Showing papers in "Mathematical Methods of Operations Research in 2016"

Discrete Wasserstein barycenters: optimal transport for discrete data

Abstract: Wasserstein barycenters correspond to optimal solutions of transportation problems for several marginals, and as such have a wide range of applications ranging from economics to statistics and computer science. When the marginal probability measures are absolutely continuous (or vanish on small sets) the theory of Wasserstein barycenters is well-developed [see the seminal paper (Agueh and Carlier in SIAM J Math Anal 43(2):904–924, 2011)]. However, exact continuous computation of Wasserstein barycenters in this setting is tractable in only a small number of specialized cases. Moreover, in many applications data is given as a set of probability measures with finite support. In this paper, we develop theoretical results for Wasserstein barycenters in this discrete setting. Our results rely heavily on polyhedral theory which is possible due to the discrete structure of the marginals. The results closely mirror those in the continuous case with a few exceptions. In this discrete setting we establish that Wasserstein barycenters must also be discrete measures and there is always a barycenter which is provably sparse. Moreover, for each Wasserstein barycenter there exists a non-mass-splitting optimal transport to each of the discrete marginals. Such non-mass-splitting transports do not generally exist between two discrete measures unless special mass balance conditions hold. This makes Wasserstein barycenters in this discrete setting special in this regard. We illustrate the results of our discrete barycenter theory with a proof-of-concept computation for a hypothetical transportation problem with multiple marginals: distributing a fixed set of goods when the demand can take on different distributional shapes characterized by the discrete marginal distributions. A Wasserstein barycenter, in this case, represents an optimal distribution of inventory facilities which minimize the squared distance/transportation cost totaled over all demands.

Computational optimization of gas compressor stations: MINLP models versus continuous reformulations

Abstract: When considering cost-optimal operation of gas transport networks, compressor stations play the most important role. Proper modeling of these stations leads to nonconvex mixed-integer nonlinear optimization problems. In this article, we give an isothermal and stationary description of compressor stations, state MINLP and GDP models for operating a single station, and discuss several continuous reformulations of the problem. The applicability and relevance of different model formulations, especially of those without discrete variables, is demonstrated by a computational study on both academic examples and real-world instances. In addition, we provide preliminary computational results for an entire network.

On the quantification of nomination feasibility in stationary gas networks with random load

Abstract: The paper considers the computation of the probability of feasible load constellations in a stationary gas network with uncertain demand. More precisely, a network with a single entry and several exits with uncertain loads is studied. Feasibility of a load constellation is understood in the sense of an existing flow meeting these loads along with given pressure bounds in the pipes. In a first step, feasibility of deterministic exit loads is characterized algebraically and these general conditions are specified to networks involving at most one cycle. This prerequisite is essential for determining probabilities in a stochastic setting when exit loads are assumed to follow some (joint) Gaussian distribution when modeling uncertain customer demand. The key of our approach is the application of the spheric-radial decomposition of Gaussian random vectors coupled with Quasi Monte-Carlo sampling. This approach requires an efficient algorithmic treatment of the mentioned algebraic relations moreover depending on a scalar parameter. Numerical results are illustrated for different network examples and demonstrate a clear superiority in terms of precision over simple generic Monte-Carlo sampling. They lead to fairly accurate probability values even for moderate sample size.

Systemic risk measures on general measurable spaces

Abstract: In view of the recent financial crisis systemic risk has become a very important research object. It is of significant importance to understand what can be done from a regulatory point of view to make the financial system more resilient to global crises. Systemic risk measures can provide more insight on this aspect. The study of systemic risk measures should support central banks and financial regulators with information that allows for better decision making and better risk management. For this reason this paper studies systemic risk measures on locally convex-solid Riesz spaces. In our work we extend the axiomatic approach to systemic risk, as introduced in Chen et al. (Manag Sci 59(6):1373–1388, 2013), in different directions. One direction is the introduction of systemic risk measures that do not have to be positively homogeneous. The other direction is that we allow for a general measurable space whereas in Chen et al. (2013) only a finite probability space is considered. This extends the scope of possible loss distributions of the components of a financial system to a great extent and introduces more flexibility for the choice of suitable systemic risk measures.

