Estimating energy consumption of transport modes in India using DEA and application to energy and environmental policy

Energy is consumed both by passenger and freight traffic, and it is generally not possible to apportion energy consumption exclusively to either of the traffic without making restrictive assumptions on the energy consumption norms of vehicles. The use of data envelopment analysis (DEA) has been suggested earlier in the literature for comparing energy efficiencies without requiring any such restrictive assumptions. In this paper, the applicability of DEA is further extended to estimate energy consumption of rail and/or road transport that would result in a pre-specified DEA efficiency. Using this new approach, effects of capturing some modal split in favour of rail transport on future energy consumption and emissions of carbon dioxide are presented in the paper via a scenario analysis. It is shown that if rail could capture 50% modal share in the years 2005–2006 and 2020–2021, it would save nearly 35% energy consumption and emissions compared to the situation when existing patterns of modal split are allowed to continue without policy intervention.     

Multiobjective optimization and marginal pollution abatement cost in the electricity sector – An Israeli case study

The multiple objective optimization models for capacity expansion problem of power generation system in the long run as a base for setting up the marginal abatement cost were examined. In the optimization model the objective function is considered as the weighted sum of several objective functions. Air pollutants are taken into account in both the objective function and the constraints. Different scenarios of pollutant reduction were analyzed. The periods of the years 2003–2013 were taken into account and the results are based on the real data of the Israel electricity sector. Several environmental policies were considered by using the CAPEX system to evaluate the environmental and economic deficiencies in different abatement cost scenarios. The following are obtained: abatement cost for each pollutant, amount of emissions and additional cost connected with the pollutants. Modern decision tools are implemented, such as data envelopment analysis (DEA) and reasonable goal method/interactive decision maps (RGM/IDM) technique as a base for decision-makers to make decisions on energy and environmental policy.     

System Dynamics Modelling for Residential Energy Efficiency Analysis and Management

A System Dynamics model to simulate the substitution of installed household appliances by more efficient ones is presented. The model allows the construction of scenarios and also the analyses of several other issues such as: alternatives for technology penetration, electricity consumption growth, gas consumption growth and effects of pricing policies on various energy demands. The proposed methodology has been applied to assist the decision process in relation to gas penetration policies. The model also supports policy making on energy efficiency and it allows the calculation of total energy savings under different scenarios. Furthermore, government underpricing policies on tariffs and appliance acquisition (longer loan terms, lower interest rates and grace periods) may also be analysed.     

A simulation optimization-based decision support tool for mitigating traffic congestion

Traffic congestion has grown considerably in the United States over the past 20 years. In this paper, we develop a robust decision support tool based on simulation optimization to evaluate and recommend congestion mitigation strategies to transportation system decision-makers. A tabu search-based optimizer determines different network design strategies on the road network while a traffic simulator evaluates the goodness of fit. The tool is tested with real-world traffic data.     

Modelling energy-economy interactions

A model is developed to enable policy analysis for coping with changes in the price and/or availability of energy. A simulation approach is used and the model is validated for the Turkish economy. The model allows the evaluation of tax/subsidy policies, effects of foreign borrowing, etc. on near and medium term macroeconomic factors such as GNP, consumption, capital stock, energy, and non-energy imports.     

Performance modeling and analysis of integrated logistic chains: An analytic framework

This paper is geared toward developing a network of inventory-queue models for the performance modeling and analysis of an integrated logistic network. An inventory-queue is a queueing model that incorporates an inventory replenishment policy for a store, which is a basic modeling element for an integrated logistic network. To achieve this objective, first, this paper presents an analytical modeling framework for integrated logistic chains, in which the interdependencies between model components are captured. Second, a network of inventory-queue models for performance analysis of an integrated logistic network with inventory control at all sites is developed. Then this paper extends the previous work done on the supply network model with base-stock control and service requirements. Instead of one-for-one base stock policy, batch-ordering policy and lot-sizing problems are considered. In practice, the assumption of uncapacitated production is often not true, therefore, GI^x/G/1 queueing analysis is used to replace the M^x/G/∞ queue based method. To include lot-sizing issue in the analysis of stores, a fixed-batch target-level production authorization mechanism is employed to explicitly obtain performance measures of the logistic chain queueing model. The validity of the proposed model is illustrated by comparing the results from the analytical performance evaluation model and those obtained from the simulation study.     

Integrating link-based discrete credit charging scheme into discrete network design problem

In this paper, we present an optimization model for integrating link-based discrete credit charging scheme into the discrete network design problem, to improve the transport performance from the perspectives of both transport network planning and travel demand management. The proposed model is a mixed-integer nonlinear bilevel programming problem, which includes an upper level problem for the transport authority and a lower level problem for the network users. The lower level sub-model is the traffic network user equilibrium (UE) formulation for a given network design strategy determined by the upper level problem. The network user at the lower level tries to minimize his/her own generalized travel cost (including both the travel time and the value of the credit charged for using the link) by choosing his/her route. While the transport authority at the upper level tries to find the optimal number of lanes and credit charging level with their locations to minimize the total system travel time (or maximize the transportation system performance). A genetic algorithm is used to solve the proposed mixed-integer nonlinear bilevel programming problem. Numerical experiments show the efficiency of the proposed model for traffic congestion mitigation, reveal that interaction effects across the tradable credit scheme and the discrete network design problem which amplify their individual effects. Moreover, the integrated model can achieve better performance than the sequential decision problems.     

