# Integer And Goal Programming Quiz

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| By Devyani Bijoor
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Devyani Bijoor
Community Contributor
Quizzes Created: 1 | Total Attempts: 23,762
Questions: 35 | Attempts: 23,792  Settings  Welcome to the thrilling world of optimization! Get ready to put your problem-solving skills to the test with our Integer and Goal Programming Quiz. This quiz will take you on a journey through the fascinating realms of mathematical programming, where you'll encounter challenging scenarios and make strategic decisions to find the best solutions.

Are you up for the challenge? Whether you're a seasoned optimizer or just starting out, this quiz will provide an engaging and interactive experience that will keep you on the edge of your seat. So, grab a pen and some paper, and let's dive right in!

But be Read moreprepared, time will be ticking! This quiz is designed to test your ability to think quickly and make efficient decisions under pressure. Can you handle the challenge and prove your optimization prowess?
So, get ready to unlock your inner mathematician, step into the shoes of an optimization expert, and embark on this thrilling journey through Integer and Goal Programming. Are you ready to optimize your way to victory? Let's begin the quiz and see who can conquer the world of optimization!

• 1.

### Mark the correct statement about integer programming problems (IPPs):

• A.

Pure IPPs are those problems in which all the variables are non-negative integers.

• B.

The 0-1 IPPs are those in which all variables are either 0 or all equal to 1.

• C.

Mixed IPPs are those where decision variables can take integer values only but the slack/surplus variables can take fractional values as well.

• D.

In real life, no variable can assume fractional values. Hence we should always use IPPs.

A. Pure IPPs are those problems in which all the variables are non-negative integers.
Explanation
The correct statement is that pure IPPs are those problems in which all the variables are non-negative integers. This means that in pure IPPs, the variables cannot have fractional values or be negative. This distinction is important because it helps to define the specific characteristics and constraints of a pure IPP, which can be useful in solving and analyzing these types of problems.

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• 2.

### Consider the following problem: Max. Z = 28x1 + 32x2, subject to 5x1 + 3x2 ≤ 23, 4x1 + 7x2 ≤ 33, and x1 ≥ 0, x2 ≥ 0. This problem is:

• A.

A pure IPP.

• B.

A 0-1 IPP.

• C.

A mixed IPP.

• D.

Not an IPP.

D. Not an IPP.
Explanation
The given problem is not an IPP (Integer Programming Problem) because it does not have any integer constraints. In an IPP, at least some of the decision variables are required to take integer values. However, in this problem, the decision variables x1 and x2 are only constrained to be non-negative, without any requirement for them to be integers. Therefore, the correct answer is "Not an IPP".

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• 3.

### Mark the correct statement:

• A.

A facility location problem can be formulated and solved as a 0-1 IPP.

• B.

Problems involving piece-wise linear functions can be modeled as mixed linear programming problems.

• C.

Solution to an IPP can be obtained by first solving the problem as an LPP then rounding off the fractional values.

• D.

If the optimal solution to an LPP has all integer values, it may or may not be an optimal integer solution.

B. Problems involving piece-wise linear functions can be modeled as mixed linear programming problems.
• 4.

### Mark the wrong statement:

• A.

To solve an IPP using cutting plane algorithm, the integer requirements are dropped in the first instance to obtain LP relaxation.

• B.

A cut is formed by choosing a row in the optimal tableau that corresponds to a non-integer variable.

• C.

A constraint picked from the optimal tableau is: 0x1 + x2 + 1/2 S1 – 1/3 S2 = 7/2. With S3 being a slack variable introduced, the cut would be: -1/2 S1 – 2/3 S2 + S3 = -1/2

• D.

The optimal solution to LPP satisfies the cut that is introduced on the basis of it.

D. The optimal solution to LPP satisfies the cut that is introduced on the basis of it.
Explanation
The statement is incorrect. The optimal solution to the Linear Programming Problem (LPP) does not necessarily satisfy the cut that is introduced based on it. The cut is introduced to strengthen the relaxation and improve the lower bound, but it does not guarantee that the optimal solution will satisfy it.

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• 5.

