# Operations Research 2

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1 Operations Research 2 Lecturer: David Ramsey Room B August 31, / 80

2 Recommended Text Operations Research- An Introduction. Hamdy A. Taha (003/TAH) The 7th edition is the most up to date in the library. The library has 4 copies of this edition. In addition, there are around 25 copies of older editions. Also, Introduction to Operations Research. Hillier and Lieberman. Course notes and tutorial sheets will be available on the Internet. 2 / 80

3 Module Outline 1. Integer Programming. Applications to the travelling salesperson s problem and the knapsack (packing) problem. 2. Dynamic Programming. Deterministic. Stochastic. Finite horizon. Infinite horizon problems. Discounting and average-cost problems. 3. Game Theory. Matrix Games. Minimax solutions. Nash equilibria. Correlated Equilibrium. Applications to economics and biology. 3 / 80

4 CHAPTER 1 - INTEGER PROGRAMMING AND THE BRANCH AND BOUND ALGORITHM 1.1 The definition of an integer programming problem An integer programming problem can be defined by the description of a linear programming problem together with the constraint that the variables must be integers. It is assumed that the variables are constrained to be non-negative. 4 / 80

5 Example of an integer programming problem subject to max z = 4x 1 + 5x 2 x 1 + x 2 5 6x x 2 45, where x 1, x 2 are non-negative integers. Note that we can describe any integer programming problem as a maximisation problem (the minimisation of 4x 1 + 5x 2 is equivalent to the maximisation of 4x 1 5x 2 ). The function z = 4x 1 + 5x 2 is called the objective function. 5 / 80

6 1.2 The Branch and Bound Method - The initial bound We may find an initial upper bound on the optimal value in the integer programming problem by solving the corresponding linear programming problem. Consider the problem given above. If the values of x 1 and x 2 at the optimal solution to this problem are integers, then this must be the optimal solution to the integer programming problem. 6 / 80

7 The Branch and Bound Method - The initial branching step Suppose x 1 = c at this optimal solution, where c is not an integer. We can split the initial linear programming problem into two linear programming problems. The first problem is obtained by adding the constraint x 1 c ( c is the largest integer not greater than c). The second is obtained by adding the constraint x 1 c ( c is the smallest integer not less than c). 7 / 80

8 The initial branching step The union of the feasible sets for these two LP problems is the feasible set for the initial LP problem with the set of (x 1, x 2 ) satisfying c < x 1 < c removed. Since no feasible solutions in which both x 1 and x 2 are integers have been removed (see diagram below, the dots show the feasible points in the integer programming problem), the optimal solution to the integer programming problem must lie in one of the feasible regions of these two new problems. 8 / 80

9 Solution of the IP problem Consider the linear programming problem max z = 4x 1 + 5x 2 subject to x 1 + x 2 5 6x x 2 45, The feasible region is illustrated in the figure on the next slide. 9 / 80

10 Soultion of the IP problem 5 F 1 F / 80

11 Solution of the initial LP problem We can solve this graphically. The solution must lie at one of the apexes of the feasible set. The point of intersection of the two lines representing the linear constraints is given by x 1 + x 2 =5 6x x 2 =45. This leads to x 1 = 1.25, x 2 = The value obtained at this point is = / 80

12 Solution of the initial LP problem The other apexes are (0, 0), (0, 4.5) and (5, 0) and the corresponding values are 0, 22.5, 20. It follows that the optimal solution of this LP problem is (1.25, 3.75) and the optimal value is This value is an upper bound on the optimal value in the integer programming (IP) problem. In addition, since the coefficients of the objective function are integers, then the value at any feasible point must also be an integer. It immediately follows that 23 is an upper bound on the optimal value in the IP problem. 12 / 80

13 The initial branch After the initial step, which produces an upper bound on the optimal value, we carry out a branching step. This involves restricting the values of one of the variables and creating 2 new linear programming problems. Suppose (x 1, x 2,..., x n ) = (c 1, c 2,..., c n ) is the optimal solution of the present LP problem, where c i is not an integer. We add the constraint x i c i to obtain one of the LP problems and add the constraint x i c i to obtain the other LP problem. 13 / 80

14 The two initial branch LPs In this case setting i = 1, the first LP problem obtained is thus max z = 4x 1 + 5x 2 subject to x 1 + x 2 5 6x x 2 45 x 1 1. The feasible region of this problem is denoted in the diagram as F / 80

