INTEGRATED PRODUCTION-DISTRIBUTION PLANNING OPTIMIZATION USING NEUTROSOPHIC PROGRAMMING

Consideration and management of uncertainties are critical in effective decision-making in production-distribution planning problems. Further, these decisions often involve multiple objectives which are conflicting in nature. More importantly, the decisions represented by these objective functions are often based on imprecise or uncertain data either due to unavailability or lack of objectivity of information, therefore cannot be solved by classical deterministic modeling techniques. To that end, this paper presents a neutrosophic programming-based approach to solve an integrated production-distribution planning problem in a two-echelon supply chain by considering uncertainties and indeterminateness in the data. The problem is formulated as a tri-objective mixed-integer linear programming model considering important features of production distribution planning decisions. The three objectives considered are to minimize: total cost, delivery time, and backorder level. These objectives are represented by membership functions of the neutrosophic set, i.e., truth, indeterminacy, and falsity. The efficacy of the proposed methodology is illustrated by considering problem instances inspired by a real-world case in the automotive industry. A Pareto optimality test performed on the proposed neutrosophic model shows the existence of a strong optimal solution of the proposed neutrosophic model.