, duas heurísticas e três versões da metaheurística Greedy Randomized Adaptive Search Procedure (GRASP) foram desenvolvidas. Testes computacionais foram realizados para os procedimentos propostos em instâncias da literatura. As abordagens propostas para a solução apresentaram boas soluções em relação ao número de sobras geradas. ABSTRACT In this article we present a study of the one-dimensional Cutting Stock Problem with Usable Leftovers Stock (CSPUL). The Cutting Stock Problem (CSP) consists of cutting a set of objects, available in stock, to produce a set of items in specified quantities and sizes. CSPUL is a CSP in which not all leftovers from a cutting process are considered waste, that is, when a leftover is greater than a certain length, it returns to the stock to be used in the subsequent cutting processes. Therefore, the objective of the addressed problem is to optimize the cutting planes to be applied to standardized objects and non-standardized objects (leftovers from previous cuts); meet the known demand for smaller items, in order to minimize the waste of material, controlling the amount of generated leftovers. In order to solve this problem, two heuristics and three versions of the Greedy Randomized Adaptive Search Procedure metaheuristic (GRASP) were developed. Computational tests were performed for the procedures proposed in instances of the literature. The approaches proposed for the solution presented good solutions in relation to the number of surpluses generated.

Made available in DSpace on 2018-12-11T17:03:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-01-01. Added 1 bitstream(s) on 2019-10-09T18:30:28Z : No. of bitstreams: 1 S0101-74382016000100167.pdf: 615169 bytes, checksum: 14c69842e4d9e1b83cb0fc7aab7c1c36 (MD5) Production processes comprising both the lot-sizing problem and the cutting stock problem are frequent in various industrial sectors. However these problems are usually treated separately, which can generates suboptimal overall solution and consequently causes production losses. In this paper, we propose different mathematical models for the integrated problem combining alternative models for the lot-sizing and the cutting stock problem, in order to evaluate and indicate the impact of these changes on the models’ performance. An extensive computational study is done using randomly generated data and as a solution strategy we used a commercial optimization package and the application of a column generation technique. Departamento de Matemática Aplicada IBILCE UNESP Universidade Estadual Paulista HEC Montréal and CIRRELT Departamento de Matemática Aplicada IBILCE UNESP Universidade Estadual Paulista

Submitted by Ana Rebelo (amsr@isep.ipp.pt) on 2019-01-08T12:24:00Z No. of bitstreams: 1 DM_HelderRibeiro_2018_MEGI.pdf: 6313129 bytes, checksum: c1640b9a807b33f3410f67542cb1ad5a (MD5) Approved for entry into archive by Ana Rebelo (amsr@isep.ipp.pt) on 2019-01-08T12:26:59Z (GMT) No. of bitstreams: 1 DM_HelderRibeiro_2018_MEGI.pdf: 6313129 bytes, checksum: c1640b9a807b33f3410f67542cb1ad5a (MD5) Made available in DSpace on 2019-01-08T12:26:59Z (GMT). No. of bitstreams: 1 DM_HelderRibeiro_2018_MEGI.pdf: 6313129 bytes, checksum: c1640b9a807b33f3410f67542cb1ad5a (MD5) Previous issue date: 2018

A cutting stock problem is one of the main and classical problems in operations research that is modeled as LP problem. Because of its NP-hard nature, finding an optimal solution in reasonable time is extremely difficult and at least non-economical. In this paper, two meta-heuristic algorithms, namely simulated annealing (SA) and tabu search (TS), are proposed and developed for this type of the complex and large-sized problem. To evaluate the efficiency of these proposed approaches, several problems are solved using SA and TS, and then the related results are compared. The results show that the proposed SA gives good results in terms of objective function values rather than TS.

This paper presents a two-stage approach for pattern generation and cutting plan determination of the one-dimensional cutting stock problem. Calculation of the total number of patterns that will be cut and generation of the cutting patterns are performed in the first stage. On the other hand, the second stage determines the cutting plan. The proposed approach makes use of two separate integer linear programming models. One of these models is employed by the first stage to generate the cutting patterns through a heuristic procedure with the objective of minimizing trim loss. The cutting patterns obtained from Stage I are then fed into the second stage. In this stage, another integer linear programming model is solved to form a cutting plan. The objective of this model is to minimize a generalized total cost function consisting of material inputs, number of setups, labor hours and overdue time: subject to demand requirements, material availability, regular and overtime availability, and due date constraints. The study also demonstrates an implementation of the proposed approach in a coronary stent manufacturer. The case study focuses on the cutting phase of the manufacturing process followed by manual cleaning and quality control activities. The experiments show that the proposed approach is suitable to the conditions and requirements of the company. (C) 2008 Elsevier B.V. All rights reserved.

Its linear relaxation is often solved instead of the one-dimensional cutting stock problem (1CSP). This causes a difference between the optimal objective function values of the original problem and its relaxation, called a gap. The size of this gap is considered in this paper with the aim to formulate principles for the construction of instances of the 1CSP with large gaps. These principles are complemented by examples for such instances.

In case of the one-dimensional cutting stock problem (CSP) one can observe for any instance a very small gap between the integer optimal value and the continuous relaxation bound. These observations have initiated a series of investigations. An instance possesses the integer round-up property (IRUP) if its gap is smaller than 1. In the last 15 years, some few instances of the CSP were published possessing a gap greater than 1.In this paper, various families of non-IRUP instances are presented and methods to construct such instances are given, showing in this way, there exist much more non-equivalent non-IRUP instances as computational experiments with randomly generated instances suggest. Especially, an instance with gap equal to 109 is obtained. Furthermore, an equivalence relation for instances of the CSP is considered to become independent from the real size parameters.