### In cutting plane algorithm, each cut which is made involves the introduction of

• A.

An ‘=’ constraint

• B.

An artificial variable

• C.

A ‘≤’ constraint

• D.

A ‘≥’ constraint

C. A ‘≤’ constraint
Explanation
In the cutting plane algorithm, each cut that is made involves the introduction of a "

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• 6.

### Which of the following effects does the addition of a Gomory have? (i) adding a new variable to the tableau; (ii) elimination of non-integer solutions from the feasibility region; (iii) making the previous optimal solution infeasible by eliminating that part of the feasible region which contained that solution.

• A.

(i) only

• B.

(i) and (ii) only

• C.

(i) and (iii) only

• D.

All the above

C. (i) and (iii) only
Explanation
The addition of a Gomory cut adds a new variable to the tableau (i), as it introduces an artificial variable to represent the fractional part of the solution. It also makes the previous optimal solution infeasible by eliminating that part of the feasible region which contained that solution (iii), as the Gomory cut removes non-integer solutions from the region. Therefore, the correct answer is (i) and (iii) only.

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• 7.

### Mark the incorrect statement about Branch and Bound method.

• A.

It is not a particular method and is used differently in different kinds of problems.

• B.

It is generally used in combinatorial problems.

• C.

It divides the feasible region into smaller parts by the process of branching.

• D.

It can be used for solving any kind of programming problem.

D. It can be used for solving any kind of programming problem.
Explanation
The given statement "It can be used for solving any kind of programming problem" is incorrect. Branch and Bound method is not applicable to all types of programming problems. It is primarily used for solving combinatorial problems, where the goal is to find an optimal solution from a large set of possible solutions. The method divides the feasible region into smaller parts through branching, which allows for a more efficient search for the optimal solution. However, it may not be suitable or effective for solving other types of programming problems that do not fall into the combinatorial category.

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• 8.

### Mark the wrong statement:

• A.

Goal programming deals with problems with multiple goals.

• B.

Goal programming realizes that goals may be under-achieved, over-achieved, or met exactly.

• C.

The inequalities or equalities representing goal constraints are flexible.

• D.

The initial tableau of a goal programming problem should never have a variable in the basis which is an under-achievement variable.

D. The initial tableau of a goal programming problem should never have a variable in the basis which is an under-achievement variable.
Explanation
The statement "The initial tableau of a goal programming problem should never have a variable in the basis which is an under-achievement variable" is incorrect. In goal programming, the initial tableau can indeed have a variable in the basis that represents an under-achievement variable. The goal programming approach allows for the possibility of under-achieving certain goals, so it is possible for such variables to be included in the initial tableau.

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• 9.

### Mark the wrong statement:

• A.

A travelling salesman problem can be solved using Branch and Bound method.

• B.

An assignment problem can be formulated as a 0-1 IPP and solved using Branch and Bound method.

• C.

The Branch and Bound method terminates when the upper and lower bounds become identical and the solution is that single value.

• D.

The Branch and Bound method can never reveal multiple optimal solutions to a problem, if they exist.

D. The Branch and Bound method can never reveal multiple optimal solutions to a problem, if they exist.
Explanation
The statement "The Branch and Bound method can never reveal multiple optimal solutions to a problem, if they exist" is incorrect. The Branch and Bound method can indeed reveal multiple optimal solutions to a problem, if they exist. This is because the method explores different branches and bounds the search space to find all possible solutions. If there are multiple solutions with the same optimal value, the method can uncover them.

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• 10.

### If two deviational variables, d- or d+ for over-achievement, are introduced in a goal constraint, then which of the following would not hold:

• A.

Each of them can be positive or zero.

• B.

Either d- or d+ is zero.

• C.

Both are non-zero.

• D.

Both are equal to zero.

D. Both are equal to zero.
Explanation
If both deviational variables, d- and d+, are equal to zero, it means that there is no deviation from the goal constraint. This implies that the goal is being achieved exactly as planned, with no over-achievement or under-achievement. Therefore, the statement "Both are equal to zero" would not hold in this case.

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• 11.

### Mark the wrong statement:

• A.

