Robust Fuzzy chance constraint programming for multi-item EOQ model with random disruption and partial backordering under uncertainty

• Published in: Journal of industrial and production engineering Vol. 36; no. 5; pp. 276 - 285
• Main Authors: Khalilpourazari, Soheyl, Teimoori, Shima, Mirzazadeh, Abolfazl, Pasandideh, Seyed Hamid Reza, Ghanbar Tehrani, Nasim
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 04.07.2019; Taylor & Francis Ltd
• Subjects: BASIC (programming language); chance constraint programming; Constraints; defective supply batches; Disruption; Economic models; Economic order quantity; Inventory management; Mathematical programming; Parameter uncertainty; partial backordering; Robust Fuzzy chance constraint programming; Robustness (mathematics); Sensitivity analysis
• ISSN: 2168-1015; 2168-1023
• DOI: 10.1080/21681015.2019.1646328

In this paper, a new mathematical model for multi-item Economic Order Quantity model is proposed considering defective supply batches and partial backordering. The proposed model assumes that the supply batches may be defective and can be rejected. The aim is to minimize total inventory costs by determining optimal values of the decision variables including Time interval between successive perfect supply deliveries. Since in real-world situations the main parameters are uncertain, two mathematical programming approaches called: Basic Chance Constraint Programming and Robust Fuzzy Chance Constraint programming are used to handle uncertain parameters. The performance of the mathematical programming models is investigated in a numerical example. The results show that the RFCCP model is able to provide risk-averse solutions comparing to the BCCP model. In the end, sensitivity analyses are performed to determine the effect of any change in the main parameters on objective function value to determine the most critical parameters