Selection of the optimal number of shifts in fuzzy environment: manufacturing company’s facility application

This paper addresses the selection of optimal shift numbers considering inventory information, customer requirements and machine reliability using fuzzy logic. Number of shift is one of the most important criteria for the production planners to minimize the production costs and is essential for appropriate production planning. The main task involves optimizing the shift periods considering constraints of raw material, due date, demand, finished goods inventory and machine breakdown. A model is developed for any kind of manufacturing company where shift periods affect company’s profit and cost. Fuzzy control is used to optimize the number of shifts under the constraints of raw material, due date, demand, finished goods inventory and machine breakdown. MATLAB Fuzzy Logic Tool Box is used to develop the model.

[1]  Ginalber Luiz de Oliveira Serra,et al.  Methodology for fuzzy identification in a noisy environment , 2006, Int. J. Model. Identif. Control..

[2]  Siegfried Gottwald,et al.  Mathematical fuzzy logic as a tool for the treatment of vague information , 2005, Inf. Sci..

[3]  L. G. Tan,et al.  An investigation on energy regeneration in a CFA6470 parallel hybrid electric vehicle using fuzzy logic , 2008 .

[4]  R. Hedjar.  Fuzzy control of periodic-review state-dependent production systems with unknown deterioration rate , 2008 .

[5]  Antonio Rizzi,et al.  A fuzzy logic based methodology to rank shop floor dispatching rules , 2002 .

[6]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[7]  Sharon M. Ordoobadi,et al.  Development of a justification tool for advanced manufacturing technologies: system-wide benefits value analysis , 2001 .

[8]  Stephen C. Graves,et al.  A Multi-Echelon Inventory Model for a Repairable Item with One-for-One Replenishment , 1985 .

[9]  Adrijit Goswami,et al.  A fuzzy replenishment policy for deteriorating items with ramp type demand rate under inflation , 2009 .

[10]  Prem Vrat,et al.  REDUCING THE BULLWHIP EFFECT IN A SUPPLY CHAIN WITH FUZZY LOGIC APPROACH , 2007 .

[11]  Samir Allet,et al.  Handling flexibility in a "generalised job shop" with a fuzzy approach , 2003, Eur. J. Oper. Res..

[12]  Paul R. Kleindorfer,et al.  Optimal stocking policies for low usage items in multi‐echelon inventory systems , 1986 .

[13]  Wallace J. Hopp,et al.  An easily implementable hierarchical heuristic for a two-echelon spare parts distribution system , 1999 .

[14]  Paul H. Zipkin,et al.  Evaluation of one-for-one replenishment policies for multiechelon inventory systems , 1991 .

[15]  Sven Axsäter,et al.  Simple Solution Procedures for a Class of Two-Echelon Inventory Problems , 1990, Oper. Res..

[16]  V. Novák,et al.  Mathematical Principles of Fuzzy Logic , 1999 .

[17]  Petri Mähönen,et al.  Fuzzy logic-based forecasting model , 2001 .

[18]  Bernard Grabot,et al.  Dispatching rules in scheduling Dispatching rules in scheduling: a fuzzy approach , 1994 .