A ‘lower’ one-sided goal sets a lower limit that we do not want to fall under.

• B.

A two-sided goal sets a specific target missing which from either side is not desired.

• C.

In goal programming, an attempt is made to minimize deviations from targets.

• D.

In using goal programming, one has to specify clearly the relative importance of the various goals involved by assigning weights to them.

C. In goal programming, an attempt is made to minimize deviations from targets.
Explanation
The statement "In goal programming, an attempt is made to minimize deviations from targets" is incorrect. In goal programming, the objective is to minimize the deviations from multiple goals or targets simultaneously, not just from a single target. The aim is to find the best possible solution that satisfies all the goals to the maximum extent possible.

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• 12.

### Mark the wrong statement:

• A.

Goal programming assumes that the decision-maker has a linear utility function with respect to the objectives.

• B.

Deviations for various goals may be given penalty weights in accordance with the relative significance of the objectives.

• C.

The penalty weights measure the marginal rate of substitution between the objectives.

• D.

A goal programming problem cannot have multiple optimal solutions.

D. A goal programming problem cannot have multiple optimal solutions.
Explanation
The statement "A goal programming problem cannot have multiple optimal solutions" is incorrect. In goal programming, it is possible for a problem to have multiple optimal solutions. This can occur when there is more than one combination of decision variables that satisfy all the constraints and achieve the same level of goal achievement. Therefore, the correct answer is that the given statement is wrong.

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• 13.

### Mark the wrong statement:

• A.

In goal programming, the goals are ranked from the least important (goal 1) to the most important (goal n), with objective function co-efficients Pi.

• B.

Existence of (multiple ∆j rows) Net Evaluation containing priority terms indicate a prioritized goal-programming problem.

• C.

A lower priority is never sought to be achieved at the expense of higher-priority goal.

• D.

The co-efficients, Pi’s are not assigned any actual values.

C. A lower priority is never sought to be achieved at the expense of higher-priority goal.
Explanation
The statement "A lower priority is never sought to be achieved at the expense of higher-priority goal" is incorrect. In goal programming, it is possible for a lower priority goal to be partially achieved at the expense of a higher-priority goal. The objective is to minimize the deviations from all the goals, and sometimes it may be necessary to sacrifice some higher-priority goals in order to achieve a satisfactory level of lower-priority goals.

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• 14.

### Which of the following is not an essential condition in a situation for linear programming to be useful?

• A.

An explicit objective function

• B.

Uncertainty

• C.

Linearity

• D.

Limited resources

• E.

Divisibility

B. Uncertainty
Explanation
Linear programming is a mathematical technique used to optimize a given objective function under certain constraints. In order for linear programming to be useful, it requires an explicit objective function, linearity (meaning that the objective function and constraints can be expressed as linear equations or inequalities), limited resources (meaning that there are constraints on the availability of resources), and divisibility (meaning that the decision variables can take on fractional values). Uncertainty, on the other hand, refers to a lack of knowledge or information about future events or outcomes, and it is not an essential condition for linear programming to be useful.

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• 15.

### There are other related mathematical programming techniques that can be used instead of linear programming if the problem has a unique characteristic. If the problem has multiple objectives we should use which of the following methodologies?

• A.

Goal programming

• B.

Orthogonal programming

• C.

Integer programming

• D.

Multiplex programming

• E.

Dynamic programming

A. Goal programming
Explanation
Goal programming is the methodology that should be used if the problem has multiple objectives. Goal programming allows for the consideration of multiple objectives and the optimization of each objective individually, while also considering their relative importance. This technique is particularly useful when there are conflicting objectives that need to be balanced and prioritized.

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• 16.

### There are other related mathematical programming techniques that can be used instead of linear programming if the problem prevents divisibility of products or resources. We should use which of the following methodologies?

• A.

Goal programming

• B.

Primary programming

• C.

Integer programming

• D.

Unit programming

• E.

Dynamic programming

C. Integer programming
Explanation
Integer programming is the appropriate methodology to use when the problem prevents divisibility of products or resources. Unlike linear programming, which allows for fractional solutions, integer programming only allows for integer values in the decision variables. This is useful in situations where the decision variables represent quantities that cannot be divided, such as the number of units of a product to produce or the number of employees to hire. By restricting the variables to integer values, integer programming ensures that the solutions are feasible and realistic in the given context.

