Fair allocation using a fast smart market auction

Allocation of network resources may be formulated as an optimization problem seeking to maximize the sum of the utility function of each flow. Individual flows collectively achieve social welfare with a constraint that global fairness must be provided by a congestion control scheme using congestion indication feedback signals. However, in the reality, non-cooperative flows without employing any congestion control scheme do not respond to congestion indication feedback signals. In this paper, we analyze fairness of congestion control with cooperative and non-cooperative flows for communication networks. Furthermore, we propose a fast smart-market auction (FSMA) approach to achieve social welfare in the environment that cooperative and non-cooperative flows coexist. Through detailed simulations, the proposed approach is proven to be effective in providing fairness.     

基于成本效益分析的救护车多时段布局优化研究

院前急救服务水平和救护资源之间存在悖反效益,本文综合考虑急救服务效果与急救网络成本,应用延误成本刻画急救效果,运营成本刻画急救资源使用,同时考虑需求规模、需求空间分布、救护车行驶速度以及救护车不可获得率随时间变化的影响,建立以最小化社会总成本为目标的救护车多时段布局优化模型,应用上海市松江区的实际数据,系统研究多时段救护车布局优化问题。计算结果表明优化后的系统在保证80%的高标准覆盖水平下,社会总成本比原系统下降32.23%。相比静态的情况,考虑时变因素可以使社会总成本下降15.8%,双覆盖率提高12.84%,各时段车辆繁忙率方差下降91.67%。     

运输问题的合作对策求解方法

产地间或销地间往往存在竞争,在这种情况下,使用运输问题最优化方法是不合理的。因此,从个体理性的视角提出运输问题的合作对策求解方法,方法将运输问题看作是一个博弈问题,各个产地或销地是博弈的局中人,求解其纳什均衡与纳什讨价还价解。在此基础上,说明了运输问题的非合作形式是一个指派问题,并证明指派问题的最优解是一个纳什均衡点。接着,通过实验验证运输问题的最优解是一个纳什讨价还价解,满足产地或销地的自身利益。在此基础上,针对纳什讨价还价解不唯一的问题,从决策者的视角给出最大可能激励成本的计算方法。最后,为弥补纳什讨价还价解不唯一及纳什讨价还价解不允许出现子联盟的缺陷,给出运输收益分配或成本分摊的Shapely值计算方法。     

The price of fairness with the extended Perles–Maschler solution

In Nash bargaining problem, due to fairness concerns of players, instead of maximizing the sum of utilities of all players, an implementable solution should satisfy some axioms or characterizations. Such a solution can result in the so-called price of fairness, because of the reduction in the sum of utilities of all players. An important issue is to quantify the system efficiency loss under axiomatic solutions through the price of fairness. Based on Perles–Maschler solution of two-player Nash bargaining problem, this paper deals with the extended Perles–Maschler solution of multi-player Nash bargaining problem. We give lower bounds of three measures of the system efficiency for this solution, and show that the lower bounds are asymptotically tight.     

公平偏好下考虑互补效应的项目投资收益分配研究

决策者的公平偏好对项目投资合作的形成和推进有重要影响。本文从创业企业和风险投资家的双重道德风险出发,选取Nash谈判解为公平偏好参照点,构建创业企业具有公平偏好的项目投资委托代理模型,研究委托方和代理方的能力存在互补效应的情形下,公平偏好对项目收益分配及双方努力水平的影响。结果表明:项目收益的最优分配比例和双方的最高努力水平均与创业企业的公平偏好程度相关。在互补效应存在时,双方的努力水平不会随收益分配比例的变化呈现单调变化趋势。如果双方的能力互补程度较小,具有公平偏好的创业企业会以Nash谈判解为自己的收益下限,风险投资家需要向其让渡更多的项目收益才能实现有效激励;如果双方的能力互补程度较大,创业企业会将Nash谈判解作为自己的收益上限,风险投资家即使不给予其大于Nash谈判解的收益也可实现有效激励。     

