Integrated production planning and warehouse storage assignment problem: An IoT assisted case

Abstract

This study is motivated by a real-world problem in a food company, where production planning is restricted by the available warehouse space for the finished goods. A novel integrated strategy that combines production planning with a randomized storage assignment policy is presented. The strategy takes advantage of greater visibility and traceability of items provided by IoT-enabled tracking systems in order to increase space utilization. An integer linear programming model is developed to formulate the strategy to minimize the total cost of production and warehouse operations. Our model is the first dynamic model for a randomized storage assignment policy. The model's feasibility, complexity, and its lower bound are presented. A heuristic algorithm is developed to obtain the near-optimal solution for the large-scale real-world problem. Based on numerical experiments, comparisons between our solutions and the solutions to model with a dedicated storage policy are also presented. The results show that the integrated strategy with a randomized storage policy can significantly reduce the total cost (up to 16.84% with an average of 9.95%) and increase space utilization (up to 26.1% with an average of 14.8%), compared to the strategy with a dedicated policy. Such results provide evidence that may justify the cost of applying the new technologies, such as IoT-enabled tracking systems, in warehouse management.