An integrated planning approach for perishable goods with stochastic lifespan: Production, inventory, and routing

Abstract

This paper focuses on addressing the crucial challenges posed by the uncertainty surrounding perishable goods within the supply chain. With staggering socioeconomic costs and significant environmental implications, effective management of perishable goods emerges as a critical imperative. Globally, the annual loss of approximately 14% of food, amounting to a staggering USD 1 trillion, highlights the urgency of the issue. However, Prevailing classification methods for perishable products oversimplify their complexity by dividing them into fixed or variable lifespans, neglecting the random lifespans endured due to fluctuating storage conditions, blurring traditional boundaries between fixed and variable classifications. In response to these pressing challenges, the research proposes a novel approach: the development of a nonlinear mixed-integer programming model for a green closed-loop supply chain. This innovative model seamlessly integrates production, inventory, and routing decisions for both main and secondary products of a manufacturer. Moreover, it optimizes order splitting and transportation modes to efficiently convert perished items into raw materials under conditions of uncertainty. Central to the approach is the adoption of a scenario-based methodology to model uncertainties, particularly focusing on the variability in the lifespan of perishable products. This approach allows for a more nuanced understanding of the complex dynamics inherent in managing perishable goods within the supply chain. To solve the proposed model, a novel hybrid algorithm is introduced: the Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm, or PSOSA, ensuring robust optimization under uncertainty and enhancing decision-making within the perishable goods supply chain. The research findings underscore the inadequacy of prevailing assumptions regarding the fixed lifespan of perishable products, commonly observed in the literature. By accounting for uncertainty in perishable goods’ lifespan, a more accurate representation of total producer costs is achieved, highlighting the misleading reduction of 25% observed when neglecting such uncertainty.