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• 17.

### Types of integer programming models are _____________.

• A.

Total

• B.

0 - 1

• C.

Mixed

• D.

All of the above

D. All of the above
Explanation
The types of integer programming models include total, 0 - 1, and mixed. The total integer programming model allows all variables to take on integer values. The 0 - 1 integer programming model restricts variables to only take on values of 0 or 1. The mixed integer programming model allows some variables to take on integer values while others can take on non-integer values. Therefore, all of the above options are correct.

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• 18.

### Which of the following is not an integer linear programming problem?

• A.

Pure integer

• B.

Mixed integer

• C.

0-1integer

• D.

Continuous

D. Continuous
Explanation
An integer linear programming problem involves finding the optimal solution for a linear objective function while considering integer constraints on the variables. The options "pure integer," "mixed integer," and "0-1 integer" all refer to types of integer linear programming problems where the variables are restricted to specific types of integers. However, "continuous" refers to a type of linear programming problem where the variables can take on any real value within a given range, without any integer restrictions. Therefore, "continuous" is not an integer linear programming problem.

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• 19.

### Which of the following is not a requirement for a goal programming problem?

• A.

Prioritization of goals

• B.

A single objective function

• C.

Linear constraints

• D.

Linear objective function

• E.

None of the above

B. A single objective function
Explanation
A goal programming problem requires prioritization of goals, linear constraints, and a linear objective function. However, it does not require a single objective function. In goal programming, multiple objectives are considered simultaneously, and the goal is to minimize the deviations from the desired values for each objective. Therefore, the correct answer is "a single objective function."

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• 20.

### If we wish to develop a stock portfolio wherein we maximize return and minimize risk, we would have to use

• A.

Pure integer programming

• B.

Goal programming

• C.

Zero-one integer programming

• D.

Mixed-integer programming

• E.

Nonlinear programming

B. Goal programming
Explanation
Goal programming is the correct answer because it allows us to simultaneously consider multiple objectives, such as maximizing return and minimizing risk, when developing a stock portfolio. It is a mathematical optimization technique that helps in finding the best possible solution by balancing different goals or objectives. By using goal programming, we can assign priorities to different objectives and find the optimal allocation of stocks that achieves the desired balance between return and risk.

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• 21.

### Solving an integer programming problem by rounding off answers obtained by solving it as a linear programming problem (using simplex), we find that

• A.

The values of decision variables obtained by rounding off are always very close to the optimal values.

• B.

The value of the objective function for a maximization problem will likely be less than that for the simplex solution.

• C.

The value of the objective function for a minimization problem will likely be less than that for the simplex solution.

• D.

All constraints are satisfied exactly.

• E.

None of the above.

B. The value of the objective function for a maximization problem will likely be less than that for the simplex solution.
Explanation
When solving an integer programming problem by rounding off the answers obtained from solving it as a linear programming problem using simplex, the values of the decision variables obtained by rounding off are always very close to the optimal values. However, the value of the objective function for a maximization problem will likely be less than that for the simplex solution. This is because rounding off the values can lead to a suboptimal solution that is slightly lower than the true optimal solution. Therefore, the correct answer is "the value of the objective function for a maximization problem will likely be less than that for the simplex solution."

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• 22.

### When using the branch and bound method in integer programming maximization problem, the stopping rule for branching is to continue until

• A.

The objective function is zero.

• B.

The new upper bound exceeds the lower bound.

• C.

The new upper bound is less than or equal to the lower bound or no further branching is possible.

• D.

The lower bound reaches zero.

• E.

None of the above

C. The new upper bound is less than or equal to the lower bound or no further branching is possible.
Explanation
The stopping rule for branching in the branch and bound method in integer programming maximization problem is to continue until the new upper bound is less than or equal to the lower bound or no further branching is possible. This means that if the new upper bound, which represents the best possible solution found so far, is not better than the lower bound, or if there are no more feasible solutions to explore, then the branching process stops. This ensures that the algorithm does not waste time exploring unpromising branches and can efficiently find the optimal solution.

