Robust-fuzzy-probabilistic optimization for a resilient, sustainable supply chain with an inventory management approach by the seller - Fateme Zahra Montazeri; Ali Sorourkhah; Dragan Marinković; Vesko Lukovac (Big Data and Computing Visions)
The current paper deals with modeling a resilient, sustainable supply chain with an inventory management approach by the seller under the uncertainty of demand and system costs. The importance of inventory management by the seller in the sustainable supply chain has led them to consider a set of buyers and sellers whose goal is to minimize the total costs of ordering, shortage, maintenance, and use of the vehicle to make the right decisions to fulfill the orders. Due to the indeterminacy of the model parameters, the robust-fuzzy-probabilistic optimization method has been used. The calculation results with the invasive weed optimization algorithm and the Baron method show that with the increase in the uncertainty rate in the network, the amount of demand has increased. Therefore, the total ordering, maintenance, and shortage costs have increased. Also, with the increase of the stability coefficient of the model, the total cost of inventory management by the seller has increased, and a greater amount of customer demand has been estimated. On the other hand, with the increase in resilience, the amount of orders transferred to the buyer has decreased. Also, the calculation results show the high efficiency of the invasive weed optimization algorithm in solving the resilient, sustainable supply chain model with the seller's inventory management approach.