15 The two initial branch LPs The second LP problem is subject to max z = 4x 1 + 5x 2 x 1 + x 2 5 6x x 2 45 x 1 2. The feasible region is denoted F 2 in the diagram above. The interior of the shaded area has been removed from our considerations. 15 / 80

16 The branching mechanism This is the branching part of the algorithm. Each branch creates 2 LP problems from one LP problem and thus it may seem that the number of LP problems considered increases. However, 1. If the solution of one of the branch LP problems is a vector of integers, then we do not have to branch any longer. Also, we can use bounds on the optimal value to check whether a solution is or cannot be optimal. 2. Many of the branches will lead to LP problems whose set of feasible solutions is empty. It can be shown that the branch and bound algorithm will always come to a halt. 16 / 80

17 Choice of way to split It should be noted that after solving the initial LP problem, we can form the next 2 LP problems by adding constraints on x 2. In this case, the first LP problem is max z = 4x 1 + 5x 2 subject to x 1 + x 2 5 6x x 2 45 x / 80

18 Choice of way to split The second LP problem is given by max z = 4x 1 + 5x 2 subject to x 1 + x 2 5 6x x 2 45 x / 80

19 Bounds on the optimal value and adding branches 1. If the solution of one of the branch LP problems is a vector of integers, then this gives a lower bound on the optimal value. This branch does not need to investigated any further. 2. If the solution of one of the branch LP problems contains a component that is not an integer (say x i = c, where c / Z), then the optimal value of this problem is an upper bound for the value of the IP problem obtained in any the lower branch problems resulting from this one. Furthermore, if the coefficients are integers and the variables must be integers, the integer part of the value is an upper bound on valid solutions found in lower branches. 19 / 80

20 Bounds on the optimal value and adding branches If we have already found a feasible solution whose value is greater or equal to the upper bound on the value in a particular branch, then this branch does not need to be investigated any further (since splitting this branch will not lead to a better solution). Otherwise, we may split this branch into two LP problems. The first is obtained by adding the constraint x i c, the second is obtained by adding the constraint x i c (where c is the value of x i in the optimal solution of the appropriate LP problem). A branch is said to be presently active, if it has not yet been split and a split is under consideration under the criterion above, or we have not yet solved both LP problems of a branch that has been split. 20 / 80

21 Bounds on the optimal value and adding branches 3. The present upper bound on the objective function is the maximum of the upper bounds found in the branches that are presently active. 4. The present lower bound on the objective function is the value of the best feasible solution found so far. 21 / 80

22 Example 1.1 Solve the IP problem. max z = 4x 1 + 5x 2 subject to x 1 + x 2 5 6x x 2 45, where x 1 and x 2 are non-negative integers. 22 / 80

23 Solution The first part of the solution is to find an upper bound on the objective function by solving the corresponding LP problem. The solution to this problem has already been shown to be (1.25, 3.75) and the optimal value in this problem is As argued above, this means that 23 is an upper bound on the value in the IP problem. We now split this LP problem into 2 LP problems. The first is obtained by adding the constraint x 1 1. The second is obtained by adding the constraint x / 80

24 Solution We now consider these LP problems in turn. The apexes of the feasible region F 1 of the first problem (see diagram) are (0, 0), (1, 0), (0, 4.5) and (1, 3.9) [the intersection point of the lines x 1 = 1 and 6x x 2 = 45]. The corresponding values of the objective function are 0, 4, 22.5 and Hence, the optimal solution of this problem is (1, 3.9) and the optimal value Arguing as above, since x 2 is not an integer at this point, it follows that 23 is an upper bound on the value of the IP problem obtained in any of the branches starting from this problem. 24 / 80

25 Solution We now consider the second LP problem. The apexes of the feasible region are (2, 0), (5, 0) and (2, 3). The corresponding values of the objective function are 8, 20 and 23. Hence, the optimal solution of this problem is (2, 3) and the optimal value 23. Since, both components of the solution are integers this is a lower bound on the optimal value in the IP problem. Since we have already shown that 23 is an upper bound on the optimal value of the IP problem, it is clear that this solution is an optimal solution to the IP problem. 25 / 80

26 Summary of the branch and bound procedure The branch and bound method may be summarised as follows Figure: Summary of the branch and bound process for Example / 80

