The bi-objective Pollution-Routing Problem

The bi-objective Pollution-Routing Problem is an extension of the Pollution-Routing Problem (PRP) which consists of routing a number of vehicles to serve a set of customers, and determining their speed on each route segment. The two objective functions pertaining to minimization of fuel consumption and driving time are conflicting and are thus considered separately. This paper presents an adaptive large neighborhood search algorithm (ALNS), combined with a speed optimization procedure, to solve the bi-objective PRP. Using the ALNS as the search engine, four a posteriori methods, namely the weighting method, the weighting method with normalization, the epsilon-constraint method and a new hybrid method (HM), are tested using a scalarization of the two objective functions. The HM combines adaptive weighting with the epsilon-constraint method. To evaluate the effectiveness of the algorithm, new sets of instances based on real geographic data are generated, and a library of bi-criteria PRP instances is compiled. Results of extensive computational experiments with the four methods are presented and compared with one another by means of the hypervolume and epsilon indicators. The results show that HM is highly effective in finding good-quality non-dominated solutions on PRP instances with 100 nodes.     

我国渔业企业经济效益分析研究——以2013年我国上市渔业公司数据为例

随着居民收入水平的提高,我国水产品消费数量不断增加.水产品消费需求的增加,一方面导致水产品养殖企业数量增多,水产品养殖产业初具规模;另一方面也导致了市场竞争压力的增大,使得水产品养殖企业经济效益降低.利用2013年我国上市渔业公司财务数据,基于因子分析法研究了我国上市渔业公司经济效益水平.研究结果显示,2013年大湖股份、国联水产各公共因子及综合因子得分都较高,表明其公司盈利能力、偿债能力及运营能力较强,企业后期发展前景较好.而好当家、武昌鱼的综合因子得分较低,需要引起公司重视.最后针对2013年我国上市渔业公司的整体得分情况,提出了提高我国渔业企业经济效益水平的建议.     

Modelling day‐ahead electricity prices

A production‐based approach is introduced to take into account different attitudes and liabilities of market participants to discuss the equilibrium day‐ahead prices on electricity. Conditions ensuring the existence of the equilibrium are given and price distribution is considered. A discussion of reasons for high price volatility is given.     

Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms

Any decision-making process that relies on a probabilistic forecast of future events necessarily requires a calibrated forecast. This article proposes new methods for empirically assessing forecast calibration in a multivariate setting where the probabilistic forecast is given by an ensemble of equally probable forecast scenarios. Multivariate properties are mapped to a single dimension through a prerank function and the calibration is subsequently assessed visually through a histogram of the ranks of the observation’s preranks. Average ranking assigns a prerank based on the average univariate rank while band depth ranking employs the concept of functional band depth where the centrality of the observation within the forecast ensemble is assessed. Several simulation examples and a case study of temperature forecast trajectories at Berlin Tegel Airport in Germany demonstrate that both multivariate ranking methods can successfully detect various sources of miscalibration and scale efficiently to high-dimensional settings. Supplemental material in form of computer code is available online.     

Microwave pyrolysis of straw bale and energy balance analysis

The compressed wheat and corn straw bale were pyrolyzed on a microwave heating device self-designed and built with respect to the time-resolved temperature distribution, mass loss and product properties. Considering scale up and technology promotion of microwave pyrolysis (MWP), the investigations on electricity consumption and energy balance of MWP were carried out emphatically. The results indicated that MWP had obvious advantages over conventional pyrolysis, such as heating rapid and more valuable products obtained. The distribution of pyrolysis products such as gas, liquid and char was close to 1:1:1 due to the medium pyrolysis temperature and the slow heating rate, which was not favorable for the formation of gas and/or liquid products. The content of H₂ attained the highest value of 35 vol.% and syngas (H₂ and CO) was greater than 50 vol.%. The electricity consumption of MWP was between 0.58 and 0.65 kW h (kg straw)⁻¹ and with the increase of microwave power, the electricity consumption required for pyrolysis of unit mass of straw increased. The minimum microwave power for MWP was about 0.371 kW (kg straw)⁻¹ and the proportion of heat loss and conversion loss of electricity to microwave energy occupied in the total input energy was 42%. Data and information obtained are useful for the design and operation of pyrolysis of large-sized biomass via microwave heating technology.     

基于对应分析法的不同收入等级居民的消费特征分析——以广东省城镇居民为例

根据2008年广东省不同收入等级城镇居民的人均消费构成的统计数据,采用对应分析法,并结合可支配收入、平均消费倾向分析不同收入等级城镇居民的消费特征.针对不同的收入人群提出了不同的扩大内需措施的政策建议.     

基于出行目的链的出行生成—分布组合模型分析

目前,我国城市轨道交通客流预测大多采用交通产生、吸引、出行分布、出行方式划分四阶段模型,其中出行生成与分布是方式划分和交通分配的前提,是模型重要的组成部分.在对传统的四阶段模型的分析基础上,结合非集计模型的优点,论文提出了基于出行目的链的出行生成—分布组合模型.模型充分考虑了居民出行链、出行目的等对客流预测的影响,在完善我国轨道交通客流预测理论和方法,提高预测精度等方面具有一定的理论价值和实际意义.     

Analysis of energy consumption in microwave and convective drying process of multi-layered porous material inside a rectangular wave guide

This work applies the first law of thermodynamics to estimate the ratio of energy utilization in microwave drying process using a rectangular waveguide. Two porous packed bed systems are considered such as attaching fine bed on coarse bed (F-C) and attaching coarse bed on fine bed (C-F). The effects of layered configuration and layered thickness on drying rate, power absorbed efficiency, specific energy consumption (SEC), and energy efficiency are studied in detail. The results show that the variations of all parameters have strongly affected on microwave penetration depth and power absorbed within the packed bed. Furthermore, F-C bed with equal layer thickness corresponds to great energy efficiency.     

An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada,United Kingdom,and South Korea

This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries.