Showing papers in "Mathematical Methods of Operations Research in 2012"

Efficient solution of interval optimization problem

Abstract: In this paper the interval valued function is defined in the parametric form and its properties are studied. A methodology is developed to study the existence of the solution of a general interval optimization problem, which is expressed in terms of the interval valued functions. The methodology is applied to the interval valued convex quadratic programming problem.

Could we use a million cores to solve an integer program

Abstract: Given the steady increase in cores per CPU, it is only a matter of time before supercomputers will have a million or more cores In this article, we investigate the opportunities and challenges that will arise when trying to utilize this vast computing power to solve a single integer linear optimization problem We also raise the question of whether best practices in sequential solution of ILPs will be effective in massively parallel environments

On solvability of a two-sided singular control problem

Abstract: We study a two-sided singular control problem in a general linear diffusion setting and provide a set of conditions under which an optimal control exists uniquely and is of singular control type. Moreover, under these conditions the associated value function can be written in a quasi-explicit form. Furthermore, we investigate comparative static properties of the solution with respect to the volatility and control parameters. Lastly we illustrate the results with two explicit examples.

Stochastic differential portfolio games for an insurer in a jump-diffusion risk process

Abstract: We discuss an optimal portfolio selection problem of an insurer who faces model uncertainty in a jump-diffusion risk model using a game theoretic approach. In particular, the optimal portfolio selection problem is formulated as a two-person, zero-sum, stochastic differential game between the insurer and the market. There are two leader-follower games embedded in the game problem: (i) The insurer is the leader of the game and aims to select an optimal portfolio strategy by maximizing the expected utility of the terminal surplus in the “worst-case” scenario; (ii) The market acts as the leader of the game and aims to choose an optimal probability scenario to minimize the maximal expected utility of the terminal surplus. Using techniques of stochastic linear-quadratic control, we obtain closed-form solutions to the game problems in both the jump-diffusion risk process and its diffusion approximation for the case of an exponential utility.

Integrating inventory control and a price change in the presence of reference price effects: a two-period model

Abstract: Demand and procurement planning for consumer electronics products must cope with short life cycles, limited replenishment opportunities and a willingness to pay that is influenced by past prices and decreases over time. We therefore propose the use of an integrated pricing and inventory control model with a two-period linear demand model, in which demand also depends on the difference between a price-history-based reference price and the current price. For this model we prove that the optimal joint pricing/inventory policy for the replenishment opportunity after the first period is a base-stock list-price policy. That is, stock is either replenished up to a base-stock level and a list-price is charged, or it is not replenished and a discount is given that increases with the stock-level. Furthermore, we use real-world cell phone data to study the differences between an integrated policy and traditional sequential optimization, where prices are initially optimized based on the expected demand and ordering cost, and the resulting demand distribution is used to determine an optimal inventory policy. Finally, we discuss possible extensions of the model.

Reflecting Brownian motion in three dimensions: a new proof of sufficient conditions for positive recurrence

Abstract: Let Z = {Z(t), t ≥ 0} be a semimartingale reflecting Brownian motion that lives in the three-dimensional non-negative orthant. A 2002 paper by El Kharroubi, Ben Tahar and Yaacoubi gave sufficient conditions for positive recurrence of Z. Recently Bramson, Dai and Harrison have shown that those conditions are also necessary for positive recurrence. In this paper we provide an alternative proof of sufficiency, the salient feature of which is its use of a linear Lyapunov function.

The multiple-partners assignment game with heterogeneous sales and multi-unit demands: competitive equilibria

Abstract: A multiple-partners assignment game with heterogeneous sales and multi-unit demands consists of a set of sellers that own a given number of indivisible units of potentially many different goods and a set of buyers who value those units and want to buy at most an exogenously fixed number of units. We define a competitive equilibrium for this generalized assignment game and prove its existence by using only linear programming. In particular, we show how to compute equilibrium price vectors from the solutions of the dual linear program associated to the primal linear program defined to find optimal assignments. Using only linear programming tools, we also show (i) that the set of competitive equilibria (pairs of price vectors and assignments) has a Cartesian product structure: each equilibrium price vector is part of a competitive equilibrium with all optimal assignments, and vice versa; (ii) that the set of (restricted) equilibrium price vectors has a natural lattice structure; and (iii) how this structure is translated into the set of agents’ utilities that are attainable at equilibrium.