Optimal mean–variance reinsurance and investment in a jump-diffusion financial market with common shock dependence

Abstract: In this paper, we study the optimal reinsurance-investment problems in a financial market with jump-diffusion risky asset, where the insurance risk model is modulated by a compound Poisson process, and the two jump number processes are correlated by a common shock. Moreover, we remove the assumption of nonnegativity on the expected value of the jump size in the stock market, which is more economic reasonable since the jump sizes are always negative in the real financial market. Under the criterion of mean–variance, based on the stochastic linear–quadratic control theory, we derive the explicit expressions of the optimal strategies and value function which is a viscosity solution of the corresponding Hamilton–Jacobi–Bellman equation. Furthermore, we extend the results in the linear–quadratic setting to the original mean–variance problem, and obtain the solutions of efficient strategy and efficient frontier explicitly. Some numerical examples are given to show the impact of model parameters on the efficient frontier.

An algorithm for a class of split feasibility problems: application to a model in electricity production

Abstract: We propose a projection algorithm for solving split feasibility problems involving paramonotone equilibria and convex optimization. The proposed algorithm can be considered as a combination of the projection ones for equilibrium and convex optimization problems. We apply the algorithm for finding an equilibrium point with minimal environmental cost for a model in electricity production. Numerical results for the model are reported.

Equivalence between polyhedral projection, multiple objective linear programming and vector linear programming

Abstract: Let a polyhedral convex set be given by a finite number of linear inequalities and consider the problem to project this set onto a subspace. This problem, called polyhedral projection problem, is shown to be equivalent to multiple objective linear programming. The number of objectives of the multiple objective linear program is by one higher than the dimension of the projected polyhedron. The result implies that an arbitrary vector linear program (with arbitrary polyhedral ordering cone) can be solved by solving a multiple objective linear program (i.e. a vector linear program with the standard ordering cone) with one additional objective space dimension.

Computing tight bounds via piecewise linear functions through the example of circle cutting problems

Abstract: This paper discusses approximations of continuous and mixed-integer non-linear optimization problems via piecewise linear functions. Various variants of circle cutting problems are considered, where the non-overlap of circles impose a non-convex feasible region. While the paper is written in an “easy-to-understand” and “hands-on” style which should be accessible to graduate students, also new ideas are presented. Specifically, piecewise linear functions are employed to yield mixed-integer linear programming problems which provide lower and upper bounds on the original problem, the circle cutting problem. The piecewise linear functions are modeled by five different formulations, containing the incremental and logarithmic formulations. Another variant of the cutting problem involves the assignment of circles to pre-defined rectangles. We introduce a new global optimization algorithm, based on piecewise linear function approximations, which converges in finitely many iterations to a globally optimal solution. The discussed formulations are implemented in GAMS. All GAMS-files are available for download in the Electronic supplementary material. Extensive computational results are presented with various illustrations.

Continuous-time Markov decision processes with risk-sensitive finite-horizon cost criterion

Abstract: This paper studies continuous-time Markov decision processes with a denumerable state space, a Borel action space, bounded cost rates and possibly unbounded transition rates under the risk-sensitive finite-horizon cost criterion. We give the suitable optimality conditions and establish the Feynman–Kac formula, via which the existence and uniqueness of the solution to the optimality equation and the existence of an optimal deterministic Markov policy are obtained. Moreover, employing a technique of the finite approximation and the optimality equation, we present an iteration method to compute approximately the optimal value and an optimal policy, and also give the corresponding error estimations. Finally, a controlled birth and death system is used to illustrate the main results.

