Abstract Distributed manufacturing is gaining traction in various industries with fast growth of sensor, IoT and advanced manufacturing technologies. It is a promising new business paradigm to achieve mass customization and facilitate the shared and circular economy. The goal of this proposed project is to develop a new optimization approach for the strategic design of distributed manufacturing system (DMS) in terms of facility locations and dynamic sharing of manufacturing resources to meet time-varying demand, while considering autonomous/distributed planning decisions of supply-production networks (SPNs) in the DMS. The main challenge and technical advancement of the project is the design and implementation of a decision-game-theoretic model to capture the autonomous decision-making feature of each local SPN and to satisfy time-varying customer demand in a dynamic way. Advanced computational algorithms will also be developed to obtain quality solutions efficiently. This project aligns with DOT’s strategic goals of economic strength and global competitiveness, safety and equity, and supports MATS-TSE’s themes on resilient supply chains and transportation systems of the future.
Description "Task 1: Conduct Literature review on distributed manufacturing.
Task 2: Build the decision-game-theoretic model.
Task 2.1: Modeling the leader’s problem
Task 2.2: Modeling the follower’s problem
Task 2.3: Develop the bilevel programming model
Task 3: Develop and implement solution methods.
Task 3.1: Exact method using Kuhn-Tucker condition
Task 3.2: Heuristic or metaheuristic algorithms
Task 4: Conduct case study on a real world distributed manufacturing application, e.g., in generic drug manufacturing or food/agriculture.
Task 5: Conduct computational experiment to examine the performance of the model and the algorithms when the problem input data varies. We also plan to compare the performance of an optimized DMS with that of a tradition manufacturing system.
Task 6: Write the project final report and a research article for journal publication."
Objective "Our optimization model is expected to be a first one that captures the main characteristics of distributed manufacturing. The solution methods are expected to obtain optimal or near optimal solutions efficiently. We also expect to show benefit of the optimal DMS compared to the traditional manufacturing system. This project has the following deliverables:
Deliverable 1: A research article for journal publication. Targeted journals include Transportation Research Part C: Emerging Technologies, Computers & OR, International Journal of Production Economics
Deliverable 2: Two presentations at national conferences, e.g., INFORMS, DSI, CSCMP, etc."
Impacts/Benefits This project has the following broader impacts. St. Louis recently established its Advanced Manufacturing Innovation Center (AMIC, https://www.amicstl.org/). The technology developed in this project will not only benefit companies in the AMIC, it will also facilitate the workforce development of the greater St. Louis region. In the long run, it is our plan to develop an advanced manufacturing curriculum which contains DMS design as a main module.