Reactive scheduling of a distributed network for the supply of perishable products

This paper considers the problem of coordinating the production and distribution activities of a network of independent supply centers. In particular, we focus on the supply of rapidly perishable goods (ready-mixed concrete) that must be delivered to customers in strict time windows. The problem presents three main challenges. First, it includes several interrelated scheduling and routing problems, each affected by nearly prohibitive combinatorial complexity. Second, due to the perishable nature of the supplied products, effective solutions must not only optimize the objective function related to resource utilization and cost minimization, but also tolerate the small and frequent stochastic perturbations (transport delays) of the operating environment. Third, if major perturbations occur, the decision strategy must be able to respond in real time with effective rescheduling interventions restoring the indispensable synchronization of activities in progress, and avoiding extremely undesirable circumstances related to product decay. After providing a detailed mathematical model of the considered problem, this paper proposes a hybrid metaheuristic approach integrating a genetic algorithm with a number of problem-specific constructive heuristics. The effectiveness of the approach is evaluated against other scheduling heuristics on an industrial case study. Note to Practitioners-This research work focuses on a challenging rescheduling problem related to the production and distribution of rapidly perishable goods. The approach presented in the paper aims at achieving a compromise between four fundamental issues (quality of solution, search time, robustness to perturbations, and transparency of the decision algorithm) that cannot be easily obtained with general-purpose solvers proposed to practitioners. The presented algorithm integrates three tools (a mathematical programming model, a genetic algorithm, and a set of constructive heuristics) in a global metaheuristic rescheduling algorithm. The approach does not provide guarantees about the closeness to optimality of the solutions, but gives interesting results on numerical experiments based on two large-scale industrial case studies. Although the presented rescheduling method is tailored for a specific just-in-time supply problem, it may be profitably extended to a number of similar production and distribution problems by redefining the model constraints and objectives, and adapting the reconstructive heuristics accordingly.