An Algorithm for the Manufacturing Equipment Selection Problem

Bard, Jonathan F.; Feo, Thomas A.

doi:10.1080/07408179108963843

Cited by 50 publications

(19 citation statements)

References 18 publications

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“…Bard et al (1991) proposed a nonlinear cost minimization model that can be used by facility planners to analyze the general manufacturing equipment selection. The objective is to determine how many of each machine type to purchase, as well as what fraction of time each machine has to be charged to a particular type of operation.…”

Section: Optimization Modelsmentioning

confidence: 99%

Design of a web-based integrated material handling system for manufacturing applications

Cho

Egbelu

2005

International Journal of Production Research

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This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproductionis dependent upon the quality off the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send UMi a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps.

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Section: Optimization Modelsmentioning

confidence: 99%

Design of a web-based integrated material handling system for manufacturing applications

Cho

Egbelu

2005

International Journal of Production Research

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“…Their emphasis was on the generation of manufacturing cells and a heuristic algorithm was developed to solve the problem. Bard and Feo (1991) also presented a mixed integer programming model for manufacturing equipment selection. A branch-andbound based algorithm was developed to reduce the computation in solving the problem.…”

Section: Introductionmentioning

confidence: 99%

“…One of the disadvantages is that the model is more complicated and may require more computation and analysis. As discussed in Bard and Feo (1991), one should consider alternate manufacturing processes and other aspects in developing a dynamic equipment selection model. These may be important issues for a static model but are essential in developing a multi-period dynamic model.…”

Section: Introductionmentioning

confidence: 99%

A heuristic for solving manufacturing process and equipment selection problems

Chen¹

1999

International Journal of Production Research

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A heuristic for solving manufacturing process and equipment selection problems M. CHEN ² In a dynamic manufacturing environment, machines and machining process selection based on current part mix may need to be revised if part mix has changed or new machine tools have become available and economical. However, the costs of acquiring new machines and revising manufacturing process may exceed the bene® ts derived from such expansion or revision. The problem of selecting the best machining process and equipment in a dynamic manufacturing environment is studied in this paper. An integer programming model and a heuristic algorithm were developed to solve the problem of multiple time periods. Lagrangian relaxation was used to generate lower bounds of the integer programming model for testing the optimality of the heuristic solution. Numerical examples are presented to illustrate the model and the solution technique.

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“…(a) manufacturing [9,26,27,33,83,101]; (b) transportation [21,25,48,136]; (c) telecommunications [13,15,40,81,93,110,114,137]; (d) graph and map drawing [58,59,88,94,118]; (e) power systems [30,31,46]; (f) computational biology [54,70,77]; (g) VLSI [18,19], among other areas of application.…”

Section: Introductionmentioning

confidence: 99%

GRASP: basic components and enhancements

Festa

Resende

2010

Telecommun Syst

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GRASP (Greedy Randomized Adaptive Search Procedures) is a multistart metaheuristic for producing good-quality solutions of combinatorial optimization problems. Each GRASP iteration is usually made up of a construction phase, where a feasible solution is constructed, and a local search phase which starts at the constructed solution and applies iterative improvement until a locally optimal solution is found. While, in general, the construction phase of GRASP is a randomized greedy algorithm, other types of construction procedures have been proposed. Repeated applications of a construction procedure yields diverse starting solutions for the local search. This paper gives an overview of GRASP describing its basic components and enhancements to the basic procedure, including reactive GRASP and intensification strategies

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An Algorithm for the Manufacturing Equipment Selection Problem

Cited by 50 publications

References 18 publications

Design of a web-based integrated material handling system for manufacturing applications

Design of a web-based integrated material handling system for manufacturing applications

A heuristic for solving manufacturing process and equipment selection problems

GRASP: basic components and enhancements

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