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Yayın On the sensitivity of desirability functions for multiresponse optimization(American Institute of Mathematical Sciences, 2008-11) Aksezer, Sezgin ÇağlarDesirability functions have been one of the most important multiresponse optimization technique since the early eighties. Main reasons for this popularity might be counted as the convenience of the implementation of the method and it's availability in many experimental design software packages. Technique itself involves somehow subjective parameters such as the importance coefficients between response characteristics that are used to calculate overall desirability, weights used in determining the shape of each individual response and the size of the specification band of the response. However, the impact of these sensitive parameters on the solution set is mostly uninvestigated. This paper proposes a procedure to analyze the sensitivity of the important characteristic parameters of desirability functions and their impact on pareto-optimal solution set. The proposed procedure uses the experimental design tools on the solution space and estimates a prediction equation on the overall desirability to identify the sensitive parameters. For illustration, a classical desirability example is selected from the literature and results are given along with the discussion.Yayın Tractable supply chain production planning, modeling nonlinear lead time and quality of service constraints(Elsevier Ltd, 2007) Anlı, Osman Murat; Caramanis, Michael C.; Paschalidis, Ioannis Ch.This paper addresses the task of coordinated planning of a supply chain (SC). Work in process (WIP) in each facility participating in the SC, finished goods inventory, and backlogged demand costs are minimized over the planning horizon. In addition to the usual modeling of linear material flow balance equations, variable lead time (LT) requirements, resulting from the increasing incremental WIP as a facility's utilization increases, are also modeled. In recognition of the emerging significance of quality of service (QoS), that is control of stockout probability to meet demand on time, maximum stockout probability constraints are also modeled explicitly. Lead time and QoS modeling require incorporation of nonlinear constraints in the production planning optimization process. The quantification of these nonlinear constraints must capture statistics of the stochastic behaviour of production facilities revealed during a time scale for shorter than the customary weekly time scale of the planning process. The apparent computational complexity of planning production against variable LT and QoS constraints has long resulted in MRP-based scheduling practices that ignore the LT and QoS constraints has long resulted in MRP-based scheduling practices that ignore the LT and QoS impact to the plan's detriment. The computational complexity challenge was overcome by proposing and adopting a time-scale decomposition approach to production planning where short-time-scale stochastic dynamics are modeled in multiple facility-specific subproblems that receive tentative targets from a deterministic master problem and return statistics to it. A converging and scalable iterative methodology is implemented, providing evidence that significantly lower cost production plans are achievable in a computationally tractable manner.Yayın Multiresponse optimisation of powder metals via probabilistic loss functions(Inderscience Enterprises Ltd, 2013) Aksezer, Sezgin Çağlar; Benneyan, James C.Quadratic loss functions have been used extensively within the context of quality engineering and experimental design for process and product optimisation and robust design. In general, this approach determines optimal parameter settings based on minimising the sum of individual or mean loss of the associated response(s) of interest in a defined response surface. While the method is neat and handy, it totally neglects the effect of deviations on the desirable value of loss function. This paper utilises variance and probability distribution of loss functions for developing an in depth optimisation scheme that balances mean and variance of loss in a Pareto optimal manner. Since losses are usually defined in financial terms, this model then further improved to handle the user determined risk levels so that financial losses are being restricted within a certain region of interest. Application of the model is illustrated on a multiresponse optimisation problem from powder metallurgy industry.Yayın Probability distributions and variances of quadratic loss functions(2006) Benneyan, James C.; Aksezer, Sezgin ÇağlarThe use of quadratic loss functions has been advocated in quality engineering and experimental design for process optimization and robust design. We derive theoretical density functions and variances for nominal-the-best, smaller-the-better, and larger-the-better quadratic loss functions in general and when the response variable has a specified distribution. While considerable attention has been given to individual and mean loss, in some applications it is of interest also to know something about the loss distribution and variance. Results frequently exhibit high variance and skew and unique density functions in cases for which it is not advisable to base decisions on expected loss alone.Yayın Interdependent network restoration: On the value of information-sharing(Elsevier Science BV, 2015-07-01) Sharkey, Thomas C.; Çavdaroğlu, Burak; Nguyen, Huy; Holman, Jonathan; Mitchell, John E. M.; Wallace, William AlWe consider restoring multiple interdependent infrastructure networks after a disaster damages components in them and disrupts the services provided by them. Our particular focus is on interdependent infrastructure restoration (IIR) where both the operations and the restoration of the infrastructures are linked across systems. We provide new mathematical formulations of restoration interdependencies in order to incorporate them into an interdependent integrated network design and scheduling (IINDS) problem. The IIR efforts resulting from solving this IINDS problem model a centralized decision-making environment where a single decision-maker controls the resources of all infrastructures. In reality, individual infrastructures often determine their restoration efforts in an independent, decentralized manner with little communication among them. We provide algorithms to model various levels of decentralization in IIR efforts. These algorithms are applied to realistic damage scenarios for interdependent infrastructure systems in order to determine the loss in restoration effectiveness resulting from decentralized decision-making. Our computational tests demonstrate that this loss can be greatly mitigated by having infrastructures share information about their planned restoration efforts.
