An adaptive large neighbourhood search for the operational integrated production and distribution problem of perishable products

TLDR

Production and distribution of perishable goods requires integrated sizing, scheduling, and vehicle routing, a joint decision that becomes difficult for large product portfolios. The study aims to improve company competitiveness by developing an integrated supply‑chain planning approach for perishable products. An adaptive large‑neighbourhood search framework based on mixed‑integer linear programming models and tools is proposed to solve the integrated problem. The ALNS consistently outperforms exact methods and fix‑and‑optimize, achieving better solutions in nine of ten runs and improving solution quality by an average of 12.7%.

Abstract

Production and distribution problems with perishable goods are common in many industries. For the sake of the competitiveness of the companies, the supply chain planning of products with restricted lifespan should be addressed with an integrated approach. Particularly, at the operational level, the sizing and scheduling of production lots have to be decided together with vehicle routing decisions to satisfy the customers. However, such joint decisions make the problems hard to solve for industries with a large product portfolio. This paper proposes an adaptive large neighbourhood search (ALNS) framework to tackle the problem. This metaheuristic is well known to be effective for vehicle routing problems. The proposed approach relies on mixed-integer linear programming models and tools. The ALNS outperforms traditional procedures of the literature, namely, exact methods and fix-and-optimize, in terms of quality of the solution and computational time of the algorithms. Nine in ten runs of ALNS yielded better solutions than traditional procedures, outperforming on average 12.7% over the best solutions provided by the latter methods.