27 Note on choice of branches It is not clear what the optimal procedure for solving such a problem should be. For example, if we first solved the LP problem with feasible region F 2, we would find the optimal solution immediately. However, in general after solving the LP problem with feasible region F 1, there is no reason why we cannot go on to split this feasible region into 2. Since the optimal solution to this LP problem is (1,3.9), the first LP problem is obtained by adding the constraint x 2 3 and the second LP problem is obtained by adding the constraint x / 80

28 Note on choice of branches The apexes of the feasible region in the first problem are (0, 0), (1, 0), (0, 3) and (1, 3). The values corresponding to these points are 0, 4, 15 and 19. Hence, 19 is a lower bound on the optimal value. The apexes of the feasible region in the second problem are (0, 4), (0, 4.5) and ( 5 6, 4). The values at these are 20, 22.5 and This gives an upper bound of 23 for solutions to the IP problem in this region. This region may then be split into two. 28 / 80

29 Note on choice of branches It can be seen that the complexity of solution depends on the choice of LP problem to be solved at each stage. There is no general rule that guarantees that the optimal choice is made at each stage. One general rule of thumb would be to solve all the LP problems of a given depth, starting with the branches associated with the largest upper bounds (unless it becomes clear that the optimal solution has been found), before splitting these LP problems. The depth of an LP problem is understood to be the number of splits required to obtain it from the original LP problem. Such a procedure is likely to avoid the extremely long search times that may result from exhausitively searching a non-optimal branch before searching along other branches. 29 / 80

30 1.3 Mixed Integer/Linear Programming problems (IP/LP problems) In such problems some, but not all, of the variables are constrained to be integers. The branch and bound algorithm can easily be adapted to such problems. Suppose (x 1, x 2,..., x k ) are constrained to be integers and (x k+1, x k+2..., x n ) are not. It is assumed that the problem is a maximisation problem. 30 / 80

31 Mixed Integer/Linear Programming problems (IP/LP problems) At the first step of the algortithm, we solve the corresponding LP problem in which none of the variables are constrained to be integers. The optimal value for this problem is an upper bound for the optimal value in the IP/LP problem. If the values of (x 1, x 2,... x k ) are integers, then we have found the optimal solution. Otherwise, we can split the initial LP problem into two problems as before. If x i = c / Z, 1 i k at the optimal solution of this problem, then the first LP problem is obtained by adding the constraint x i c and the second LP problem is obtained by adding the constraint x i c. 31 / 80

32 The bounding procedure The bounding procedure is as for pure IP problems with the following change: 1. If the components of the solution of one of the branch LP problems that are supposed to be integers are integers, then this gives a lower bound on the optimal value. This branch does not need to investigated any further. 32 / 80

33 Example 1.2 Solve the mixed LP/IP problem. max z = 4x 1 + 5x 2 subject to x 1 + x 2 5 6x x 2 45, where x 1, x 2 are non-negative and x 2 is an integer. 33 / 80

34 Solution As before the first step is to solve the LP problem obtained by ignoring the constraint that x 2 is an integer. The solution of this problem (as shown above) is (1.25, 3.75) and the optimal value is It should be noted that the optimal value in this problem is not necessarily an integer and hence we cannot immediately improve this upper bound as in the pure IP problem with integer coefficients. 34 / 80

35 Solution We now split the initial linear programming problem into 2 problems. Since x 1 does not have to be an integer, the two LP problems are obtained as follows: 1. The first problem is obtained by adding the constraint x 2 3 to the initial LP problem. 2. The second problem is obtained by adding the constraint x 2 4 to the initial LP problem. 35 / 80

36 Graphical solution The problems obtained are illustrated below: 5 F 1 F / 80

37 Solution The apexes of F 1 are (0,4), (0,4.5), ( 5 6, 4) and the corresponding values of the objective function are 20, 22.5, Since x 2 is an integer at the final solution, this gives us a lower bound on the optimal value of the mixed IP/LP problem. We do not have to split this LP problem into two. The apexes of F 2 are (0,0), (0,3), (2,3) and (5,0). The corresponding values of the objective function are 0, 15, 23, 20. It follows that the solution of this mixed IP/LP problem is ( 5 6, 4) and the optimal value / 80

