Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Solution of Interval-valued Manufacturing Inventory Models With Shortages
Authors: Susovan Chakrabortty, Madhumangal Pal, Prasun Kumar Nayak
Abstract:
A manufacturing inventory model with shortages with carrying cost, shortage cost, setup cost and demand quantity as imprecise numbers, instead of real numbers, namely interval number is considered here. First, a brief survey of the existing works on comparing and ranking any two interval numbers on the real line is presented. A common algorithm for the optimum production quantity (Economic lot-size) per cycle of a single product (so as to minimize the total average cost) is developed which works well on interval number optimization under consideration. Finally, the designed algorithm is illustrated with numerical example.Keywords: EOQ, Inventory, Interval Number, Demand, Production, Simulation
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329038
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1650References:
[1] A. Sengupta and T. K. Pal, On comparing interval numbers, European Journal of Operational Research, 127(1) (2000) 28-43.
[2] A. H. Lau and H. S. Lau, Effects of a demand-curve-s shape on the optimal solutions of a multi-echelon inventory/pricing model, European Journal of Operational Research, 147 (2003) 530 - 548.
[3] D. C. Lin and J.S. Yao, Fuzzy economic production for production inventory,Fuzzy Sets and Systems, 111 (2000) 465-495.
[4] F. Haris, How many parts to make at once Factory, The Magazine of Management, 1913, 10(2): 13-5-136, 152.
[5] G.C. Mahata and A. Goswami, Fuzzy EOQ models for deteriorating items with stock dependent demand and non-linear holding costs, International Journal of Applied Mathematics and Computer Sciences , 5(2) (2009) 94 - 98.
[6] G.C. Mahata and A. Goswami, An EOQ models with fuzzy lead time , fuzzy demand and fuzzy cost coefficients, International Journal of Mathematical, Physical and Engineering Sciences , 3:1 2009.
[7] H.J.Zimmermann, Fuzzzy mathematical programming, Computer Operational Research, 10(1983) 291-298.
[8] H. Ishibuchi and H. Tanaka, Multiobjective programming in optimization of the interval objective function, European Journal of Operational Research, 48 (1990) 219-225.
[9] J. Kacprzyk, P. Staniewski, Long term inventory policy making through fuzzy decisionmaking models, Fuzzy Sets and Systems, 8 (1982) 117- 132.
[10] K. J. Chung, An algorithm for an inventory model with inventory-leveldependent demand rate, Computational Operation Research, 30 (2003) 1311-1317.
[11] K. S. Park, Fuzzy set theoretic interpretation of economic order quantity, IEEE Transactions on Systems, Man and Cybernetics, SMC, 17 (1987) 1082-1084.
[12] L.A.Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338-352.
[13] M. Vujosevic, D. Petrovic and R. Petrovic, EOQ formula when inventory cost is fuzzy, International Journal of Production Economics, 45(1996) 499-504.
[14] M. Gen, Y. Tsujimura and D. Zheng, An application of fuzzy set theory to inventory contol models, Computers and Industrial Engineering, 33 (1997) 553-556.
[15] M. Sakawa, Interactive computer program for fuzzy linear programming with multiple objectives, International Journal of Man-Machine Studies, 18 (1983) 489-503.
[16] N. Brahimi, S. Dauzere-Peres and A. Nordli, Single item lot sizing problems, European Journal of Operational Research, 168(1) (2006) 1 - 16.
[17] P.K. Nayak and M. Pal, The Bi-matrix Games with Interval Payoffs and its Nash Equilibrium Strategy, The Journal of Fuzzy Mathematics, 17(2)(2009) 421-435.
[18] R.E. Moore, Method and Application of Interval Analysis, SIAM, Philadelphia, 1979.
[19] R.E. Steuer, Algorithm for linear programming problem with interval objective function coefficients, Mathematics of Operation Research, 6(1981) 333-348.
[20] T. K.Roy and M. Maity, A fuzzy EOQ model with demand dependent unit cost under limited stroage capacity, European Journal of Operational Research, 99 (1997) 425 - 432.