Sensible decisions based on QoS

Network Quality of Service (QoS) criteria of interest include conventional metrics such as throughput, delay, loss, and jitter, as well as new QoS criteria based on power utilization, reliability and security. Variable and adaptive routing have again become of interest in networking because of the increasing importance of mobile ad-hoc networks. In this paper we develop a probability model of adaptive routing algorithms which use the expected QoS to select paths in the network. Our objective is not to analyze QoS, but rather to design randomized routing policies which can improve QoS. We define QoS metrics as non-negative random variables associated with network paths which satisfy a sub-additivity condition along each path. We define the QoS of a path, under some routing policy, as the expected value of a non-decreasing measurable function of the QoS metric. We discuss sensitive and insensitive QoS metrics, the latter being dependent on the routing policy which is used. We describe routing policies simply as probabilistic choices among all possible paths from some source to some given destination. Incremental routing policies are defined as those which can be derived from independent decisions taken at certain points (or nodes) along paths. Sensible routing policies are then introduced: they take decisions based simply on the QoS of each available path. Sensible policies, which make decisions based on the QoS of the paths, are introduced. We prove that the routing probability of a sensible policy can always be uniquely obtained. A hierarchy of m-sensible probabilistic routing policies is then introduced. A 0-sensible policy is simply a random choice of routes with equal probability, while a 1-sensible policy selects a path with a probability which is inversely proportional to the (expected) QoS of the path. We prove that an m + 1-sensible policy provides better QoS on the average than an m-sensible policy, if the QoS metric is insensitive. We also show that under certain conditions, the same result also holds for sensitive QoS metrics.Accepted: May 2003, This work was supported by the U.S. Army and Navy under contracts N611339-00-K-0002, N61339-02-C0050, N61339-02-C0080, N61339-02-C0117, and by NSF grants EIA0086251, EIA0203446, ECS0216381.     

The maximin HAZMAT routing problem

The hazardous material routing problem from an origin to a destination in an urban area is addressed. We maximise the distance between the route and its closest vulnerable centre, weighted by the centre’s population. A vulnerable centre is a school, hospital, senior citizens’ residence or the like, concentrating a high population or one that is particularly vulnerable or difficult to evacuate in a short time. The potential consequences on the most exposed centre are thus minimized. Though previously studied in a continuous space, the problem is formulated here over a transport (road) network. We present an exact model for the problem, in which we manage to significantly reduce the required variables, as well as an optimal polynomial time heuristic. The integer programming formulation and the heuristic are tested in a real-world case study set in the transport network in the city of Santiago, Chile.     

Implementation of goal programmingin long-range resource planning in university management

This paper presents a resource allocation model via Goal Programming (GP) in a long-range planning horizon in university management. In the proposed model, the introduction of new courses in the academic units and the allocation of available budget within the academic units as well as the forecast of the budget which is actually needed for better academic performance are taken into consideration. An illustrative example is presented to expound the model     

Strategic interactions between channel structure and demand enhancing services

In this paper, we investigate how opportunities to invest in demand enhancing services for a product line affect the interactions between a manufacturer and her dealer. Many demand enhancing services, e.g. after sales support, warranty repair etc. can be provided either by the manufacturer or they can be delegated to the dealer. We first show that when a manufacturer retains control of such services, the dealer will have an incentive to choose a decentralized organizational structure as a means of committing to non-product line pricing since this will encourage the manufacturer to invest more in demand enhancing services. We then consider a game that is played between the manufacturer and the dealer in which the dealer chooses between centralized (product line pricing) or decentralized (non-product line pricing) operations, and the manufacturer chooses between insourcing the services, i.e. providing them herself, or outsourcing them to the dealer. We find that the equilibrium depends on which, if any, channel partner has the ability to act as a Stackelberg leader. If the dealer can move first, then the equilibrium will always be outsourced services and centralized dealer operations. However, if either the manufacturer moves first or if neither partner can move first, then the equilibrium can be either insourced services and decentralized dealer operations or outsourced service and centralized dealer operations.     

New formulations and valid inequalities for a bilevel pricing problem

Consider the problem of maximizing the toll revenue collected on a multi-commodity transportation network. This fits a bilevel framework where a leader sets tolls, while users respond by selecting cheapest paths to their destination. We propose novel formulations of the problem, together with valid inequalities yielding improved algorithms.     

