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Adapa FINAL 4 25 25 with cert.pdf (2.27 MB)

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Supply Chain Reallocation Problem in the Automotive Industry: A Mixed-Integer Linear Programming Approach

Adapa, Sairam Bhaskar Sri Harsha
http://rave.ohiolink.edu/etdc/view?acc_num=ysu1745592080700631

Abstract Details

2025, Master of Science in Engineering, Youngstown State University, Department of Mechanical, Industrial and Manufacturing Engineering.
In the highly structured and cost-sensitive environment of automotive manufacturing, the efficiency of delivery scheduling plays a critical role in determining overall supply chain performance. Traditional shipment planning methods, often static and heuristic in nature, fail to exploit available flexibility in delivery windows—leading to frequent inefficiencies such as underutilized truck capacity, excessive setup frequency, and fragmented shipments. This thesis addresses these operational shortcomings through the development of a progressive suite of deterministic optimization models designed to reallocate deliveries across fixed time slots. Three mixed integer linear programming (MILP) models are proposed, each adding successive layers of real-world complexity. The basic model introduces a time-windowed reallocation framework aimed at balancing delivery volumes and reducing setup and holding costs. The enhanced model builds upon this by incorporating truck capacity constraints and underutilization penalties to simulate more realistic logistics scenarios. Finally, the supplier-integrated model introduces supplier selection logic, binary activation decisions, and inter-supplier constraints, offering a more holistic view of cost and operational feasibility in multi-source environments. All models are implemented using Python and the PuLP optimization library and validated using synthetic data reflective of real-world automotive delivery patterns. Results demonstrate that reallocation, even within a deterministic and disruption-free environment, can yield substantial logistics cost reductions in some cases while improving resource utilization and scheduling efficiency. The models serve not only as theoretical constructions but also as practical decision-support tools that can be embedded within existing enterprise planning systems. By systematically restructuring delivery plans before execution, this research bridges the gap between strategic supply chain theory and actionable, mid-horizon logistics planning.
Seokgi Lee, PhD (Advisor)
Bharat Yelamanchi, PhD (Committee Member)
Zefeng Lyu, PhD (Committee Member)
76 p.
Automotive Engineering; Industrial Engineering; Operations Research
Supply Chain Optimization; Mixed Integer Linear Programming; MILP; Automotive Logistics; Delivery Reallocation Modeling

Recommended Citations

Citations

  • Adapa, S. B. S. H. (2025). Supply Chain Reallocation Problem in the Automotive Industry: A Mixed-Integer Linear Programming Approach [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1745592080700631

    APA Style (7th edition)

  • Adapa, Sairam Bhaskar Sri Harsha. Supply Chain Reallocation Problem in the Automotive Industry: A Mixed-Integer Linear Programming Approach. 2025. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu1745592080700631.

    MLA Style (8th edition)

  • Adapa, Sairam Bhaskar Sri Harsha. "Supply Chain Reallocation Problem in the Automotive Industry: A Mixed-Integer Linear Programming Approach." Master's thesis, Youngstown State University, 2025. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1745592080700631

    Chicago Manual of Style (17th edition)

ysu1745592080700631
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This open access ETD is published by Youngstown State University and OhioLINK.
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