We model and solve the production routing problem (PRP) with time windows, product deterioration and split delivery. A bi-objective PRP model for a single perishable product, which is subject to deterioration, is presented. The two objectives represent the economic and social aspects of sustainability, whereas the environmental impact is enforced by incorporating ad-hoc constraints. The economic dimension of sustainability consists of minimizing the costs related to the production, setup, holding, transportation and lateness penalty. The social responsibilities are modelled through maximizing the total freshness of the delivered products at all nodes over the planning horizon. The outcomes of our formulation are represented by the lot sizes, and the amounts of product to be delivered, as well as the routing at each planning period. To solve the resulting problem, we develop an interval robust possibilistic approach, and we carry out an experimental study and a sensitivity analysis. Finally, we further validate our optimization model and solution method using a real-life case of a food factory producing a product that is subject to perishability and deterioration.
Today's transportation, which is primarily based on the combustion of fossil fuels, contributes significantly to energy-related GHG emissions which is one of aspects of sustainability. Incorporating sustainable concerns in cross-dock scheduling can affect decision making for logistics systems. In the current contribution, a sustainable vehicle routing problem with cross-docking is presented. In addition to the economic considerations, this paper addresses the environmental impacts of CO2 emissions and the social impacts, including equity between drivers as well as customer satisfaction. A metaheuristic, composed of GA and mixed integer programming, is proposed as the solution approach. For validating the presented method, instances of various sizes are solved. The results of the GA in small-sized instances, has a small deviation from the optimal fitness values. Finally, a real case study is provided to demonstrate the applicability of the model in a real-world environment, and several in-depth analyses are conducted to infer managerial implications.
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