A traffic shaping model for optimizing network operations

The management of technology in multi-service computer networks, such as university networks, has become a challenge with the explosive growth of entertainment oriented peer-to-peer (P2P) traffic. Traffic shaping is one of the tools used to manage bandwidth to improve system performance by allocating bandwidth between P2P and non-peer-to-peer (NP2P) traffic. We present a model for traffic shaping and bandwidth management that considers the trade-offs from allocating different amounts of bandwidths for different application categories and use data from a university network. The current policy allocates varying bandwidths over the day to P2P and NP2P traffic to reflect the importance of not letting entertainment based traffic choke the network during the day time at the expense of the more important traffic, such as Web traffic. We highlight the difficulties in obtaining data in the form required for analysis, and the need to estimate demand for allocations not covered by current policy. We present a goal programming model for this estimation task. We also model the traffic shaping problem as a Markov decision process and develop an algorithm for determining the optimal bandwidth allocation to maximize the utility of all users. Finally we use a numerical example to illustrate our approach.     

A-P-S组合策略的多重绩效及其“褪色”效应分析

为了降低城市交通拥堵及空气污染程度,本文在现有研究的基础上引入罚款政策,利用系统动力学方法原理构建了机动车污染物减排管控模型。通过仿真和比较分析来探索空气污染收费(Air pollution charging fee,简称APCF)、罚款(penalty)和补贴(subsidy)政策(简称A-P-S策略)的作用效果。研究结果表明:一方面,组合策略具有多重绩效,不但能够降低污染损失(经济-环境效益)、死亡人口的生命价值(社会-经济效益)、交通拥堵程度(社会效益)和机动车污染物总量(环境效益),而且能大大改善环境生态承载力(环境效益)和机动车非法出行增长量(社会安全绩效);另一方面,政策在实施过程中也会遇到一定的局限性,如初期的滞后性,后期的“褪色”效应和“反弹”效应,因此,从长期来看,需要及时调整组合方案。最后,根据仿真结果提出了相应的政策建议,进而为城市交通和环保等相关部门提供决策参考。     

基于系统动力学的新能源汽车“绿色悖论”效应研究

面对日益严峻的环境问题, 为减少机动车排放污染气体, 我国各地政府纷纷出台相应鼓励发展新能源汽车的政策。新能源汽车主要是采用电作为推动力, 能够有效降低空气污染程度。本文运用系统动力学方法建立模型, 从系统的角度出发, 采用VENSIM软件进行仿真模拟, 探究产生“绿色悖论”的原因。动态仿真结果显示, 政策对新能源汽车的影响实施阶段有显著效果, 但是随着时间的推移, 政策的边际效用会逐渐降低; 新能源汽车总量的上升直接吞噬甚至超出了其节约的资源, 而且带来了停车位紧张、道路拥挤度上升的不良后果。     

基于计量经济模型的煤炭消费与经济增长关系实证研究——以世界主要煤炭消费国为例

经济的发展和能源(包括煤炭)的利用有着密不可分的关系.运用国际上研究能源消费与经济增长之间关系的重要工具-协整分析、格兰杰因果关系检验对世界主要煤炭消费国家(中国、美国、印度、俄罗斯、日本和南非)1981-2005的煤炭消费与GDP之间因果关系及其内在规律进行了分析和研究.实证研究结果表明,这些国家能源消费结构、经济政策不同,煤炭消费同经济增长之间的因果关系也不完全一致,中国、日本和南非存在经济增长对煤炭消费的因果关系,俄罗斯存在煤炭消费对经济增长的因果关系,而印度煤炭消费和经济增长之间则不存在因果关系.据此各国根据自身能源国情及国际比较形势,制定科学的能源战略与政策.     

Energy aware proactive optimized link state routing in mobile ad-hoc networks

In this paper we develop a novel energy aware routing approach for mobile ad hoc network (MANET) problems. The approach is based on using Optimized Link State Routing Protocol. Our Energy Aware OLSR labeled as OLSR_EA measures and predicts per-interval energy consumptions using the well-known Auto-Regressive Integrated Moving Average time series method. We develop a composite energy cost, by considering transmission power consumption and residual energy of each node, and use this composite energy index as the routing metric. Our extensive ns2 simulation experiments show that OLSR_EA substantially prolongs the network lifetime and saves total energy used in MANET. In our experiments we considered different scenarios considering a variety of traffic loads, node mobilities, homogeneous power consumption, and heterogeneous power consumption. Simulation results also confirm that OLSR_EA improves the traffic balance between nodes, and packet delivery ratio in higher node speed. We further develop characteristics of OLSR_EA in power-wise heterogeneous MANET to achieve efficient energy preserving performance.     