Optimal paths in bi-attribute networks with fractional cost functions

An important routing problem is to determine an optimal path through a multi-attribute network which minimizes a cost function of path attributes. In this paper, we study an optimal path problem in a bi-attribute network where the cost function for path evaluation is fractional. The problem can be equivalently formulated as the “bi-attribute rational path problem” which is known to be NP-complete. We develop an exact approach to find an optimal simple path through the network when arc attributes are non-negative. The approach uses some path preference structures and elimination techniques to discard, from further consideration, those (partial) paths that cannot be parts of an optimal path. Our extensive computational results demonstrate that the proposed method can find optimal paths for large networks in very attractive times.     

On the economics of GPRS networks with Wi-Fi integration

Wi-Fi provides an appealing opportunity for GSM/GPRS operators to enhance their data capability. By integrating both networks, operators are able to provide 3G-like services. However, both networks have different data rates and capacity, which makes economics of the network integration and pricing of services a challenging issue. In this paper we introduce a novel pricing model for GPRS networks integrated with Wi-Fi networks. The model identifies how the integration can play a significant role in increasing operators’ overall revenue and potentially improve the performance of GPRS networks. We identify the optimal GPRS charging rate and Wi-Fi connection fee that yields maximum network revenue. In addition, we conduct a case study of a wireless operator that considers network integration, adopting our pricing model. The investment analysis provides the insightful information for profitable business cases of GPRS networks with Wi-Fi integration.     

An exact algorithm for the network pricing problem

This work focuses on an improved exact algorithm for addressing an NP-hard network pricing problem. The method involves an efficient and partial generation of candidate solutions, a recursive scheme for generating improved upper bounds, and a column generation procedure for solving the network-structured subproblems. Its efficiency is assessed against both randomly generated instances involving three distinct topologies as well as instances based on real life situations in telecommunication and freight transportation.     

The optimal pricing and ordering policy for an integrated inventory model when trade credit linked to order quantity

In traditional inventory models, it is implicitly assumed that the buyer must pay for the purchased items as soon as they have been received. However, in many practical situations, the vendor is willing to provide the buyer with a permissible delay period when the buyer’s order quantity exceeds a given threshold. Therefore, to incorporate the concept of vendor–buyer integration and order-size-dependent trade credit, we present a stylized model to determine the optimal strategy for an integrated vendor–buyer inventory system under the condition of trade credit linked to the order quantity, where the demand rate is considered to be a decreasing function of the retail price. By analyzing the total channel profit function, we developed some useful results to characterize the optimal solution and provide an iterative algorithm to find the retail price, buyer’s order quantity, and the numbers of shipment per production run from the vendor to the buyer. Numerical examples and sensitivity analysis are given to illustrate the theoretical results, and some managerial insights are also obtained.     

Centralized resource allocation for enhanced Russell models

This paper proposes a centralized resource allocation (CRA) model for the enhanced Russell model. All the DMUs can be easily projected onto the efficient frontier by solving only one model. This projection can be made by transforming the proposed model to a linear programming problem. In this paper, instead of non-radially increasing or decreasing the inputs or outputs individually, we increase or decrease non-radially all of the inputs and outputs at the same time. By solving a single model, we can provide targets for all DMUs. By the proposed approximation, different targets can be found for all DMUs, as compared to those obtained by the previous approximations. The proposed model can be developed to CRA models. Finally, an applied example emphasizes the importance of the proposed model.     

Capacity and price setting for dispersed,time-sensitive customer segments

We consider joint pricing and capacity decisions for a facility serving heterogeneous consumers that span a continuous range of locations, and are sensitive to time delays. Within this context, we analyze two contrasting service strategies: segmentation and pooling. Consumer segments differ with respect to their reservation prices and time sensitivities, and are dispersed over a single distance dimension. The firm serves these consumers using a process that we initially model as an M/M/1 queuing system. We analyze profit-maximizing price and capacity levels for a monopolist, and contrast the optimal segmentation and pooling policies. We find that when consumers are time sensitive, and can expect queuing delays at the firm’s facility (due to random arrival and service times), then scale economies from pooling can outweigh segmentation benefits. Yet, segmentation outperforms pooling when consumer segments differ substantively, in which case the firm can use capacity as a lever to price discriminate between the segments. Moreover, by contrasting a dedicated-services strategy, which directly targets specific segments and serves them separately, with the alternative of allowing consumers to self-select, we find that self-selection has a moderate negative influence on profits. We also examine the profit impact of employing alternative queuing systems, and find that a hybrid strategy based on a prioritized queuing discipline, that combines elements of segmentation (by offering different waiting times) and pooling (by sharing capacity across consumer segments), can outperform both the pure segmentation and pooling strategies.     