Modeling values for TU-games using generalized versions of consistency, standardness and the null player property

Abstract: In the paper we discuss three general properties of values of TU-games: λ-standardness, general probabilistic consistency and some modifications of the null player property. Necessary and sufficient conditions for different families of efficient, linear and symmetric values are given in terms of these properties. It is shown that the results obtained can be used to get new axiomatizations of several classical values of TU-games.

On the solution continuity of parametric set optimization problems

Abstract: The aim of this paper is to investigate the continuity of the solution set maps of set-valued vector optimization problems with set optimization criterion. First, we introduce a new concept, which is called a u-lower level map. Then, we give some sufficient conditions for the upper and lower semicontinuities of the generalized lower level map. Finally, by virtue of the semicontinuity of the u-lower level map, we obtain the continuity of the minimal solution set map to parametric set-valued vector optimization problems with set optimization criterion.

Robust unit commitment with n - 1 security criteria

Abstract: The short-term unit commitment and reserve scheduling decisions are made in the face of increasing supply-side uncertainty in power systems. This has mainly been caused by a higher penetration of renewable energy generation that is encouraged and enforced by the market and policy makers. In this paper, we propose a two-stage stochastic and distributionally robust modeling framework for the unit commitment problem with supply uncertainty. Based on the availability of the information on the distribution of the random supply, we consider two specific models: (a) a moment model where the mean values of the random supply variables are known, and (b) a mixture distribution model where the true probability distribution lies within the convex hull of a finite set of known distributions. In each case, we reformulate these models through Lagrange dualization as a semi-infinite program in the former case and a one-stage stochastic program in the latter case. We solve the reformulated models using sampling method and sample average approximation, respectively. We also establish exponential rate of convergence of the optimal value when the randomization scheme is applied to discretize the semi-infinite constraints. The proposed robust unit commitment models are applied to an illustrative case study, and numerical test results are reported in comparison with the two-stage non-robust stochastic programming model.

Functional central limit theorems for Markov-modulated infinite-server systems

Abstract: In this paper we study the Markov-modulated M/M/ $$\infty $$ queue, with a focus on the correlation structure of the number of jobs in the system. The main results describe the system’s asymptotic behavior under a particular scaling of the model parameters in terms of a functional central limit theorem. More specifically, relying on the martingale central limit theorem, this result is established, covering the situation in which the arrival rates are sped up by a factor N and the transition rates of the background process by $$N^\alpha $$ , for some $$\alpha >0$$ . The results reveal an interesting dichotomy, with crucially different behavior for $$\alpha >1$$ and $$\alpha <1$$ , respectively. The limiting Gaussian process, which is of the Ornstein–Uhlenbeck type, is explicitly identified, and it is shown to be in accordance with explicit results on the mean, variances and covariances of the number of jobs in the system.

Performance analysis of a reflected fluid production/inventory model

Abstract: We study the performance of a reflected fluid production/inventory model operating in a stochastic environment that is modulated by a finite state continuous time Markov chain. The process alternates between ON and OFF periods. The ON period is switched to OFF when the content level reaches a predetermined level q and returns to ON when it drops to 0. The ON/OFF periods generate an alternative renewal process. Applying a matrix analytic approach, fluid flow techniques and martingales, we develop methods to obtain explicit formulas for the cost functionals (setup, holding, production and lost demand costs) in the discounted case and under the long-run average criterion. Numerical examples present the trade-off between the holding cost and the loss cost and show that the total cost appears to be a convex function of q.

The online knapsack problem with incremental capacity

Abstract: We consider an online knapsack problem with incremental capacity. In each time period, a set of items, each with a specific weight and value, is revealed and, without knowledge of future items, it has to be decided which of these items to accept. Additionally, the knapsack capacity is not fully available from the start but increases by a constant amount in each time period. The goal is to maximize the overall value of the accepted items. This setting extends the basic online knapsack problem by introducing a dynamic instead of a static knapsack capacity and is applicable to classic problems such as resource allocation or one-way trading. In contrast to the basic online knapsack problem, for which no competitive algorithms exist, the setting of incremental capacity facilitates the development of competitive algorithms for a bounded time horizon. We provide a competitive analysis of deterministic and randomized online algorithms for the online knapsack problem with incremental capacity and present lower bounds on the competitive ratio achievable by online algorithms for the problem. Most of these lower bounds match the competitive ratios achieved by our online algorithms exactly or differ only by a constant factor.