A perturbation solution for bacterial growth and bioremediation in a porous medium with bio-clogging

We investigate a flow problem of relevance in bioremediation and develop a mathematical model for transport of contamination by groundwater and the spreading, confinement, and remediation of chemical waste. The model is based on the fluid mass and momentum balance equations and simultaneous transport and consumption of the pollutant (hydrocarbon) and nutrient (oxygen). Particular emphasis is placed on the study of processes involving the full coupling of reaction, transport and mechanical effects. Dimensional analysis and asymptotic reduction are used to simplify the governing equations, which are then solved numerically.     

容许借贷的消费投资策略研究

考虑了容许借贷的消费投资决策问题,投资者选择债券和带有红利回报的风险股票,在效用最大化的标准下,研究了最优消费投资策略。最后就ＨＡＲＡ效用函数提供了最优策略。     

Analysis of optimal and nearly optimal sequencing policies for a closed queueing network

We analyze sequencing policies designed to most effectively utilize the resources of a closed queueing network representation of a manufacturing system. A continuous time Markov decision process formulation is used to compare the performance of optimal sequencing policies and a heuristic developed by analyzing a heavy traffic approximation of the system.     

A quality of service negotiation-based vertical handoff decision scheme in heterogeneous wireless systems

This paper introduces a novel vertical handoff decision scheme. The objective is to provide users with enhanced quality of service (QoS) and maximize the network revenue. This scheme balances both-side interests via a suitably defined network merit function and a user–operator negotiation model. The merit function evaluates network performance based on user preferences and decides the most appropriate network for users. The negotiation model is defined as a semi-Markov decision process (SMDP). An optimal policy that maximizes the network revenue without violating QoS constraints is found by resolving the SMDP problem using Q-learning. Furthermore, a time-adaptive QoS monitoring mechanism is combined with the merit function in order to decrease the power consumption on terminal interface activation. The simulation results demonstrate that the proposed vertical handoff decision scheme enhances the performance in terms of power consumption, handoff call-dropping probability (HCDP) and network revenue.     

Non-compensatory composite indicators for the evaluation of urban planning policy: The Land-Use Policy Efficiency Index (LUPEI)

In this research paper, we define and test an ELECTRE III-based approach to the construction of non-compensatory composite indicators; these indicators are used for the evaluation of environmental and social performances of urban and regional planning policies. We tested the methodology for the construction of the Land-Use Policy Efficiency Index (LUPEI) on the municipal scale applied to a sample of municipalities in the Apulia Region (Southern Italy). Based on the literature review concerning composite indicators, we found that linear aggregation rules are the most widely applied aggregation procedures for composite indicators. However, their applicability depends on a set of strong theoretical and operational conditions. If these conditions do not hold, then other aggregation and weighting procedures must be applied to construct the composite indicators. We tested the ELECTRE III via a fruitful interaction with three experts who were participating in a focus group. We found that composite indicators are powerful tools when it comes to the assessment of multidimensional planning issues. Since each sub-indicator provides different information and responds to different goals, rankings and assessment based on mono-indicator frameworks can lead to incomplete or even biased results that do not consider an integrated approach to land-use policy efficiency. Moreover, both experts and decision-makers appreciated the role of composite indicators in increasing knowledge and providing deeper insights into complex phenomena in the domains of urban and regional planning.     

基于动态综合评价模型的街区型住区开放对城市交通拥堵影响的研究（英文）

The growing use of vehicles with the development of China brings greater pressure on the existing road traffic network. Based on the domestic and international research experiences and the domestic congestion situations, this paper selected six indicators that affect the traffic conditions. As each single indicator cannot reflect the detailed traffic quality separately, the dynamic comprehensive evaluation principle is adopted.Then we used the principle of "Variation Driven" to determine the weight vector of each sub-indicator, and established a multi-indicator comprehensive evaluation system through the dynamic weight of each sub-indicator. Finally, the rationality and feasibility of the system is demonstrated by case analysis. This provides a strong theoretical foundation and basis to mitigate traffic congestion.     

The analytic hierarchy process in medical and health care decision making: A literature review

This paper presents a literature review of the application of the analytic hierarchy process (AHP) to important problems in medical and health care decision making. The literature is classified by year of publication, health care category, journal, method of analyzing alternatives, participants, and application type. Very few articles were published prior to 1988 and the level of activity has increased to about three articles per year since 1997. The 50 articles reviewed were classified in seven categories: diagnosis, patient participation, therapy/treatment, organ transplantation, project and technology evaluation and selection, human resource planning, and health care evaluation and policy. The largest number of articles was found in the project and technology evaluation and selection category (14) with substantial activity in patient participation (9), therapy/treatment (8), and health care evaluation and policy (8). The AHP appears to be a promising support tool for shared decision making between patient and doctor, evaluation and selection of therapies and treatments, and the evaluation of health care technologies and policies. We expect that AHP research will continue to be an important component of health care and medical research.     

基于学习向量量化神经网络的水环境质量综合评价

针对水环境质量综合评价中指标权重确定方法的不足,利用学习向量量化(LVQ)神经网络具有的强大的非线性运算和相似特征聚类功能,提出一种基于学习向量量化(LVQ)神经网络的水质综合评价决策方法.将它应用于水质综合指标评价,为改进水质综合评价提供了一种简捷的分类评价方法.