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• 23.

### A goal programming problem had two goals (with no priorities assigned). Goal number 1 was to achieve a cost of \$3,600 and goal number 2 was to have no wasted material. The optimal solution to this problem resulted in a cost of \$3,900 and no wasted material. What was the value for the objective function for this goal programming problem?

• A.

300

• B.

-300

• C.

3300

• D.

0

• E.

None of the above

A. 300
Explanation
The value for the objective function in this goal programming problem is 3300. This is because the optimal solution resulted in a cost of \$3,900, which is \$300 more than the desired cost of \$3,600. However, the second goal of having no wasted material was achieved. Therefore, the objective function value is calculated by subtracting the excess cost from the desired cost, which is \$3,900 - \$3,600 = \$300.

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• 24.

### Which of the following is not a type of integer programming problem?

• A.

Pure integer programming problem

• B.

Blending problem

• C.

Zero-one programming problem

• D.

Mixed-integer programming problem

B. Blending problem
Explanation
A blending problem is not a type of integer programming problem. Integer programming involves optimizing a linear function subject to integer constraints, while a blending problem involves determining the optimal combination of ingredients to create a mixture with certain characteristics. Blending problems typically do not involve integer constraints and can be solved using linear programming techniques. Therefore, the blending problem is not categorized as an integer programming problem.

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• 25.

### Potential problems with the cutting plane method include

• A.

It may never converge to a solution.

• B.

It can be used only for problems with two dimensions.

• C.

It may take a great deal of computer time to find a solution.

• D.

It does not produce a good integer solution until the final solution is reached.

• E.

Both c and d

E. Both c and d
Explanation
The cutting plane method is a technique used in mathematical optimization to solve linear programming problems. It involves iteratively adding constraints to a linear program until an optimal solution is found. However, there are potential problems with this method. One problem is that it may never converge to a solution, meaning that it may not be able to find an optimal solution within a reasonable amount of time. Another problem is that it may take a great deal of computer time to find a solution, making it computationally expensive. Additionally, the cutting plane method may not produce a good integer solution until the final solution is reached, which can be a limitation in certain applications. Therefore, the correct answer is both c and d.

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• 26.

### The first step in a branch and bound approach to solving integer programming problems is to

• A.

Graph the problem.

• B.

Change the objective function coefficients to whole integer numbers.

• C.

Solve the original problem using LP by allowing continuous noninteger solutions.

• D.

Compare the lower bound to any upper bound of your choice.

• E.

None of the above

E. None of the above
Explanation
The first step in a branch and bound approach to solving integer programming problems is to create an initial relaxation of the problem by solving the LP relaxation, which allows continuous noninteger solutions. This relaxation provides an upper bound on the optimal integer solution. Then, the solution is branched into subproblems, and the process continues by solving each subproblem iteratively, comparing the lower bound of each subproblem to the current upper bound. Therefore, the correct answer is "none of the above" as none of the options provided accurately describe the first step in a branch and bound approach.

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• 27.

### Goal programming can be used to ensure that we maximise profit while at the same time we use the entire amount of an available resource.

• A.

True

• B.

False

A. True
Explanation
Goal programming is a mathematical optimization technique used to solve problems with multiple conflicting objectives. In this case, the objective is to maximize profit while utilizing the entire amount of an available resource. By using goal programming, we can set the goal of maximizing profit and simultaneously set a constraint that requires the resource to be fully utilized. This allows us to find an optimal solution that achieves both objectives, making the statement true.

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• 28.

### Goal programming models are all maximization problems.

• A.

True

• B.

False

B. False
Explanation
The statement is false because goal programming models can be both maximization problems and minimization problems. Goal programming models are used to optimize multiple objectives simultaneously, and these objectives can be either maximized or minimized depending on the specific problem at hand. Therefore, it is incorrect to claim that all goal programming models are maximization problems.

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• 29.

### Pure integer programming, mixed integer programming, and __________________ are all examples of integer programming problems.

• A.

Goal programming

• B.

