function initUtilityConstraint()
    c = categories[1]
    me = attack_methods[1]
    t = teams[1]
    tuple = Simulate.cmt(c, me, t)
    w = windows[1]
    r = resources[1]
    wrtuple = Simulate.wr(w, r)
    for ni in 1:size(list,1), c in categories, f in **flights**
        performloop(ni, c, f, tuple, wrtuple)
    end
end

function performloop(ni, c, f, tuple, wrtuple)
    fi = findfirst(flights, f)
    for w in windows, me in attack_methods
        tuple.c = c
        tuple.m = me
        total = 0.0
        for t in teams
            tuple.t = t
            strat = linearizeStrategyS(f, c, t, w, ni)
            total = total + effectiveness[tuple]*strat
        end

        total = ( total*(flight_vals[fi]*(-1)) + flight_vals[fi] )

        for w2 in owindows, r2 in resources
            wrtuple.w = w2
            wrtuple.r = r2
            over = linearizeOverflow(w2, r2, ni)
            total = total - resource_fines[wrtuple]*over
        end
        # println(string( sc[c], "<=", ( total*(flight_vals[fi]*(-1)) + flight_vals[fi] )))
        @addConstraint(m, sc[c] <= total)
    end
end

1 item in flights array - performloop consumes about 620KB per iteration - peak memory consumption by program is 8.84GBs
2 items - 1.01MB per performloop iteration - peak 8.87GBs
3 items - 3.45MB - peak 9.60GBs
4 items - 4.35MB - peak 10.24GBs
5 items - 10.78MB - peak 15.63GBs