The proper relaxation and the proper gap of the skiving stock problem

Abstract: We consider the 1D skiving stock problem (SSP) which is strongly related to the dual bin packing problem: find the maximum number of products with minimum length L that can be constructed by connecting a given supply of $$ m \in {\mathbb {N}} $$ smaller item lengths $$ l_1,\ldots ,l_m $$ with availabilities $$ b_1,\ldots , b_m $$ . For this NP-hard optimization problem, we focus on the proper relaxation and introduce a modeling approach based on graph theory. Additionally, we investigate the quality of the proper gap, i.e., the difference between the optimal objective values of the proper relaxation and the SSP itself. As an introductorily motivation, we prove that the SSP does not possess the integer round down property (IRDP) with respect to the proper relaxation. The main contribution of this paper is given by a construction principle for an infinite number of non-equivalent non-proper-IRDP instances and an enumerative approach that leads to the currently largest known (proper) gap.

On the extreme points of moments sets

Abstract: Necessary and sufficient conditions for a measure to be an extreme point of the set of measures on a given measurable space with prescribed generalized moments are given, as well as an application to extremal problems over such moment sets; these conditions are expressed in terms of atomic partitions of the measurable space. It is also shown that every such extreme measure can be adequately represented by a linear combination of k Dirac probability measures with nonnegative coefficients, where k is the number of restrictions on moments; moreover, when the measurable space has appropriate topological properties, the phrase “can be adequately represented by” here can be replaced simply by “is”. Applications to specific extremal problems are also given, including an exact lower bound on the exponential moments of truncated random variables, exact lower bounds on generalized moments of the interarrival distribution in queuing systems, and probability measures on product spaces with prescribed generalized marginal moments.

Long run risk sensitive portfolio with general factors

Abstract: In the paper portfolio optimization over long run risk sensitive criterion is considered. It is assumed that economic factors which stimulate asset prices are ergodic but non necessarily uniformly ergodic. Solution to suitable Bellman equation using local span contraction with weighted norms is shown. The form of optimal strategy is presented and examples of market models satisfying imposed assumptions are shown.

G/{ GI}/N(+{ GI}) queues with service interruptions in the Halfin–Whitt regime

Abstract: We study $$G/GI/N(+GI)$$ queues with alternating renewal service interruptions in the Halfin–Whitt regime. The systems experience up and down alternating periods. In the up periods, the systems operate normally as the usual $$G/GI/N(+GI)$$ queues with non-idling first-come–first-served service discipline. In the down periods, arrivals continue entering the systems, but all servers stop functioning while the amount of service that each customer has received will be conserved and services will resume when the next up period starts. For models with abandonment, interruptions do not affect customers’ patience times. We assume that the up periods are of the same order as the service times but the down periods are asymptotically negligible compared with the service times. We establish the functional central limit theorems for the queue-length processes and the virtual-waiting time processes in these models, where the limit processes are represented as stochastic integral convolution equations driven by jump processes. The convergence in these limit theorems is proved in the space $${\mathbb D}$$ endowed with the Skorohod $$M_1$$ topology.

