Adaptive Sequential Optimization Decision Making Approach to Procurement, Storage and Distribution of Perishable Food Items to Fast-Food Chain Restaurants at Regional Level

Abstract

The fast-food restaurant chain industry currently relies on frozen ingredients to reduce the cost of procurement of raw-material. In recent years, consumers are shifting their habit of eating out from fast-food chain restaurants due to its prevailing perception of unhealthy menu choices and the frozen-food ingredient concept.

This thesis addresses the problem of integrating sourcing, storage, and distribution strategies for a fast-food restaurant chain at the regional level. We present an adaptive sequential optimization decision-making approach for procurement, storage and distribution of perishable food products to multi-unit restaurants at the regional level. This solution approach uses shelf-life considerations in developing a procurement and distribution strategy for raw-materials. In this thesis, three models are developed using mixed Integer Linear Programming (MILP). First, a procurement model is developed to find the cost-effective supplier for each produce category based on shelf-life. Second, a distribution model is developed to find the cost-optimal solution for distributing produce

to multiple restaurant locations considering weight, volume, and operation hours. Finally, an integrated model is developed to optimally combine procurement and distribution options generated by the first two models to minimize costs while respecting total shelf-life constraints. A numerical example is developed based on realistic data to illustrate how the three models can be used sequentially to configure the fast-food supply chain. Other examples are presented to illustrate the effect of price, shelf-life, and demand changes on the supply chain.