How to check if my linear constraints and bounds are infeasible & reduce execution time?
Hello,
I’m making a code where it creates an optimized engine usage schedule based on engine’s reliability using genetic algorithm solver. The following is the code that I’ve made:
tic
clear
% Create arrays to store total Hour & daily result
totalHourWeekday = zeros(5,13);
result = zeros(5,13);
% Calculate the maximum engine by adding all of the engine’s status
engineStatus = ones(1,13);
maxEngine = sum(engineStatus);
% Initialize optimization problem
rel = zeros(1,13);
decision = optimproblem("ObjectiveSense","max");
engine = optimvar(‘engine’,13,’Type’,’integer’,’LowerBound’,0,’UpperBound’,1);
% Define days
for day = 1:5
% calculate maximum duration
maxDuration = 24*max(day);
% Initialize demand
for demand = 1:5
disp([‘Day: ‘,num2str(day),’ Demand: ‘,num2str(demand)])
switch demand
case 1
hourDemand = 3;
engineDemand = 9;
% Calculate reliability based on the sum between the hour
% demand & previous hour
if day == 1
timeCalc = hourDemand + totalHourWeekday(demand,:);
else
timeCalc = hourDemand + result(day-1,:);
end
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
% Set reliability as objective function
decision.Objective = relSys;
% Initialize constraint #1: engines should be equal to the
% demand
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
% Initialize constraint #2: engine’s hour should be less
% than max duration
if day == 1
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand,:) <= maxDuration;
else
decision.Constraints.durationConstraint = engine*result(day-1,:) <= maxDuration;
end
% Solve optimization problem
sol = solve(decision);
% Add the hour demand to the chosen engines
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 2
hourDemand = 2;
engineDemand = 6;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 3
hourDemand = 5;
engineDemand = 10;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 4
hourDemand = 8;
engineDemand = maxEngine-1;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 5
% Special case
for i = 1:13
if engineStatus(1,i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + 6;
end
end
end
% Add the total hour from all hour demands within the day
if day == 1
result(day,:) = sum(totalHourWeekday);
else
result(day,:) = result(day-1,:) + sum(totalHourWeekday);
end
end
end
The code ran well until it reached 4th day. When calculating 4th day’s 1st demand, it gave me the following error:
I guessed that, since it gave the infeasible linear constraints and bounds, an error was detected on line 57. That’s why I am wondering how to solve this problem or how to check for linear constraints and bounds infeasibility?
Furthermore, is there any way to reduce the code’s execution time?
Thank you in advanceHello,
I’m making a code where it creates an optimized engine usage schedule based on engine’s reliability using genetic algorithm solver. The following is the code that I’ve made:
tic
clear
% Create arrays to store total Hour & daily result
totalHourWeekday = zeros(5,13);
result = zeros(5,13);
% Calculate the maximum engine by adding all of the engine’s status
engineStatus = ones(1,13);
maxEngine = sum(engineStatus);
% Initialize optimization problem
rel = zeros(1,13);
decision = optimproblem("ObjectiveSense","max");
engine = optimvar(‘engine’,13,’Type’,’integer’,’LowerBound’,0,’UpperBound’,1);
% Define days
for day = 1:5
% calculate maximum duration
maxDuration = 24*max(day);
% Initialize demand
for demand = 1:5
disp([‘Day: ‘,num2str(day),’ Demand: ‘,num2str(demand)])
switch demand
case 1
hourDemand = 3;
engineDemand = 9;
% Calculate reliability based on the sum between the hour
% demand & previous hour
if day == 1
timeCalc = hourDemand + totalHourWeekday(demand,:);
else
timeCalc = hourDemand + result(day-1,:);
end
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
% Set reliability as objective function
decision.Objective = relSys;
% Initialize constraint #1: engines should be equal to the
% demand
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
% Initialize constraint #2: engine’s hour should be less
% than max duration
if day == 1
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand,:) <= maxDuration;
else
decision.Constraints.durationConstraint = engine*result(day-1,:) <= maxDuration;
end
% Solve optimization problem
sol = solve(decision);
% Add the hour demand to the chosen engines
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 2
hourDemand = 2;
engineDemand = 6;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 3
hourDemand = 5;
engineDemand = 10;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 4
hourDemand = 8;
engineDemand = maxEngine-1;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 5
% Special case
for i = 1:13
if engineStatus(1,i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + 6;
end
end
end
% Add the total hour from all hour demands within the day
if day == 1
result(day,:) = sum(totalHourWeekday);
else
result(day,:) = result(day-1,:) + sum(totalHourWeekday);
end
end
end
The code ran well until it reached 4th day. When calculating 4th day’s 1st demand, it gave me the following error:
I guessed that, since it gave the infeasible linear constraints and bounds, an error was detected on line 57. That’s why I am wondering how to solve this problem or how to check for linear constraints and bounds infeasibility?
