نتایج جستجو برای: variable neighborhood search
تعداد نتایج: 568841 فیلتر نتایج به سال:
although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. but, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. this paper considers the problem of scheduling no-idle hybrid flow shops. a mixed integer linear programming model is first developed to mathematically form...
In this paper, an effective approach based on the variable neighborhood search (VNS) algorithm is presented to solve the uncapacitated multilevel lot-sizing (MLLS) problems with component commonality and multiple end-items. A neighborhood structure for the MLLS problem is defined, and two kinds of solution move policies, i.e., move at first improvement (MAFI) and move at best improvement (MABI)...
In this paper a variable neighborhood search approach as a method for solving combinatory optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algor...
Given an undirected weighted graph G = (V,E) with vertex set V, edge set E and weights wi ∈ R associated to V or to E. Minimum weighted k-Cardinality tree problem (k-CARD for short) consists of finding a subtree of G with exactly k edges whose sum of weights is minimum [4]. There are two versions of this problem: vertex-weighted and edge-weighted, if weights to V or to E are associated, respect...
The High School Timetabling Problem consists in assigning timeslots and resources to events, satisfying constraints which heavily depend on the specific institution. This work deals with the problem of the ongoing III International Timetabling Competition (ITC), which includes a diverse set of instances from many educational institutions around the world. We proposed an approach based on Simula...
Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications are briefly summarized. They comprise heuristic solution of a variety of optimization problems, wa...
The recent Variable Neighborhood Search (VNS) metaheuristic combines local search with systematic changes of neighborhood in the descent and escape from local optimum phases. When solving large instances of various problems, its efficiency may be enhanced through decomposition. The resulting two level VNS, called Variable Neighborhood Decomposition Search (VNDS), is presented and illustrated on...
An uncapacitated facility location problem considers a set of n warehouses W and a set of m stores S. Each warehouse w has a fixed cost fw, and the transportation cost from warehouse w to store s is given by cws. The problem is to find a subset of warehouses and an assignment of warehouses to stores in order to minimize the fixed and transportation costs. Observe that once the warehouses are se...
In this paper we propose three different VNS variants to solve the Max-Mean Dispersion Problem. This problem consists of selecting a set of elements in such a way that the average distance between them is maximized. To tackle this problem, we propose a Basic Variable Neighborhood Search (BVNS), a Variable Neighborhood Descent (VND) and a Generalized Variable Neighborhood Search (GVNS) that hybr...
In this issue we present a peer-reviewed selection of short papers that were accepted for presentation in the 4th International Conference on Variable Neighborhood Search (ICVNS’16) which was held in Málaga, Spain, during October 3-5, 2016. This conference is devoted to the Variable Neighborhood Search metaheuristic which was originally proposed by Nenad Mladenović and Pierre Hansen. This metho...
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