A robust decentralized decision-making approach for mobile supply chains under uncertainty

The mobile supply chain (MSC) is a new concept that allows companies more adaptability and flexibility. In MSCs, a product family can be produced, distributed, and delivered by a mobile factory, carried by trucks, and shared among different customers. In this paper, to optimize production scheduling and the mobile factory routing problem under uncertainty, a robust decentralized decision-making approach (RDDMA) based on the Analytical Target Cascading (ATC) approach is developed. The RDDMA is a bi-level hierarchical optimization method that divides an all-in-one model into sub-problems and aims to address each agent's target. It is a 4-phase procedure, including time window determination, robust mobile factory routing, actual production scheduling, and adjustment. In real-world applications, the service time at each site is uncertain. Therefore, a scenario-based robust optimization approach is utilized to manage the uncertainties of the problem. Finally, the RDDMA performance is evaluated using several instances. The results suggest the proposed approach can provide robust solutions for such a multi-agent problem.