A branching algorithm to solve binary problem in uncertain environment: an application in machine allocation problem

AbstractThis paper studies a new algorithm to solve the uncertain generalized assignment problem. The presented technique is based on the concept of branch and bound rather than the usual simplex based techniques. At first, the problem is relaxed to the transportation model which is easy to handle and work with. The model, so obtained is solved by the conventional transportation technique. The obtained solution serves as starting solution for further sub problems. The ambiguity in parameters is represented by triangular fuzzy numbers. We propose a linear ranking function, called the grade function which is based on the centroid method. The grade function is used to rank the triangular fuzzy numbers. The proposed approach is justified numerically by showing its application in generalized machine allocation problem.

[1]  Lale Özbakir,et al.  Solving fuzzy multiple objective generalized assignment problems directly via bees algorithm and fuzzy ranking , 2013, Expert Syst. Appl..

[2]  Wayne L. Winston,et al.  Introduction to mathematical programming , 1991 .

[3]  Fred W. Glover,et al.  A path relinking approach with ejection chains for the generalized assignment problem , 2006, Eur. J. Oper. Res..

[4]  Linzhong Liu,et al.  Fuzzy weighted equilibrium multi-job assignment problem and genetic algorithm , 2009 .

[5]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[6]  Lale Özbakir,et al.  Bees algorithm for generalized assignment problem , 2010, Appl. Math. Comput..

[7]  Adil Baykasoglu,et al.  A review and classification of fuzzy mathematical programs , 2008, J. Intell. Fuzzy Syst..

[8]  V. Jeet,et al.  Lagrangian relaxation guided problem space search heuristics for generalized assignment problems , 2007, Eur. J. Oper. Res..

[9]  Asoke Kumar Bhunia,et al.  Elitist genetic algorithm for assignment problem with imprecise goal , 2007, Eur. J. Oper. Res..

[10]  L. V. Wassenhove,et al.  A survey of algorithms for the generalized assignment problem , 1992 .

[11]  Fred W. Glover,et al.  An Ejection Chain Approach for the Generalized Assignment Problem , 2004, INFORMS J. Comput..

[12]  Uwe Aickelin,et al.  An Indirect Genetic Algorithm for a Nurse Scheduling Problem , 2004, Comput. Oper. Res..

[13]  Ichiro Nishizaki,et al.  Interactive fuzzy programming for multi-level 0-1 programming problems with fuzzy parameters through genetic algorithms , 2001, Fuzzy Sets Syst..

[14]  E. Ertugrul Karsak,et al.  A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system , 2002 .

[15]  George J. Klir,et al.  On Measuring Uncertainty and Uncertainty-Based Information: Recent Developments , 2001, Annals of Mathematics and Artificial Intelligence.

[16]  Paul R. Harper,et al.  A genetic algorithm for the project assignment problem , 2005, Comput. Oper. Res..

[17]  Caroline M. Eastman,et al.  Response: Introduction to fuzzy arithmetic: Theory and applications : Arnold Kaufmann and Madan M. Gupta, Van Nostrand Reinhold, New York, 1985 , 1987, Int. J. Approx. Reason..

[18]  Liang-Hsuan Chen,et al.  An extended assignment problem considering multiple inputs and outputs , 2007 .

[19]  Chi-Jen Lin,et al.  A labeling algorithm for the fuzzy assignment problem , 2004, Fuzzy Sets Syst..

[20]  Etienne E. Kerre,et al.  Reasonable properties for the ordering of fuzzy quantities (II) , 2001, Fuzzy Sets Syst..

[21]  Martin Bichler,et al.  Generalized assignment problem: Truthful mechanism design without money , 2016, Oper. Res. Lett..

[22]  John N. Hooker,et al.  Generalized resolution for 0–1 linear inequalities , 1992, Annals of Mathematics and Artificial Intelligence.

[23]  Lean Yu,et al.  Multi-depot vehicle routing problem for hazardous materials transportation: A fuzzy bilevel programming , 2017, Inf. Sci..

[24]  Settimo Termini,et al.  On Some Vagaries of Vagueness and Information , 2002, Annals of Mathematics and Artificial Intelligence.