A method for solving the minimization of the maximum number of open stacks problem within a cutting process

Becceneri, José Carlos; Yanasse, Horácio Hideki; Soma, Nei Yoshihiro

doi:10.1016/s0305-0548(03)00189-8

Cited by 40 publications

(50 citation statements)

References 12 publications

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“…For the purpose of comparison, the criteria adopted in this study were the same as those adopted by [5] and [18]. As [3,5] used quantitative methods and this work adopted the qualitative methodology, the data were converted to the In the particular case, as the criteria 'Stakeholders' And 'Customer requirement value' have more than one customer by punctuating the same requirement, the arithmetic mean between the scores indicated by those clients for the classification represented in table 2 was considered. 4.…”

Section: D1mentioning

confidence: 99%

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Selection and prioritization of software requirements using the Verbal Decision Analysis paradigm

Barbosa

Pinheiro

Silveira

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Selecting and prioritizing the most stable software requirements within a set of requirements and engaging them in releases that satisfy the most customers is a difficult task for the decision maker. Many methods have been employed to solve this type of problem. But we do not find many solutions that use Verbal Decision Analysis. Therefore, in this paper we aim to select and prioritize software requirements using Verbal Decision Analysis techniques as a tool, exploring the ZAPROS III-i method and comparing the results with those obtained by the NSGA-II, SPEA2 and Mocell metaheuristics and also with a random algorithm.

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Section: D1mentioning

confidence: 99%

“…A common method is the use of metaheuristics that are algorithms used to solve optimization problems. Becceneri [3] say that metaheuristics is algorithmic tool, which can be applied to different optimization problems, with relatively small modifications, in order to make them adaptable to a specific problem.…”

Section: Introductionmentioning

confidence: 99%

Selection and prioritization of software requirements using the Verbal Decision Analysis paradigm

Barbosa

Pinheiro

Silveira

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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“…Lower bounds are often based on the co-demand graph G which has been defined in the literature in [1]. The nodes of G are associated to orders and an edge (i, j) is added if and only if orders i and j share at least one product.…”

Section: Preprocessing and Lower Boundsmentioning

confidence: 99%

“…The nodes of G are associated to orders and an edge (i, j) is added if and only if orders i and j share at least one product. Several lower bounds can be defined from this graph, and we use the size of a clique obtained as a minor of G [1] by edge contraction as it appeared the most effective in our experimentation. These bounds may also be used during search on the problem restricted to P − S by taking into account the current open orders.…”

Section: Preprocessing and Lower Boundsmentioning

confidence: 99%

Learning from the Past to Dynamically Improve Search: A Case Study on the MOSP Problem

Cambazard

Jussien²

2008

Lecture Notes in Computer Science

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Abstract.This paper presents a study conducted on the minimum number of open stacks problem (MOSP) which occurs in various production environments where an efficient simultaneous utilization of resources (stacks) is needed to achieve a set of tasks. We investigate through this problem how classical lookback reasonings based on explanations could be used to prune the search space and design a new solving technique. Explanations have often been used to design intelligent backtracking mechanisms in Constraint Programming whereas their use in nogood recording schemes has been less investigated. In this paper, we introduce a generalized nogood (embedding explanation mechanisms) for the MOSP that leads to a new solving technique and can provide explanations. The Minimum number of Open Stacks ProblemThe Minimum number of Open Stacks Problem (MOSP) has been recently used to support the IJCAI 2005 constraint modeling challenge [14]. This scheduling problem involves a set of products and a set of customer's orders. Each order requires a specific subset of the products to be completed and sent to the customer. Once an order is started (i.e. its first product is being made) a stack is created for that order. At that time, the order is said to be open. When all products that an order requires have been produced, the stack/order is closed. Because of limited space in the production area, the maximum number of stacks that are used simultaneously, i.e. the number of customer orders that are in simultaneous production, should be minimized.Therefore, a solution for the MOSP is a total ordering of the products describing the production sequence that minimizes the set of simultaneously opened stacks. This problem is known to be NP-hard and is related to well known graph problems such as the minimum path-width or the vertex separation problems.

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“…Additionally, the problem is also known to be NP-Hard (Linhares & Yanasse, 2002) and therefore, difficult to be solved exactly. In Becceneri et al (2004) it is presented the best heuristic, up to now, in terms of the quantity of open stacks, despite a much worst running time than Yuen (1995) and our new heuristic presented here. Metaheuristics solutions for the MOSP were also suggested (Faggioli & Bentivoglio, 1998;Fink & Voss, 1999).…”

Section: Introductionmentioning

confidence: 97%

A heuristic for the minimization of open stacks problem

Ashikaga

Soma²

2009

Pesqui. Oper.

Self Cite

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It is suggested here a fast and easy to implement heuristic for the minimization of open stacks problem (MOSP). The problem is modeled as a traversing problem in a graph (G mosp ) with a special structure (Yanasse, 1997b). It was observed in Ashikaga (2001) that, in the mean experimental case, G mosp has large cliques and high edge density. This information was used to implement a heuristic based on the extension-rotation algorithm of Pósa (1976) for approximation of Hamiltonian Circuits. Additionally, an initial path for Pósa's algorithm is derived from the vertices of an ideally maximum clique in order to accelerate the process. Extensive computational tests show that the resulting simple approach dominates in time and mean error the fast actually know Yuen (1991 and heuristic to the problem.

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A method for solving the minimization of the maximum number of open stacks problem within a cutting process

Cited by 40 publications

References 12 publications

Selection and prioritization of software requirements using the Verbal Decision Analysis paradigm

Selection and prioritization of software requirements using the Verbal Decision Analysis paradigm

Learning from the Past to Dynamically Improve Search: A Case Study on the MOSP Problem

A heuristic for the minimization of open stacks problem

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