Best-matching with interdependent preferences - implications for capacitated cluster formation and evolution

Generalized best-matching refers to matching the elements of two or more sets, on a many-to-one or many-to-many basis, with respect to their mutual preferences and capacity requirements/limits. Generalized best-matching problem (BMP) has a variety of applications in areas such as team and network design, scheduling, transportation, routing, production planning, facility location, allocation, and logistics. The problem is indeed analogous to the capacitated clustering problem, where a set of individuals are partitioned into disjoint clusters with certain capacities. This work defines, formulates, and analyzes an important behavior associated with the generalized BMP: The mutual influence of the elements of the same set on each other's preferences, if matched to the same element of the other set. Such preferences are referred to as interdependent preferences (IP). A binary program is developed to formulate the problem and provide the basis for analyzing the impact of IP on generalized best-matching decisions from two perspectives: Optimal cluster formation (fixed sets) and evolution (emergent sets). A set of evolutionary algorithms is then developed to handle the complexity of the cluster formation problem, and enable the network of clusters to autonomously adapt to random changes, recover, and evolve. Results from several experiments indicate (a) significant impact of IP on the optimality of cluster formation and evolution decisions, and (b) efficiency of the developed evolutionary algorithms in handling the problem's complexity, and the emergent behavior of matching. The notion of interdependent preferences is introduced to the generalized best matching problem.The problem (BMP-IP) is analogous to the capacitated clustering problem.A novel binary quadratic programming formulation is developed for the BMP-IP.Efficient evolutionary heuristics are developed to handle the formation and evolution of clusters.Results show significant impacts of IP on the static and dynamic BMP.

[1]  R Reznick,et al.  Forming professional identities on the health care team: discursive constructions of the ‘other’ in the operating room , 2002, Medical education.

[2]  Wulf Gaertner,et al.  A dynamic model of interdependent consumer behavior , 1974 .

[3]  Nicos Christofides,et al.  Capacitated clustering problems by hybrid simulated annealing and tabu search , 1994 .

[4]  Andrew Postlewaite,et al.  The social basis of interdependent preferences , 1998 .

[5]  Shimon Y. Nof,et al.  Combined demand and capacity sharing with best matching decisions in enterprise collaboration , 2014 .

[6]  Fred Glover,et al.  Genetic algorithms and scatter search: unsuspected potentials , 1994 .

[7]  H. Kuhn The Hungarian method for the assignment problem , 1955 .

[8]  L. Shapley,et al.  College Admissions and the Stability of Marriage , 1962 .

[9]  Florence Yean Yng Ling,et al.  Effects of interpersonal relations on public sector construction contracts in Vietnam , 2012 .

[10]  Antoni Calvó-Armengol,et al.  Interdependent preferences and segregating equilibria , 2008, J. Econ. Theory.

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

[12]  Selim G. Akl,et al.  Scheduling Algorithms for Grid Computing: State of the Art and Open Problems , 2006 .

[13]  Greg M. Allenby,et al.  Modeling Interdependent Consumer Preferences , 2003 .

[14]  Julian Jamison,et al.  Games with Synergistic Preferences , 2012, Games.

[15]  Ashish Tiwari,et al.  A greedy genetic algorithm for the quadratic assignment problem , 2000, Comput. Oper. Res..

[16]  Zvi Drezner,et al.  A New Genetic Algorithm for the Quadratic Assignment Problem , 2003, INFORMS J. Comput..

[17]  N. Tomes,et al.  Income distribution, happiness and satisfaction: A direct test of the interdependent preferences model , 1986 .

[18]  Shimon Y. Nof,et al.  Location-Allocation Decisions in Collaborative Networks of Service Enterprises , 2014 .

[19]  Temel Öncan,et al.  A Survey of the Generalized Assignment Problem and Its Applications , 2007, INFOR Inf. Syst. Oper. Res..

[20]  Cyril Fonlupt,et al.  Parallel Ant Colonies for the quadratic assignment problem , 2001, Future Gener. Comput. Syst..

[21]  Sina Risse,et al.  Two-stage group rent-seeking with negatively interdependent preferences , 2011 .

[22]  David W. Pentico,et al.  Assignment problems: A golden anniversary survey , 2007, Eur. J. Oper. Res..

[23]  Mauro Dell'Amico,et al.  Assignment Problems , 1998, IFIP Congress: Fundamentals - Foundations of Computer Science.

[24]  C. Burns,et al.  Trust tokens in team development , 2014 .

[25]  Ann Maria Bell,et al.  Locally interdependent preferences in a general equilibrium environment , 2002 .

[26]  É. Taillard COMPARISON OF ITERATIVE SEARCHES FOR THE QUADRATIC ASSIGNMENT PROBLEM. , 1995 .

[27]  Mauro Dell'Amico,et al.  8. Quadratic Assignment Problems: Algorithms , 2009 .

[28]  Zvi Drezner,et al.  Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem , 2008, Comput. Oper. Res..

[29]  Tony Manning,et al.  Interpersonal influence in the workplace - part one: an introduction to concepts and a theoretical model , 2008 .

[30]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[31]  K. Cox,et al.  The effects of unit morale and interpersonal relations on conflict in the nursing unit. , 2001, Journal of advanced nursing.

[32]  Bongsoon Cho,et al.  Interpersonal Trust and Emotion as Antecedents of Cooperation: Evidence From Korea , 2011 .

[33]  Shimon Y. Nof Collaborative control theory for e-Work, e-Production, and e-Service , 2007, Annu. Rev. Control..

[34]  Efe A. Ok,et al.  Evolution of Interdependent Preferences in Aggregative Games , 2000, Games Econ. Behav..

[35]  Vittorio Maniezzo,et al.  The Ant System Applied to the Quadratic Assignment Problem , 1999, IEEE Trans. Knowl. Data Eng..

[36]  Collin R. Payne,et al.  One-to-One and One-to-Many Business Relationship Marketing: Toward a Theoretical Framework , 2013 .

[37]  Shimon Y. Nof,et al.  A best-matching protocol for collaborative e-work and e-manufacturing , 2008, Int. J. Comput. Integr. Manuf..

[38]  M Dorigo,et al.  Ant colonies for the quadratic assignment problem , 1999, J. Oper. Res. Soc..

[39]  Jorge Peña,et al.  The Influence of Social Categories and Interpersonal Behaviors on Future Intentions and Attitudes to Form Subgroups in Virtual Teams , 2014, Commun. Res..

[40]  T. Koopmans,et al.  Assignment Problems and the Location of Economic Activities , 1957 .

[41]  Pawel Wozniak,et al.  Modelling Interpersonal Relations in Surgical Teams with Fuzzy Logic , 2012, MICAI.

[42]  Shimon Y. Nof,et al.  Real-time administration of tool sharing and best matching to enhance assembly lines balanceability and flexibility , 2015 .

[43]  Joel Sobel,et al.  INTERDEPENDENT PREFERENCES AND RECIPROCITY , 2005 .

[44]  Teofilo F. Gonzalez,et al.  P-Complete Approximation Problems , 1976, J. ACM.

[45]  R. Hall,et al.  Occupational Mobility and the Distribution of Occupational Success among Young Men , 1976 .

[46]  A. Roth,et al.  The Market for Federal Judicial Law Clerks , 2007 .