Information Economics Question

Project Overview:I am looking for an academic expert in Operations Research, Supply Chain Management, and Data Science to write a 12-page seminar paper. The paper must explore the use of Mixed-Integer Linear Programming (MILP) to optimize the location of energy infrastructure (e.g., EV charging hubs) and how Large Language Models (LLMs) can assist in translating business logic into Python code.

Academic Level: University / Master’s degreeLanguage: English (Academic, flawless grammar)Length: Approx. 12 pages (excluding title page, bibliography, and appendices)Formatting: Standard academic format (APA or Harvard), proper citations required.

Required Deliverables:

1. Research Highlights & Outline: 5 bullet points summarizing the core hypotheses and a clear table of contents.
2. Main Seminar Paper (12 pages): Following the provided structure (see below).
3. Abstracts: One short abstracts (max. 1750 characters each) in English
4. Python Script: A fully functional, well-commented Python script using the pulp library that solves the case study described in the paper. (Note: I have a working draft of the code that can be provided as a baseline).

Topic Focus & Specific Requirements:

• Background: The paper should be written from a Business/IT perspective. Please avoid overly complex stochastic or biological algorithms. The focus must remain on clear, deterministic decision-making (Fixed vs. Variable Costs).
• The Mathematical Model: The core of the paper is based on the Capacitated Facility Location Problem (CFLP).
• The “AI” Component: The paper must discuss how LLMs (like ChatGPT) were used to bridge the gap between theoretical business constraints and executable Python code, emphasizing that the code is deterministic, “hardcoded” (no external CSV dependencies), and permanently functional.
• Literature: The theoretical foundation must reference standard logistics and facility management concepts (e.g., distinguishing between qualitative and quantitative location factors, discrete location planning).

Mandatory Paper Outline (To be followed strictly):

1. Introduction

• 1.1 Problem Statement: Energy infrastructure as a strategic success factor.
• 1.2 Objective and structure of the paper.

2. Theoretical Foundations of Location Planning

• 2.1 Strategic facility management and relevant location factors (quantitative vs. qualitative).
• 2.2 Introduction to discrete location planning within logistics networks (specifically CFLP).
• 2.3 Basics of Mixed-Integer Linear Programming (MILP).

3. Model Development for Energy Infrastructure

• 3.1 Adapting logistics models to EV charging infrastructure.
• 3.2 Mathematical formulation of the objective function (cost minimization).
• 3.3 Definition of decision variables (binary and continuous) and constraints (capacity, demand).

4. AI-Assisted Implementation and Case Study

• 4.1 Methodology: Generating the optimization code via LLM prompting.
• 4.2 Presentation of the case study (5 demand zones, 3 potential facility locations with varying fixed costs and capacities).
• 4.3 Solving the model and business interpretation of the algorithmic decisions (e.g., avoiding over-engineering).

5. Critical Reflection

• 5.1 Evaluation of the model’s quality and the transferability of logistics theory.
• 5.2 Opportunities and limitations of using AI for implementing Operations Research models.

6. Conclusion and Outlook

• Summary of core findings and future research potential.

Case Study Data (To be used in Chapter 4):

The writer must use this specific dataset for the Python implementation and the interpretation:

• Demand Zones: Zone 1 (150 units), Zone 2 (100 units), Zone 3 (80 units), Zone 4 (120 units), Zone 5 (50 units). Total: 500 units.
• Potential Locations:
  • Location A: Capacity = 500, Fixed Cost = 100,000
  • Location B: Capacity = 300, Fixed Cost = 55,000
  • Location C: Capacity = 250, Fixed Cost = 40,000
• Variable Transport Costs (per unit):
  • Zone 1 to [A: 2, B: 5, C: 8]
  • Zone 2 to [A: 4, B: 2, C: 7]
  • Zone 3 to [A: 3, B: 6, C: 2]
  • Zone 4 to [A: 5, B: 3, C: 4]
  • Zone 5 to [A: 6, B: 7, C: 3]

(The optimal solution proves that building B + C is cheaper than building the single “Super-Hub” A, despite differing transport costs. This trade-off must be discussed in Chapter 4.3).

“Note: The original assignment suggests German textbooks (Lasch 2020; Mattfeld & Vahrenkamp 2014 [chapters are attached in german language]). Since this paper is in English, please use equivalent international standard literature for Operations Research and Supply Chain Network Design (e.g., Chopra/Meindl for strategic location factors, and Daskin or Hillier/Lieberman for discrete location modeling and MILP).”

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