Devising a Cooperation Policy for Emergency Networks

A mathematical programming model is proposed to select an optimal cooperation policy between autonomous service networks for dispatching purposes. In addition to traditional characteristics such as network topology and station location, this model takes into account 'political' constraints on minimum response-time in certain subzones. Such constraints are translated into performance requirements, which are imposed on the cooperation policy. Testing the model under different assumptions can be useful for analysing various cooperation policies. The paper formulates a mathematical programming model, derives example policies for various circumstances, and tests the sensitivity of the resultant policies to some parameters, such as the penalty for not providing service, and distances between adjacent networks. The paper suggests also a less constrained approach, which entails a linear programming model. A comparison between the two approaches is presented.     

线性与非线性规划算法与理论

线性规划与非线性规划是数学规划中经典而重要的研究方向. 主要介绍该研究方向的背景知识,并介绍线性规划、无约束优化和约束优化的最新算法与理论以及一些前沿与热点问题. 交替方向乘子法是一类求解带结构的约束优化问题的方法,近年来倍受重视. 全局优化是一个对于应用优化领域非常重要的研究方向. 因此也试图介绍这两个方面的一些最新研究进展和问题.     

基于多任务委托代理模型的导游动态薪酬契约设计

旅游服务质量是旅游业可持续发展的重要决定因素,如何通过设计合理的薪酬契约激励导游努力提高服务质量是现今旅行社面临的难题。基于Holmstrom提出的多任务代理模型,考虑导游的纵向多任务特性,将导游投入划分为追求当期业绩和追求服务质量的旅行社声誉建设两个维度的多任务问题,导游的服务绩效由其个人业绩和服务质量决定,以此为基础构建了多周期动态契约,与没有考虑服务质量投入的单周期静态契约进行比较分析,最后给出了其数值仿真结果。结果表明:本文所提多周期动态契约对旅行社和导游双方具有明显的帕累托效率改进,且从长远来看,对旅行社声誉的提高以及游客满意度的提升效果显著。     

Interactive bicriterion solution method and its application to critical path method problems

An interactive solution method is developed for bicriterion mathematical programming (BCMP) problems. The new method, called the dichotomous bicriterion mathematical programming (DBCMP) method, combines Tchebycheff theory and the existing paired comparison method (PCM). The DBCMP method is then compared with the PCM method based on critical path method problems with two conflicting objectives: minimizing the total crashing cost and minimizing the total project completion time. The extension of the DBCMP method to BCMP problems with multiple decision makers is also discussed.     

A dual-relax penalty function approach for solving nonlinear bilevel programming with linear lower level problem

The penalty function method, presented many years ago, is an important numerical method for the mathematical programming problems. In this article, we propose a dual-relax penalty function approach, which is significantly different from penalty function approach existing for solving the bilevel programming, to solve the nonlinear bilevel programming with linear lower level problem. Our algorithm will redound to the error analysis for computing an approximate solution to the bilevel programming. The error estimate is obtained among the optimal objective function value of the dual-relax penalty problem and of the original bilevel programming problem. An example is illustrated to show the feasibility of the proposed approach.     

The Transit Route Arc-Node Service Maximization problem

This article presents a new method for determining optimal transit routes. The Transit Route Arc-Node Service Maximization model is a mathematical model that maximizes the service value of a route, rather than minimizing cost. Cost (distance) is considered as a budget constraint on the extent of the route. The mathematical formulation modifies and exploits the structure of linear programming problems designed for the traveling salesman problem. An innovative divide-and-conquer solution procedure is presented that not only makes the transit routing problem tractable, but also provides a range of high-quality alternate routes for consideration, some of which have substantially varying geometries. Variant formulations are provided for several common transit route types. The model is tested through its application to an existing street network in Richardson, TX. Optimal numeric results are obtained for several problem instances, and these results demonstrate that increased route cost is not correlated with increased service provision.     

Optimal dynamic asset allocation of pension fund in mortality and salary risks framework

In this paper, we consider the optimal dynamic asset allocation of pension fund with mortality risk and salary risk. The managers of the pension fund try to find the optimal investment policy (optimal asset allocation) to maximize the expected utility of terminal wealth. The market is a combination of financial market and insurance market. The financial market consists of three assets: cashes with stochastic interest rate, stocks and rolling bonds, while the insurance market consists of mortality risk and salary risk. These two non-hedging risks cause incompleteness of the market. By martingale method and dynamic programming principle we first derive the approximate optimal investment policy to overcome the difficulty, then investigate the efficiency of the approximation. Finally, we solve an optimal assets liabilities management(ALM) problem with mortality risk and salary risk under CRRA utility, and reveal the influence of these two risks on the optimal investment policy by numerical illustration.     

