Smart multi-commodity location-routing model for perishable goods with an emphasis on big data under uncertainty and congestion

In recent decades, the integrated optimization approach of information-based logistics systems has been one of the most important aspects of optimization in supply chain management. This approach deals with the simultaneous investigation of dependencies between facility location, allocation of suppliers/customers to facilities, the structure of transportation routes, planning, and inventory control. One of the most critical issues related to logistics is location routing. Therefore, in this research, a multi-objective mathematical model for locating and routing multiple perishable goods is presented, considering the objectives of minimizing logistics costs and transportation costs, minimizing product distribution time among customers, and maximizing customer service. Among the most critical limitations considered are the capacities of suppliers, vehicles, and producers and the soft time window of product distribution. Due to the uncertainty in the number of customers' orders for product delivery in the supply chain and the large volume of big data, the queue model based on M/M/C/K was introduced in the fuzzy conditions of customer demand. Finally, the mathematical model was optimized and analyzed with MOSA and MOKA. The analysis results of two meta-heuristic algorithms in the studied problem showed that the MOSA has better efficiency.