In this study, an attempt is made to examine the planning for supply, production, and distribution with an integrated approach to reduce waste and returned products in such a way that the supplier and manufacturer exchange information in supply chain. In this paper, manufacturer share information with suppliers through IoT and blockchain. For best implementation of supply chain based on these technologies, a manufacturer must chose the optimal combination of suppliers that can better meet the criteria of technologies implementation. In this paper, IoT and blockchain are examined to supply chain management by considering the impactful criteria of implementation in suppliers' selection and optimization. For this purpose, this paper applies the multi criteria decision making (MCDM) methods for prioritizing the suppliers. Then, the obtained weights are plugged into the model as the inputs of the designed model. In this study chance constraint programming (CCP) is leveraged to develop optimization models that take the uncertainties and randomness into account. Since the developed model is a non-differentiable and non-convex, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the formulated model. Finally, the efficiency of the presented method is verified through a case study from the food supply chain.

In today's competitive world, the quality of raw material plays a significant role in the success of organizations. Since the quality of raw materials is a factor that leads to a reduction in return products, the selection of raw material supplier's problem has always been daunting challenges in supply chain. The supplier's selection problem is a multi-criteria group decision-making (MCGDM) that the preferences over criteria, and suppliers are highly dependent on the opinions of experts. In the real world, decision-makers cannot express their opinions with certainty. In this study, a novel hesitant fuzzy linguistic Term set Vlsekriterijumska Optimizacija I Kompromisno Resenje (HFLTs-VIKOR) approach is proposed for solving supplier selection problems with hesitant fuzzy linguistic set information. In this regard, a new context-free grammar has been proposed. In the present study, the quality of raw materials due to their impact on environmental issues and the amount of product waste has been investigated using the proposed method. Then, using a designed model, an appropriate ordering policy is identified to minimize costs and maximize the value of suppliers at supply chain, production, and distribution levels. Finally, the efficiency of the presented method is verified through a case study with real data.

This paper has addressed the new model for the hub location-allocation problem that can evaluate the impact of cost and time of tardiness in delivering goods to the destination, find the optimal number of hubs and vehicles with different capacities, and congestion in hubs. In addition, this study investigates the problem from two perspectives: scheduled time and real-time. Also, it compares the problem in circumstances with tardiness and solves the problem with two objective functions by minimizing the total costs, handling time, and delay time caused by congestion in the hubs. Moreover, the uncertainty of transportation costs and hub establishment are considered. For this purpose, the Monte-Carlo simulation is used. Due to the models being non-convex and non-derivative, they are NP-hard. So, the models are solved with the particle swarm optimization (PSO) algorithm. The models are tested on Australian Post (AP) data for single and multiple allocations. Also, we presented a model that can illustrate the effect of tardiness on the results and service time. The results show multiple allocation than single ones needs the smaller hubs and cheaper vehicles for transportation due to access the nodes to several hubs.