L-class enumeration algorithms for a discrete production planning problem with interval resource quantities

Devyaterikova, M. V.; Колоколов, А. А.; Kolosov, A. P.

doi:10.1016/j.cor.2007.10.005

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Devyaterikova et al [19] presented discrete production planning problem which may be formulated as the multidimensional knapsack problem is considered, while resource quantities of the problem are supposed to be given as intervals. An approach for solving this problem based on using its relaxation set is suggested.…”

Section: Simoes and Costamentioning

confidence: 99%

Meta-Heuristics Approach to Knapsack Problem in Memory Management

Oppong

Asamoah

et al. 2019

AJRCoS

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The Knapsack Problems are among the simplest integer programs which are NP-hard. Problems in this class are typically concerned with selecting from a set of given items, each with a specified weight and value, a subset of items whose weight sum does not exceed a prescribed capacity and whose value is maximum. The classical 0-1 Knapsack Problem arises when there is one knapsack and one item of each type. This paper considers the application of classical 0-1 knapsack problem with a single constraint to computer memory management. The goal is to achieve higher efficiency with memory management in computer systems. This study focuses on using simulated annealing and genetic algorithm for the solution of knapsack problems in optimizing computer memory. It is shown that Simulated Annealing performs better than the Genetic Algorithm for large number of processes.

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Section: Simoes and Costamentioning

confidence: 99%

Meta-Heuristics Approach to Knapsack Problem in Memory Management

Oppong

Asamoah

et al. 2019

AJRCoS

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“…The cutting plane method does not work due to the aforementioned two reasons . The enumeration method does not work on account of the second reason . Thus, the hybrid inheritance algorithm combined with HIA-ES is adopted in this work.…”

Section: Methodsmentioning

confidence: 99%

“…30 The enumeration method does not work on account of the second reason. 31 Thus, the hybrid inheritance algorithm combined with HIA-ES 32 is adopted in this work.…”

Section: Establishment Of Model For Selecting the Number Ofmentioning

confidence: 99%

Improved Inheritance Algorithm for the Assembly of Coal Fragments

Zhao

Yang

2011

Ind. Eng. Chem. Res.

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There are many methods to simulate coal structure that might be beneficial to coal technologies. However, they often suffer from producing an impractical structure and a large calculation time and could not be applied to most rank coal. Hence, the hybrid inheritance algorithms combined with Elitist strategy (HIA-ES) and Monte Carlo and Elitist Strategy (HIA-MC-ES) are proposed to simulate the coal structure. The reported fragment structures of the coal macromolecule are summarized, and the fragment structure is accurately quantified with an adjacency matrix, an atom identification vector, bonding parameters, a bond characteristic index, and a functional group characteristic parameter, respectively. The humic acid (HA) is used to test the effectiveness of two algorithms since HA has the similar fragment structures with coal. With the use of the method of HIA-ES, the numbers of fragment structures is gained. Then, the parameters of the simulated structures are obtained with the HIA-MC-ES method. Simulated HA and Shenfu coal structures are highly similar with the reported results, indicating that the two novel hybrid inheritance algorithms are effective to simulate a molecular structure for the most rank coal.

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“…Several new analytical algorithms for APP were found in the recent literature: integer programming (Devyaterikova, Kolokolov, and Kolosov 2009), approximation (Achary and Geetha 2007) and non-iterative algorithm (Singhal and Singhal 1996). However, the solution algorithms used in the models have remained relatively the same (Kempf, Keskinocak, and Uzsoy 2011) and the algorithm which can effectively solve convex APP models was not proposed.…”

Section: Optimalmentioning

confidence: 99%

Convex optimisation for aggregate production planning

Bushuev

2013

International Journal of Production Research

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This paper presents a new solution approach to the problem of aggregate production planning (APP). As identified by many researchers, the APP cost function is convex and piecewise. Thus, the convex optimisation approach can be applied to the APP problem. Solving the APP problem using convex optimisation is attractive since it leads to an improved solution over the classical solution methods and it can be applied to a wider range of functions. The classical Linear Decision Rule model of APP is solved using convex optimisation and the resulting solution is compared to three solution approaches which have been historically used to solve this model. The results suggest that convex optimisation may be an effective approach for solving certain types of planning models. IntroductionIn general, production is a process of the transformation of inputs (raw materials, information, etc.) into finished products (goods or services). To effectively manage a large number of components of a manufacturing system, a considerable number of decisions should be made on several organisational levels. The taxonomy proposed by Anthony (1965) classifies managerial decisions into three categories: strategic planning, tactical planning and operations control. This taxonomy has been used by many researchers (see e.g. Shim et al. 2002;Kempf, Keskinocak, and Uzsoy 2011).Strategic planning decisions have long-term implications and are affected by both internal and external information. Tactical planning is a middle-level activity which connects strategic planning and operations control. For tactical planning, the basic problem to be resolved is the allocation of resources such as capacity, work force availability, storage and resource distribution over a medium range planning horizon. Operations control decisions deal with short-term operational and scheduling problems which require complete disaggregation of the information generated at higher levels of the taxonomy. At the tactical level, aggregate production planning (APP) models are concerned with determining the production rate and work force level that should be set over a given horizon in order to meet the total demand and minimise the total expected cost (Kempf, Keskinocak, and Uzsoy 2011).All APP techniques face a problem of trade-off between the accuracy of the model in capturing the relevant features of the production-planning environment and the resulting model complexity (Nam and Logendran 1992). The accuracy means that the model precisely describes the problem. The complexity implies that an optimal solution algorithm of solving the model exists and small changes in the model will not require a new algorithm. Some of APP models are not accurate, because they assume linear or quadratic cost function which does not match the data (Groff and Muth 1972; Kempf, Keskinocak, and Uzsoy 2011). Other APP models use algorithms that do not fit the complexity requirement or do not guarantee an optimal solution. The above-mentioned problems are one of the main reasons why APP techniques are not...

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L-class enumeration algorithms for a discrete production planning problem with interval resource quantities

Cited by 4 publications

References 17 publications

Meta-Heuristics Approach to Knapsack Problem in Memory Management

Meta-Heuristics Approach to Knapsack Problem in Memory Management

Improved Inheritance Algorithm for the Assembly of Coal Fragments

Convex optimisation for aggregate production planning

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