On the complexity of the FIFO stack-up problem

Abstract: We study the combinatorial FIFO stack-up problem. In delivery industry, bins have to be stacked-up from conveyor belts onto pallets with respect to customer orders. Given k sequences $$q_1, \ldots , q_k$$ of labeled bins and a positive integer p, the aim is to stack-up the bins by iteratively removing the first bin of one of the k sequences and put it onto an initially empty pallet of unbounded capacity located at one of p stack-up places. Bins with different pallet labels have to be placed on different pallets, bins with the same pallet label have to be placed on the same pallet. After all bins for a pallet have been removed from the given sequences, the corresponding stack-up place will be cleared and becomes available for a further pallet. The FIFO stack-up problem is to find a stack-up sequence such that all pallets can be build-up with the available p stack-up places. In this paper, we introduce two digraph models for the FIFO stack-up problem, namely the processing graph and the sequence graph. We show that there is a processing of some list of sequences with at most p stack-up places if and only if the sequence graph of this list has directed pathwidth at most $$p-1$$ . This connection implies that the FIFO stack-up problem is NP-complete in general, even if there are at most 6 bins for every pallet and that the problem can be solved in polynomial time, if the number p of stack-up places is assumed to be fixed. Further the processing graph allows us to show that the problem can be solved in polynomial time, if the number k of sequences is assumed to be fixed.

Optimal investment and consumption under partial information

Abstract: We present a unified approach for partial information optimal investment and consumption problems in a non-Markovian Ito process market. The stochastic local mean rate of return and the Wiener process cannot be observed by the agent, whereas the path-dependent volatility, the path-dependent interest rate and the asset prices can be observed. The main assumption is that the asset price volatility is a nonanticipative functional of the asset price trajectory. The utility functions are general and satisfy standard conditions. First, we show that the corresponding full information market is complete and in this setting we solve the problem using standard methods. Second, we transform the original partial information problem into a corresponding full information problem using filtering theory, and show that it follows that the market is observationally complete in the sense that any contingent claim adapted to the observable filtration is replicable. Using the solutions of the full information problem we then easily derive solutions to the original partial information problem.

A trust-region method with improved adaptive radius for systems of nonlinear equations

Abstract: In this study, a new adaptive trust-region strategy is presented to solve nonlinear systems. More specifically, we propose a new method leading to produce a smaller trust-region radius close to the optimizer and a larger trust-region radius far away from the optimizer. Accordingly, it can lead to a smaller step-size close to the optimizer and a larger one far away from the optimizer. The new strategy includes a convex combination of the maximum norm of function value of some preceding successful iterates and the current norm of function value. The global convergence of the proposed approach is established while the local q-quadratic convergence rate is proved under local error bound condition, which is weaker than the nonsingularity. Numerical results of the proposed algorithm are also reported.

Minimality of EDF networks with resource sharing

Abstract: We consider a general real-time, multi-resource network with soft customer deadlines, in which users require service from several shared resources simultaneously. We show that the preemptive earliest-deadline-first scheduling strategy minimizes, in a suitable sense, the system resource idleness with respect to customers with lead times not greater than any given threshold value on all the routes of the network. Related methods of performance evaluation for such systems are also discussed. Our arguments are pathwise, requiring no assumptions on the network topology and very mild assumptions, or even no assumptions, on the model stochastic primitives.

Relationships between constrained and unconstrained multi-objective optimization and application in location theory

Abstract: This article deals with constrained multi-objective optimization problems. The main purpose of the article is to investigate relationships between constrained and unconstrained multi-objective optimization problems. Under suitable assumptions (e.g., generalized convexity assumptions) we derive a characterization of the set of (strictly, weakly) efficient solutions of a constrained multi-objective optimization problem using characterizations of the sets of (strictly, weakly) efficient solutions of unconstrained multi-objective optimization problems. We demonstrate the usefulness of the results by applying it on constrained multi-objective location problems. Using our new results we show that special classes of constrained multi-objective location problems (e.g., point-objective location problems, Weber location problems, center location problems) can be completely solved with the help of algorithms for the unconstrained case.