Newsvendor-type models with decision-dependent uncertainty

Abstract: Models for decision-making under uncertainty use probability distributions to represent variables whose values are unknown when the decisions are to be made. Often the distributions are estimated with observed data. Sometimes these variables depend on the decisions but the dependence is ignored in the decision maker’s model, that is, the decision maker models these variables as having an exogenous probability distribution independent of the decisions, whereas the probability distribution of the variables actually depend on the decisions. It has been shown in the context of revenue management problems that such modeling error can lead to systematic deterioration of decisions as the decision maker attempts to refine the estimates with observed data. Many questions remain to be addressed. Motivated by the revenue management, newsvendor, and a number of other problems, we consider a setting in which the optimal decision for the decision maker’s model is given by a particular quantile of the estimated distribution, and the empirical distribution is used as estimator. We give conditions under which the estimation and control process converges, and show that although in the limit the decision maker’s model appears to be consistent with the observed data, the modeling error can cause the limit decisions to be arbitrarily bad.

Optimal stopping with random exercise lag

Abstract: We study optimal stopping with exponentially distributed exercise lag. We formalize the problem first in a general Markovian setting and derive a set of conditions under which the solution exists. In particular, no semicontinuity assumptions of the payoff function are needed. We analyze also some specific classes of lagged optimal stopping problems with one-dimensional diffusion dynamics where the solution can be characterized in closed form. Finally, the results are illustrated with an explicit example.

The opportunistic replacement problem: theoretical analyses and numerical tests

Abstract: We consider a model for determining optimal opportunistic maintenance schedules with respect to a maximum replacement interval. This problem generalizes that of Dickman et al. (J Oper Res Soc India 28:165–175, 1991) and is a natural starting point for modelling replacement schedules of more complex systems. We show that this basic opportunistic replacement problem is NP-hard, that the convex hull of the set of feasible replacement schedules is full-dimensional, that all the inequalities of the model are facet-inducing, and present a new class of facets obtained through a {0,1/2}-Chvatal–Gomory rounding. For costs monotone with time, a class of elimination constraints is introduced to reduce the computation time; it allows maintenance only when the replacement of at least one component is necessary. For costs decreasing with time, these constraints eliminate non-optimal solutions. When maintenance occasions are fixed, the remaining problem is stated as a linear program and solved by a greedy procedure. Results from a case study on aircraft engine maintenance illustrate the advantage of the optimization model over simpler policies. We include the new class of facets in a branch-and-cut framework and note a decrease in the number of branch-and-bound nodes and simplex iterations for most instance classes with time dependent costs. For instance classes with time independent costs and few components the elimination constraints are used favorably. For fixed maintenance occasions the greedy procedure reduces the computation time as compared with linear programming techniques for all instances tested.

Estimation of more than one parameters in stratified sampling with fixed budget

Abstract: In a multivariate stratified sampling more than one characteristic are defined on every unit of the population. An optimum allocation which is optimum for one characteristic will generally be far from optimum for others. A compromise criterion is needed to work out a usable allocation which is optimum, in some sense, for all the characteristics. When auxiliary information is also available the precision of the estimates of the parameters can be increased by using it. Furthermore, if the travel cost within the strata to approach the units selected in the sample is significant the cost function remains no more linear. In this paper an attempt has been made to obtain a compromise allocation based on minimization of individual coefficients of variation of the estimates of various characteristics, using auxiliary information and a nonlinear cost function with fixed budget. A new compromise criterion is suggested. The problem is formulated as a multiobjective all integer nonlinear programming problem. A solution procedure is also developed using goal programming technique.

Accessibility measures to nodes of directed graphs using solutions for generalized cooperative games

Abstract: The aim of this paper consists of constructing accessibility measures to the nodes of directed graphs using methods of Game Theory. Since digraphs without a predefined game are considered, the main part of the paper is devoted to establish conditions on cooperative games so that they can be used to measure accessibility. Games that satisfy desirable properties are called test games. Each ranking on the nodes is then obtained according to a pair formed by a test game and a solution defined on cooperative games whose utilities are given on ordered coalitions. The solutions proposed here are extensions of the wide family of semivalues to games in generalized characteristic function form.

Between a rock and a hard place: the two-to-one assignment problem

Abstract: We describe the two-to-one assignment problem, a problem in between the axial three-index assignment problem and the three-dimensional matching problem, having applications in various domains. For the (relevant) case of decomposable costs satisfying the triangle inequality we provide, on the positive side, two constant factor approximation algorithms. These algorithms involve solving minimum weight matching problems and transportation problems, leading to a 2-approximation, and a $${\frac{3}{2}}$$ -approximation. Moreover, we further show that the best of these two solutions is a $${\frac{4}{3}}$$ -approximation for our problem. On the negative side, we show that the existence of a polynomial time approximation scheme for our problem would imply P = NP. Finally, we report on some computational experiments showing the performance of the described heuristics.

