A production inventory supply chain model with partial backordering and disruption under triangular linguistic dense fuzzy lock set approach

• Published in: Soft computing (Berlin, Germany) Vol. 24; no. 7; pp. 5053 - 5069
• Main Authors: De, Sujit Kumar, Mahata, Gour Chandra
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Case studies; Computational Intelligence; Control; Cost function; Decision making; Decision theory; Disruption; Engineering; Fuzzy sets; Inventory; Linguistics; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Order quantity; Random variables; Raw materials; Robotics; Social networks; Supply chains; Supply chain; Partial backlogging; Linguistic triangular dense fuzzy lock set; Disruption; Production inventory; Optimization
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-019-04254-2

This paper focuses on a three-level distribution process in a supply chain (SC) modeling where the raw materials are received batchwise with imperfect quality. Defective batches are rejected instantly under “all or none” policy. Allowing partial backlogging and random disruption in supply, we develop an expected average cost function of the production inventory SC model first. Then, considering the several cost components of the model as linguistic triangular dense fuzzy lock set, the cost function itself has been fuzzified according to the needs of the problem defined at case study. Utilizing the proper application (growth) of key vectors, the objective function has been solved under crisp, general fuzzy, dense fuzzy, dense fuzzy lock of single and double keys environment, respectively. For managerial importance, numerical results and graphical illustrations are made to justify the novelty.