我国房地产行业问题研究

结合数学、计量经济学和国民经济核算等方面的知识,分别从四个方面系统地研究了房地产行业的相关问题,首先,考虑到我国房地产市场存在地区差异性,利用地理加权回归方法(GWR)分别建立房地产行业需求和供给模型来反映该行业市场供需状况;其次,基于VAR模型分区域建立有效的房地产行业定价模型对未来房价走向进行预测,以西部地区的货币供应量对房价的影响为例,其滞后一期的货币供应量发生1个单位的正向变动,房价发生0.85个单位的正向变动;再次,对房地产行业关联度和发展态势建立了投入产出模型和固定效应的变系数面板数据模型,研究发现房地产行业与金融业、租赁和商务服务业、建筑业存在显著关联度,且根据固定效应的变系数面板数据模型结果,可具体分析房地产行业增加值的提高对其他行业的影响程度;最后,基于熵值法建立综合评价模型对天津市房地产行业的可持续发展性进行了研究和深入探讨.     

Models and algorithms for a staff scheduling problem

We present mathematical models and solution algorithms for a family of staff scheduling problems arising in real life applications. In these problems, the daily assignments to be performed are given and the durations (in days) of the working and rest periods for each employee in the planning horizon are specified in advance, whereas the sequence in which these working and rest periods occur, as well as the daily assignment for each working period, have to be determined. The main objective is the minimization of the number of employees needed to perform all daily assignments in the horizon. We decompose the problem into two steps: the definition of the sequence of working and rest periods (called pattern) for each employee, and the definition of the daily assignment to be performed in each working period by each employee. The first step is formulated as a covering problem for which we present alternative ILP models and exact enumerative algorithms based on these models. Practical experience shows that the best approach is based on the model in which variables are associated with feasible patterns and generated either by dynamic programming or by solving another ILP. The second step is stated as a feasibility problem solved heuristically through a sequence of transportation problems. Although in general this procedure may not find a solution (even if one exists), we present sufficient conditions under which our approach is guaranteed to succeed. We also propose an iterative heuristic algorithm to handle the case in which no feasible solution is found in the second step. We present computational results on real life instances associated with an emergency call center. The proposed approach is able to determine the optimal solution of instances involving up to several hundred employees and a working period of up to 6 months. Mathematics Subject Classification (2000): 90B70, 90C10, 90C27, 90C39, 90C57, 90C59     

房地产开发的最优时间和最优强度

房地产投资具有许多不确定性,对房地产投资进行评估尤为重要.利用实物期权理论,对房地产投资进行建模分析,确定出最优开发时间和最优开发强度;最后根据模型推导出来的结论进行数值分析.     

A stochastic programming approach for supply chain network design under uncertainty

This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the sample average approximation (SAA) scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the stochastic model as well as the efficiency of the proposed solution strategy.     

An improved origin-based algorithm for solving the combined distribution and assignment problem

This paper is to further study the origin-based (OB) algorithm for solving the combined distribution and assignment (CDA) problem, where the trip distribution follows a gravity model and the traffic assignment is a user-equilibrium model. Recently, the OB algorithm has shown to be superior to the Frank–Wolfe (FW) algorithm for the traffic assignment (TA) problem and better than the Evans’ algorithm for the CDA problem in both computational time and solution accuracy. In this paper, a modified origin–destination (OD) flow update strategy proposed by Huang and Lam [Huang, H.J., Lam, W.H.K., 1992. Modified Evans’ algorithms for solving the combined trip distribution and assignment problem. Transportation Research B 26 (4), 325–337] for CDA with the Evans’ algorithm is adopted to improve the OB algorithm for solving the CDA problem. Convergence proof of the improved OB algorithm is provided along with some preliminary computational results to demonstrate the effect of the modified OD flow update strategy embedded in the OB algorithm.     