Approximation algorithms for TTP(2)

Abstract: We consider the traveling tournament problem, which is a well-known benchmark problem in tournament timetabling. It consists of designing a schedule for a sports league of n teams such that the total traveling costs of the teams are minimized. The most important variant of the traveling tournament problem imposes restrictions on the number of consecutive home games or away games a team may have. We consider the case where at most two consecutive home games or away games are allowed. We show that the well-known independent lower bound for this case cannot be reached and present two approximation algorithms for the problem. The first algorithm has an approximation ratio of \({3/2+\frac{6}{n-4}}\) in the case that n/2 is odd, and of \({3/2+\frac{5}{n-1}}\) in the case that n/2 is even. Furthermore, we show that this algorithm is applicable to real world problems as it yields close to optimal tournaments for many standard benchmark instances. The second algorithm we propose is only suitable for the case that n/2 is even and n ≥ 12, and achieves an approximation ratio of 1 + 16/n in this case, which makes it the first \({1+\mathcal{O}(1/n)}\) -approximation for the problem.

A private contributions game for joint replenishment

Abstract: We study a non-cooperative game for joint replenishment by n firms that operate under an EOQ-like setting. Each firm decides whether to replenish independently or to participate in joint replenishment, and how much to contribute to joint ordering costs in case of participation. Joint replenishment cycle time is set by an intermediary as the lowest cycle time that can be financed with the private contributions of participating firms. We characterize the behavior and outcomes under undominated Nash equilibria.

A very short algebraic proof of the Farkas Lemma

Abstract: We present a very short algebraic proof of a generalisation of the Farkas Lemma: we set it in a vector space of finite or infinite dimension over a linearly ordered (possibly skew) field; the non-positivity of a finite homogeneous system of linear inequalities implies the non-positivity of a linear mapping whose image space is another linearly ordered vector space. In conclusion, we briefly discuss other algebraic proofs of the result, its special cases and related results.

A new second-order corrector interior-point algorithm for semidefinite programming

Abstract: In this paper, we propose a second-order corrector interior-point algorithm for semidefinite programming (SDP). This algorithm is based on the wide neighborhood. The complexity bound is $${O(\sqrt{n}L)}$$ for the Nesterov-Todd direction, which coincides with the best known complexity results for SDP. To our best knowledge, this is the first wide neighborhood second-order corrector algorithm with the same complexity as small neighborhood interior-point methods for SDP. Some numerical results are provided as well.

Packing chained items in aligned bins with applications to container transshipment and project scheduling

Abstract: Bin packing problems are at the core of many well-known combinatorial optimization problems and several practical applications alike. In this work we introduce a novel variant of an abstract bin packing problem which is subject to a chaining constraint among items. The problem stems from an application of container handling in rail freight terminals, but is also of relevance in other fields, such as project scheduling. The paper provides a structural analysis which establishes computational complexity of several problem versions and develops (pseudo-)polynomial algorithms for specific subproblems. We further propose and evaluate simple and fast heuristics for optimization versions of the problem.

Steiner tree packing revisited

Abstract: The Steiner tree packing problem (STPP) in graphs is a long studied problem in combinatorial optimization. In contrast to many other problems, where there have been tremendous advances in practical problem solving, STPP remains very difficult. Most heuristics schemes are ineffective and even finding feasible solutions is already NP-hard. What makes this problem special is that in order to reach the overall optimal solution non-optimal solutions to the underlying NP-hard Steiner tree problems must be used. Any non-global approach to the STPP is likely to fail. Integer programming is currently the best approach for computing optimal solutions. In this paper we review some “classical” STPP instances which model the underlying real world application only in a reduced form. Through improved modelling, including some new cutting planes, and by employing recent advances in solver technology we are for the first time able to solve those instances in the original 3D grid graphs to optimimality.

Finding a core of a tree with pos/neg weight

Abstract: Let T = (V, E) be a tree. A core of T is a path P, for which the sum of the weighted distances from all vertices to this path is minimized. In this paper, we consider the semi-obnoxious case in which the vertices have positive or negative weights. We prove that, when the sum of the weights of vertices is negative, the core must be a single vertex and that, when the sum of the vertices’ weights is zero there exists a core that is a vertex. Morgan and Slater (J Algorithms 1:247–258, 1980) presented a linear time algorithm to find the core of a tree with only positive weights of vertices. We show that their algorithm also works for semi-obnoxious problems.

Scalarization method for Levitin–Polyak well-posedness of vectorial optimization problems

Abstract: In this paper, we develop a method of study of Levitin-Polyak well- posedness notions for vector valued optimization problems using a class of scalar optimization problems We first introduce a non-linear scalarization function and con- sider its corresponding properties We also introduce the Furi-Vignoli type measure and Dontchev-Zolezzi type measure to scalar optimization problems and vectorial optimization problems, respectively Finally, we construct the equivalence relations between the Levitin-Polyak well-posedness of scalar optimization problems and the vectorial optimization problems

Consistent cost sharing

Abstract: A new concept of consistency for cost sharing solutions is discussed, analyzed, and related to the homonymous and natural property within the rationing context. Main result is that the isomorphism in Moulin and Shenker (J Econ Theory 64:178–201, 1994) pairs each additive and consistent single-valued mechanism with a corresponding monotonic and consistent rationing method. Then this answers the open question in Moulin (Econometrica 68:643–684, 2000; Handbook of social choice and welfare. Handbooks in economics, pp 289–357, 2002) whether such notion for cost sharing exists. The conclusion is that renown solutions like the average and serial cost sharing mechanisms are consistent, whereas the Shapley–Shubik mechanism is not. Average cost sharing is the only strongly consistent element in this class. The two subclasses of incremental and parametric cost sharing mechanisms are further analyzed as refinement of the main result.

