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System dynamics as a decision support system for machine tool selection

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The worldwide competitive economy, the increase in sustainable issue and investment of new production line is demanding companies to choose the right machine from the available ones. An improper selection can negatively affect the overall performance of the manufacturing system like productivity, quality, cost and companys responsive manufacturing capabilities. Thus, selecting the right machine is desirable and substantial for the company to sustain competitive in the market. The ultimate objective of this paper is to formulate a framework for machining strategy and also provide methodology for selecting machine tool from two special purpose machine tools in consideration of interaction of attributes. A decision support system for the selection of machine tool is developed. It evaluates the performance of the machining process and enhances the manufacturer (decision maker) to select the machine with respect to the performance and the pre-chosen criteria. Case study was conducted in a manufacturing company. A system dynamics modelling and simulation techniques is demonstrated towards efficient selection of machine tool that satisfy the future requirement of engine-block production.
Rocznik
Strony
102--125
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
  • KTH Royal Institute of Technology, Department of Production Engineering, Division of Machine and Process Technology, Stockholm, Sweden
autor
  • KTH Royal Institute of Technology, Department of Production Engineering, Division of Machine and Process Technology, Stockholm, Sweden
Bibliografia
  • [1] ADANE T.F., BIANCHI M.F., ARCHENTI A., NICOLESCU M., 2015, Performance evaluation of machining strategy for engine-block manufacturing, Journal of Machine Engineering, 15/4, 81-102.
  • [2] ADANE T.F., NICOLESCU M., 2014, System dynamics analysis of energy usage: case studies in automotive manufacturing, Int. J. Manufacturing Research, 9/2, 131-156.
  • [3] ATMANI A., LASHKARI R.S., 1998, A model of machine‐tool selection and operation allocation in flexible manufacturing system, International Journal of Production Research, 36/5, 1339‐49.
  • [4] AYAG Z., 2007, A hybrid approach to machine-tool selection through AHP and simulation, International Journal of Production Research, 45/9, 2029-2050.
  • [5] AYAG Z., OZDEMIR R., 2012, Evaluating machine tool alternatives through modified TOPSIS and alpha-cut based fuzzy ANP, International Journal of Production Economics, 140/2, 630-636.
  • [6] AZAR A.T., 2012, System dynamics as a useful technique for complex systems, International Journal of Industrial and Systems Engineering, 10/4, 377–410.
  • [7] BRAILSFORD S.C., LATTIMER V.A., TARNARAS P., TURNBULL J.C., 2004, Emergency and on-demand health care: modeling a large complex system, Journal of the Operational Research Society, 55/1, 34-42.
  • [8] CAGDAC ARSLAN M., CATAY B., BUDAK E., 2004, A decision support system for machine tool selection, Journal of Manufacturing Technology Management, 15/1, 101-109.
  • [9] CIMREN E., CATAY B., BUDAK, E., 2007, Development of a machine tool selection system using AHP, Int. J. of Advanced Manufacturing, 35/3-4, 363-376.
  • [10] GERRARD W., 1988, A strategy for selecting and introducing new technology machine tools, Advances in Manufacturing Technology, 3, 532-536.
  • [11] GOH C.H., TUNG Y.C.A., CHENG C.H., 1995, A revised weighted sum decision model for robot selection, Computers and Industrial Engineering, 30/2, 193-199.
  • [12] HASAN AGHDAIE M., HASHEMKHANI ZOLFANI S., ZAVADSKAS E.K., 2013, Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods, Engineering Economics, 24/1, 5-17.
  • [13] IC Y., YURDAKUL M., ERASLAN E., 2012, Development of a component-based machining centre selection model using AHP, International Journal of Production Research, 50/22, 6489–6498.
  • [14] LIN Z.C., YANG C.B., 1994, Evaluation of machine selection by the AHP method, Journal of Materials Processing Technology, 57/3, 253-258.
  • [15] NGUYEN H.T., DAWAL S.Z.M., NUKMAN Y., AOYAMA H., 2014, A hybrid approach for fuzzy multiattribute decision making in machine tool selection with consideration of the interactions of attributes, Expert system with application, 41/6, 3078–3090.
  • [16] RAI R., KAMESHWARAN S., TIWARI M, 2002, Machine-tool selection and operation allocation in FMS: Solving a fuzzy goal-programming model using a genetic algorithm, Int. J. of Production Research, 40/3, 641-665.
  • [17] STERMAN J.D., 2000, Business Dynamics: System Thinking and Modeling for a Complex World, Irwin/McGraw-Hill, Boston.
  • [18] STAMATOPOULOUS D., 2014, Six cylinder engine block. In: https://grabcad.com/library/6-cylinder-engine-block-1, retrieved June 2016.
  • [19] TABUCANON M.T., BATANOV D.N., VERMA D.K., 1994. Decision support system for multi-criteria machine selection for flexible manufacturing systems, Computers in Industry, 25/2, 131-143.
  • [20] TAHA Z., ROSTAM S., 2012, A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell, Journal of Intelligent Manufacturing, 23/6, 2137-2149.
  • [21] TSAI J.P., CHENG H.Y., WANG S., KAO Y.C., 2010, Multi-criteria decision making method for selection of machine tool, In computer communication control and automation (3CA), 2, 49-52, IEEE.
  • [22] WANG T.Y., SHAW C.F., CHEN Y.L., 2000, Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision-making approach, International Journal of Production Research, 38/9, 2079-2097.
  • [23] WARREN K., LANGLEY P., 1999, The effective communication of system dynamics to improve insight and learning in management education, Journal of the Operational Research Society, 396-404.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9d9eaa03-05c4-4aa9-98fd-1af07b54fdc6
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