Research and Models for Automated Manufacturing

Singhal, Kalyan; Fine, Charles H.; Meredith, Jack R.; Suri, Rajan

doi:10.1287/inte.17.6.5

Cited by 58 publications

(26 citation statements)

References 27 publications

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“…Para Singhal et alii [19], atualmente é virtualmente impossível projetar e instalar um sistema desses onde cada um dos cinco subsi temas possa completamente explorar os outros quatro. Isso naturalmente configura um campo motivador de pesquisas.…”

Section: Produçãounclassified

A função de controle nos sistemas integrados de manufatura

Fernandes

1991

Prod.

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Section: Produçãounclassified

A função de controle nos sistemas integrados de manufatura

Fernandes

1991

Prod.

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“…Automated systems can provide a heretofore unmatched level of performance along the four strategic dimensions of cost, quality, delivery, and flexibility (Singhal 1987). Parts of this presentation are based on Singhal et al (1987) and Singhal (2011).…”

Section: Introductionmentioning

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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“…Although AMSs can improve overall manufacturing performance, there is still an ambiguous decision about the action to invest in such systems. The subject of the economics of AMSs has become of significant interest to the management science community recently (Meredith 1986, Canada 1986, Singhal et al 1987). In general, economic justification approaches can be classified into four categories according to different levels of AMSs, from stand-alone systems to completely integrated systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal timing of investments in advanced manufacturing systems under competition

Chung

Tsou

1995

Production Planning & Control

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Large initial investment in advanced manufacturing systems (AMS) makes it imperative to develop economic jusrificution methods. Once the new system has been properly justified, a critical issue facing the firm is the optimal timing of the installation in an industry. In this paper we use a gametheoretic model by which the optimal timing of the investments in AMSs is analysed. The existence of timing equilibria is demonst ratcd and simple decision rules of optimal strategy are provided. Economic interpretations of the decision rules are subsequently made clear. Finally, illustrative examples are used to demonstrate the correctness and usefulness of the analysis.

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Research and Models for Automated Manufacturing

Cited by 58 publications

References 27 publications

A função de controle nos sistemas integrados de manufatura

A função de controle nos sistemas integrados de manufatura

A* Algorithm

Optimal timing of investments in advanced manufacturing systems under competition

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