Multi-item imperfect production inventory model in Bi-fuzzy environments

被引:16
作者
Bera A.K. [1 ]
Jana D.K. [2 ]
机构
[1] Department of Mechanical Engineering, Haldia Institue of Technology, Haldia, Purba Midnapur, 721657, West Bengal
[2] Department of Engineering Science, Haldia Institute of Technology, Haldia, Purba Midnapur, 721657, West Bengal
关键词
Bi-fuzzy; Imperfect production; Inventory; Possibility measure; Stochastic demand;
D O I
10.1007/s12597-016-0283-4
中图分类号
学科分类号
摘要
In this paper, we propose a mathematical model for a single period multi-product production inventory model producing stochastically imperfect items with continuous stochastic demand under budget and limited shortage constraints in Bi-fuzzy environment. The stochastic constraints are first converted into corresponding crisp values using expected value method. Here, we have considered the model as single period’s inventory for each item and the cycle lengths for different items are constant but different. Total demand for a cycle and the rate of production of defective units is considered as stochastic. The model is formulated and the expected average profits for each product are calculated from density function of demand and percentage of imperfectness in general form and then particular expressions are obtained by using appropriate boundary conditions. Here, all the constraints are Bi-fuzzy in nature and represented by possibility constraints. The deterministic problem is then solved by using generalized reduced gradient method. The model is illustrated through numerical examples. Sensitivity analysis on profit functions due to different aspiration and confidence level is presented via graphically. © 2016, Operational Research Society of India.
引用
收藏
页码:260 / 282
页数:22
相关论文
共 40 条
[1]  
Harris F.W., How many parts to make at once, Fact. Mag. Manag., 10, 152, pp. 135-136, (1913)
[2]  
Taft E.W., The most economical production lot, Iron Age, 101, pp. 1410-1412, (1918)
[3]  
Goswami A., Chaudhuri K.S., EOQ model for inventory with a linear trend in demand and finite rate of replenishment considering shortages, Int. J. Syst. Sci., 22, pp. 181-187, (1991)
[4]  
Love S.F., Inventory Control, (1979)
[5]  
Jana D.K., Das B., Maiti M., Multi-item partial backlogging inventory models over random planning horizon in random fuzzy environment, Appl. Soft Comput., 21, pp. 12-27, (2014)
[6]  
Jana D.K., Maity K., Roy T.K., Multi-objective imperfect production inventory model in fuzzy rough environment via genetic algorithm, Int. J. Oper. Res., 18, 4, pp. 365-385, (2013)
[7]  
Cardenas-Barron L.E., Sana S.S., A production-inventory model for a two-echelon supply chain when demand is dependent on sales teams’ initiatives, Int. J. Prod. Econ., 155, pp. 249-258, (2014)
[8]  
Pasandideh S.H.R., Niaki S.T.A., Nobil A.H., Cardenas-Barron L.E., A multiproduct single machine economic production quantity model for an imperfect production system under warehouse construction cost, Int. J. Prod. Econ., 169, pp. 203-214, (2015)
[9]  
Agnihothri S.R., Kenett R.S., The impact of defects on a process with rework, Eur. J. Oper. Res., 80, 2, pp. 308-327, (1995)
[10]  
Porteus E., Optimallotsizingprocessqualityimprovementandsetupcost reduction, Oper. Res. Soc. Am., 34, 1, pp. 137-144, (1986)