Collusive game solutions via optimization

A Nash-based collusive game among a finite set of players is one in which the players coordinate in order for each to gain higher payoffs than those prescribed by the Nash equilibrium solution. In this paper, we study the optimization problem of such a collusive game in which the players collectively maximize the Nash bargaining objective subject to a set of incentive compatibility constraints. We present a smooth reformulation of this optimization problem in terms of a nonlinear complementarity problem. We establish the convexity of the optimization problem in the case where each player's strategy set is unidimensional. In the multivariate case, we propose upper and lower bounding procedures for the collusive optimization problem and establish convergence properties of these procedures. Computational results with these procedures for solving some test problems are reported. It is with great honor that we dedicate this paper to Professor Terry Rockafellar on the occasion of his 70th birthday. Our work provides another example showing how Terry's fundamental contributions to convex and variational analysis have impacted the computational solution of applied game problems. This author's research was partially supported by the National Science Foundation under grant ECS-0080577. This author's research was partially supported by the National Science Foundation under grant CCR-0098013.     

Optimization reformulations of the generalized Nash equilibrium problem using regularized indicator Nikaidô–Isoda function

In this paper, we extend the literature by adapting the Nikaidô–Isoda function as an indicator function termed as regularized indicator Nikaidô–Isoda function, and this is demonstrated to guarantee existence of a solution. Using this function, we present two constrained optimization reformulations of the generalized Nash equilibrium problem (GNEP for short). The first reformulation characterizes all the solutions of GNEP as global minima of the optimization problem. Later this approach is modified to obtain the second optimization reformulation whose global minima characterize the normalized Nash equilibria. Some numerical results are also included to illustrate the behaviour of the optimization reformulations.     

考虑繁忙率的多时段救护车优化布局研究

救护车布局对院前急救服务中需求的响应具有决定性作用。本文重点研究了考虑繁忙率的多时段救护车优化布局问题,在传统双覆盖模型基础上引入救护车繁忙率因素,提出改进后的双覆盖模型。首先计算考虑繁忙率的期望覆盖需求量,进而结合实际,将一天以早晚高峰划分为5个时段,探究不同时段下繁忙率差异带来的不同布局方案。以上海市松江区2014年数据为例,应用改进后的模型进行了系统深入的实证研究,并绘制繁忙率对需求覆盖率的影响曲线。结果表明,本文提出的布局方案比实际方案得到的期望覆盖需求量提高了3.19%,比传统双覆盖模型得到的期望覆盖需求量提高了0.54%,证明了改进后模型的有效性;需求覆盖率曲线随繁忙率增加而下降,与实际意义相符。该方法能够直观简洁地得出救护车布局方案,利于院前急救服务水平的提升,为社会安全提供有力保障。     

Efficient and fair routing for mesh networks

Inspired by the One Laptop Per Child project, we consider mesh networks that connect devices that cannot recharge their batteries easily. We study how the mesh should retransmit information to make use of the energy stored in each of the nodes effectively. The solution that minimizes the total energy spent by the whole network may be very unfair to some nodes because they bear a disproportionate burden of the traffic. A Nash equilibrium—achieved when nodes minimize the energy they spend—does not model the situation well because, as retransmissions consume battery without increasing the node’s utility, it predicts that nodes refuse to participate. Actually, there are wireless communication protocols, peer-to-peer networks and other systems that provide incentives or impose penalties to encourage nodes to be active and to participate. We explicitly aim at the solution that minimizes the total energy spent by nodes among those that satisfy a fairness constraint. Although this does not guarantee that the solution is at equilibrium, nodes do not have a big incentive to deviate from the proposed solution since they do not view the situation as extremely unfair to them. This is consistent with the recommendation of Beccaria and Bolelli (Proceedings of the 3rd IEEE Vehicle Navigation & Information Systems Conference, pp. 117–126, Oslo, 1992) who proposed to optimize social welfare keeping user needs as constraints. We propose a distributed and online routing algorithm and compare it to an offline, centralized approach. The centralized approach, besides being unrealistic in terms of information requirements, is also NP-hard to solve. For both reasons, we focus on the former and evaluate it by conducting an extensive set of computational experiments that evaluate the efficiency and fairness achieved by our algorithm.     

Scheduling ambulance crews for maximum coverage

This paper addresses the problem of scheduling ambulance crews in order to maximize the coverage throughout a planning horizon. The problem includes the subproblem of locating ambulances to maximize expected coverage with probabilistic response times, for which a tabu search algorithm is developed. The proposed tabu search algorithm is empirically shown to outperform previous approaches for this subproblem. Two integer programming models that use the output of the tabu search algorithm are constructed for the main problem. Computational experiments with real data are conducted. A comparison of the results of the models is presented.     

