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ردیف | عنوان | نوع |
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1 |
Assessing the impacts of uncertain future closure costs when evaluating strategies for tailings management
ارزیابی اثرات هزینه های بسته نشده آینده در هنگام ارزیابی استراتژی های مدیریت دنباله ها-2020 This paper reports on the economic evaluation of alternative strategies for managing a hypothetical gold
tailings stream in Western Australia as a slurry, as a thickened, and as a filtered tailings. Suitable project
designs were developed and the lifecycle cost was calculated for each project under two different scenarios;
(i) baseline scenario in which a traditional accounting approach was adopted for assessing the
options in terms of the total cost of the project, and (ii) an alternative scenario which considered higher
rehabilitation costs compared to the costs envisaged in the baseline scenario, and a lower discount rate
for discounting them. This was done to examine the extent to which uncertainties and potential sources
of variability surrounding mine closure planning, accounting and provisioning impact the costs of tailings
management. The results revealed that under the baseline scenario, managing thickened tailings is
the option of lowest cost, but under the alternative scenario, managing slurry tailings resulted in the
most economically feasible solution. However, the better cost performance of the slurry option should be
viewed with caution given the difficulties associated with the closure of slurry tailings storage facilities
and increasingly stringent regulations with regards to the rehabilitation of areas disturbed by mining
operations. Although presenting the highest cost under both scenarios, filtered tailings has the lowest
cost sensitivity to alternative input values and assumptions associated with mine closure. This is
important information to be conveyed to decision-makers when a solution for tailings management is to
be selected. Designing with closure in mind, and continued focus on developing leading practices for
tailings management are essential to achieve optimal sustainability performance goals in mining. Keywords: Filtered tailings | Life cycle costing | Cost comparison | Discount rate | Sustainable mining |
مقاله انگلیسی |
2 |
Impact of Lean, Agile and Green (LAG) on business competitiveness: An empirical study of fast moving consumer goods businesses
تأثیر Lean ، Agile و Green (LAG) بر رقابت تجاری: مطالعه تجربی تجارت سریع کالاهای مصرفی-2020 The adoption/utilisation of Lean, Agile and Green (LAG) practices in both the manufacturing and service sector is rising. However, there yet remain a research gap to precisely evaluate the relationship between LAG practices and business competitiveness (e.g, achieving reduction in cost, lead time and environmental recyclable waste). This research aims to explore this relationship, specifically in fast-moving consumer goods (FMCG) businesses. The hypothesised relationships are tested with data collected from 96 FMCG companies. Structural Equation Modelling is applied to evaluate different channels of achieving business competitiveness through the adoption of Lean, Agile and Green. The findings suggest that competitive outcomes vary with the adoption of LAG practices in specific product life cycle stages. This implies that awareness of the product life cycle concept is essential. A combination of LAG practices for the sole purpose of reducing environmental waste is negatively related to environmental waste reduction. LAG practices are more efficiently adopted when the adopters are equipped with expert knowledge on the paradigms and their individual practices. This research has approached the attainment of competitiveness in the FMCG businesses by analysing management efforts that improve cost performance, lead time and environmental sustainability aspects of business operations. The research has also considered the product life cycle stages in analysing the impacts of management efforts. Keywords: Lean | Green | Agile | Product life cycle | Competitiveness |
مقاله انگلیسی |
3 |
Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships
تقویت انرژی مقرون به صرفه مدیریت انرژی یادگیری برای سلولهای سوختی هیبریدی پلاگین و باتری ها-2020 Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for
coastal ships with access to H2 replenishment and battery charging infrastructures in ports. However, such
systems could be constrained by high power source degradation and energy costs. Cost-effective energy management
strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double
Q reinforcement learning based energy management system for such systems to achieve near-optimal average
voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring
of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management
strategies generated by the agent were validated using another test dataset collected over a different
period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion
systems for ships. The results show that without prior knowledge of future power demands, the strategies
can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming
with the equivalent state space resolution. Keywords: Coastal ferry | Hybrid fuel cell and battery | Continuous monitoring | Energy management system | Reinforcement learning |
مقاله انگلیسی |
4 |
Performance outcomes of offshoring, backshoring and staying at home manufacturing
خروجی های عملکرد دور از ساحل، پشت ساحل، و ماندن در ساخت و تولید خانه-2018 The objective of this paper is to advance the understanding of performance outcomes of companies pursuing different strategies in moving manufacturing abroad and moving it back again. The study is based on a large-scale survey of perceptual data from 233 senior managers in manufacturing companies. Furthermore, secondary performance data of return on capital employed is included in the analysis. Companies that have an explicit corporate manufacturing strategy report better operational performance in terms of product quality, lead time and flexibility than companies that do not have such a strategy. The analysis does not reveal any differences in productivity among companies that have offshored, backshored, or maintained manufacturing at home. Companies that have offshored manufacturing report lower unit costs than companies that have applied a staying at home strategy. No significant level of difference in unit costs was found when comparing companies that have backshored manufacturing and companies that have maintained manufacturing at home. Companies that have offshored manufacturing report that they can extract detailed component, product and process data from their cost-accounting systems to a higher degree. The paper stresses the importance of access to, and the quality of, cost-accounting data to make informed strategic manufacturing decisions, and how such decisions may affect operational and cost performance. The paper provides novel empirical insights on cost performance, operational performance and cost accounting data among manufacturers pursuing different strategies of moving manufacturing abroad and back to home destinations.
