A new model for production and distribution planning based on data envelopment analysis with respect to traffic congestion, Blockchain technology and uncertain conditions

The production and distribution planning problem, where incomplete information from the manufacturer factories is available to the distributor, is prevalent in the real world. Yet, researchers have not focused sufficiently on this field of research. Therefore, our paper offers a two-level multi-objective optimization of production and distribution planning for the two-stage supply chain based on the Data Envelopment Analysis method, which minimizes both the cost and the traffic congestion caused by the establishment of distributor warehouses. In addition, transparency through the blockchain technology and uncertain conditions through stochastic and fuzzy programming are considered in the supply chain design. The proposed model is first transformed by using Karush–Kuhn–Tucker into single-level linear programming and then solved by fuzzy goal programming. Next, the obtained solutions are measured and sorted in terms of efficiency. In this regard, the solutions are categorized in terms of cost-based strategies, traffic congestion, demand satisfaction, and optimality. For the purpose of enabling comprehensive supply chain management, the solutions are aggregated according to various strategies and the results show that two solutions are the most efficient, one focusing on cost and the other focusing on traffic congestion.