Energy-efficient optimization of multi-echelon inventory systems

Efficient energy management in the cold supply chain is crucial for reducing costs and environmental impact. This study presents an integrated distribution inventory system that focuses on energy considerations throughout the supply chain. The model incorporates energy usage from production, warehousing, and transportation processes into the average total cost of the system, providing a comprehensive analysis of energy cost components. By considering various factors such as production rate, ordering policy, warehouse filling level, and truck types, the model offers insights into the energy efficiency of the system. The model is formulated as a mixed-integer nonlinear programming (MINLP) problem. To solve this problem, a heuristic algorithm is proposed, aiming to optimize the total cost, including energy costs, while providing near-optimal decision variables. A study based on a real-world company serves as a practical illustration of the model’s effectiveness. A comparison between the integrated inventory system and a non-inventory system reveals significant reductions in energy consumption for warehousing (23.85%) and the overall system costs (2.74%). After testing four groups of datasets, the proposed heuristic algorithm outperforms the LINGO solver in terms of cost minimization (for the first two groups) and computational time, validating its efficiency. Sensitivity analyses are performed to assess the impact of key parameters such as energy unit costs, distance, transportation speed, and demand on energy costs and system performance. These analyses provide valuable insights for decision-makers, supporting informed decision-making and the identification of practical strategies for optimizing energy usage.