Optimal planning of liquefied natural gas deliveries

We investigate the problem of designing an optimal annual delivery plan for Liquefied Natural Gas (LNG). This problem requires determining the long-term cargo delivery dates and the assignment of vessels to the cargoes while accommodating several constraints, including berth availability, liquefaction terminal inventory, planned maintenance, and bunkering requirements. We describe a novel mixed-integer programming formulation that captures important industry requirements and constraints with the objective of minimizing the vessel fleet size. A peculiar property of the proposed formulation is that it includes a polynomial number of variables and constraints and is, in our experience, computationally tractable for large problem instances using a commercial solver. Extensive computational runs demonstrate the efficacy of the proposed model for real instances provided by a major energy company that involve up to 118 cargoes and a 373-day planning horizon.     

港口干散货物流供应链信息协同运作模式分析

在国内外供应链管理研究及河北港口群煤炭业务现状调研分析的基础上,剖析现阶段港口煤炭供应链运营管理的问题。聚焦河北港口群煤炭供应链模式构建过程,梳理港口煤炭供应链管理流程,构建河北港口群供应链管理模式、物流协同模式、物流体系及物流供应链协同信息化支撑模式,解决制约港口煤炭供应链的信息共享不及时、信息流通不畅等问题。对港口煤炭供应链协同运作信息化体系进行总体框架设计,并选择典型平台建设探讨供应链协同运作信息化体系支撑实现模式,形成高效、科学、先进的河北港口群煤炭供应链管理模式。     

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.     

潜艇舷间舾装的精度分配

[目的]潜艇建造是一个多工序耦合的工艺过程,随着模块化建造模式的发展,广义的“舾装”包含了材料成型、结构建造、设备管系制造安装及涂装等全流程,在设计阶段开展潜艇舾装的精度分配顶层规划,可有效影响潜艇建造工艺过程,确保潜艇总体性能。[方法]针对潜艇舷间舾装,首先提出精度分配的流程,然后对基于尺寸链的精度分配方法的内涵及其适用范围进行探讨,最后对舷间的关键项目及其精度控制对象进行识别,依据尺寸链原理方法对舾装全流程的未知精度项进行分配。[结果]得到了可满足建造精度需求的舷间舾装关键项目中未知精度项的分配值。[结论]通过研究,形成了包含材料、结构、设备、安装等广义舾装过程的精度分配流程及方法,并可用于指导潜艇舾装的各工艺环节和工艺设计。     

公路城际出行链模式划分及其形成机制

为划分公路城际出行链模式并分析其形成机制,本文考虑城市内部换乘过程,解析了公路旅客城际出行过程.利用昆明市1 757份公路旅客出行调查问卷数据,提取了公路城际出行链结构特征.通过测算出行链结构值,提出了公路城际出行链模式的划分方法,将公路客运城际出行链模式划分为Ⅰ、Ⅱ、Ⅲ类并分别获取了其形成机制.结果表明:昆明市公路城际通道的平均运距为237.95 km,公共交通是公路城际出行过程中城市内部换乘的主要交通方式;昆明市公路城际出行链的3类模式占比为13.9∶6.4∶4.7,整体结构较合理;其中,Ⅱ类出行链模式最能体现均衡、稳定的形成机制.对公路城际出行链的形成机制分析发现,收入因素是促使出行链结构合理化的重要动力;造成出行链结构不合理的重要因素是市内出行延误,而非城际通道出行过程.     

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。     

Situation analysis forecasting: the case of European maritime clusters

Within the literature concerned with aspects of competitiveness, innovation and strategic management of industrial clusters, the body regarding forecasting of strategic management is still nascent. This work aspires to render a contribution within the domain of strategic management forecasting, through the indicative case of European maritime clusters. For this end, a two-tier model is formulated. A quantitative strengths, weaknesses, opportunities, and threats (SWOT) methodology that derives from the fusion of a quondam situation analysis and crosstabs’ theory is generated. This leads to the utilization of the crosstab’s conditional probabilities as transition probabilities; the latter are called to compile the transition matrix of a Markov chain. Through this methodology, and the successive Markov chain’s transition matrices, strategic forecasts are extracted for a devised European maritime cluster case. This work relinquishes a novel application with respect to strategic management forecasting, that provides a dichotomy of practical interpretations and scenarios for quantitative situation analysis. Thus, it may enable effective real-time decision-making for strategic management and/or policy drafting. The situation analysis forecasting model may find applicability in a plethora of practical and theoretical cases, wherein forecasting may be desirable. It may as well pertain to an intrinsic methodology for situation analysis forecasting of maritime clusters.