Multistage stochastic programming in strategic telecommunication network planning

Mobile communication is taken for granted in these days. Having started primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. Operators are facing the challenge to match the demand by continuously expanding and upgrading the network infrastructure. However, the evolution of the customer's demand is uncertain. We introduce a novel (long-term) network planning approach based on multistage stochastic programming, where demand evolution is considered as a stochastic process and the network is extended so as to maximize the expected profit. The approach proves capable of designing large-scale realistic UMTS networks with a time horizon of several years. Our mathematical optimization model, the solution approach, and computational results are presented.     

MOP/GP models for machine learning

Techniques for machine learning have been extensively studied in recent years as effective tools in data mining. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problems with two class sets, its idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. This task is performed by solving a quadratic programming problem in a traditional formulation, and can be reduced to solving a linear programming in another formulation. However, the idea of maximal margin separation is not quite new: in the 1960s the multi-surface method (MSM) was suggested by Mangasarian. In the 1980s, linear classifiers using goal programming were developed extensively.This paper presents an overview on how effectively MOP/GP techniques can be applied to machine learning such as SVM, and discusses their problems.     

Probabilistic programming models for traffic incident management operations planning

This paper proposes mathematical programming models with probabilistic constraints in order to address incident response and resource allocation problems for the planning of traffic incident management operations. For the incident response planning, we use the concept of quality of service during a potential incident to give the decision-maker the flexibility to determine the optimal policy in response to various possible situations. An integer programming model with probabilistic constraints is also proposed to address the incident response problem with stochastic resource requirements at the sites of incidents. For the resource allocation planning, we introduce a mathematical model to determine the number of service vehicles allocated to each depot to meet the resource requirements of the incidents by taking into account the stochastic nature of the resource requirement and incident occurrence probabilities. A detailed case study for the incident resource allocation problem is included to demonstrate the use of proposed model in a real-world context. The paper concludes with a summary of results and recommendations for future research.     

Decomposition in global optimization

The purpose of this article is to propose a simple framework for the various decomposition schemes in mathematical programming.Special instances are discussed. Particular attention is devoted to the general mathematical programming problem with two sets of variables. An economic interpretation in the context of hierarchical planning is done for the suggested decomposition procedure.The framework is based on general duality theory in mathematical programming and thus focussing on approaches leading to global optimality.     

Saddle-Point Criteria in an η-Approximation Method for Nonlinear Mathematical Programming Problems Involving Invex Functions

In this paper, the η-approximation method introduced by Antczak (Ref. 1) for solving a nonlinear constrained mathematical programming problem involving invex functions with respect to the same function η is extended. In this method, a so-called η-approximated optimization problem associated with the original mathematical programming problems is constructed; moreover, an η-saddle point and an η-Lagrange function are defined. By the help of the constructed η-approximated optimization problem, saddle-point criteria are obtained for the original mathematical programming problem. The equivalence between an η-saddle point of the η-Lagrangian of the associated η-approximated optimization problem and an optimal solution in the original mathematical programming problem is established.     

A two-phase heuristic approach to the bike repositioning problem

An approach to overcome the bike imbalance problem is to transfer excess bikes to branches with bike shortages. This study develops a constrained mathematical model to deal with a multi-vehicle bike-repositioning problem, and aims to minimize the sum of transportation and unmet demand costs over a planning horizon through bike-transfer strategies under a minimum service requirement. A two-phase heuristic based on linear programming was proposed to solve the problem and produce compromising solutions. In the first phase, the paper developed a linear programming model to quickly develop decisions related to bike inventory, unloading, and loading for all stations for each time slot. In the second phase, this paper proposed an iterative approach through two parameter sensitive mathematical models to sequentially reduce the problem scale to develop decisions related to bike transfers. Computational results show that the proposed approach is superior to a CPLEX optimizer and a hybrid heuristic based on a genetic algorithm. The proposed approach was used to analyze the bicycle system in Taiwan. The impacts of various system parameters on the system were also investigated.     

A heuristic approach for the truck and trailer routing problem

In this paper, we propose an approach based on mathematical programming and local search to cope with the truck and trailer vehicle routing problem. The mathematical programming framework models two subproblems that are solved sequentially, that is, the customer-route assignment problem (CAP), with the objective of minimizing the fleet size used to service clients, and the route definition problem, with the objective of minimizing the total tour length given the set of clients assigned to each vehicle. Since the route assignment model can return infeasible solutions, the local search plays the role of possibly retrieving a feasible solution. The mathematical formulations and the local search work iteratively, embedded in a multiple restarting mechanism able to diversify solutions by (i) identifying additional constraints for the CAP formulation to be taken into account during the algorithm progress, (ii) using a tabu like customer-route matrix to avoid assignments already analysed in the previous iterations of the algorithm. Also a lower bound to assess the solution quality is given. Experiments and comparison with competing approaches suggest that the results of the proposed machinery are promising, producing, on average,a smaller total tour lengths on benchmarks.     

Nonlinear leastpth optimization and nonlinear programming

Over the past few years a number of researchers in mathematical programming became very interested in the method of the Augmented Lagrangian to solve the nonlinear programming problem. The main reason being that the Augmented Lagrangian approach overcomes the ill-conditioning problem and the slow convergence of the penalty methods. The purpose of this paper is to present a new method of solving the nonlinear programming problem, which has similar characteristics to the Augmented Lagrangian method. The original nonlinear programming problem is transformed into the minimization of a leastpth objective function which under certain conditions has the same optimum as the original problem. Convergence and rate of convergence of the new method is also proved. Furthermore numerical results are presented which illustrate the usefulness of the new approach to nonlinear programming.This work was supported by the National Research Council of Canada and by the Department of Combinatorics and Optimization of the University of Waterloo.     

Examining the effect of second-order terms in mathematical programming approaches to the classification problem

Research on mathematical programming approaches to the classification problem has focused almost exclusively on linear discriminant functions with only first-order terms. While many of these first-order models have displayed excellent classificatory performance when compared to Fisher's linear discriminant method, they cannot compete with Smith's quadratic discriminant method on certain data sets. In this paper, we investigate the appropriateness of including second-order terms in mathematical programming models. Various issues are addressed, such as performance of models with small to moderate sample size, need for crossproduct terms, and loss of power by the mathematical programming models under conditions ideal for the parametric procedures. A simulation study is conducted to assess the relative performance of first-order and second-order mathematical programming models to the parametric procedures. The simulation study indicates that mathematical programming models using polynomial functions may be prone to overfitting on the training samples which in turn may cause rather poor fits on the validation samples. The simulation study also indicates that inclusion of cross-product terms may hurt a polynomial model's accuracy on the validation samples, although omission of them means that the model is not invariant to nonsingular transformations of the data.     

The school class allocation problem—maximising friendship requests subject to diversity requirements

Friendship groups are an important element in both the social and academic well-being of school students, particularly in their younger years. Each year, school management has to assign students to a particular class in their new grade in junior school, and in doing so make and break friendship preferences as other criteria need to be satisfied in order to ensure diversity. This ongoing administrative task is time consuming and does not always result in the most equitable allocation. Coupled with the lack of transparency of the process, this can lead to teacher and parent frustration. This paper formulates and solves a mathematical programming model for this problem and shows that better solutions can likely be found in a fraction of the time using software that is freely available.     

A multiple objective programming approach for determining faculty salary equity adjustments

A multiple objective programming approach is proposed as a new analytical tool to fit a model which is used to project faculty salaries. The projected salaries are used as a basis to allocate the available budget for faculty salary equity adjustments. The model is also used to classify all faculty members into two categories with one category consisting of those faculty members being underpaid and the other consisting of those being overpaid. The multiple objective programming approach is much more powerful than regression analysis for this purpose because budgetary and policy restrictions can be included in the model as constraints. It is also more flexible than the goal programming approach because it has desirable solution properties. Salary data from a public university are used as an example to demonstrate the use of the approach. In addition, determinants of faculty salaries and variables to be included in the model are briefly discussed.     

Invex Functions and Generalized Convexity in Multiobjective Programming

Martin (Ref. 1) studied the optimality conditions of invex functions for scalar programming problems. In this work, we generalize his results making them applicable to vectorial optimization problems. We prove that the equivalence between minima and stationary points or Kuhn–Tucker points (depending on the case) remains true if we optimize several objective functions instead of one objective function. To this end, we define accurately stationary points and Kuhn–Tucker optimality conditions for multiobjective programming problems. We see that the Martin results cannot be improved in mathematical programming, because the new types of generalized convexity that have appeared over the last few years do not yield any new optimality conditions for mathematical programming problems.