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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6

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Department: search.filters.department.Department of Business AdministrationSubject: search.filters.subject.Mathematics

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Modelling and analysis of a network organization for cooperation of manufacturers on production capacity
    (Hindawi, 2006) Department of Business Administration; Tan, Barış; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600
    We present an analytical model to analyze the operation of a productive cooperation network where producers cooperate on production capacity. Producers have limited capacity and have access to subcontractors at a higher cost. A single-unit auction-based allocation mechanism is proposed to allocate an arriving order based on the producers' cost structures and their current loads to maximize the total profit. We show that when the costs are private information, producers are willing to cooperate in order to increase their expected profit. Furthermore, it is shown that there is an equilibrium where producers bid their actual costs. The cooperation can also generate extra profit to cover a part of its operating expenses with this allocation mechanism. A continuous-time Markov chain model is utilized to evaluate the performance of the allocation mechanism where producers submit their myopic best response bids. The cooperation case is also compared with the no-cooperation case and also with the centralized operation of producers.
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    PublicationOpen Access
    Bilevel programming for generating discrete representations in multiobjective optimization
    (Springer, 2018) Kirlik, Gökhan; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
    The solution to a multiobjective optimization problem consists of the nondominated set that portrays all relevant trade-off information. The ultimate goal is to identify a Decision Maker's most preferred solution without generating the entire set of nondominated solutions. We propose a bilevel programming formulation that can be used to this end. The bilevel program is capable of delivering an efficient solution that maps into a given set, provided that one exits. If the Decision Maker's preferences are known a priori, they can be used to specify the given set. Alternatively, we propose a method to obtain a representation of the nondominated set when the Decision Maker's preferences are not available. This requires a thorough search of the outcome space. The search can be facilitated by a partitioning scheme similar to the ones used in global optimization. Since the bilevel programming formulation either finds a nondominated solution in a given partition element or determines that there is none, a representation with a specified coverage error level can be found in a finite number of iterations. While building a discrete representation, the algorithm also generates an approximation of the nondominated set within the specified error factor. We illustrate the algorithm on the multiobjective linear programming problem.
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    PublicationOpen Access
    Portfolio optimization based on stochastic dominance and empirical likelihood
    (Elsevier, 2018) Post, Thierry; Arvanitis, Stelios; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819
    This study develops a portfolio optimization method based on the Stochastic Dominance (SD) decision criterion and the Empirical Likelihood (EL) estimation method. SD and EL share a distribution-free assumption framework which allows for dynamic and non-Gaussian multivariate return distributions. The SD/EL method can be implemented using a two-stage procedure which first elicits the implied probabilities using Convex Optimization and subsequently constructs the optimal portfolio using Linear Programming. The solution asymptotically dominates the benchmark and optimizes the goal function in probability, for a class of weakly dependent processes. A Monte Carlo simulation experiment illustrates the improvement in estimation precision using a set of conservative moment conditions about common factors in small samples. In an application to equity industry momentum strategies, SD/EL yields important out-of-sample performance improvements relative to heuristic diversification, Mean-Variance optimization, and a simple 'plug-in' approach.