Real Estate Appraisal: A Double Perspective Data Envelopment Analysis Approach

This paper proposes a new methodology for the assessment of the value range for real estate units. The theoretical basis of the methodology is built on the Data Envelopment Analysis—DEA approach, which has its original concept adapted to the case where the units under assessment consist of transactions among sellers and buyers. The proposed approach—christened Double Perspective-Data Envelopment Analysis (DP-DEA)—is applied to a database comprising the prices and features of the units under assessment. It is shown that the DP-DEA presents some specific advantages when compared to the usual regression analysis method employed in real estate value assessment.     

The evaluation criteria of the venture capital investment activity: An interactive assessment

In this paper the modelling of venture capital decision making is being attempted, using a multicriteria decision support system (the Minora system) based on the interactive use of the Uta ordinal regression model. First, a review of the literature on the evaluation criteria for venture capital investment is outlined, and the general methodology of financing firms is presented. Then, a real world application of the Minora system is developed; this has been done in a French venture capital firm and concerned the assessment of an analytical evaluation model of firms.     

Competence-driven project portfolio selection,scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.     

基于MIV-BP型网络实验的房地产项目风险识别研究

为了更准确更客观地识别房地产项目中的风险,为房地产项目投资决策提供科学依据和参考,有效地规避风险,本研究在BP神经网络 (Back-Propagation Neural Network)建模的基础上,采取MIV(Mean Impact Value)算法对BP神经网络模型进行变量筛选的网络优化和改良,从而形成新的优化后的MIV-BP(Mean Impact Value Back-Propagation Neural Network)神经网络,并以此用于评价房地产项目中的风险度以及各因素在风险度中的影响作用大小;同时选取目前相关的房地产项目数据进行仿真实证分析和验证。验证实验结果表明,MIV-BP型神经网络对于房地产项目风险度识别具有良好的适应性和准确性,实验结果客观,达到专家评价的要求,并在风险因素作用度分析上具有良好的应用价值。     

基于VAR—GM(1.1)—SVR模型的房地产价格预测研究

为提高房地产价格预测精度,克服传统统计数据真实性低、时效性差的缺点,本文以网络搜索数据为基础,首先通过斯皮尔曼相关分析和时差相关分析筛选出与房地产价格具有高度相关性的先行关键词,并利用向量自回归模型(VAR)和GM(1.1)模型分别预测房地产价格;然后构建基于向量自回归模型和GM(1.1)模型的VAR—GM(1.1)—SVR模型将以上两个模型的预测结果进行预测融合,并以西安市数据为例进行验证,得出均方误差(MSE)和标准平均方差(NMSE)分别为0.97和0.03,优于单一模型预测效果.     

Solving a Real World Assignment Problem with a Metaheuristic

This paper investigates a real world assignment problem, which slightly differs from the classical generalized assignment problem (GAP). The large-scale number of variables in the related 0-1 linear program makes the use of commercial optimization packages impractical. We present here a metaheuristic using simulated annealing. It is based on successive reductions of the search space by identification of locally active constraints. Our approach employs a heuristic procedure to compute an initial (feasible or infeasible) 0/1 solution, and a double-criterion acceptance rule. The performance of the algorithm is demonstrated on real data sets.     

Approximate solutions to the turbine balancing problem

The turbine balancing problem (TBP) is an NP-Hard combinatorial optimization problem arising in the manufacturing and maintenance of turbine engines. Exact solution methods for solving the TBP are not appropriate since the problem has to be solved in real time and the input data is itself inaccurate. In this paper the TBP is formulated as a quadratic assignment problem (QAP) and we propose a heuristic algorithm for solving the resulting problem. Computational results on a set of instances provided by Pratt & Whitney (P&W) and from the literature, indicate that the proposed algorithm outperforms the current methods used for solving the TBP, and has the best overall performance with respect to other heuristic algorithms in the literature.     

