Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data

Assessing sustainability of supply chains is a critical and increasingly complex problem. In recent years sustainability has received more attention in supply chain management (SCM) literature with triple bottom lines including social, environmental, and economic factors. Conventional data envelopment analysis (DEA) models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration internal interactions of DMUs. Two-stage DEA models deal with such DMUs. However, existing two-stage DEA models are applicable only in technologies characterized by positive inputs/outputs. This paper aims to build and present a new two-stage DEA model considering negative input-intermediate-output data. Some numerical examples along with some theorems and properties are given to show capability of proposed method. The proposed ideas are used in a case study where 29 Iranian supply chains producing equipment of expendable medical devices are evaluated in terms of sustainability.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

Reducing CO2 emissions in temperature-controlled road transportation using the LDVRP model

Temperature-controlled transport is needed to maintain the quality of products such as fresh and frozen foods and pharmaceuticals. Road transportation is responsible for a considerable part of global emissions. Temperature-controlled transportation exhausts even more emissions than ambient temperature transport because of the extra fuel requirements for cooling and because of leakage of refrigerant. The transportation sector is under pressure to improve both its environmental and economic performance. To explore opportunities to reach this goal, the Load-Dependent Vehicle Routing Problem (LDVRP) model has been developed to optimize routing decisions taking into account fuel consumption and emissions related to the load of the vehicle. However, this model does not take refrigeration related emissions into account. We therefore propose an extension of the LDVRP model to optimize routing decisions and to account for refrigeration emissions in temperature-controlled transportation systems. This extended LDVRP model is applied in a case study in the Dutch frozen food industry. We show that taking the emissions caused by refrigeration in road transportation can result in different optimal routes and speeds compared with the LDVRP model and the standard Vehicle Routing Problem model. Moreover, taking the emissions caused by refrigeration into account improves the estimation of emissions related to temperature-controlled transportation. This model can help to reduce emissions of temperature-controlled road transportation.     

递进式连锁经营管理专业实践教学改革研究——基于加盟创业项目视角

连锁经营管理专业人才培养目标大多定位在一线服务员或基层管理者，所培养的学生必须具备较强的连锁专业能力和创业能力，而大多数高职院校培养出来的学生在这些方面所表现出来的能力却差强人意，尤其是创业能力方面。本文提出将加盟创业项目贯穿于连锁经营管理实践教学全过程，按照“仿真训练-校内孵化-校内模拟-市场全真”四个阶段，形成一个递进式的实践教学流程，从而促使学生的专业能力和创业能力实现递进式提升。     

基于供应链的汽车制造业物流一体化研究

从物流一体化的原因和目标出发,分析中国汽车制造业物流发展的现状,提出了实施物流一体化的几项方面,即首先从思想上重视物流一体化,改变传统的物流观;重构业务流程,为企业实施物流一体化打下基础;充分利用第三方物流和建立物流信息系统。     

一类供应链下游企业合作利润分配机制研究

仅以合作关系作为供应链利润分配机制是与共享原则相悖的,且也无法实现。基于合作利润分配机制应包括的结构要素和运行要素之间的辩证关系,建立了一厂一商的供应链合作利润分配机制的数学模型。通过对该模型的求解,确定了厂商和渠道利润的数学表达式,定量描述了机制结构要素与运行要素之间的“硬件”与“软件”关系,并对它们的变化区域与对各决策变量及利润的影响程度进行了深入的分析,对现行返利系数的应用进行了讨论;构造出了一种较为有效的供应链合作利润分配机制。     

抗干扰技术在数控机床中的应用

该文介绍了抗干扰技术在数控机床中的应用,阐述了五种抗干扰的方法,特别对屏蔽(包括电场屏蔽,磁场屏蔽和电磁场屏蔽)和正确的布线方式做了详细说明,这对于避免干扰造成数控系统的“软”故障将有所帮助．     

孕源断链在土地沙漠化防御技术中的实践剖析

基于灾害的链式理论对现今土地沙漠化问题进行了分析,指出土地沙漠化无论在成因上还是在危害性的表现结果上都呈现出复杂的链式效应,并依据灾害链式效应指出不合理耕作、开采地下水、砍伐树木、过度放牧及日益恶化的生态环境是形成土地沙漠化灾害链的源头因素.根据孕源断链的治灾思路,提出治理沙漠化应从其孕育初期的源头上控制灾害蔓延,切断灾害传播链,并从工程实践入手对其成效进行剖析。     

RFID技术在供应链中的应用

介绍了RFID的发展历程和关键技术,对电子标签的应用及工作原理、优点等进行了阐述.从信息与利润共享入手论述了电子标签对供应链产生的影响,对RFID技术在供应链中的应用做了分析和预测,并结合中国实际对如何发展RFID在物流供应链中的应用提出了一些建议.