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For the pseudo-random-proportional action choice rule, J V is selected according to the distribution, (t) ij (t) ij if j Nik (t) (t) (t) k (1730) pij (t) = iu u N k (t) iu i 0 otherwise For the random-proportional action choice rule, r0 = 0 in equation (1729) The next node is therefore always selected randomly based on the distribution given by equation (1730) Gambardella and Dorigo show that the pseudo-random-proportional action choice rule is best for Ant-Q (considering the traveling salesman problem (TSP)) AQ-values are learned using the updating rule (very similar to that of Q-learning), ij (t + 1) = (1 ) ij (t) + ij (t) + max { ju (t)}

.

k u Nj (t)

(Keep for your records)

(1731)

Enter 1 for yourself if no one else can claim you as a dependent You are single and have only one job; or Enter 1 if: You are married, have only one job, and your spouse does not work; or Your wages from a second job or your spouse s wages (or the total of both) are $1,000 or less

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where is referred to as the discount factor (by analogy with pheromone evaporation) and is the learning step size Note that if = 0, then equation (1731) reduces to the global update equation of ACS (refer to equation (1719)) In Ant-Q, update equation (1731) is applied for each ant k after each new node j has been selected, but with ij (t) = 0 The e ect is that the AQ-value associated with link (i, j) is reduced by a factor of (1 ) each time that the link is selected to form part of a candidate solution At the same time the AQ-value is reinforced with an amount proportional to the AQ-value of the best link, (j, u), leaving node j such that the future desirability of link (j, u) is increased The update according to equation (1731) can be seen as the local update rule, similar to equation (1721) of ACS In addition to the above local update, a delayed reinforcement is used, similar to the global update rule of ACS (refer to equation (1719)) After all paths have been constructed, one for each ant, AQ-values are updated using equation (1731), where ij (t) is calculated using equation (1720) with x+ (t) either the global-best or the iteration-best path Gambardella and Dorigo found from their experimental results (with respect to the TSP), that the iteration-best approach is less sensitive to changes in parameter , and is faster in locating solutions of the same quality as the global-best approach

increasing pressure on government and private enterprise for better use of scarce resources, as invested in information technology, can be improved by commercial offthe-shelf technologies that can accelerate adoption of smart data strategy Finally, the companies forget that there are requirements for management and technical training, in order to keep the enterprise competitive Now, let us see how companies such as Honeywell and IBM apply these general considerations by developing a strategy that will address the following issues It is generally accepted that integrating the elements of an enterprise is a good thing Honeywell has integrated people with change and used smart data to accomplish this It has produced corresponding bene ts by developing process maps and continuous improvement It is generally recognized that information, whereby information data facts meaning, and information context knowledge, is an asset that can sometimes become noise Therefore question everything and demand team ownership It is generally accepted that information technology infrastructure and software has evolved into something that is useful and essential while costly, unwieldy, and unmanageable Honeywell realizes that IT is a necessary, but not a suf cient, enabler It is generally accepted that interoperability among data, processes, interfaces, applications, taxonomies, policies, and social networks is highly desirable, including the latest semantic interoperability Therefore Honeywell realizes that execution is the real difference between success and failure Case Study: Honeywell Business Process Reengineering (BPR) We compare Fannie Mae to another example of an organization that embraced the smart data, SOE paradigm: Honeywell In the case entitled A BPR Case Study at Honeywell [23], we embarked on a case study to explore one organization s experiences with radical change for the purpose of uncovering how it achieved success The organization we examined was Honeywell Inc in Phoenix, Arizona From the interview data, we were able to devise a set of 10 lessons to help others transform successfully Two important lessons stand out above the rest First, execution of a carefully developed change plan separates the high performers from less successful BPR projects Second, recognition that dealing with change is dif cult and complicated is not enough Top management should make change management a top priority and communicate the change vision across the organization From the case study, we developed a set of general lessons The case experience allowed us to speak in-depth with people involved in enterprise transformation, which should make the lessons more practical Lesson 1: People are the key enablers of change Business processes are complex, but process mapping offers a comprehensive blueprint of the existing state The blueprint enables systematic identi cation of opportunities for improvement IT is complex, but vendors, consultants, and system designers can create models of the system In contrast, people are unpredictable They cannot be modeled or categorized universally.

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