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where L(x, g , h ) is de ned in equation (A27) The second population maximizes the tness function, (1247) f ( g , h ) = min L(x, g , h )

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Both populations use an ES as search algorithm Kramer et al [488] developed a biased mutation operator to lead the search to more promising, feasible areas The mean of the Gaussian distribution, from which mutational step sizes are sampled, is biased to shift the center of the mutation distribution as illustrated in Figure 123 Let i (t) = ( i1 (t), , inx (t)) be the bias coe cient vector, with ij (0) U ( 1, 1), for all j = 1, , nx The bias vector, i (t), is then de ned as ij (t) = ij (t) ij (t) Mutational step sizes are calculated as xij (t) = ij (t)Nj (0, 1) + ij (t) = Nj ( ij (t), ij (t)) Bias coe cients are self-adapted using ij (t) = ij (t) + N (0, 1) (1249) (1248)

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43,400 78,459 161,143 51,378 43,400 43,400 51,378 36,198 36,198 75,752 108,500 69,000 98,912 51,378 78,459 43,400 55,071 75,600 46,026

x2 2

49,815 90,000 220,000 59,748 49,815 49,815 59,748 41,954 41,954 81,387 130,000 88,500 131,897 59,748 90,000 49,815 63,461 84,600 50,040

Figure 123 Biased Mutation for Evolution Strategies with = 01 (suggested in [488]) A very simple approach to handle constraints is to change the selection operator to select the next population as follows: Until individuals have been selected, First select the best feasible individuals If all feasible solutions have been selected, select those that violate the fewest constraints As last resort, when individuals are infeasible, and they violate the same number of constraints, select the most t individuals When a selection operator is applied to select one of two individuals, the following rules can be applied: If both are feasible, select the one with the best tness If one is feasible, and the other infeasible, select the feasible solution If both are infeasible, select the one that violates the fewest constraints If constraint violation is the same, select the most t individual

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By contrast, the proprietary aspect is external to smart data strategy that leverages open and interoperable standards instead The Austin-Tetra product addresses data quality requirements: data cleansing (eliminating duplicates and xing errors), standardizing data formats, con guration management (ensuring data currency), and maintenance These functionalities are essential to smart data strategy By contrast, smart data strategy does not standardize data formats; it maps and translates, providing data exchange based on open and interoperable standards for both syntax and semantics Other Austin-Tetra capability includes risk assessment, analyzing possible disruption in the supply chain, which is an application of decision support The AustinTetra product addresses process improvement that is a theme for smart data strategy In the supplier data context, data is used to pro le and evaluate suppliers, for prequali cation, and for work ow and monitoring that is a part of policy compliance In another example, The Digital Entertainment Group and consultants Capgemini and Teradata nished the initial phase of an industry- rst supply chain study analyzing merchandising execution at what s known as the the last 100 feet of retail, from the stockroom to the store oor What they found was signi cant room for improvement in data synchronization between suppliers and retailers [8] Large scale industry and government initiatives were initiated in the early 1990s to attend to supplier data as electronic commerce took off These efforts are the bureaucratic-intensive initiatives that consumed many resources to harmonize the use of standards and implementation conventions While such efforts were useful in focusing attention on supplier and prime producer interaction, they had dif culty accommodating advancing technology as the perception was that it was easier to standardize in rigid electronic data interchange technology than to accommodate the emerging service-oriented architecture and web services Looking at Boeing, for instance, the company governs supplier relationships with a standard trading partner agreement.

One of the rst, simple ES for solving multi-objective (MOO) problems was developed by Knowles and Corne [469] The Pareto archived evolution strategy (PAES)

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