Real estate price estimation in French cities using geocoding and machine learning

This paper reviews real estate price estimation in France, a market that has received little attention. We compare seven popular machine learning techniques by proposing a different approach that quantifies the relevance of location features in real estate price estimation with high and fine levels of granularity. We take advantage of a newly available open dataset provided by the French government that contains 5 years of historical data of real estate transactions. At a high level of granularity, we obtain important differences regarding the models’ prediction powers between cities with medium and high standards of living (precision differences beyond 70% in some cases). At a low level of granularity, we use geocoding to add precise geographical location features to the machine learning algorithm inputs. We obtain important improvements regarding the models’ forecasting powers relative to models trained without these features (improvements beyond 50% for some forecasting error measures). Our results also reveal that neural networks and random forest techniques particularly outperform other methods when geocoding features are not accounted for, while random forest, adaboost and gradient boosting perform well when geocoding features are considered. For identifying opportunities in the real estate market through real estate price prediction, our results can be of particular interest. They can also serve as a basis for price assessment in revenue management for durable and non-replenishable products such as real estate.

     

Modelling variations in hospital service delivery based on real time locating information

Variations in service delivery have been identified as a major challenge to the success of process improvement studies in service departments of hospital such as radiology. Largely, these variations are due to inherent system level factors, i.e., system variations such as unavailability of resources (nurse, bed, doctors, and equipment). These system variations are largely unnecessary/unwarranted and mostly lead to longer waiting times, delays, and lowered productivity of the service units. There is limited research on identifying system variations and modelling them for service improvements within hospital. Therefore, this paper proposes a modelling methodology to model system variations in radiology based on real time locating system (RTLS) tracking data. The methodology employs concepts from graph theory to identify and represent system variations. In particular, edge coloured directed multi-graphs (ECDMs) are used to model system variations which are reflected in paths adopted by staff, i.e., sequence of rooms/areas traversed while delivering services. The main steps of the methodology are: (i) identifying the most standard path followed by staff for service delivery; (ii) filtering the redundant events in RTLS tracking database for analysis; (iii) identifying rooms/areas of hospital site involved in the service delivery; (iv) determining patterns of paths adopted by staff from filtered tracking database; and, (v) representation of patterns in graph based model called as edge coloured directed multigraphs (ECDMs) of a role. A case study of MR scanning process is utilized to illustrate the implementation of the proposed methodology for modelling system variations reflected in the paths adopted by staff.     

A matheuristic algorithm for stochastic home health care planning

Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensioning, resource assignment, scheduling, and routing. In this study, a two-stage stochastic mixed integer model is proposed that considers these decisions simultaneously. In the planning phase of a home health care system, the main uncertain parameters are travel and service times. Hence, the proposed model takes into account the uncertainty in travel and service times. Districting and staff dimensioning are defined as the first stage decisions, and assignment, scheduling, and routing are considered as the second stage decisions. A novel algorithm is developed for solving the proposed model. The algorithm consists of four phases and relies on a matheuristic-based method that calls on various mixed integer models. In addition, an algorithm based on the progressive hedging and Frank and Wolf algorithms is developed to reduce the computational time of the second phase of the proposed matheuristic algorithm. The efficiency and accuracy of the proposed algorithm are tested through several numerical experiments. The results prove the ability of the algorithm to solve large instances.     

Solving a tactical operating room planning problem by a column-generation-based heuristic procedure with four criteria