38 1.4 The Travelling Salesperson Problem (TSP) The problem is to find the shortest tour which visits all of n cities and finishes in the starting city. Without loss of generality, we can assume that the initial city is labelled 1. Suppose the distance between city i and j is d i,j. For convenience, define d i,i =. Let x i,j = 1 if a tour goes to city j after visiting city i, otherwise x i,j = / 80

39 The Travelling Salesperson Problem The mathematical formulation of the TSP is as follows: Minimize n n z = d i,j x i,j, i=1 j=1 such that n x i,j = 1, j=1 n x i,j = 1, i=1 x i,j 0 i = 1, 2,..., n j = 1, 2,..., n x i,j is an integer 39 / 80

40 The Travelling Salesperson Problem The first constraint states that exactly one city must be visited immediately after city i. The second constraint states that exactly one city must be visited immediately before city j. It follows that the TSP is an integer programming problem. It should be noted that in the natural interpretation of this problem d i,j = d j,i. However, the algorithms considered below can be used to solved more general interpretations of the TSP. The distances d i,j are normally given in a matrix where d i,j is the value in row i and column j. 40 / 80

41 1.4.1 The Subtour Reversal Heuristic for Finding Good Solutions of the TSP problem The algorithm works as follows: 1. An initial tour is found by at each step travelling from the present city to the nearest city that has not yet been visited. When all the cities have been visited, then we return to city We then see whether a shorter tour can be found by reversing the order of two cities in the tour, then by reversing the order of three cities (based on the shortest tour found so far) and so on until the order of n 1 cities is reversed. This algorithm will find a good solution, but not necessarily the optimal solution. 41 / 80

42 Example 1.3 Consider the following 5-city TSP problem given by the distance matrix D = / 80

43 Example 1.3 First we find an initial tour. Starting from city 1, we go to city 2 (the closest). Distance 12 From city 2, we can go to any city except city 1. City 3 is the closest. Distance 8 From city 3, we can go to city 4 or 5. City 4 is the closest. Distance 16 From city 4, we must go to city 5. Distance 19. Finally, we return to city 1. Distance / 80

44 Example 1.3 Hence, the initial tour is The length of this tour is =76. We now consider reversing any two cities in the tour. It should be noted that city 1 should not be moved, since the resulting sequence does not define a tour. 44 / 80

45 Example 1.3 Hence, we consider the following reversals Old Tour New Tour Length = = =79 None of these tours is better than the initial tour. 45 / 80

46 Example 1.3 Next we consider the reversal of three cities in a tour in the best tour found so far (the initial tour). Old Tour New Tour Length = =82 Hence, the best tour found so far is Length / 80

47 Example 1.3 Finally, we consider the reversal of four cities in this new tour. Old Tour New Tour Length =73 Hence, we have found two tours ( and ) which are of the same length. It should be noted that if the distance matrix is symmetric, then reversing n 1 cities in a tour results in a tour of unchanged length as the tour obtained is a pure reversal of the original tour. 47 / 80

48 1.4.2 The Branch and Bound Method for Solving the TSP This method finds the optimal tour by solving an appropriate sequence of assignment problems. This approach uses the fact that every tour can be interpreted as an assignment by assuming that individual i is assigned to task j where j is the city which comes immediately after city i in the tour. The cost of assigning individual i to task j is defined to be d i,j. Since d i,i =, individual i will not be assigned to task i. The tour above can be interpreted as the following assignment: Individual 1 does task 4, individual 4 does task 3, individual 3 does task 2, individual 2 does task 5 and individual 5 does task / 80

49 1.4.2 The Branch and Bound Algorithm for Solving the TSP However, not every assignment can be interpreted as a tour. For example, if individual 1 does task 3, individual 3 does task 4 and individual 4 does task 1, then we have obtained a cycle which does not visit all the cities (this assignment corresponds to the cycle ). In this case, individual 2 would be assigned to task 5 and individual 5 would be assigned to task 2 (corresponding to the cycle 2-5-2). It can be shown that every assignment that cannot be interpreted as a tour includes two or more cycles. 49 / 80

50 The Branch and Bound Algorithm for Solving the TSP The algorithm is as follows: 1. Since this is a cost minimisation problem, the value of the solution to an assignment problem is a lower bound on the optimal value of the TSP. A simple upper bound is given by the length of the initial tour using the subtour reversal heuristic. 2. Solve the assignment problem corresponding to the distance matrix D. If the assignment corresponds to a tour, then this is the optimal tour. 3. If the assignment does not correspond to a tour, then it contains at least two cycles. We split the present assignment problem into a set of new assignment problems, each obtained by setting one of the distances in a chosen (normally the shortest) cycle to be. 50 / 80

