Based on our work with ConAgra Foods (http://www.conagrafoods.com), a leading U.S. food manufacturer, we study a large-scale production-planning problem. The problem incorporates several distinguishing characteristics of production in the processed-food industry, including (i) production patterns that define specific combinations of weeks in which products can be produced, (ii) food groups that classify products based on the allergens they contain, (iii) sequence-dependent setup times, and (iv) manufacture of a large number of products (typically, around 200-250) on multiple production lines (typically, around 15-20) in the presence of significant inventory holding costs and production setup costs. The objective is to obtain a minimum-cost four-week cyclic schedule to resolve three basic decisions: (a) the assignment of products to each line, (b) the partitioning of the demand of each product over the lines to which it is assigned, and (c) the sequence of production on each line. We show that the general problem is strongly NP-hard. To develop intuition via theoretical analysis, we first obtain a polynomially solvable special case by sacrificing as little of its structure as possible and then analyzing the impact of imposing production patterns. A mixed-integer programming model of the general problem allows us to assess the average impact of production patterns and production capacities on the cost of an optimal schedule. Next, to solve practical instances of the problem, we develop an easy-to-implement heuristic. We first demonstrate the effectiveness of the heuristic on a comprehensive test bed of instances; the average percentage gap of the heuristic solution from the optimum is about 3%.Then, we show savings of about 28% on a real-world instance (283 products, 17 production lines) by comparing the schedule obtained from the heuristic to one that was in use (at ConAgra) based on an earlier consultant's work. Finally, we discuss the IT infrastructure implemented to enable the incorporation of optimized (or near-optimized) solutions for ongoing use.
- Food industry
- Intege programming algorithms
- Production planning