(Steps Shown) In this question, please reconsider the Filatoi Riuniti case and linear optimization model. Recall that Filatoi Riuniti seeks to minimize:
Question: In this question, please reconsider the Filatoi Riuniti case and linear optimization model. Recall that Filatoi Riuniti seeks to minimize:
total production and transportation costs
subject to:
constraints to meet demand for the four sizes of yarn, and
constraints that limit the machine hours used by each supplier to the supplier’s monthly capacity.
The attached spreadsheet output entitled "Filatoi Riuniti – Optimal production schedule – March" contains data for a modified version of the Filatoi case and model. The optimal solution of the resulting linear optimization model is shown in the table on the upper left of the spreadsheet.
| Filatoi Riuniti - Optimal production schedule – March | |||||||||||||
| DECISION VARIABLES | MACHINE HOURS REQUIRED PER PRODUCT | PRODUCTION | |||||||||||
| Yarn produced by each factory (Kg/month) | (Hours/Kg) | CAPACITY | |||||||||||
| Size | Size | (Machine hours | |||||||||||
| Supplier | Extrafine | Fine | Medium | Coarse | Supplier | Extrafine | Fine | Medium | Coarse | per month) | |||
| Ambrosi | - | - | - | 10,000 | Ambrosi | 0.400 | 0.375 | 0.250 | 2,500 | ||||
| Bresciani | - | 6,000 | - | - | Bresciani | 0.700 | 0.500 | 0.350 | 0.250 | 3,000 | |||
| Castri | 3,704 | - | - | - | Castri | 0.675 | 0.450 | 0.400 | 0.250 | 2,500 | |||
| De Blasi | - | 448 | - | - | De Blasi | 0.450 | 0.350 | 0.200 | 2,600 | ||||
| Estensi | 3,846 | - | - | - | Estensi | 0.650 | 0.450 | 0.400 | 0.250 | 2,500 | |||
| Filatoi R. | 13,879 | 19,552 | 28,000 | 18,000 | Filatoi R. | 0.625 | 0.500 | 0.425 | 0.425 | 38,000 | |||
| Giuliani | 3,571 | - | - | - | Giuliani | 0.700 | 0.450 | 0.350 | 0.400 | 2,500 | |||
| OBJECTIVE FUNCTION (PRODUCTION COST + TRANSPORTATION COST ) | |||||||||||||
| ($/Kg) | |||||||||||||
| Size | |||||||||||||
| Supplier | Extrafine | Fine | Medium | Coarse | |||||||||
| Ambrosi | 14.05 | 11.10 | 8.45 | ||||||||||
| Bresciani | 17.80 | 13.15 | 11.80 | 10.05 | |||||||||
| Castri | 18.30 | 15.02 | 12.20 | 10.70 | |||||||||
| De Blasi | 14.20 | 12.05 | 10.65 | ||||||||||
| Estensi | 18.60 | 14.50 | 12.45 | 10.65 | |||||||||
| Filatoi R. | 18.25 | 13.90 | 11.40 | 8.90 | |||||||||
| Giuliani | 18.25 | 14.40 | 12.15 | 9.50 | |||||||||
| DEMAND TO MEET | |||||||||||||
| (Kg/month) | |||||||||||||
| Extrafine | Fine | Medium | Coarse | ||||||||||
| 25,000 | 26,000 | 28,000 | 28,000 | ||||||||||
- [10 points] Write a concise memo (e.g., for your boss), detailing the optimal procurement strategy and briefly explaining the linear optimization methodology.
- [10 points] Suppose that due to contractual obligations, if Filatoi outsources to Castri, then they also have to outsource to Estensi. Let C be a binary variable that is equal to 1 if Filatoi outsources to Castri and 0 otherwise. Let E be a binary variable that is equal to 1 if Filatoi outsources to Estensi. Write a linear binary constraint that enforces this requirement.
- [10 extra credit points] Recall from lecture that the linear programming variables can be represented succinctly as X ys , where y ranges from 1 to 4 (representing extrafine, fine, medium and course yarn) and s ranges from A to G (representing the yarn producers). Write linear constraints that require the outsourcing quantities from Castri to be zero if we decide not to outsource with them, by combining appropriate X ys variables with the binary variable C (hint: note that if we do outsource to Castri, we do not want our outsourcing quantities to be restricted by this constraint).
Deliverable: Word Document 