3_R_32 - p Decision Variables var x{i in 1.N number of workers for shift i v Objective Function maximize profit sum{i in 1.N

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# Index Bounds ############# param N; # number of shifts (3) p # Scalar Parameters ############# param HoursPerShift; # number of hours per shift (8) param MaxWorkers; # max workers (25) param MaxSold; # max number capacitors sold a day (250) param MaxProd; # max produced per shift (10) param MaxShift; # max workers per shift (10) param MaxAvgDefect; # maximum average number of defects per capacitor (3) p # Vector Parameters ############# param Salary{i in 1. .N}; # hourly salary for shift i param Defects{i in 1. .N}; #defects per capacitor for shift i param Price{i in 1. .N};#price per capacitor for shift i
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Unformatted text preview: p # Decision Variables ############# var x{i in 1. .N}; # number of workers for shift i v # Objective Function ############# maximize profit: sum{i in 1. .N} x[i]*(Price[i]*MaxProd-Salary[i]*HoursPerShift); m # Constraints ############# subject to worker_total: sum{i in 1. .N} x[i] <= MaxWorkers; subject to max_sold: sum{i in 1. .N} 10*x[i] <= MaxSold; subject to defects: sum{i in 1. .N} x[i]*MaxProd*Defects[i]<=sum{i in 1. .N} MaxAvgDefect*MaxProd*x[i]; subject to shift_limits{i in 1. .N}: x[i]<=MaxShift;...
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This note was uploaded on 01/21/2010 for the course IEOR 162 taught by Professor Zhang during the Fall '07 term at University of California, Berkeley.

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