Heuristic option pricing with neural networks and the neuro-computer synapse 3

Today’s option and warrant pricing is based on models developed by Black, Scholes and Merton in 1973 and Cox, Ross and Rubinstein in 1979. The price movement of the underlying asset is modeled by continuous-time or discrete-time stochastic processes. Unfortunately these models are based on severely unrealistic assumptions. Permanently an unsatisfactory and quite artificial adaption to the true market conditions is necessary (future volatility of the underlying price). Here, an alternative heuristic approach with a highly accurate neural network approximation is presented. Market prices of options and warrants and the values of the influence variables form the usually very large output/ input data set. Thousands of multi-layer perceptrons with various topologies and with different weight initializations are trained with a fast sequential quadratic programming (SQP) method. The best networks are combined to an expert council network to synthesize market prices accurately. All options and warrants can be compared to single out overpriced and underpriced ones for each trading day. For each option and warrant overpriced and underpriced trading days can be used to ascertain a better buy and sell timing. Furthermore the neural model gains deep insight into the market price sen-sitivities (option Greeks), e.g., ?, Г, Θ and Ω. As an illustrative example we inves-tigate BASF stock call warrants. Time series from the beginning of 1996 to mid 1997 of 74 BASF call warrant prices at the Frankfurter Wertpapierborse (Frankfurt Stock Exchange) form the data basis. Finally a possible speed up of the training with the neuro-computer SYNAPSE 3 is briefly discussed     

Output learning and duality in joint production programs

Summary Output learning is incorporated into a short-run static cost-minimizing model of the multiproduct, multifactor firm which employs a fixed-coefficients technology. The firm's output processes or activities are ultimately specified as functions of the activity variables themselves, thus rentering a generalization to a concave program. A Lagrange dual formulation is then used to obtain the indirect cost objective. Given that this optimal cost function is differentiable and satisfies a regularity condition, its price derivatives serve as input demand functions while its derivatives with respect to the minimum output requirements yield a set of (implicit) marginal costs or dual variables.     

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts.     

A library for continuous convex separable quadratic knapsack problems

The Continuous Convex Separable Quadratic Knapsack problem (CQKnP) is an easy but useful model that has very many different applications. Although the problem can be solved quickly, it must typically be solved very many times within approaches to (much) more difficult models; hence an efficient solution approach is required. We present and discuss a small open-source library for its solution that we have recently developed and distributed.     

随机的库存-路径问题的机会约束规划模型与算法

随机需求下的库存-路径问题是一类复杂的组合优化问题.本文讨论了VMI背景下的库存-路径联合优化问题,构建了问题的机会约束规划模型,并将随机模拟、人工神经网络和遗传算法结合在一起,设计了求解问题的混合智能算法.实验表明算法性能良好.     

A novel mechanism for provisioning of high-level quality of service information in grid environments

Grids, like other heterogeneous systems, have reached the level of maturity where they are addressing the Quality of Service (QoS) provision aspect as a fundamental part of their architecture. However, due to the multi-layer nature of these environments, the QoS characteristics of the infrastructure are revealed to the service-consumer through a complex procedure, in which each layer maps its own QoS parameters to another. Most of these approaches are starting the delegation of QoS information from the service provider’s side, resulting in delivering low-level terms to the service consumer. In this paper, we define the architecture of a mechanism that is able to provide application-oriented and user-specific QoS information in the internal layers of the Grid middleware. This way, it enables the service consumer and especially the end-user to express his business perspectives through the service level agreement life cycle. We also demonstrate the operation of this mechanism and evaluate its performance using a specific Grid scenario.     

Pricing peer-produced services: Quality,capacity, and competition issues

Peer production has played an important role in the economics of Web 2.0 related services in which user participation and contribution become the main driving dynamics. However, the quality of peer-produced services is uncertain because of inherently decentralized and heterogeneous participants. In the paper, utilizing reliability and game theoretic models, we develop a QoS measure and pricing schemes for this emerging type of service under various market structures. Our results suggest that a monopolistic platform provider has no incentive to offer multiple quality classes of service. Two competing platform providers may offer identical service contracts but still receive non-negative profit. If they offer heterogeneous service contracts, the provider with the lower quality service may provide higher quality than he advertises. This research contributes to the literature with a number of unique and interesting implications for the issues of service contract design, capacity planning, and market interactions for operations of community-based or peer-produced services.