The nucleolus and the core-center of multi-sided Böhm-Bawerk assignment markets

Abstract: We prove that both the nucleolus and the core-center, i.e., the mass center of the core, of an m-sided Bohm-Bawerk assignment market can be respectively computed from the nucleolus and the core-center of a convex game defined on the set of m sectors. What is more, in the calculus of the nucleolus of this latter game only singletons and coalitions containing all agents but one need to be taken into account. All these results simplify the computation of the nucleolus and the core-center of a multi-sided Bohm-Bawerk assignment market with a large number of agents. As a consequence we can show that, contrary to the bilateral case, for multi-sided Bohm-Bawerk assignment markets the nucleolus and the core-center do not coincide in general.

Continuous learning methods in two-buyer pricing problem

Abstract: This paper presents continuous learning methods in a monopoly pricing problem where the firm has uncertainty about the buyers’ preferences. The firm designs a menu of quality-price bundles and adjusts them using only local information about the buyers’ preferences. The learning methods define different paths, and we compare how much profit the firm makes on these paths, how long it takes to learn the optimal tariff, and how the buyers’ utilities change during the learning period. We also present a way to compute the optimal path in terms of discounted profit with dynamic programming and complete information. Numerical examples show that the optimal path may involve jumps where the buyer types switch from one bundle to another, and this is a property which is difficult to include in the learning methods. The learning methods have, however, the benefit that they can be generalized to pricing problems with many buyers types and qualities.

Optimality of ( s , S ) policies for jump inventory models

Abstract: This paper is concerned with the optimality of (s, S) policies for a single-item inventory control problem which minimizes the total expected cost over an infinite planning horizon and where the demand is driven by a piecewise deterministic process. Our approach is based on the theory of quasi-variational inequality.

A Bankruptcy Approach to the Core Cover

Abstract: In this paper we establish a relationship between the core cover of a compromise admissible game and the core of a particular bankruptcy game: the core cover of a compromise admissible game is, indeed, a translation of the set of coalitionally stable allocations captured by an associated bankruptcy game. Moreover, we analyze the combinatorial complexity of the core cover and, consequently, of the core of a compromise stable game.

Optimal partial hedging of an American option: shifting the focus to the expiration date

Abstract: As a main contribution we present a new approach for studying the problem of optimal partial hedging of an American contingent claim in a finite and complete discrete-time market. We assume that at an early exercise time the investor can borrow the amount she has to pay for the option holder by entering a short position in the numeraire asset and that this loan in turn will mature at the expiration date. We model and solve a partial hedging problem, where the investor’s purpose is to find a minimal amount at which she can hedge the above-mentioned loan with a given probability, while the potential shortfall is bounded above by a certain number of numeraire assets. A knapsack problem approach and greedy algorithm are used in solving the problem. To get a wider view of the subject and to make interesting comparisons, we treat also a closely related European case as well as an American case where a barrier condition is applied.

A semismooth Newton method for nonlinear symmetric cone programming

Abstract: In this paper, we employ the projection operator to design a semismooth Newton algorithm for solving nonlinear symmetric cone programming (NSCP). The algorithm is computable from theoretical standpoint and is proved to be locally quadratically convergent without assuming strict complementarity of the solution to NSCP.

A matroid view of key theorems for edge-swapping algorithms

Abstract: We demonstrate that two key theorems of Amaldi et al. (Math Methods Oper Res 69:205–223, 2009), which they presented with rather complicated proofs, can be more easily and cleanly established using a simple and classical property of binary matroids. Besides a simpler proof, we see that both of these key results are manifestations of the same essential property.

Quantitative stability of mixed-integer two-stage quadratic stochastic programs

Abstract: For our introduced mixed-integer quadratic stochastic program with fixed recourse matrices, random recourse costs, technology matrix and right-hand sides, we study quantitative stability properties of its optimal value function and optimal solution set when the underlying probability distribution is perturbed with respect to an appropriate probability metric. To this end, we first establish various Lipschitz continuity results about the value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of linear constraints. The obtained results extend earlier results about quantitative stability properties of stochastic integer programming and stability results for mixed-integer parametric quadratic programs.