Stochastic optimization approach for green routing and planning in perishable food production

Perishable food products present a production-inventory routing dilemma critical to logistics planning for several industries. Due to the speedy deterioration of perishable food products, proper production, inventory, and shipping planning are essential. Meanwhile, the growing impact of greenhouse emissions due to fuel usage indicates that mitigation for these impacts must be factored into the routing problem. As a result, the perishable food product industry's logistics planning must include production-inventory-routing coordination with carbon footprint considerations. This paper aimed to create an integrated production-inventory-routing problem for perishable food products that took into account capacity, time windows, and carbon emissions reduction. The inventory routing problems are NP-hard in nature. Therefore, the proposed problems were solved using two nature-inspired algorithms: flower pollination algorithm (FPA) and cuckoo search algorithm (CSA). The effect of model parameters on the response value was investigated using sensitivity analysis, and the computational experiment discussed the comparative results obtained from both algorithms for different case scenarios. The outcomes of the two algorithms were evaluated for ten instances, and the CSA consistently outperformed the FPA. By incorporating uncertain demands via a random variable with a specified probability distribution, the proposed framework generates compelling opportunities. Individual logistics units may use the proposed framework to mitigate the stochastic nature of problems in uncertain circumstances.