Fresh produce supply chain network design and management using swarm intelligence: A case study of Egypt

Abstract

Purpose: The objective of this work is to fulfil a strategic requirement in Egypt’s agriculture industry by establishing a fresh produce supply chain network (SCN) that manages the collection, processing, packaging, and distribution of products. Design/methodology/approach: A two-phase approach is proposed. In the first, a network of food aggregation hubs is strategically located across the country for the collection, consolidation, and distribution of products. This is accomplished by modeling and solving a cost minimization dynamic facility location-allocation (FLA) problem using a hybrid binary particle swarm optimization (BPSO) algorithm. The second phase of the approach is to complement the hub FLA decision with optimal fleet size, transportation schedules, and routing decisions. This is achieved by solving the split-delivery vehicle routing problem (SDVRP) using a hybrid ant-colony optimization (ACO) algorithm, considering positioning loading constraints, and shelf-lives of products. Findings: There is a strong correlation between the geographical locations and capacities of the established hubs, and the proximity of supply points and the populations in the demand areas. In addition, accounting for spoilage of products has a significant effect on network design, and collection and distribution decisions. Practical implications: Establishment of the intended SCN can reduce the proportion of wasted product during transit, and improve the quality of the delivered product. Social implications: Establishment of the SCN will increase the exposure of small farmers to wider markets, and hence their return and standard of living, and potentially reduce the prices for the final customer. Originality/value: This study is the first attempt to establish an efficient fresh produce supply chain network in Egypt. In addition, the proposed solution approach considered a multitude of problem characteristics, simultaneously for the first time.

Keywords

• supply chain network design, aggregation hubs, split-delivery vehicle routing, particle swarm optimization, ant colony optimization