Projectlfiloduc ion, demand, revenue and cost information for a three-month period (January, February, march is shown below

(I.) Simulation Proiect
A cellulosic ethanol plant which will use energy cane biomass as the primary feedstock is currently being constructed with production operations to begin in 2012. This plant will have an initial annual ethanol production capacity of approximately 500,000 gallons. Using a conversion factor of 65 gallons of ethanol per dry ton of feedstock and a biomass wet ton dry matter content of 35%, the plant will need to purchase 7,692 dry tons (21,978 wet tons) of energy cane biomass for conversion to ethanol. It is anticipated that the annual quantity of biomass required to support ethanol production will be obtained from two potential sources. A portion of the biomass supply will be obtained under a forward contract arrangement from local growers for a specific acreage level. A second feedstock source would be from uncontracted acreage of sugarcane biomass being grown by local growers in the area.
Since energy cane takes a couple years to expand enough seedcane to plant desired acreage levels, the company will have to utilize biomass from sugarcane available for harvest in 2012. Sugarcane yields are projected to average 30 tons per acre in 2012 with a minimum of 25 and a maximum of 37. Sugar recovery (CRS = raw sugar recovered per ton of sugarcane) is projected to average 220 pounds with a standard deviation of 10. Prices to be paid for sugarcane biomass in 2012 is a function of the expected gross revenue per ton from sugar sales received by the grower. With a mill share of 40% taken out of revenue for processing, the resulting grower share of total revenue is 60%. Contracted acres next year for ethanol production will be paid the grower gross revenue per ton (received from sugar sales) plus $5. Production purchased from noncontracted acres will be paid the gross sugar revenue per ton plus $15.

A major uncertainty tbr next year is what will be the market price of raw sugar. It is expected to be about $0.35 per pound, but could easily be $0.30 or $0.40 per pound. If the sugar price is relatively low, it is assumed a lower biomass price increase over sugar revenue per ton could be offered. However, it the price of sugar next year is high, it would be expected that a higher biomass price markup over sugar revenue per ton would be required to secure enough noncontracted acreage. As a result, the company has specified the following policy for purchase of biomass from noncontracted acreage: if the sugar price is less than $0.32 then noncontracted biomass will be priced at sugar revenue plus $10 per ton and if the sugar price is more than $0.38, noncontracted biomass will be price at sugar revenue plus $20 per ton. Biomass trucking cost is $3 per wet ton on contracted acreage and $5 per wet ton on noncontracted acreage. Any surplus biomass above demand incurs a $5 per ton surplus charge to transport it to a raw sugar mill for processing. 

Realizing the potential impact of the uncertain raw sugar price on biomass purchase cost, the company would like to evaluate this problem as a decision analysis problem with three sugar price levels: $0.30, $0.35 and $0.40 (each with a standard deviation of $0.02). Decision variable choices selected are contract acreages equal to 50%, 75% and 100% of required acreage. Perform 10,000 iterations using each of the three mean price levels. Using estimated mean total costs from your simulation runs, conduct a decision analysis evaluation for the contract acreage decision choice assuming, alternatively, that each of the three sugar price levels occurs with a 60% probability and the remaining two price levels occurring with a 20% probability each. Your assignment is to determine the optimal quantity of biomass acreage to purchase under forward contract for next year with the goal of providing the appropriate quantity of biomass required at the ethanol facility at the lowest total cost. 

(a.) Develop a simulation model which could be used to solve this problem. Three simulation scenarios will be run, simulating the three contract acreage choices. To provide data for the decision analysis problem, three price distributions (and the associated three cost estimates) will be simulated within each simulation run. Use SAS to solve this problem and include SAS program and output with PROC MEAN results.
(b.) Summarize each simulation scenario runs in a table (proc means output) presenting the following data:
(a) biomass demand (dry tons/acre) (b) contracted acreage (acres) (c) sugarcane yield (wet tons/acre) (d) sugarcane yield (dry tons/acre) (e) crs (pounds of raw sugar/ton cane) (0 sugar production per acre (lbs/acre) (g) contracted biomass production (dry tons) (h) noncontracted biomass production (dry tons) (i) noncontracted acreage (acres) (j) total biomass production (dry tons) • (k) surplus biomass production (dry tons)
(I) raw sugar price 1-3 (Sib) (m) grower gross revenue 1-3 ($/acre) (n) grower gross revenue 1-3 ($/wet ton) (o) contract biomass price 1-3 ($/wet ton) (p) noncontract biomass price 1-3 ($/wet ton) (q) total contract biomass cost 1-3 ($) (r) total noncontract biomass cost 1-3 ($) (s) total surplus disposal cost 1-3 ($) (t) total cost 1-3 ($)