1391 Words6 Pages

Capital Budgeting Essay

(Derived from Chapter 17: Long-Term Investment Analysis)

Title: The Lorie-Savage Problem

BUS 505 – Multinational Economics of Technology

Table of Contents

1.0 Introduction – Lorie-Savage Problem 3

1.1 Thesis Statement 3

2.0 Supporting Research 4

3.0 Conclusions and Recommendations 6

References 7

1.0 Introduction – Lorie-Savage Problem The Lorie-Savage problem is a problem introduced in 1955 that addresses the issue in how to allocate capital (or resources) among competing investment opportunities with constraints on the available resources. (Lorie & Savage, 1955, p. 229) In defining this problem, Lorie-Savage structures it by outlining three separate scenarios:

1) Given the cost of*…show more content…*

(Lorie & Savage, 1955, p. 234) Turns out this is an Integer Programming optimization problem as it has identified constraints with the end output being to either accept an investment (using the integer 1), or decline (using the integer 0). Since the Lagrange Multipliers are real values, this is more specifically classified as a Mixed Integer Programming problem. (Trick, 1998) Their research proved to be revolutionary as this strayed from the traditional accepted method in using IRR, and this research has evolved since. One example of this is demonstrated where Seymour Kaplan introduced the concept of applying the Generalized Lagrange Multiplier (G.L.M.) method with Integer Programming, using the Lorie-Savage problem as a basis for comparison, that found favorable results in the effectiveness of G.L.M. in producing optimal solutions using NPV to make investment decisions. (Kaplan, 1966, p. 1136) Building on this research was the introduction of using genetic algorithms (GA) to solve capital budgeting problems in allowing financial analysts to find optimal investment combinations for various situations, such as the multiple tax-structures a company may encounter. (Berry & Manongga, 2006, p. 96) Expanding on the GA implementation was research conducted that incorporated fuzzy set theory on problems when investment parameters contained scant or vague information and therefore had great uncertainty. Xiaoxia Huang created a new mean

(Derived from Chapter 17: Long-Term Investment Analysis)

Title: The Lorie-Savage Problem

BUS 505 – Multinational Economics of Technology

Table of Contents

1.0 Introduction – Lorie-Savage Problem 3

1.1 Thesis Statement 3

2.0 Supporting Research 4

3.0 Conclusions and Recommendations 6

References 7

1.0 Introduction – Lorie-Savage Problem The Lorie-Savage problem is a problem introduced in 1955 that addresses the issue in how to allocate capital (or resources) among competing investment opportunities with constraints on the available resources. (Lorie & Savage, 1955, p. 229) In defining this problem, Lorie-Savage structures it by outlining three separate scenarios:

1) Given the cost of

(Lorie & Savage, 1955, p. 234) Turns out this is an Integer Programming optimization problem as it has identified constraints with the end output being to either accept an investment (using the integer 1), or decline (using the integer 0). Since the Lagrange Multipliers are real values, this is more specifically classified as a Mixed Integer Programming problem. (Trick, 1998) Their research proved to be revolutionary as this strayed from the traditional accepted method in using IRR, and this research has evolved since. One example of this is demonstrated where Seymour Kaplan introduced the concept of applying the Generalized Lagrange Multiplier (G.L.M.) method with Integer Programming, using the Lorie-Savage problem as a basis for comparison, that found favorable results in the effectiveness of G.L.M. in producing optimal solutions using NPV to make investment decisions. (Kaplan, 1966, p. 1136) Building on this research was the introduction of using genetic algorithms (GA) to solve capital budgeting problems in allowing financial analysts to find optimal investment combinations for various situations, such as the multiple tax-structures a company may encounter. (Berry & Manongga, 2006, p. 96) Expanding on the GA implementation was research conducted that incorporated fuzzy set theory on problems when investment parameters contained scant or vague information and therefore had great uncertainty. Xiaoxia Huang created a new mean

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