Nash bargaining and leader–follower models in water allocation: Application to the Zarrinehrud River basin,Iran

Mathematical models for conflict resolution are very important in integrated water resources and environmental management. This study proposes a new methodology to resolve conflicts among different water users and water suppliers while considering environmental requirements and the system’s constraints. A two-level leader–follower model is applied to maximize the net benefit with the Iran Water Resources Management Company as the leader and agricultural, domestic, and industrial users as followers subject to the system’s constraints. As a comparison, the Nash bargaining solution is also used to find a solution when simultaneous moves are assumed by the participants. The suggested method is then applied to the real case of the Zarrinehrud River basin that is one of the areas facing water shortages in Iran. For the actual optimization, Genetic Algorithm is used in order to avoid local optimum. As the contribution of this study, the results show that benefits for the leader in the leader–follower model increased in comparison with the Nash bargaining solutions.     

An MPCC approach for stochastic Stackelberg–Nash–Cournot equilibrium

In this article, we investigate a Stochastic Stackelberg–Nash–Cournot Equilibrium problem by reformulating it as a Mathematical Program with Complementarity Constraints (MPCC). The complementarity constraints are further reformulated as a system of nonsmooth equations. We characterize the followers’ Nash–Cournot equilibria by studying the implicit solution of a system of equations. We outline numerical methods for the solution of a stochastic Stackelberg–Nash–Cournot Equilibrium problem with finite distribution of market demand scenarios and propose a discretization approach based on implicit numerical integration to deal with stochastic Stackelberg–Nash–Cournot Equilibrium problem with continuous distribution of demand scenarios. Finally, we discuss the two-leader Stochastic Stackelberg–Nash–Cournot Equilibrium problem.     

Ambulance location and relocation problems with time-dependent travel times

EMERGENCY SERVICE PROVIDERS ARE FACING THE FOLLOWING PROBLEM: how and where to locate vehicles in order to cover potential future demand effectively. Ambulances are supposed to be located at designated locations such that in case of an emergency the patients can be reached in a time-efficient manner. A patient is said to be covered by a vehicle if (s)he can be reached by an ambulance within a predefined time limit. Due to variations in speed and the resulting travel times it is not sufficient to solve the static ambulance location problem once using fixed average travel times, as the coverage areas themselves change throughout the day. Hence we developed a multi-period version, taking into account time-varying coverage areas, where we allow vehicles to be repositioned in order to maintain a certain coverage standard throughout the planning horizon. We have formulated a mixed integer program for the problem at hand, which tries to optimize coverage at various points in time simultaneously. The problem is solved metaheuristically using variable neighborhood search. We show that it is essential to consider time-dependent variations in travel times and coverage respectively. When ignoring them the resulting objective will be overestimated by more than 24%. By taking into account these variations explicitly the solution on average can be improved by more than 10%.     

A FPTAS for computing a symmetric Leontief competitive economy equilibrium

In this paper, we consider a linear complementarity problem (LCP) arisen from the Nash and Arrow–Debreu competitive economy equilibria where the LCP coefficient matrix is symmetric. We prove that the decision problem, to decide whether or not there exists a complementary solution, is NP-complete. Under certain conditions, an LCP solution is guaranteed to exist and we present a fully polynomial-time approximation scheme (FPTAS) for approximating a complementary solution, although the LCP solution set can be non-convex or non-connected. Our method is based on approximating a quadratic social utility optimization problem (QP) and showing that a certain KKT point of the QP problem is an LCP solution. Then, we further show that such a KKT point can be approximated with a new improved running time complexity ${{O}((\frac{n^4}{\epsilon})\log\log(\frac{1}{\epsilon}))}$ arithmetic operation in accuracy ${\epsilon \in (0,1)}$ . We also report preliminary computational results which show that the method is highly effective. Applications in competitive market model problems with other utility functions are also presented, including global trading and dynamic spectrum management problems.     