51 The Branch and Bound Algorithm for Solving the TSP 4. These problems can be solved in the same way. 5. If the solution of an assignment problem does not correspond to a tour, we can split it (if it seems possible that a better solution than the best found so far can be found) into a set of new assignment problems as above. 6. If the solution of an assignment problem corresponds to a tour, then we do not need to split this problem and a we obtain an upper bound on the minimum cost. 51 / 80

52 Example 1.4 Solve the TSP problem given by the matrix D = / 80

53 Example 1.4 We can find an initial upper bound on the minimum distance by using the shortest distance heuristic. From 1 we go to 3 (the nearest city). Distance 3. From 3 we go to 4. Distance 7. From 4 we go to 2. Distance 1. From 2 we must go to 5. Distance 2. From 5 we return to 1. Distance 3. The total distance is / 80

54 Example 1.4 We now solve the corresponding assignment problem, call this A 1. First we subtract the minimum value in a row from the other row values. This gives us the matrix / 80

55 Example 1.4 Next we subtract the minimum value in a column from the other column values. This gives us the matrix The sum of the numbers subtracted gives us a lower bound on the cost of the optimal assignment ( =14). 55 / 80

56 Example 1.4 It is not possible to choose 5 zeroes (5 is the number of cities) such that there is one zero in each row and one zero in each column. In this case, we highlight rows and columns such that all zeroes are highlighted. The minimum value of all the non-highlighted columns is subtracted from the non-highlighted elements. This value is added to the elements at the intersection of the highlighted rows and columns. 56 / 80

57 Example 1.4 Highlighting the appropriate rows and columns in red (those doubly highlighted are in blue) leads to The minimum value of the non-highlighted (black) elements (1) is subtracted from the black elements. This element is added to the blue elements and the lower bound on the optimal cost (which is now 14+1=15). 57 / 80

58 Example 1.4 This gives There is a unique set of fives zeroes such that there is one zero in each row and one in each column (marked in green). The optimal assignment is 1 3, 2 5, 3 1, 4 2, 5 4. The optimal cost is / 80

59 Example 1.4 This assignment contains two cycles and We obtain one new assignment problem, A 2, by setting d 1,3 = (the distance of one of the transitions in the shortest cycle). The other assignment problem, A 3, is obtained by setting d 3,1 =. 59 / 80

60 Example 1.4 The matrix corresponding to A 2 is given by D = The solution to this assignment problem (the calculations are analogous to those made above and are thus omitted) is 1 4, 2 5, 3 1, 4 3, 5 2 and the cost of this assignment is / 80

61 Example 1.4 This assignment contains two cycles and Hence, we could define two new assignment problems to solve. One by setting d 2,5 = and one by setting d 5,2 =. However, the cost of this assignment is 17 (i.e. already more than the solution found using the shortest distance heuristic). Hence, we do not need to investigate this branch any more. 61 / 80

62 Example 1.4 The matrix corresponding to A 3 is given by D = The solution to this assignment problem (details omitted) is 1 3, 2 5, 3 4, 4 2, 5 1 and the cost of this assignment is / 80

63 Example 1.4 This solution corresponds to the tour and so this branch does not need to be investigated any further. Since it is impossible to find a better solution in the other branch, we have found the optimal solution. It should be noted that this is the solution found by the shortest route heuristic. However, while the branch and bound algorithm will always find an optimal solution, the shortest route and subtour reversal heuristics are not guaranteed to do so. 63 / 80

64 Example 1.4 The solution can be illustrated using a branch and bound tree as shown below: Figure: Summary of the branch and bound process for Example / 80

65 1.5 The Knapsack Problem The problem is to load a rucksack in such a way as to maximize the value of the objects in the rucksack while not exceeding the maximum weight. Suppose there are n types of object and k i objects of type i are available. The weight of objects of type i is w i. The value of objects of type i is v i. 65 / 80

66 The Knapsack Problem Let x i be the number of objects of type i packed into the rucksack. Let W be the maximum weight that can be carried. In mathematical terms, we wish to maximise z = n x i v i i=1 subject to n w i W ; x i {0, 1, 2,..., k i }, 1 i n. i=1 66 / 80

