The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to custo...

The traditional evolutionary algorithms dividing the complex networks community have some inevitable deficiencies such as low searching accuracy, high computing time complexity, local optimal solution and so on. To address this issue, this paper proposes a novel community structure partition algorithm based on multi-gene families (MGF). First, this algorithm respectively encodes the network ent...

One important issue regarding the implementation of cellular manufacturing systems relates to deciding whether to convert an existing job shop into a cellular manufacturing system comprehensively in a single go, or in stages incrementally by forming cells one after the other taking the advantage of the experiences of implementation. In this paper two heuristic methods based on multi-stage progr...

Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective ...

In this study the advantages of statistical gene selection are combined with the power of Genetic Programming (GP) to build classifiers for assigning gene expression microarray data samples to categories characteristic of certain cell states. To that end we implemented different statistical measures in a program called GENEACTIVATOR and tested their applicability to gene selection. Subsequently...

In this study the advantages of statistical gene selection are combined with the power of Genetic Programming (GP) to build classifiers for assigning gene expression microarray data samples to categories characteristic of certain cell states. To that end we implemented different statistical measures in a program called GENEACTIVATOR and tested their applicability to gene selection. Subsequently...

In practice, there are many problems which decision parameters are fuzzy numbers, and some kind of this problems are formulated as either possibilitic programming or multi-objective programming methods. In this paper, we consider a multi-objective programming problem with fuzzy data in constraints and introduce a new approach for solving these problems base on a combination of the multi-objecti...