• C.

Ranked goals models

• D.

0-1 integer programming

• E.

Nonlinear programming

D. 0-1 integer programming
Explanation
0-1 integer programming is a type of integer programming problem where the decision variables can only take on binary values, 0 or 1. In other words, the variables are either selected or not selected for the solution. This type of programming is commonly used in optimization problems where decisions need to be made on whether to include or exclude certain elements or options. It is called 0-1 integer programming because the variables can only be 0 or 1, representing the absence or presence of the element or option in the solution.

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• 30.

### The _______________ divides a set of feasible solutions into subsets that are examined systematically.

• A.

Cutting plane method

• B.

Simplex method

• C.

Branch and bound method

• D.

Mixed-integer method

• E.

0-1 binary model

C. Branch and bound method
Explanation
The branch and bound method is used to divide a set of feasible solutions into subsets and then systematically examine them. This method is commonly used in optimization problems to find the optimal solution by progressively narrowing down the search space. It involves branching, where the problem is divided into smaller subproblems, and bounding, where bounds are established to eliminate certain subsets of solutions. By iteratively applying these steps, the branch and bound method efficiently explores the solution space and eventually identifies the best feasible solution.

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• 31.

### ___________________ programming is an extension to linear programming models that allows more than one objective to be stated.

• A.

Goal

• B.

• C.

Mixed-integer

• D.

Branch and bound

• E.

Nonlinear

A. Goal
Explanation
Goal programming is an extension to linear programming models that allows more than one objective to be stated. It is used when there are multiple goals or objectives that need to be considered simultaneously. In goal programming, each objective is assigned a priority level and a target value, and the goal is to minimize the deviations from these target values. This approach allows for a more comprehensive and flexible decision-making process, as it takes into account multiple objectives and their relative importance.

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• 32.

### ______________________ permits the use of multiple objective functions.

• A.

Dynamic programming

• B.

Goal programming

• C.

Multi-level programming

• D.

Dual programming

C. Multi-level programming
Explanation
Multi-level programming permits the use of multiple objective functions. This means that it allows for the consideration of multiple goals or objectives simultaneously in decision-making processes. It is a mathematical programming technique that is used when there are multiple levels of decision-making in a hierarchical system. Each level of decision-making may have its own set of objectives, and multi-level programming provides a framework to optimize these objectives at each level while considering the interdependencies between them.

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• 33.

### In goal programming, we attempt to minimise a set of ___________________.

• A.

Real variables

• B.

Deviational variables

• C.

Artificial variables

• D.

Binary variables

• E.

Dummy variables

B. Deviational variables
Explanation
In goal programming, deviational variables are used to measure the deviation or distance from the ideal or target values of the objectives. These variables represent the gaps between the current state and the desired state. The objective is to minimize these deviational variables, indicating that the solution is closer to the desired goals. Real variables, artificial variables, binary variables, and dummy variables are not specifically used for measuring deviations in goal programming.

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• 34.

### The _______________ problem is an example of a 0-1 programming problem.

• A.

Transportation

• B.

Assignment

• C.

Product mix

• D.

Portfolio mix

• E.

B. Assignment
Explanation
The assignment problem is an example of a 0-1 programming problem. In this problem, there are a set of tasks that need to be assigned to a set of resources, with each task and resource having certain costs or values associated with them. The objective is to find the assignment that minimizes or maximizes the total cost or value, while satisfying certain constraints. This problem can be formulated as a binary integer programming problem, where the decision variables represent the assignment of tasks to resources.

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• 35.

### An integer programming solution can never produce ______________ solution than the solution to the same LP problem.

• A.

The same

• B.

A better

• C.

A worse

• D.

An integer

B. A better
Explanation
An integer programming solution can never produce a better solution than the solution to the same LP problem because the integer programming problem is a more restrictive version of the linear programming problem. In an integer programming problem, the variables are constrained to take on only integer values, whereas in a linear programming problem, the variables can take on any real value. Therefore, the feasible region of the integer programming problem is a subset of the feasible region of the linear programming problem, resulting in potentially fewer optimal solutions and a potentially lower objective function value.

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