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Engineering - Production & Process Engineering | Design of Advanced Manufacturing Systems - Models for Capacity Planning in Advanced Manufacturing

Design of Advanced Manufacturing Systems

Models for Capacity Planning in Advanced Manufacturing Systems

Matta, Andrea, Semeraro, Quirico (Eds.)

2005, XVI, 267 p.

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Since manufacturing has acquired industrial relevance, the problem of adequately sizing manufacturing plants has always been discussed and has represented a di?cult problem for the enterprises, which prepare strategic plans to competitively operate in the market. Manufact- ing capacity is quite expensive and its exploitation and planning must be carefully designed in order to avoid large wastes, or to preserve the survival of enterprises in the market. Indeed a good choice of ma- facturing capacity can result in improved performance in terms of cost, innovativeness, ?exibility, quality and service delivery. Unfortunately the capacity planning problem is not easy to solve because of the lack of clarity in the decisional process, the large number of variables involved, the high correlation among variables and the high level of uncertainty that inevitably a?ects decisions. The aim of this book is to provide a framework and speci?c methods and tools for the selection and con?guration of capacity of Advanced Manufacturing Systems (AMS). In particular this book de?nes an - chitecture where the multidisciplinary aspects of the designofAMSare properly organized and addressed. The tool will support the decisi- maker in the de?nition of the con?guration of the system which is best suited for the particular competitive context where the ?rm operates or wants tooperate. Thisbookisofinterest for academic researchers in the ?eldofind- trial engineering and particularly indicated in the areas of operations and manufacturing strategy.

Content Level » Research

Keywords » Fuzzy - Operations Research - Simulation - algorithm - algorithms - architecture - environment - fuzzy set - linear optimization - manufacturing engineering - model - optimization - planning - production - programming

Related subjects » Civil Engineering - Computational Intelligence and Complexity - Production & Process Engineering

Table of contents 

List of Figures List of Tables Preface Contributing Authors 1: A framework for long term capacity decisions in AMSs; A. Matta, Q. Semeraro, T. Tolio 1 Introduction 2 Manufacturing capacity 3 Manufacturing strategy 4 Advanced Manufacturing Systems 5 A framework for capacity problems 2: A DSS for strategic planning; M. Bruccoleri, G. Lo Nigro, S. Noto La Diega, P. Retina, C. Perrone 1 The strategic planning process 2 Models for Production Strategy Planning 3 Models for Long-term Capacity Planning 4 DSS description 5 Tests and results 6 Conclusions 3: Stochastic programming models for manufacturing applications; A. Alfieri, P. Brandimarte 1 Introduction 2 The newsvendor problem 3 Stochastic linear programming 4 General structure of two-stage stochastic linear programs 5 Solution methods 6 Multi-stage stochastic programming models 7 Strong mixed-integer model formulations 8 Scenario generation 9 Models for capacity planning 10 An alternative approach to cope with uncertainty: robust optimization 11 Conclusions 4: Configuration of AMSs; A. Matta. Q. Semeraro. T. Tolio 1 Introduction 2 Problem description 3 Description of Automated Manufacturing Systems 4 Design of Automated Manufacturing Systems 5 Performance evaluation of Dedicated Manufacturing Flow Lines 6 Performance evaluation of Flexible Manufacturing Systems 7 Conclusions 5: Selecting capacity plan; A. Anglani, P. Caricato, A. Grieco, F. Nucci 1 Introduction 2 Problem statement 3 The proposed methodology 4 Case study 5 Conclusions 6: Fuzzy performance evaluator of AMSs; F. Caiazzo, R. Pasquino, V. Sergi, B. Spiezio 1 Introduction 2 Fuzzy sets arid fuzzy numbers 3 Describing uncertainty 4 Linguistic modifiers 5 Constructing fuzzy sets 6 Queuing systems 7 Open queuing network models 8 Closed queuing network models 9 The method proposed: single-class case 10 The method proposed: multi-class case 11 The algorithm for the method proposed: single-class case 12 A sample application 13 Conclusions

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