Electricity market near-equilibrium under locational marginal pricing and minimum profit conditions

This paper provides a tool to determine the near-equilibrium of an electric energy market. This market works under locational marginal pricing, i.e., generating units and demand loads are paid and pay, respectively, the locational marginal prices corresponding to the nodes they are connected to. The near-equilibrium is defined as the energy transaction levels for which generating companies maximize their respective profits and consumption companies maximize their respective utilities. An independent system operator clears the market maximizing the social welfare. Conditions that ensure minimum profit for generating units can be included. However, these conditions may render a generating unit uncompetitive and expel it from the market. Demands are taken to be non-constant and values are determined as part of the solution. The near-equilibrium is obtained through the solution of a mixed-integer quadratic problem equivalent to a mixed linear complementarity problem that includes the minimum profit conditions. It is important to note that the near-equilibrium concept presented in this paper does not solve a market equilibrium when indivisibilities such as start up costs or the like are present. Lastly, we validate the proposed model on a case study using data from the IEEE Reliability Test System.     

Optimal television schedules in alternative competitive environments

We formulate network-television-scheduling problems as integer programs for three different competitive environment—smyopic, Nash competitive, and cooperative. To provide input data for the scheduling models, we develop and estimate a regression model in which show-part ratings are regressed on variables that influence television viewership, including day, time slot, show attribute, and competitive effects, as well as lead-in from the previous show part. We apply our models by solving for optimal myopic (noncompetitive) and Nash competitive week-long prime-time schedules for the three major networks for two specific weeks. We find that there are substantial gains to optimization and that those gains are not diminished much by competition. We illustrate the cooperative problem for six time slots and show how the solution differs from the Nash solution. We discuss the use of simple programming heuristics such as counterprogramming.     

Pavement rehabilitation scheduling and toll pricing under different regulatory regimes

This paper addresses the highway pavement rehabilitation scheduling and toll pricing issues over a planning horizon. In the highway system concerned, two types of agents are considered, namely highway operator and road users. Two models, which account for different highway regulatory regimes (i.e. public and private), are proposed. In the public regulatory model, the government aims to maximize total discounted social welfare of the transportation system over the planning horizon by determining the optimal pavement rehabilitation schedule and toll level. In the private regulatory regime, a profit-driven private operator seeks to optimize the pavement rehabilitation schedule and toll level to maximize its own discounted net profit over the planning horizon. The proposed models treat the interactions between the highway operator and the road users in the system as a bi-level hierarchical problem in which the upper level is a multi-period pavement rehabilitation scheduling and toll pricing problem, while the lower level is a multi-period route choice equilibrium problem. A heuristic solution algorithm that combines a greedy approach and a sensitivity analysis based approach is developed to solve the proposed bi-level multi-period optimization models. An illustrative example is used to show the applications of the proposed models. The findings show that the highway regulatory regime, pavement deterioration parameter and the roughness-induced vehicle operating cost can significantly affect the pavement rehabilitation schedules and the toll level as well as the performance of transportation system in terms of total life-cycle travel demand, net profit and social welfare.     

A Lagrangean-based heuristics for the target covering problem in wireless sensor network

We study the target coverage problem in wireless sensor networks. The problem consists in maximizing the network lifetime by grouping the sensors in disjoint set covers of the targets. A binary integer programing model is formulated to maximize the network lifetime. Since the problem is NP-complete, we provide an iterative approximation based on Lagrangean relaxation and subgradient optimization.     

求解一类广义Nash均衡问题

Generalized Nash equilibrium problem (GNEP) is an important model that has many applications in practice. However, a GNEP usually has multiple or even infinitely many Nash equilibrium points and it is not easy to choose a favorable solution from those equilibria. This paper considers a class of GNEP with some kind of separability. We first extend the so-called normalized equilibrium concept to the stationarity sense and then, we propose an approach to solve the normalized stationary points by reformulating the GNEP as a single optimization problem. We further demonstrate the proposed approach on a GNEP model in similar product markets.     

Spatial duopoly with cost subsidy

The effects of government subsidies are examined in a spatial duopoly with a conventional and an electronic retailer. The Nash equilibrium is first determined and then the optimal cost subsidy rates are computed that maximize social welfare. The stability of the equilibrium is also investigated and it is shown that in the case of delayed information there is the possibility of cyclic behavior.