There are varieties of QFD combination forms available that can help management to choose the right model for his/her types of problem. The proposed MOCC-QFD-FMEA model is a right model to include variety of objectives as well as the risk factors into the model of the problem. Due to the fact that the model also takes into consideration the concept of Fuzzy set, it further allows management...

1.1. Lagrange Duality . . . . . . . . . . . . . . . . . . p. 2 1.1.1. Separable Problems – Decomposition . . . . . . . p. 7 1.1.2. Partitioning . . . . . . . . . . . . . . . . . . p. 9 1.2. Fenchel Duality and Conic Programming . . . . . . . . p. 10 1.2.1. Linear Conic Problems . . . . . . . . . . . . . p. 15 1.2.2. Second Order Cone Programming . . . . . . . . . p. 17 1.2.3. Semidefinite Progr...

This paper investigates novel evidence from Russian Verb-Stranding Verb Phrase Ellipsis (VVPE), and argues for its use as a probe into the syntactic structure of morphophonologically inseparable but morphosyntactically complex verbs. The first step is to distinguish internal argument drop from VVPE, because they appear identical on the surface. I present novel evidence that Russian internal arg...

We address the problem of spatial conservation planning in which the goal is to maximize the expected spread of cascades of an endangered species by strategically purchasing land parcels within a given budget. This problem can be solved by standard integer programming methods using the sample average approximation (SAA) scheme. Our main contribution lies in exploiting the separable structure pr...

The minimum concave cost network flow problem (MCCNFP) is NP-hard, but efficient polynomial-time algorithms exist for some special cases such as the uncapacitated lot-sizing problem and many of its variants. We study the MCCNFP over a grid network with a general nonnegative separable concave cost function. We show that this problem is polynomially solvable when all sources are in the first eche...

We develop a network revenue management model to jointly make capacity control and overbooking decisions. Our approach is based on the observation that if the penalty cost of denying boarding to the reservations at the departure time were given by a separable function, then the dynamic programming formulation of the network revenue management problem would decompose by the itineraries and it co...

Symbolic regression that aims to detect underlying data-driven model, has become increasingly important for industrial data analysis when the experimental model structure is unknown or wrong, or the concerned system has changed. For most of the existing algorithms for symbolic regression, such as genetic programming, the convergence speed might be too slow for large scale problems with a large ...

We propose new interior projection type methods for solving monotone variational inequalities. The methods can be viewed as a natural extension of the extragradient and hyperplane projection algorithms, and are based on using non Euclidean projection-like maps. We prove global convergence results and establish rate of convergence estimates. The projection-like maps are given by analytical formu...

The covariance matrix in the Mahalanobis distance can be trained by semi-definite programming, but training for a large size data set is inefficient. In this paper, we constrain the covariance matrix to be diagonal and train Mahalanobis kernels by linear programming (LP). Training can be formulated by ν-LP SVMs (support vector machines) or regular LP SVMs. We clarify the dependence of the solut...