A Branch-and-Bound Algorithm Embedded with DCA for DC Programming

A Branch-and-Bound Algorithm Embedded with DCA for DC Programming

The special importance of Difference of Convex DC functions programming has been recognized in recent studies on nonconvex optimization problems. In this work, a class of DC programming derived from the portfolio selection problems is studied. The most popular method applied to solve the problem is the Branch-and-Bound B&B algorithm. However, “the curse of dimensionality” will affect the perfor...

A Branch and Bound Algorithm Based on DC Programming and DCA for Strategic Capacity Planning in Supply Chain Design for a New Market Opportunity

This paper addresses the problem of supply chain design at the strategic level when production/distribution of a new market opportunity has to be launched in an existing supply chain. The new market opportunity is characterized by a deterministic forecast expected to occur per period. The product (or service) is assumed to be produced (or provided) in a three-stage capacitated supply chain wher...

A modified branch and bound algorithm for a vague flow-shop scheduling problem

Uncertainty plays a significant role in modeling and optimization of real world systems. Among uncertain approaches, fuzziness describes impreciseness while for ambiguity another definition is required. Vagueness is a probabilistic model of uncertainty being helpful to include ambiguity into modeling different processes especially in industrial systems. In this paper, a vague set based on dista...

A Branch and Bound Algorithm for Separable Concave Programming

In this paper, we propose a new branch and bound algorithm for the solution of large scale separable concave programming problems. The largest distance bisection (LDB) technique is proposed to divide rectangle into sub-rectangles when one problem is branched into two subproblems. It is proved that the LDB method is a normal rectangle subdivision(NRS). Numerical tests on problems with dimensions...

DC Programming and DCA for Nonnegative Matrix Factorization