Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297192 
Year of Publication: 
2021
Citation: 
[Journal:] Logistics Research [ISSN:] 1865-0368 [Volume:] 14 [Issue:] 1 [Article No.:] 6 [Year:] 2021 [Pages:] 1-15
Publisher: 
Bundesvereinigung Logistik (BVL), Bremen
Abstract: 
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.
Subjects: 
Decentralized decision-making
Analytical Target Cascading
Robust optimization
Mobile supply chains
Shared factory
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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