Partner selection and synchronized planning in dynamic manufacturing networks

Viswanadham, N.; Gaonkar, R.

doi:10.1109/tra.2002.805659

Cited by 78 publications

(24 citation statements)

References 18 publications

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“…The comparison of various prediction methods was such as shown in table Ⅰ. [10] There were much such phenomena in the reality world: A particular system was in the condition that it had been known, it was only related to now that the condition of the future moment of the system was, but it had not direct relationship to the past history. This was math model which can describe the kind of stochastic phenomena called Markov model.…”

Section: A Market Demand Forecast Methodsmentioning

confidence: 99%

Research on Virtual Enterprise Market Demand Forecast Model Based on Theory of Markov Chain

Zhang¹,

Dong³

et al. 2017

Proceedings of the 2017 International Conference on Information Technology and Intelligent Manufacturing (ITIM 2017)

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Abstract-It was found that market demand was the critical factor for the uncertainty of virtual enterprise by the summarizing of the form of virtual enterprise(VE) supply chain and the analyzing of the uncertainty of the virtual enterprise supply chain. The market demand forecasting model based on Markov chain was established, aiming at the uncertainty of market demand. And it was used into the practical example to prove the feasibility of the mathematical model.

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Section: A Market Demand Forecast Methodsmentioning

confidence: 99%

Research on Virtual Enterprise Market Demand Forecast Model Based on Theory of Markov Chain

Zhang¹,

Dong³

et al. 2017

Proceedings of the 2017 International Conference on Information Technology and Intelligent Manufacturing (ITIM 2017)

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“…Transportation costs are not calculated for individual products but for families of similar products, thus reducing the model complexity. Syam (2002) and Viswanadham and Gaonkar (2003) include a fixed charge per unit using a particular link to transfer products between units. Arntzen et al (1995), Dogan and Goetschalckx (1999), and Viswanadham and Gaonkar (2003) also include the transportation time parameter.…”

Section: Transportationmentioning

confidence: 99%

Mathematical Programming Approaches

Chandra

Grabis

2016

Supply Chain Configuration

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Mathematical programming is one of the most important techniques available for quantitative decision-making. The general purpose of mathematical programming is finding an optimal solution for allocation of limited resources to perform competing activities. The optimality is defined with respect to important performance evaluation criteria, such as cost, time, and profit. Mathematical programming uses a compact mathematical model for describing the problem of concern. The solution is searched among all feasible alternatives. The search is executed in an intelligent manner, allowing the evaluation of problems with a large number of feasible solutions.Mathematical programming finds many applications in supply chain management, at all decision-making levels. It is also widely used for supply chain configuration purposes. Out of several classes of mathematical programming models, mixed-integer programming models are used most frequently. Other types of models, such as stochastic and multi-objective programming models, are also emerging to handle more complex supply chain configuration problems. Although these models are often more appropriate, computational complexity remains an important issue in the application of mathematical programming models for supply chain configuration.This chapter describes application of mathematical programming for supply chain configuration. The general overview is given in Sect. 8.2. It is followed by a description of generic supply chain configuration mixed-integer programming model in Sect. 8.3. This model is based on the data model presented in Chap. 7. Computational approaches for solving problems of large size are also discussed along with typical modifications of the generic model, especially, concerning global factors. Section 8.4 outlines the application of other classes of mathematical programming models. In Sect. 8.5, the generic optimization model is used to optimize the SCC Bike's supply chain configuration. Section 8.6 details a model

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“…Accordingly, different models have to be defined at each level of the decision hierarchy to describe the multiple aspects of the SC. While the development of formal models for SC design at strategic and tactical levels was addressed in the related literature Dotoli et al, 2006;Gaonkar & Viswanadham, 2001;Luo et al, 2001;Vidal & Goetschalckx, 1997;Viswanadham & Gaonkar, 2003), research efforts are lagging behind in the subject of modeling and analyzing the SC operational performance. At the operational level, SCs can be viewed as Discrete Event Dynamical Systems (DEDSs), whose dynamics depends on the interaction of discrete events, such as customer demands, departure of parts or products from entities, arrival of transporters at facilities, start of assembly operations at manufacturers, arrival of finished goods at customers etc (Viswanadham & Raghavan, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…While the SC optimization models presented in the related literature determine the decision parameters off-line (e.g. see Gaonkar & Viswanadham, 2001;Vidal & Goetschalckx, 1997;Viswanadham, 1999;Viswanadham & Gaonkar, 2003) in order to design and manage the SC, the task of the considered model is selecting some operational SC parameters in a short time, based on knowledge of the system state and of the occasional uncontrollable events. More precisely, the optimal mode of operation is obtained by the computation of control variables, i.e., the instantaneous firing speeds of continuous transitions, solving a linear programming problem optimizing a chosen performance index.…”

Section: Introductionmentioning

confidence: 99%