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.

One of the most common and successful approaches for the integrated supply chain management (SCM) is the vendor-managed inventory (VMI). In VMI, a vendor takes control of the inventory decisions for retailers. To establish a long-run relationship between the vendor and the retailer, it is necessary to consider two impactful factors: the vendors and retailers' reliability, and the optimal selection of retailers. For this purpose, the redundancy allocation problem (RAP), as an effective technique for increasing the reliability of vendors, is used in this paper. Also, the reliability of retailers as well as the reliability of the relationship between retailers and vendors are considered. In the retailer selection, process decisions on impactful criteria are simultaneously considered. For the retailers' selection, one analysis hierarchical process (AHP) is performed for each vendor, and the weights of retailers are obtained. Then, the obtained weights are plugged into the model as the inputs of the designed model. Since the developed model is a non-differentiable, non-convex, and mixed-integer function, 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 with data collected from the electronic 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.