Optimizing the tactical planning in the Fast Moving Consumer Goods industry considering shelf-life restrictions

• Developed two computationally efficient methods to consider shelf-life limitations.
• Hybrid method: more efficient than common methods and obtains near optimal solution.
• Indirect method: very efficient and yields solution within few percent of optimum.
• Indirect method with previously developed algorithm can solve cases up to 1000 SKUs.

This paper addresses the optimization of the tactical planning for the Fast Moving Consumer Goods industry using an MILP model. To prevent unnecessary waste and missed sales, shelf-life restrictions are introduced using three methods. The direct method tracks the age of products directly. While it provides optimal solutions, it is computationally inefficient. The indirect method forces products to leave inventory before the end of their shelf-life. It obtains solutions within a few percent of optimality. Moreover, compared to the direct method, the computational time was on average reduced by a factor 32. The hybrid method models the shelf-life directly in the first and indirectly in the second storage stage. It obtains near optimal solutions and, on average, reduces the required computational time by a factor 5 compared to the direct method. Cases containing up to 25, 100, and 1000 SKUs were optimized using the direct, hybrid and indirect method respectively.