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(D-Rank) Both A-Rank and D-Rank methods do not always lead to optimal solutions because they ignore tax considerations in IT projects, but these methods do provide exibility to solve the ITCB problem without a clear de nition of an objective function Given restrictions posed by ranking methods, and dif culty in using integer programming models, we nd that heuristic approaches are best suited for solving the ITCB problem The heuristic approaches provide us with the exibility to model the objective function appropriately, de ne accounting rules, and nd the solution to the ITCB problem in polynomial time Therefore we developed two simulated annealing (SA) procedures for solving the ITCB problem Our rst SA procedure utilizes a feasibility restoration component In our second procedure, we eliminate the feasibility restoration step from the rst SA procedure The feasibility restoration always maintains a feasible solution In the event when a solution is not feasible, failure occurs in our second SA procedure and annealing schedule is omitted We call our rst SA procedure the feasibility restoring SA (FRSA) procedure, and the second procedure the simple SA (SSA) procedure We use n object-oriented programming language C to build a simulation model for generating a real-world set of IT projects, revenues, and IT budgets The probability density functions for revenues, IT budget, and individual project types were developed using the data from an Information Week survey of Compustat s top 500 rms The descriptive statistics for revenues and IT budgets are summarized in Table 25 Table 26 illustrates the descriptive statistics for the natural log-transformed variables The correlation between log transformed revenue and IT budget variables was 0722 The simulation model used base e antilogarithm exponent transformation of Revenue and IT Budget variables to compute the actual values of revenues and IT budget for analysis Individual hardware, personnel, software, and other category projects were generated with normal distributions with means of $456 million, $709 million, $1182 million, and $405 million, respectively The standard deviations of 10% were considered for each of the categories. to tip the magnetization into the xy plane of the rotating frame of . Barcode Encoder In Visual Basic .NET Using Barcode drawer for .NET framework Control to .Related: C# Intelligent Mail Generator , Printing Data Matrix VB.NET , Generate EAN-8 VB.NET The value of corporate tax was kept constant at 35% The value of the IT Budget variable was randomly generated The expected value of the Revenue variable was generated using an expectation formula The actual values of the IT budget and revenues were then generated using the antilogarithm function A prespeci ed number of projects were generated for funding Four different approaches the A-Rank, the D-Rank, the FRSA, and the SSA were used to make four different IT project funding selections We provide a simple working example to illustrate the concept of project generation and selection using the A-Rank and D-Rank methods We assume 10 simulation replications and generation of 10 random projects for each simulation replication For 10 simulation replications, we rst generate 10 random IT budgets Using our simulation model, these 10 random IT budgets are {1705, 1647, 1845, 1855, 1858, 1552, 1629, 1752, 1723, 1766} The expected 10 revenues of each of the IT budgets are computed using the expectation equation mentioned previously These 10 expected revenue values are {2166, 2134, 2242, 2247, 2249, 2082, 2124, 2191, 2175, 2199} As the 10 random values represent the log-transformed values, we use the base e antilogarithm exponent transformation to obtain actual values of IT budget and revenue These values are represented in millions of dollars in the following sets: IT Budget 5 f25:39; 14:22; 102:97; 113:80; 117:27; 5:49; 11:87; 40:62; 30:40; 46:75g Revenues 5 f2555:02; 1863:53; 5472:97; 5779:01; 5874:12; 1111:33; 1689:67; 3299:57; 2817:92; 3560:75g For the rst replication, we use the rst elements from the IT Budget and Revenues set These elements represent an IT budget of $2539 million and revenues of $255 billion For the rst replication, we generate hardware, personnel, software, and other projects with an approximate probability of 27%, 42%, 7%, and 24%, respectively These probabilities were chosen due to previous studies The actual computer implementation can generate 10 random numbers between the interval (0, 1), and based on the value of each random number, we can allocate the project type using the following four rules: Rule 1: Rule 2: Rule 3: Rule 4: IF random number A (0, 027], THEN project 5 hardware IF random number A [027, 069), THEN project 5 personnel IF random number A [069, 076), THEN project 5 software IF random number A [076 1), THEN project 5 other. 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