Formulations and theoretical analysis of the one-dimensional multi-period cutting stock problem with setup cost

Silva, Eduardo M.; Melega, Gislaine Mara; Akartunalı, Kerem; Araújo, Silvio Alexandre de

doi:10.1016/j.ejor.2022.04.023

Cited by 11 publications

(6 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Os procedimentos heurísticos foram implementados pelo Visual Studio 2017 usando o IBM ILOG Cplex 22.1 como software de otimização. As instâncias usadas são propostas em [13] Na Tabela 2 são mostradas as médias dos resultados considerando cada classe com custo de objeto c = 10. Nota-se que o AGACAV apresenta os melhores resultados tanto para soluções inteiras quanto para tempo médio (com exceção da Classe 8 onde o AGG apresenta uma solução inteira 0.9% melhor).…”

Section: Resultados Computacionaisunclassified

Algoritmo Genético Adaptativo com Chaves Aleatórias Viciadas para um Problema de Corte de Estoque Multi-Período com Custos de Setup

Araujo,

Silva,

Jans

2023

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

View full text Add to dashboard Cite

Resumo. Este trabalho apresenta um Algoritmo Genético Adaptativo com Chaves Aleatórias Viciadas (AGACAV) para resolver o problema de Corte de Estoque Multi-período com custos de setup nos padrões de corte. Uma aplicação determinística chamada decoder que mapeia soluções factíveis do problema é necessária para a inicialização do AGACAV. Um decoder baseado na geração de estoque por período e construção de padrões de corte é proposto e comparado com um procedimento de geração de colunas. Os métodos foram comparados em instâncias com diferentes tamanhos de itens e o resultados mostram que o AGACAV obtém melhores resultados para instâncias cujo custo de setup é maior que o custo dos objetos em estoque.

show abstract

Section: Resultados Computacionaisunclassified

Algoritmo Genético Adaptativo com Chaves Aleatórias Viciadas para um Problema de Corte de Estoque Multi-Período com Custos de Setup

Araujo,

Silva,

Jans

2023

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

View full text Add to dashboard Cite

show abstract

“…The problem refers to cutting an inventory of stock materials into smaller items based on a pattern to fulfill customer orders or for the next stage of processing. CSP, especially the one-dimensional problem, is studied by many researchers, including (de Lara Andrade et al 2021;Muter and Sezer 2018;Pierini and Poldi 2021;Silva et al 2023). Muter and Sezer (2018) propose an algorithm to solve a two-stage one-dimensional CSP that the total number of stock rolls to be cut.…”

Section: Literature Reviewmentioning

confidence: 99%

Minimizing Job Tardiness in One-Dimensional Cutting Stock Problem Using Mixed Integer Linear Programming (MILP)

Sar,

Tai,

Buddhakulsomsiri

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

View full text Add to dashboard Cite

The one-dimensional cutting stock problem is a fundamental problem in manufacturing that involves the preparation of raw materials for production. An example of a cutting stock problem in the industry is steel pipe manufacturing, where the raw material preparation operation requires cutting steel sheets from stock rolls based on job specifications. Steel manufacturers usually perform job planning and scheduling on the cutting process to satisfy the production requirement, i.e. job due date, from subsequent operation. The key performance measure is job tardiness. In this paper, we propose a mixed integer linear programming model to solve the one-dimensional cutting stock problem so as to minimize total job tardiness. A numerical example is presented to demonstrate the effectiveness of the model in performing job scheduling to meet the due dates.

show abstract

“…Terefore, two wellknown transfer function families, S-shaped and V-shaped, mathematically given in Table 2 have been proposed to normalize the continuous solutions between 0 and 1. Ten, according to equation (24), these normalized values are changed to 0 and 1 to become applicable for binary problems [78]. Figure 2 illustrates the distinction between S-shaped and V-shaped transfer functions.…”

Section: Transfer Functionsmentioning

confidence: 99%

“…Updating X →t+1 i according to equations ( 20) and ( 23) (22) else / * Exploration phase2 * / (23) Updating X →t+1 i according to equations ( 21) and ( 23) (24) end if (25) t � t + 1 (26) end for (27) end while ALGORITHM 1: Steps of NOA.…”

Section: Transfer Functionsmentioning

confidence: 99%

“…Te KP01 dilemma aims to use resources efciently to maximize proft and minimize cost. KP01 is applied in several felds to save resources and maximize proft; some of which are investment decisions [13], cryptography [14], real estate property maintenance optimization [15], energy minimization [16], principal budgeting [17], the availableto-promise problem [18], cargo loading problems [19], adaptive multimedia systems [20], resource allocation [18], housing problem [21], computer memory [22], project portfolio selection [23], and cutting-stock problem [24]. Te public-key cryptosystem (PKC) is designed to encrypt any message using a public key before transmitting it to the recipient, who has a private key to decrypt the message to become readable.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Optimization and Comprehensive Analysis of Binary Nutcracker Optimization Algorithm: A Case Study of Feature Selection and Merkle–Hellman Knapsack Cryptosystem

et al. 2023

View full text Add to dashboard Cite

In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V-shaped, are extensively investigated for converting the classical NOA into a binary variant, namely BNOA, applicable for various binary problems. Additionally, BNOA is improved using a local search strategy based on effectively integrating some genetic operators into the BNOA’s exploitation and exploration; this additional variant is called BINOA. Both BNOA and BINOA are evaluated using three common binary optimization problems, including feature selection, 0-1 knapsack, and the Merkle–Hellman knapsack cryptosystem (MHKC), and are compared to several robust binary metaheuristic optimizers in terms of statistical information, statistical tests, and convergence speed. The experiential findings show that BINOA is better than the classical BNOA and the other rival optimizers for both the 0-1 knapsack problem and attacking MHKC and is on par with some algorithms, like the genetic algorithm for feature selection.

show abstract

Formulations and theoretical analysis of the one-dimensional multi-period cutting stock problem with setup cost

Cited by 11 publications

References 71 publications

Algoritmo Genético Adaptativo com Chaves Aleatórias Viciadas para um Problema de Corte de Estoque Multi-Período com Custos de Setup

Algoritmo Genético Adaptativo com Chaves Aleatórias Viciadas para um Problema de Corte de Estoque Multi-Período com Custos de Setup

Minimizing Job Tardiness in One-Dimensional Cutting Stock Problem Using Mixed Integer Linear Programming (MILP)

Performance Optimization and Comprehensive Analysis of Binary Nutcracker Optimization Algorithm: A Case Study of Feature Selection and Merkle–Hellman Knapsack Cryptosystem

Contact Info

Product

Resources

About