It is difficult to tell if there is a Condorcet spanning tree

Abstract: We apply the well-known Condorcet criterion from voting theory outside of its classical framework and link it with spanning trees of an undirected graph. In situations in which a network, represented by a spanning tree of an undirected graph, needs to be installed, decision-makers typically do not agree on the network to be implemented. Instead, each of these decision-makers has her own ideal conception of the network. In order to derive a group decision, i.e., a single spanning tree for the entire group of decision-makers, the goal would be a spanning tree that beats each other spanning tree in a simple majority comparison. When comparing two dedicated spanning trees, a decision-maker will be considered to be more satisfied with the one that is “closer” to her proposal. In this context, the most basic and natural measure of distance is the usual set difference: we simply count the number of edges the spanning tree has in common with the proposal of the decision-maker. In this work, we show that it is computationally intractable to decide (1) if such a spanning tree exists, and (2) if a given spanning tree satisfies the Condorcet criterion.

On undiscounted semi-Markov decision processes with absorbing states

Abstract: Limiting ratio average (undiscounted) reward finite (state and action spaces) semi-Markov decision processes (SMDPs) with absorbing states are considered where all but one states are absorbing. We propose a realistic inspection model that suitably fits into the class of undiscounted SMDPs with absorbing states. Existence of an optimal semi-stationary policy (i.e., a semi-Markov policy independent of decision epoch counts) is proved. A linear programming algorithm is provided to compute such an optimal policy.

Utility maximization in an illiquid market in continuous time

Abstract: A utility maximization problem in an illiquid market is studied. The financial market is assumed to have temporary price impact with finite resilience. After the formulation of this problem as a Markovian stochastic optimal control problem a dynamic programming approach is used for its analysis. In particular, the dynamic programming principle is proved and the value function is shown to be the unique discontinuous viscosity solution. This characterization is utilized to obtain numerical results for the optimal strategy and the loss due to illiquidity.

A limited memory BFGS algorithm for non-convex minimization with applications in matrix largest eigenvalue problem

Abstract: This study aims to present a limited memory BFGS algorithm to solve non-convex minimization problems, and then use it to find the largest eigenvalue of a real symmetric positive definite matrix. The proposed algorithm is based on the modified secant equation, which is used to the limited memory BFGS method without more storage or arithmetic operations. The proposed method uses an Armijo line search and converges to a critical point without convexity assumption on the objective function. More importantly, we do extensive experiments to compute the largest eigenvalue of the symmetric positive definite matrix of order up to 54,929 from the UF sparse matrix collection, and do performance comparisons with EIGS (a Matlab implementation for computing the first finite number of eigenvalues with largest magnitude). Although the proposed algorithm converges to a critical point, not a global minimum theoretically, the compared results demonstrate that it works well, and usually finds the largest eigenvalue of medium accuracy.

Construction of Nash equilibrium based on multiple stopping problem in multi-person game

Abstract: We consider a multi-person stopping game with players’ priorities and multiple stopping. Players observe sequential offers at random or fixed times. Each accepted offer results in a reward. Each player can obtain fixed number of rewards. If more than one player wants to accept an offer, then the player with the highest priority among them obtains it. The aim of each player is to maximize the expected total reward. For the game defined this way, we construct a Nash equilibrium. The construction is based on the solution of an optimal multiple stopping problem. We show the connections between expected rewards and stopping times of the players in Nash equilibrium in the game and the optimal expected rewards and optimal stopping times in the multiple stopping problem. A Pareto optimum of the game is given. It is also proved that the presented Nash equilibrium is a sub-game perfect Nash equilibrium. Moreover, the Nash equilibrium payoffs are unique. We also present new results related to multiple stopping problem.

Simple and three-valued simple minimum coloring games

Abstract: In this paper minimum coloring games are considered. We characterize the class of conflict graphs inducing simple or three-valued simple minimum coloring games. We provide an upper bound on the number of maximum cliques of conflict graphs inducing such games. Moreover, a characterization of the core is provided in terms of the underlying conflict graph. In particular, in case of a perfect conflict graph the core of an induced three-valued simple minimum coloring game equals the vital core.