A production inventory model with fuzzy random demand and with flexibility and reliability considerations |
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| Authors: | Soumen Bag Debjani Chakraborty AR Roy |
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| Affiliation: | 1. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore 721102, WB, India;2. Department of Mathematics, Mahishadal Raj College, Mahishadal 721628, WB, India;1. Department of Mathematics, Midnapore College, Vidyasagar University, Medinipur (W), W.B., India;2. Department of Mathematics, Indian Institute of Technology, Kharagpur, Medinipur (W), W.B., India;3. Department of Mathematics, Bhangar Mahavidyalaya, South 24-Pargana, W.B., India;1. Department of Industrial & Management Engineering, Hanyang University, Ansan, Gyeonggi-do 426 791, Republic of Korea;2. Department of Mathematics, Jadavpur University, Kolkata 700 032, India;3. Department of Industrial Engineering, Seoul National University, Seoul 151 744, Republic of Korea;1. Systems Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia;2. Institute of Production and Supply Chain Management, Department of Law and Economics, Technische Universität Darmstadt, Darmstadt, Germany |
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| Abstract: | The classical inventory control models assume that items are produced by perfectly reliable production process with a fixed set-up cost. While the reliability of the production process cannot be increased without a price, its set-up cost can be reduced with investment in flexibility improvement. In this paper, a production inventory model with flexibility and reliability (of production process) consideration is developed in an imprecise and uncertain mixed environment. The aim of this paper is to introduce demand as a fuzzy random variable in an imperfect production process. Here, the set-up cost and the reliability of the production process along with the production period are the decision variables. Due to fuzzy-randomness of the demand, expected average profit of the model is a fuzzy quantity and its graded mean integration value (GMIV) is optimized using unconstraint signomial geometric programming to determine optimal decision for the decision maker (DM). A numerical example has been considered to illustrate the model. |
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