In some hospitals, an “open scheduling” strategy is applied to solve the operating room planning problem; i.e., surgeons can choose any workday for his surgical cases, and the staffing of anesthetists and nurses is adjusted to maximize the efficiency of operating room utilization. In this paper, we aim at obtaining an efficient operating program for an operating theatre with several multifunctional operating rooms by using this “open scheduling” strategy. First, a mathematical model is constructed to assign surgical cases to operating rooms within one week. This model complies with the availability of operating rooms and surgeons, and its objective is not only to maximize utilization of operating rooms, but to minimize their overtime cost. Then a column-generation-based heuristic (CGBH) procedure is proposed, where four different criteria are compared with each other so as to find a solution with the best performance. In addition, the best approximate solution, obtained by this CGBH procedure after running all the criteria proposed, is compared with the lower bound obtained by an explicit column generation (CG) procedure, LP, to evaluate the distance between the approximate solution obtained and the optimum one. Although no criterion, according to the experimental results, is found superior to all other three in both robustness and quality of the solution obtained, it is found that the best solution obtained among those four criteria is often very close to LP, which means that the proposed algorithm can obtain a near optimal solution. In one word, the CGBH procedure proposed in this paper can obtain an efficient assignment of the surgical cases if the other resources (anesthesia and nursing staff, equipment, beds in the recovery room and etc.) are well organized.     

基于多角度的我国房地产行业数学模型的建立与分析

房地产业在我国国民经济中起着重要的作用,通过建立数学模型,从多个角度研究我国的房地产行业的性质与发展态势.首先,建立了基于联立方程组的住房的供给和需求模型,并结合蛛网模型进行分析,发现我国房地产目前处于一个非均衡的阶段;接着,建立了基于VAR模型的房地产行业和国民经济13个主要其他行业关系模型,找到了与房地产业互相影响的产业;再接着建立了基于组合权重的GC-TOSIS法的我国房地产行业态势分析模型,得出近几年房地产的态势不太乐观的结论;并通过态势分析模型得到我国房地产的经济发展情况,并引入可持续发展指数,建立了基于离散Hopfield神经网络的房地产行业可持续发展模型,得到我国房地产行业可持续发展程度以及可持续发展的等级正在逐渐增高.最后,结合以上四个模型,综合分析我国房地产的各个方面的发展趋势,并提供了可行性建议.     

Greedy approaches for a class of nonlinear Generalized Assignment Problems

The traditional Generalized Assignment Problem (GAP) seeks an assignment of customers to facilities that minimizes the sum of the assignment costs while respecting the capacity of each facility. We consider a nonlinear GAP where, in addition to the assignment costs, there is a nonlinear cost function associated with each facility whose argument is a linear function of the customers assigned to the facility. We propose a class of greedy algorithms for this problem that extends a family of greedy algorithms for the GAP. The effectiveness of these algorithms is based on our analysis of the continuous relaxation of our problem. We show that there exists an optimal solution to the continuous relaxation with a small number of fractional variables and provide a set of dual multipliers associated with this solution. This set of dual multipliers is then used in the greedy algorithm. We provide conditions under which our greedy algorithm is asymptotically optimal and feasible under a stochastic model of the parameters.     

Optimal berth allocation and time-invariant quay crane assignment in container terminals

Due to the dramatic increase in the world’s container traffic, the efficient management of operations in seaport container terminals has become a crucial issue. In this work, we focus on the integrated planning of the following problems faced at container terminals: berth allocation, quay crane assignment (number), and quay crane assignment (specific). First, we formulate a new binary integer linear program for the integrated solution of the berth allocation and quay crane assignment (number) problems called BACAP. Then we extend it by incorporating the quay crane assignment (specific) problem as well, which is named BACASP. Computational experiments performed on problem instances of various sizes indicate that the model for BACAP is very efficient and even large instances up to 60 vessels can be solved to optimality. Unfortunately, this is not the case for BACASP. Therefore, to be able to solve large instances, we present a necessary and sufficient condition for generating an optimal solution of BACASP from an optimal solution of BACAP using a post-processing algorithm. In case this condition is not satisfied, we make use of a cutting plane algorithm which solves BACAP repeatedly by adding cuts generated from the optimal solutions until the aforementioned condition holds. This method proves to be viable and enables us to solve large BACASP instances as well. To the best of our knowledge, these are the largest instances that can be solved to optimality for this difficult problem, which makes our work applicable to realistic problems.