Multi-modal transport of perishable products with demand uncertainty and empty repositioning: A scenario-based rolling horizon framework

Abstract

We study the planning of a multi-modal transportation system with perishable products, demand uncertainty and repositioning of the empty Returnable Transport Items (RTIs). We propose a rolling horizon framework where we periodically re-optimize. As such, relevant responses and actions to new occurred demand are taken, and possible updates to the transportation and repositioning plans can be made. Our rolling horizon framework considers the uncertainty of customer demand, formulated as a Scenario-based Two-Stage Program (STSP) for which a set of scenarios is generated. An Adaptive Large Neighborhood Search (ALNS) algorithm is used to solve this scenario-based problem. Our proposed ALNS algorithm employs new operators and strategies to solve this complex and large problem. We give detailed computational analysis on the properties of our framework, evaluating the effects of stochastic demand, and we provide practical insights.