Asset Allocation and Reinsurance Policy for a Mean-Variance-CVaR Insurer in Continuous-Time

This paper studies the optimal asset allocation and reinsurance problem under mean-variance-CVaR criteria for an insurer in continuous-time. We obtain the closed-form solution of optimization problem by using martingale method. Numerical results show the trends of optimal wealth, investment and reinsurance strategies with various parameter values.     

Greedy primal-dual algorithm for dynamic resource allocation in complex networks

In Stolyar (Queueing Systems 50 (2005) 401–457) a dynamic control strategy, called greedy primal-dual (GPD) algorithm, was introduced for the problem of maximizing queueing network utility subject to stability of the queues, and was proved to be (asymptotically) optimal. (The network utility is a concave function of the average rates at which the network generates several “commodities.”) Underlying the control problem of Stolyar (Queueing Systems 50 (2005) 401–457) is a convex optimization problem subject to a set of linear constraints. In this paper we introduce a generalized GPD algorithm, which applies to the network control problem with additional convex (possibly non-linear) constraints on the average commodity rates. The underlying optimization problem in this case is a convex problem subject to convex constraints. We prove asymptotic optimality of the generalized GPD algorithm. We illustrate key features and applications of the algorithm on simple examples. AMS Subject Classifications: 90B15 · 90C25 · 60K25 · 68M12     

Bandwidth allocation in ATM networks

In an ATM network, bandwidth is allocated at different levels and in different stages. At the physical level, the ATM topology can be dynamically reconfigured by adding/removing truns between ATM switches. This allocation of bandwidth is made possible by the SONET Synchronous Transfer Mode (STM) infrastructure equipped with Digital Cross Connect Systems (DCSs). We will refer to this allocation asSTM allocation. At the ATM level, we can allocate bandwidth to individual Virtual Circuits (ATM-VC allocation) as well as to Virtual Paths (ATM-VP allocation). For example, in order to implement the Connectionless Network Access layer functions we find it convenient to organize the Virtual Paths in a Connectionless Overlay Network. This introduces another type of bandwidth allocation (CLS allocation). In this paper, we address and formulate the above bandwidth allocation problems, and propose efficient techniques for their solution. We illustrate these techniques with examples based on STM and CLS allocation, respectively.This work was jointly supported by the Brazilian National Science Council (CNPq) and NSF.     

Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

The reliability-redundancy allocation problem is an optimization problem that achieves better system reliability by determining levels of component redundancies and reliabilities simultaneously. The problem is classified with the hardest problems in the reliability optimization field because the decision variables are mixed-integer and the system reliability function is nonlinear, non-separable, and non-convex. Thus, iterative heuristics are highly recommended for solving the problem due to their reasonable solution quality and relatively short computation time. At present, most iterative heuristics use sensitivity factors to select an appropriate variable which significantly improves the system reliability. The sensitivity factor represents the impact amount of each variable to the system reliability at a designated iteration. However, these heuristics are inefficient in terms of solution quality and computation time because the sensitivity factor calculations are performed only at integer variables. It results in degradation of the exploration and growth in the number of subsequent continuous nonlinear programming (NLP) subproblems. To overcome the drawbacks of existing iterative heuristics, we propose a new scaling method based on the multi-path iterative heuristics introduced by Ha (2004). The scaling method is able to compute sensitivity factors for all decision variables and results in a decreased number of NLP subproblems. In addition, the approximation heuristic for NLP subproblems helps to avoid redundant computation of NLP subproblems caused by outlined solution candidates. Numerical experimental results show that the proposed heuristic is superior to the best existing heuristic in terms of solution quality and computation time.     

