R MILP/LP Electricity Cost Report -- 2
Budget: ₹600 – ₹1,500 INR
My college assignment asks for a complete optimisation study on an electricity-generation portfolio. The core task is to formulate a Mixed-Integer Linear Programming model that minimises production costs, then create its LP relaxation to analyse the bound difference. Everything must be implemented in R and run with both HiGHS and GLPK solvers.
You will receive a clean CSV file containing plant capacities, fuel costs, demand by time period and emission caps. Please import it directly; no data wrangling beyond basic checks is needed. The focus is to:
• write transparent, well-commented R code (ompr, ROI.plugin.highs / ROI.plugin.glpk or any equivalent interface) that builds, solves and exports results for both MILP and LP cases;
• capture solver statistics—objective value, run-time, integrality gap, node counts—and benchmark HiGHS against GLPK;
• visualise key outputs (merit-order dispatch, cost breakdown, dual bounds) in ggplot2;
• interpret findings in a concise, academically-styled report (≈10–12 pages) that covers model formulation, relaxation theory, data description, computational results and a short conclusion.
Deliverables
1. R scripts (or RMarkdown notebook) that run end-to-end on my dataset.
2. PDF report ready for submission, including figures and tables generated from the code.
3. A brief README with setup instructions and package versions to ensure reproducibility.
Acceptance criteria: code executes without error on R ≥4.2; both solvers return feasible solutions; report clearly compares MILP vs LP bounds and HiGHS vs GLPK performance.
Timeline is flexible within the next week, but earlier is appreciated so I can review before submission.
You will receive a clean CSV file containing plant capacities, fuel costs, demand by time period and emission caps. Please import it directly; no data wrangling beyond basic checks is needed. The focus is to:
• write transparent, well-commented R code (ompr, ROI.plugin.highs / ROI.plugin.glpk or any equivalent interface) that builds, solves and exports results for both MILP and LP cases;
• capture solver statistics—objective value, run-time, integrality gap, node counts—and benchmark HiGHS against GLPK;
• visualise key outputs (merit-order dispatch, cost breakdown, dual bounds) in ggplot2;
• interpret findings in a concise, academically-styled report (≈10–12 pages) that covers model formulation, relaxation theory, data description, computational results and a short conclusion.
Deliverables
1. R scripts (or RMarkdown notebook) that run end-to-end on my dataset.
2. PDF report ready for submission, including figures and tables generated from the code.
3. A brief README with setup instructions and package versions to ensure reproducibility.
Acceptance criteria: code executes without error on R ≥4.2; both solvers return feasible solutions; report clearly compares MILP vs LP bounds and HiGHS vs GLPK performance.
Timeline is flexible within the next week, but earlier is appreciated so I can review before submission.