67 The Knapsack Problem It can thus be seen that the knapsack problem is an integer programming problem. The standard branch and bound technique can be used to find the solution of this problem. In this case, the form of the problem means that the associated linear programming problems are easy to solve. 67 / 80

68 The Knapsack Problem Intuitively, if we could pack fractions of objects (i.e. turn the problem into an LP problem), it would be optimal to start by packing objects with the highest value to weight ratio and continue until the rucksack is full. If the solution found in this way involves packing an integer number of objects of each type, then this is the optimal solution to the IP problem. 68 / 80

69 The Knapsack Problem Otherwise, only a fraction of the final object is packed. Suppose this object is of type i and x i = c. As in the general IP problem, we can split the original LP problem into two different problems. To obtain the first, we add the constraint x i c and to obtain the second, we add the constraint x i c. These LP problems can be solved in the same way as the initial LP problem. Upper and lower bounds on the optimal value to the IP problem are found in the same way as for general IP problems. 69 / 80

70 Example 1.5 A lorry can carry 4 tonnes. Three types of object can be loaded. Their values and weights (in tonnes) are given in the table below. Item i v i w i Find the load which maximises the value of the objects in the lorry. 70 / 80

71 Example 1.5 In mathematical terms, the problem to be solved is to maximise subject to z = 31x x x 3 2x 1 + 3x 2 + x 3 4. Note that the maximum number of objects of type i that can be packed is W /w i. Hence, x 1 {0, 1, 2}, x 2 {0, 1} and x 3 {0, 1, 2, 3, 4}. 71 / 80

72 Example 1.5 We first solve the linear programming problem obtained by allowing the x i to be non-negative reals. In order to do this, we calculate the value to weight ratios for each of the types of object. We have r 1 = v 1 w 1 = 15.5; r 2 = v 2 w 2 = ; r 3 = v 3 w 3 = 14 To find the optimal solution to the LP, we first load an object of type 2 (the maximum number of such objects possible). One tonne is still available, so we load half an object of type 1 (has the 2nd highest value to weight ratio and weighs 2 tonnes). 72 / 80

73 Example 1.5 Hence, the optimal solution to the initial LP problem is x 1 = 0.5, x 2 = 1 and x 3 = 0. The value of this solution is = This is an upper bound on the optimal value in the knapsack problem. Since the coefficients in the objective function are integers, the optimal value must be an integer. Hence, this upper bound can be immediately tightened to / 80

74 Example 1.5 We now split this LP problem into two LP problems. The first is obtained by adding the constraint x It follows that given this constraint, we must have x 1 = 0. The second LP problem is obtained by adding the constraint x 1 0.5, i.e. x / 80

75 Example 1.5 The first of these LP problems is to maximise subject to z = 47x x 3 3x 2 + x 3 4. This is solved as before. Since objects of type 2 have a higher value to weight ratio, we first pack an object of type 2 (the maximum number). A load of one tonne is left, hence we can pack an object of type / 80

76 Example 1.5 It follows that the optimal solution to this LP problem is x 1 = 0, x 2 = 1, x 3 = 1 and the value is = 61. Since the values of the decision variables are integers. This is a lower bound on the optimal value. 76 / 80

77 Example 1.5 We now solve the second LP problem. It should be noted that the interpretation of the problem allows us to update the constraints of the LP problem to be solved, which often allows us to find the optimal solution more quickly. In physical terms, the constraint x 1 1 means that we should first load an object of type 2 (giving a reward of 31). We then consider what other objects we can load. The residual load available is 2 tonnes. It follows that no objects of type 2 can be loaded, up to one object of type 1 and up to 2 objects of type / 80

78 Example 1.5 This LP problem is thus to maximise z = 31 + x x 3 subject to 2x 1 + x 3 2, where x 1 is the additional number of type 1 objects packed (above 1). 78 / 80

79 Example 1.5 Since objects of type 1 have a higher value to weight ratio than objects of type 3, we first load another type 1 object. There is no residual load left. It follows that the optimal solution to this problem is to load just 2 objects of type 1. The value of this solution is 2 31 = 62. Since this is a feasible solution which achieves the upper bound on the objective function, this is the optimal solution. 79 / 80

80 Example 1.5 The solution can be illustrated using a branch and bound tree as shown below: Figure: Summary of the branch and bound process for Example / 80

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