Design, planning, scheduling, and control problems of flexible manufacturing systems

Stecke, Kathryn E.

doi:10.1007/bf02023765

Cited by 269 publications

(82 citation statements)

References 2 publications

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“…This classification is nei ther entirely new nor unique; earlier work has been published by e.g. , Stecke[1985], Van Looveren et. al.…”

Section: Introductionmentioning

confidence: 78%

Flexible Manufacturing Systems: background examples and models

Zijm¹

1988

Operations Research Proceedings

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In this paper, we discuss recent innovations in manufacturing technology and their implications on the design and control of manufacturing systems. Recognizing the need to respond properly to rapidly changing market demands, we discuss several types of flexibility that can be incorporated in our production organisation to achieve this goal. We show how the concept of a Flexible Manufacturing System (FMS) naturally arises as an attempt to combine the advantages of tradi tional Job Shops and dedicated production lines.The main body of the paper is devoted to a classification of

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“…This classification is nei ther entirely new nor unique; earlier work has been published by e.g. , Stecke[1985], Van Looveren et. al.…”

Section: Introductionmentioning

confidence: 78%

Flexible Manufacturing Systems: background examples and models

Zijm¹

1988

Operations Research Proceedings

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“…• Flexible manufacturing systems (FMSs): These are manufacturing cells designed for mid-volume and mid-variety ranges of production (Stecke 1985(Stecke , 1992. Typical FMSs consist of 5-25 machines that are linked together by common computer controllers and by automated material handling devices.…”

Section: Automated Manufacturingmentioning

confidence: 99%

A* Algorithm

2013

Encyclopedia of Operations Research and Management Science

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A heuristic search procedure that selects a node in its search tree for expansion such that the selected node has minimum value of the sum of the cost to reach the node plus a heuristic cost value for that node, where the heuristic cost underestimates the true minimum cost of completion. See ▶ Artificial IntelligenceReferences Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4, 100-107. Pearl, J. (1984). Heuristics. Reading, MA: Addison-Wesley. Acceptance Sampling ▶ Quality Control Acceptance-Rejection MethodIn stochastic or Monte Carlo simulation, a method for sampling from a given difficult target probability distribution by sampling from a distribution that is close to the target distribution and relatively easy to sample but possibly rejecting the generated output. Sometimes just called the rejection method. Activity LevelThe value taken by a structural variable in an intermediate or final solution to a linear programming problem. See ▶ Structural Variables Activity-Analysis ProblemA linear-programming problem of the form Maximize cx, subject to Ax b, x ! 0. The variables x j of the vector x are quantities of products to be produced. The b i coefficients of the resource vector b represent the amount of resource i that is available for production, the c j coefficients represent the value (profit) of one unit of output x j , and the coefficients a ij of the technological matrix A represent the amount of resource i required to produce one unit of product j. The a ij are termed technological or input-output coefficients. The objective function cx represents some measure of value of the total production. Adjacent (Neighboring) Extreme PointsTwo extreme points of a polyhedron that are connected by an edge of the polyhedron. Advertising IntroductionAdvertising research has focused on three substantive areas: sales response to advertising, optimal advertising policy (constant spending or pulsing), competitive reactions and over-time effects. The research has employed econometric, time-serioes, optimization and game theoretic analytical techniques to address the issues. The advent of enormous amounts of scanner panel and internet data has led to some fruitful modeling at the individual household level. Contributions in each one of the three areas are discussed. A thorough review of optimal control advertising models is given in Feichtinger, Hartl, and Sethi (1994). Mathematical programming also has been a useful technology. Since some early successful applications of this technology for media planning, the progress has been limited because of measurement problems relating to advertising response function (Little and Lodish 1969). Advances in research, however, provide reasons for optimism in identifying the response function (Little 1979; Eastlack and Rao 1986). Heuristic approaches have been developed to estimate the media characteristics of reach and frequency (Rust and Eechambadi 1989)...

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“…The constraint-directed approach, which is the most recent, considers due dates, processing capacities of the machine tools, and tool magazine capacities (Hwang 1986). The approaches of Kiran and Tansel (1986), Rajagopalan (1986), O'Grady and Menon (1984, 1985, 1987, and Chakravarty and Shtub (1987) can also be classified as constraintdirected, even though the part type selection problem is considered along with other subproblems in a single formulation or solution procedure. Using the last type of approach, Stecke and Kim (1986) suggest a method called aflexible approach, which considers dynamic events such as changes in the part mix and machine breakdowns.…”

Section: Resource Auocationproblemmentioning

confidence: 99%

An iterative approach to system setup problems in flexible manufacturing systems

Kim

Yano

1992

Int J Flex Manuf Syst

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Abstract. System setup problems in flexible manufacturing systems deal with short-term planning problems such as part type selection, machine grouping, operation assignment, tooling, fixture and pallet allocation, and routing. In this article, we consider three of the subproblems: part type selection, machine grouping, and loading. We suggest a heuristic approach to solve the subproblems consistently with the objective of maximizing the expected production rate. The proposed procedure includes routines to generate all possible machine grouping alternatives for a given set of machines, to obtain optimal target workloads for each grouping alternative, and to allocate operations and tools to machine groups. These routines are executed iteratively until a good solution to the system setup problem is obtained. Computational experience is reported.

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Design, planning, scheduling, and control problems of flexible manufacturing systems

Cited by 269 publications

References 2 publications

Flexible Manufacturing Systems: background examples and models

Flexible Manufacturing Systems: background examples and models

A* Algorithm

An iterative approach to system setup problems in flexible manufacturing systems

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