Cost-Focused Chemical Process Optimization Model

The objectives of this project are: (i) to formulate and implement a discrete-time (timeslot)
mixed-integer linear programming (MILP) scheduler for a multiproduct/multipurpose batch plant
and apply it to the Kondili–Pantelides–Sargent (1993) benchmark data involving product
scheduling [1], or a new set of data relating to oil production maximization from offshore wells;
(ii) to maximize profit objective function under capacity tardiness, equipment assignment, finite
storage, resource, and changeover constraints; and (iii) to quantify the tradeoffs between timegrid
fidelity and computational effort
Here is what I will supply: current flow-sheet information, material and energy balances, equipment limits, pricing data for utilities and raw materials, plus any safety or throughput constraints the model must respect.

What I need back from you is a clean, reproducible optimisation model—Matlab ,Python + Pyomo, GAMS, or another transparent solver environment is fine—that can be re-run quickly when any of the economic inputs change. Alongside the code, please provide a brief write-up that shows:

• the objective function and all decision variables
• a summary of the key constraints you implemented
• the optimal cost figure your model arrives at and a short sensitivity check (e.g., ±10 % swings in major raw-material or utility prices)

I will verify the deliverable by reproducing your result on my machine and confirming that relaxing or tightening a constraint changes the solution in a predictable way. If you can automate plotting of cost versus throughput as a bonus, that would be excellent.

Let me know which platform you prefer, your estimated turnaround, and any clarifications you need on the process data so we can get started.