The mobile supply chain (MSC) is a new development that aims to help companies implement these ideas. In MSCs, production, distribution, and delivery of a product family is performed by a mobile factory (MF), which can be carried by truck and shared among different production sites. The MSC offers an alternative by providing flexibility to produce goods where and when needed, reducing inventory costs, and enhancing resource utilization. Additionally, its applications in critical sectors, including healthcare, disaster relief, and just-in-time manufacturing, highlight their potential to make a significant societal impact. The research investigates the capabilities of MSCs, with a particular emphasis on understanding the complex dynamics among key stakeholders within this type of supply chain. The main stakeholders in this supply chain model include the Mobile Factory Service Provider (MFSP), manufacturing sites, and the final customers who may have conflicting objectives. Finding a balanced solution that optimizes the performance of the entire supply chain is at the core of this research.:Table of content Table of content 2 List of Tables 5 List of Figures 6 List of abbreviations 7 List of Symbols 9 List of publications 13 Contribution to the publications 14 1. Introduction 18 1.1 Motivation 18 1.2 Research Background 21 1.2.1 Mobile factory Concept 21 1.2.2 Vehicle routing problem variants 22 1.2.3 Decentralization methods in supply chains 23 1.3 Research questions 24 1.4 Structure of this work 25 2. A hybrid robust-stochastic optimization approach for the noise pollution routing problem with a heterogeneous vehicle fleet 28 2.1 Introduction 29 2.2 Literature review 30 2.3 Model formulation 31 2.3.1 Hybrid Robust-Stochastic optimization methodology 31 2.4 Computational Experiments 35 2.5 Conclusions 37 3. Joint Optimization of Production and Routing Master Planning in Mobile Supply Chains 38 3.1 Introduction 39 3.2 Literature review 41 3.3 Problem definition and the proposed model 42 3.3.1 Assumptions 45 3.3.2 Mathematical formulation 45 3.4 Experimental results 49 3.4.1 Experimental design 49 3.4.2 Computational Results 50 3.4.3 Managerial insights 54 3.5 Conclusions 54 4. Coordinated Allocation Production Routing Problem for Mobile Supply Chains with Shared Factories 56 4.1 Introduction 57 4.2 Literature review 59 4.3 Problem definition and the proposed model 61 4.3.1 Mathematical formulation 62 4.3.2 Mathematical Formulation 64 4.3.3 Linearization 66 4.4 Solution Methodology 67 4.4.1 Solution representation 67 4.4.2 Neighborhood generation mechanism 68 4.4.3 Solution parsing heuristic (SPH) 68 4.4.4 Handling constraint violations 70 4.4.5 Simulated annealing algorithm 70 4.4.6 Evolutionary algorithm 72 4.5 Experimental Results 75 4.5.1 Data generation 75 4.5.2 parameter setting of the metaheuristic algorithm 75 4.5.3 Model implementation and initial observation 76 4.5.4 Performance evaluation of the proposed solution algorithms 79 4.5.5 Analysis on impact of the delay cost rate 81 4.5.6 Analysis on impact of assignment cost rate 82 4.6 Conclusions 83 5. A robust decentralized decision-making approach for mobile supply chains under uncertainty 84 5.1 Introduction 85 5.2 Literature review 86 5.3 Problem statement and theoretical background 88 5.3.1 ATC 89 5.3.2 Scenario-based robust optimization method 90 5.4 Mathematical formulation 91 5.4.1 Robust decentralized decision-making approach (RDDMA) 92 5.5 Experimental evaluation 97 5.6 Conclusions 102 6. Conclusions 103 6.1 Summary 103 6.2 Future research 105 A. Appendix A: A decentralized decision-making approach for production routing problem in mobile supply chains with shared factories (Research report) 107 Abstract 107 A.1 Hierarchical problem structure 108 A.1.1 Problem definition 108 A.1.2 Analytical target cascading (ATC) 109 A.2 Mathematical Formulation 111 A.2.1 Centralized decision-making mathematical model (CDMM) 113 A.2.2 Decentralized decision-making approach (DDMA) 114 A.3 Experimental Evaluation 118 A.3.2 Comparing centralized vs. decentralized approaches 123 Acknowledgements 125 References 126