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.

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.

Lean manufacturing is a strategic concern for companies which conduct mass production and it has become even more significant for those producing in a project-oriented way by modularization. In this paper, a bi-objective optimization model is proposed to design and plan a supply chain up to the final assembly centre. The delivery time and the quality in the procurement and low fluctuation of the production are the most important lean production principles that are considered. Because of the long-horizon planning and the subjective data gathered, it is necessary to handle uncertainty. Therefore, a robust credibility-based fuzzy programming (RCFP) approach is proposed to perform the robust optimization and to obtain the crisp equivalent of an MILP model using the chance constraint programming method in terms of simultaneous credibility measurement. A real industrial case study is provided to present the usefulness and applicability of the proposed model and programming approach.

In this paper, the connection between Menger probabilistic norms and Hohle probabilistic norms is discussed. In addition, the correspondence between probabilistic norms and Wu-Fang fuzzy (semi-) norms is established. It is shown that a probabilistic norm (with triangular norm min) can generate a Wu-Fang fuzzy semi-norm and conversely, a Wu-Fang fuzzy norm can generate a probabilistic norm.