Rough‐fuzzy quadratic minimum spanning tree problem. Issue 2 (16th January 2019)

• Record Type: Journal Article
• Authors: Majumder, Saibal; Kar, Samarjit; Pal, Tandra
• Is Part Of: Expert systems. Volume 36:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-16
• Subjects: chance‐constrained programming -- epsilon‐constraint method -- MOCHC -- NSGA‐II -- quadratic minimum spanning tree -- rough‐fuzzy variable; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12364
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: A quadratic minimum spanning tree problem determines a minimum spanning tree of a network whose edges are associated with linear and quadratic weights. Linear weights represent the edge costs whereas the quadratic weights are the interaction costs between a pair of edges of the graph. In this study, a bi‐objective rough‐fuzzy quadratic minimum spanning tree problem has been proposed for a connected graph, where the linear and the quadratic weights are represented as rough‐fuzzy variables. The proposed model is formulated by using rough‐fuzzy chance‐constrained programming technique. Subsequently, three related theorems are also proposed for the crisp transformation of the proposed model. The crisp equivalent models are solved with a classical multi‐objective solution technique, the epsilon‐constraint method and two multi‐objective evolutionary algorithms: (a) nondominated sorting genetic algorithm II (NSGA‐II) and (b) multi‐objective cross‐generational elitist selection, heterogeneous recombination, and cataclysmic mutation (MOCHC) algorithm. A numerical example is provided to illustrate the proposed model when solved with different methodologies. A sensitivity analysis of the example is also performed at different confidence levels. The performance of NSGA‐II and MOCHC are analysed on five randomly generated instances of the proposed model. Finally, a numerical illustration of an application of the proposed model is also presented in this study.