Drawing a block plan from a REL chart with graph theory and a microcomputer
Computers and Industrial Engineering
A new graph-theoretic heuristic for facility layout
Management Science
Proximity-based adjacency determination for facility layout
Computers and Industrial Engineering
Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization
Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization
Statistics for Managers Using Microsoft Excel
Statistics for Managers Using Microsoft Excel
Preface: focused issue on applied meta-heuristics
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Grasp and Path Relinking for 2-Layer Straight Line Crossing Minimization
INFORMS Journal on Computing
A note on computing a maximal planar subgraph using PQ-trees
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
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The weighted maximal planar graph (WMPG) problem seeks to find a subgraph from a given weighted complete graph such that the subgraph is planar--it can be embedded on the plane without any arcs intersecting. The subgraph is maximal no additional arc can be added to the subgraph without destroying its planarity and it also has the maximal sum of arc weights. In this paper, the main objective is to develop, implement and empirically analyse a new greedy random adaptive search procedure (GRASP) to solve the WMPG problem. A dynamic strategy to update the restricted candidate list is proposed. An efficient data structure is developed for the Green&Al-Hakim (GH) construction heuristic. The data structure reduces the GH complexity from O(n3) to O(n2). The GH heuristic with the data structure is then integrated with advanced moves neighbourhood to develop an efficient GRASP implementation. Further, we investigate the behaviour of GRASP parameters in relation to the problem's characteristics. Finally, the developed algorithms are compared with the best-known procedures in the literature on a set of 100 test instances of sizes varying from 20 to 100 nodes.