Furthermore, is there any way to reduce the code’s execution time?
Thank you in advance Hello,
I’m making a code where it creates an optimized engine usage schedule based on engine’s reliability using genetic algorithm solver. The following is the code that I’ve made:
tic
clear
% Create arrays to store total Hour & daily result
totalHourWeekday = zeros(5,13);
result = zeros(5,13);
% Calculate the maximum engine by adding all of the engine’s status
engineStatus = ones(1,13);
maxEngine = sum(engineStatus);
% Initialize optimization problem
rel = zeros(1,13);
decision = optimproblem("ObjectiveSense","max");
engine = optimvar(‘engine’,13,’Type’,’integer’,’LowerBound’,0,’UpperBound’,1);
% Define days
for day = 1:5
% calculate maximum duration
maxDuration = 24*max(day);
% Initialize demand
for demand = 1:5
disp([‘Day: ‘,num2str(day),’ Demand: ‘,num2str(demand)])
switch demand
case 1
hourDemand = 3;
engineDemand = 9;
% Calculate reliability based on the sum between the hour
% demand & previous hour
if day == 1
timeCalc = hourDemand + totalHourWeekday(demand,:);
else
timeCalc = hourDemand + result(day-1,:);
end
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
% Set reliability as objective function
decision.Objective = relSys;
% Initialize constraint #1: engines should be equal to the
% demand
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
% Initialize constraint #2: engine’s hour should be less
% than max duration
if day == 1
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand,:) <= maxDuration;
else
decision.Constraints.durationConstraint = engine*result(day-1,:) <= maxDuration;
end
% Solve optimization problem
sol = solve(decision);
% Add the hour demand to the chosen engines
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 2
hourDemand = 2;
engineDemand = 6;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 3
hourDemand = 5;
engineDemand = 10;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 4
hourDemand = 8;
engineDemand = maxEngine-1;
timeCalc = hourDemand + totalHourWeekday(demand-1,:);
rel = exp(-(4.44*10^-5*timeCalc));
relSys = 1-((1-engine(1)*rel(1,1))*(1-engine(2)*rel(1,2))*(1-engine(3)*rel(1,3))*(1-engine(4)*rel(1,4))*(1-engine(5)*rel(1,5))*(1-engine(6)*rel(1,6))*(1-engine(7)*rel(1,7))*(1-engine(8)*rel(1,8))*(1-engine(9)*rel(1,9))*(1-engine(10)*rel(1,10))*(1-engine(11)*rel(1,11))*(1-engine(12)*rel(1,12))*(1-engine(13)*rel(1,13)));
decision.Objective = relSys;
engineMaintOrNot = sum(engine*engineStatus);
decision.Constraints.engineDemandConstraint = engineMaintOrNot == engineDemand;
decision.Constraints.durationConstraint = engine*totalHourWeekday(demand-1,:) <= maxDuration;
sol = solve(decision);
for i = 1:13
if sol.engine(i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + hourDemand;
end
end
case 5
% Special case
for i = 1:13
if engineStatus(1,i) == 1
totalHourWeekday(demand,i) = totalHourWeekday(demand,i) + 6;
end
end
end
% Add the total hour from all hour demands within the day
if day == 1
result(day,:) = sum(totalHourWeekday);
else
result(day,:) = result(day-1,:) + sum(totalHourWeekday);
end
end
end
The code ran well until it reached 4th day. When calculating 4th day’s 1st demand, it gave me the following error:
I guessed that, since it gave the infeasible linear constraints and bounds, an error was detected on line 57. That’s why I am wondering how to solve this problem or how to check for linear constraints and bounds infeasibility?
Furthermore, is there any way to reduce the code’s execution time?
Thank you in advance genetic algorithm, optimization, error MATLAB Answers — New Questions