Reimbursement systems and regional inpatient allocation: a non-linear optimisation model

^** Email: marion.rauner{at}univie.ac.at^*** Email: georg.schneider{at}univie.ac.at^**** Email: kurt.heidenberger{at}univie.ac.at This study presents a non-linear optimisation model for investigatingthe optimal allocation of both budgets and inpatients with differenttreatments among hospitals within a geographic region such asVienna. The objective function maximises the overall qualityof treatment provided by regional hospitals. We compare theeffects of two different reimbursement systems—fixed versusvariable budgets—on optimal allocation strategies. Thecombination of modelling ideas from hospital location-allocationmodels and economic models to solve such a problem is new accordingto the literature. We found that fixed budgets outperformedvariable budgets as fewer Euros had to be invested for an incrementalunit of quality of care provided in most of the policy scenariosanalysed. Regional demand and supply patterns for differenttreatments affect the decision makers' choice of the most suitablereimbursement system. In our illustrative example, two hospitalsappeared inefficient regardless of the reimbursement system.Vienna policy makers are currently considering restructuringthese hospitals. They plan to merge one with nearby hospitalsand transform the other into a nursing home.     

Selection of certain dichotomous experiments

Summary The problem of allocating a single observation to one of the two available populations is considered. Suppose that a certain characteristic has densityf in one population, and has densityg in the other. On the basis of the value observed, one must specify which population has densityf. It is assumed that when a wrong population is chosen, a certain known loss is incurred. The problem is to allocate the observation so as to minimize the expected loss. General conditions onf andg are derived to decide which population should be selected for taking the observation. Research was supported in part by the National Science Foundation under grant No. SOC79-06386. Work done while on sabbatical leave at Carnegie-Mellon University.     

Chance-constrained activity analysis

The methods of activity analysis (Koopmans [6,7]) are re-examined in the presence of technological uncertainty. In particular, such uncertainty arises when new emerging technologies are employed in the production process and/or when new product designs are being developed. Both input coefficients and output coefficients may be uncertain. If activity levels are to be determined and fixed a priori, one may not be able to require in advance that total output suffice to cover total demand. (Indeed, demand itself may also be uncertain.) Instead, the requirement is written as a chance-constraint, to hold on some predetermined probability level only.The purpose of the present note is to discuss the economic properties of the resulting optimal solution. The certainty equivalent of the chance-constrained program and the corresponding Kuhn-Tucker conditions are written down. At the point of optimum, each producer will hold some inventories of finished goods as a contingency against variation in the output coefficients and in demand. Equilibrium prices will suffice to provide each activity some expected positive profit (an explicit formula for the calculation of the magnitude of this profit is provided).In choosing between several risky activities, each producer may attempt to establish an optimal portfolio of activities, providing a trade-off between expected cost and risk. The nature of an emerging theory of activity portfolios, developed along the lines of standard concepts in financial portfolio analysis, is indicated.     

无交双圈图的邻接矩阵的奇异性

一个无交双圈图G的邻接矩阵是奇异的当且仅当G含有4m(m∈N)阶圈，或G含有完美匹配和G—V(c1)，G-V(c2)均含有完美匹配且G中含有4κ1 3与4e1 1(κ1，e1∈N)阶圈，或G、G-V(c1)、G—V(c2)、G—V(c1)-V(c2)均无完美匹配．无交双圈图的邻接矩阵的最大行列式值为16。     

Fixed charge problems with identical fixed charges

The general problem considered by this paper is a special case of the fixed-charge problem. The further condition imposed is that all variables have the same associated fixed-charge. The problem is discussed in the context of a known commercial application, that being the cutting stock problem. The situation considered is that of cutting given numbers of small rectangles from large rectangular stock-plates. In many such situations major aims are to have low stock-plate usage and a low number of setups of the cutting equipment. These represent conflicting objectives capable of being combined by the use of fixed charges upon the setups but this paper presents an alternative approach incorporating direct manipulation of the number of setups involved in the solution. This approach is compared to a solution technique for the general fixed-charge problem.     

Optimal allocation policies for testing-resource based on a software reliability growth model

A lot of development resources are consumed during the software testing phase fundamentally consisting of module testing, integration, testing and system testing. Then, it is of great importance for a manager to decide how to effectively spend testing-resources on software testing for developing a quality and reliable software.In this paper, we consider two kinds of software testing-resource allocation problems to make the best use of the specified testing-resources during module testing. Also, we introduce a software reliability growth model for describing the time-dependent behavior of detected software faults and testing-resource expenditures spent during the testing, which is based on a nonhomogeneous Poisson process. It is shown that the optimal allocation of testing-resources among software modules can improve software reliability.