“The efficiency of alternative control mechanisms in a MTO three-stage tandem production/inventory system”

In this work we develop a SD model for a make-to-order (MTO) three-stage capacitated production/inventory system. We employ a production order release mechanism affiliated with the automated pipeline inventory and order based production control system (APIOBPCS) policies family. The production rates at each stage are defined under alternative policies. One of the policies considers the human behavior in the decision making process. The robustness of the alternative policies is investigated through the dynamic response of the system under step and pulse changes in demand. Finally, the efficiency of the alternative policies is examined by means of six performance criteria.

[1]  Denis Royston Towill,et al.  Dynamic analysis of an inventory and order based production control system , 1982 .

[2]  Uday S. Karmarkar,et al.  Chapter 6 Manufacturing lead times, order release and capacity loading , 1993, Logistics of Production and Inventory.

[3]  J. Little A Proof for the Queuing Formula: L = λW , 1961 .

[4]  Mohamed Mohamed Naim,et al.  Dynamic analysis of a WIP compensated decision support system , 1994 .

[5]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[6]  Stephen C. Graves,et al.  A Tactical Planning Model for a Job Shop , 1986, Oper. Res..

[7]  William M Glazer,et al.  What's in the pipeline. , 2007, Behavioral healthcare.

[8]  Brian G. Kingsman,et al.  Performance analysis of make-to-order manufacturing systems under different workload control regimes , 2004 .

[9]  S. Disney,et al.  THE IMPACT OF VENDOR MANAGED INVENTORY ON TRANSPORT OPERATIONS , 2003 .

[10]  Robert C. Leachman,et al.  A general framework for modeling production , 1989 .

[11]  Reha Uzsoy,et al.  An Alternative Modeling Framework for Aggregate Production Planning , 2007 .

[12]  Joakim Wikner,et al.  Dynamic analysis of a production-inventory model , 2005 .

[13]  D. Sterman,et al.  Misperceptions of Feedback in a Dynamic Decision Making Experiment , 1989 .

[14]  S. T. Enns,et al.  Work load responsive adjustment of planned lead times , 2004 .

[15]  Sangjin Choi,et al.  Use of GI/G/1 queuing approximations to set tactical parameters for the simulation of MRP systems , 2002, Proceedings of the Winter Simulation Conference.

[16]  S. Bennett,et al.  Production-inventory system controller design and supply chain dynamics , 2002, Int. J. Syst. Sci..

[17]  Uday S. Karmarkar,et al.  Batching to minimize flow times on parallel heterogeneous machines , 1989 .

[18]  Martin Land,et al.  Workload control concepts in job shops A critical assessment , 1995 .

[19]  T. Hout,et al.  Competing Against Time , 1990 .

[20]  J D Littler,et al.  A PROOF OF THE QUEUING FORMULA , 1961 .

[21]  David L. Woodruff,et al.  Production planning with load dependent lead times , 2005, 4OR.

[22]  O. Tang,et al.  The impact of information transparency on the dynamic behaviour of a hybrid manufacturing/remanufacturing system , 2004 .

[23]  Uday S. Karmarkar,et al.  Capacity analysis of a manufacturing cell , 1987 .

[24]  David L. Woodruff,et al.  Production planning with load dependent lead times: an update of research , 2007, Ann. Oper. Res..

[25]  A. Srinivasan,et al.  Resource Pricing And Aggregate Scheduling In Manufacturing Systems , 1988 .

[26]  Denis Royston Towill,et al.  A discrete transfer function model to determine the dynamic stability of a vendor managed inventory supply chain , 2002 .

[27]  John O. McClain,et al.  Mathematical Programming Approaches to Capacity-Constrained MRP Systems: Review, Formulation and Problem Reduction , 1983 .

[28]  Reha Uzsoy,et al.  Using System Dynamics Simulations to Compare Capacity Models for Production Planning , 2006, Proceedings of the 2006 Winter Simulation Conference.

[29]  Rafael Kandiyoti What's in the pipeline? , 2009 .

[30]  H. Mohr,et al.  A Critical Assessment , 1985, The Federal Estate Tax.