Hi-Cap Magazine Distribution in California Team CarRamrod

Disclaimer The following is a hypothetical scenario. The group members do not engage in, nor condone engaging in the trade of banned products.

CA Law (a) Any person in this state who does any of the following is punishable by imprisonment in a county jail not exceeding one year or in the state prison: (2) Commencing January 1, 2000, manufactures or causes to be manufactured, imports into the state, keeps for sale, or offers or exposes for sale, or who gives, or lends, any large-capacity magazine.... (c) (25) As used in this section, "large-capacity magazine" means any ammunition feeding device with the capacity to accept more than 10 rounds, but shall not be construed to include any of the following: (A) A feeding device that has been permanently altered so that it cannot accommodate more than 10 rounds. (B) A.22 caliber tube ammunition feeding device. (C) A tubular magazine that is contained in a lever- action firearm.

Motivation Law largely unenforceable o No outright prohibition of possession o No way to prove if magazine manufactured after 2000 Bad guys have them o Not illegal in most other states o Hard to control flow into CA So should you o Home invasions o Civil Unrest

LA Riots 1992: Koreatown 45% of all property damage 5 of 53 deaths Police nowhere to be seen Store-owners banded together on rooftops Defended themselves against much larger crowds using “Assault Weapons” and High-Capacity Magazines

Concept Procure High-Capacity magazines from another state Distribute by airplane, flying out of Watsonville Municipal Airport Distribute to large cities (Pop > 150,000) via small airports

The Market The Market

Assumptions/Constraints (General) Cities o California cities with populations over 150k are considered Population drives largest profit with widest distribution o 36 (of 482) cities meet population limit and are analyzed o Only 20 cities will be supplied Airports o Only airports considered small/medium by the FAA are considered o Only airports within 50 miles of each city are considered o 550 out of 1007 total California airports are analyzed Network o Start Node: Watsonville; End Node: Market o 2305 total arcs (Watsonville->Airport->City->Market) o 588 total nodes (Watsonville->Airport->City->Market)

Assumptions/Constraints (Resources) Season lasts 20 workdays (1 month, per year) Cities will be supplied only once per season Each airport will supply only 1 city AR-15 high-capacity magazines o 7.5”x2.75”x1”,.25 lbs 1 airplane will be used (Cessna Super CargoMaster) o 452 cubic feet of storage; (4500 lbs/.25 lbs) = 18,000 Magazines (180 cases) o $2.29/nm fuel cost o 871 nm range Supplier (out of State) o Able to supply in excess of the air transport constraint (20*180=3600 cases).

Assumptions/Constraints (Formulation) Demand (Capacity): o (.0525*Population)/100 o Range: o 21% of CA population own firearms* 21% used in model (conservative) o 50% of gun-owners don’t support high-capacity magazine ban** 25% used in model (conservative) Probability (of capture, relatively): o ((Distance/50)*1.24)*(CrimeRate*12) o Ranges: (Distance: ), (CrimeRate: ) Highest relative probability: 50 miles from Oakland: 97% o Not a function of population Price (Cost): o log(population(.0525)/1000)*$30 $30: Upper-end price of name-brand magazine in legal state o Range($ $68.97)

Airports U= Plane (180) Cities Cost = Prob of Getting Caught U= Inf Cost = - Revenue Cost = Flight price U = City Demand The Model Watsonville Market

Optimizing Parameters: o Probability : log (1-P ij ) o Revenue : Y ij * C ij Method Of Optimization: o Probability as a Constraint o Probability in Objective Function

Under The Hood Constraint: MaxProb.. sum((i,j), log(1-arcdata(i,j, 'Probb'))*P(i,j)) =g= log(.90) ; Objective Function: OBJECTIVE.. Zprimal =e= SUM(arcs(i,j),(arcdata(i,j,'Cost') *Y(i,j) )) + (Weight)*probab + nC/2*SUM(j,UnsatisfiedDemand(j)) ;

Results (.90) Probability = 0.90 Revenue from LosxAngeles -> is Revenue from SanxDiego -> is Revenue from SanxJose -> is Revenue from Fresno -> is Revenue from Sacramento -> is Revenue from LongxBeach -> is Revenue from Oakland -> is Revenue from Bakersfield -> is Revenue from Anaheim -> is Revenue from SantaxAna -> is Revenue from Riverside -> is Revenue from Stockton -> is Revenue from ChulaxVista -> is Revenue from Fremont -> is Revenue from Irvine -> is Revenue from SanxBernardi -> is Revenue from Modesto -> is Revenue from Oxnard -> is Revenue from Fontana -> is Revenue from Glendale -> is SupplyDemand that could not be moved on node Watsonvillex is SupplyDemand that could not be moved on node Market is total Revenue =

Results (.70) Probability = 0.70 Revenue from LosxAngeles -> is Revenue from SanxDiego -> is Revenue from SanxJose -> is Revenue from SanxFrancisc -> is Revenue from Fresno -> is Revenue from Sacramento -> is Revenue from LongxBeach -> is Revenue from Oakland -> is Revenue from Bakersfield -> is Revenue from Anaheim -> is Revenue from SantaxAna -> is Revenue from Riverside -> is Revenue from Stockton -> is Revenue from ChulaxVista -> is Revenue from Fremont -> is Revenue from Irvine -> is Revenue from SanxBernardi -> is Revenue from Modesto -> is Revenue from Oxnard -> is Revenue from Fontana -> is SupplyDemand that could not be moved on node Watsonvillex is SupplyDemand that could not be moved on node Market is total Revenue =

Results (.96) Probability = 0.96 Revenue from SanxDiego -> is Revenue from SanxJose -> is Revenue from Fresno -> is Revenue from Sacramento -> is Revenue from LongxBeach -> is Revenue from Bakersfield -> is Revenue from Anaheim -> is Revenue from Riverside -> is Revenue from Oxnard -> is Revenue from Fontana -> is Revenue from Oceanside -> is Revenue from ElkxGrove -> is Revenue from Corona -> is Revenue from Torrance -> is SupplyDemand that could not be moved on node Watsonvillex is SupplyDemand that could not be moved on node Market is total Revenue =

Results

Conclusion Results: o Market changed with different threshold values for Probability of capture. What could we have done better given more time? o More realistic model No shipping by air from Watsonville to San Jose, etc. Capability to ship to more than 20 cities (longer contract) o More realistic constraints Better Probability Function Better Demand Function