keywords: Offshoring |Backshoring |Cost performance |Operational performance |Cost accounting capabilities |
مقاله انگلیسی |
5 |
Analyze the energy consumption characteristics and affecting factors of Taiwan’s convenience stores-using the big data mining approach
ویژگی های مصرف انرژی و عوامل موثر را تحلیل کنیداز فروشگاه های راحت تایوان - با استفاده از روش کاوش داده های بزرگ-2018 This study applies big data mining, machine learning analysis technique and uses the Waikato Environ
ment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption
performance in Taiwan which consists of (a). Influential factors of architectural space environment and
geographical conditions; (b). Influential factors of management type; (c). Influential factors of business
equipment; (d). Influential factors of local climatic conditions; (e). Influential factors of service area so
cioeconomic conditions. The survey data of 1,052 chain convenience stores belong to 7-Eleven, Family
Mart and Hi-Life groups by Taiwan Architecture and Building Center (TABC) in 2014. The implicit knowl
edge will be explored in order to improve the traditional analysis technique which is unlikely to build a
model for complex, inexact and uncertain dynamic energy consumption system for convenience stores.
The analysis process comprises of (a). Problem definition and objective setting; (b). Data source selection;
(c). Data collection; (d). Data preprocessing/preparation; (e). Data attributes selection; (f). Data mining
and model construction; (g). Results analysis and evaluation; (h). Knowledge discovery and dissemination.
The key factors influencing the convenience stores energy consumption and the influence intensity order
can be explored by data attributes selection. The numerical prediction model for energy consumption is
built by applying regression analysis and classification techniques. The optimization thresholds of various
influential factors are obtained. The different cluster data are compared by using clustering analysis to
verify the correlation between the factors influencing the convenience stores energy consumption char
acteristic. The implicit knowledge of energy consumption characteristic obtained by the aforesaid analysis
can be used to (a). Provide the owners with accurate predicted energy consumption performance to opti
mize architectural space, business equipment and operations management mode; (b). The design planners
can obtain the optimum design proposal of Cost Performance Ratio (C/P) by planning the thresholds of
various key factors and the validation of prediction model; (c). Provide decision support for government
energy and environment departments, to make energy saving and carbon emission reduction policies, in
order to estimate and set the energy consumption scenarios of convenience store industry.
Keywords: Convenience store ، Data mining ، Machine learning ، Energy consumption characteristics ، Energy consumption affecting factor |
مقاله انگلیسی |
6 |
A model to control environmental performance of project execution process based on greenhouse gas emissions using earned value management
یک مدل برای کنترل عملکرد محیطی فرآیند اجرای پروژه برمبنای انتشار گاز گلخانه ای با استفاده از مدیریت ارزش درآمدی-2018 In response to recent climate change, which is believed to be attributed to the release of greenhouse gas (GHG) emissions, many countries are placing CO2 abatement programs such as carbon tax and cap-and-trade. Projects do have a significant share in GHGs and therefore their environmental performance, like their schedule and cost performance, should be monitored and controlled. Although many large projects would pass an environmental assessment in the project evaluation phase, the issue of environmental performance monitoring during the project execution phase has not been addressed in project management methodologies. The objective of this paper is to develop a model to estimate project GHG emissions, and to measure project GHG performance using the developed metrics, which can be used at any point in time over the life of a project. A comprehensive study is conducted to collect information on GHG emission factors of various project activity data (such as material use, energy and fuel consumption, transportation, etc.), and a user form interface is developed to calculate the total GHG of an activity. Also, a breakdown structure is proposed which supports managing all the project GHG accounts. The monitoring and control model is formulated based on the logic used in earned value management (EVM) methodology. The proposed model is then implemented to a work package of a real construction project. The results present the project initial GHG plan and show that the model is able to calculate project GHG variance by the reporting date and predict project final GHG based on a project GHG performance index. The method presented in this paper is general and can be applied to any type of projects in an organization that aims to reduce its carbon footprint. The same structure can be applied to monitor and control any other environmental impact associated with project execution process.
keywords: Greenhouse gas (GHG) emissions |Project control |Earned value management (EVM) |
مقاله انگلیسی |
7 |
Analyze the energy consumption characteristics and affecting factors of Taiwan’s convenience stores-using the big data mining approach
تجزیه و تحلیل ویژگی های مصرف انرژی و عوامل موثر در فروشگاه های راحتی تایوان با استفاده از روش کاوش داده های بزرگ-2018 This study applies big data mining, machine learning analysis technique and uses the Waikato Environ
ment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption
performance in Taiwan which consists of (a). Influential factors of architectural space environment and
geographical conditions; (b). Influential factors of management type; (c). Influential factors of business
equipment; (d). Influential factors of local climatic conditions; (e). Influential factors of service area so
cioeconomic conditions. The survey data of 1,052 chain convenience stores belong to 7-Eleven, Family
Mart and Hi-Life groups by Taiwan Architecture and Building Center (TABC) in 2014. The implicit knowl
edge will be explored in order to improve the traditional analysis technique which is unlikely to build a
model for complex, inexact and uncertain dynamic energy consumption system for convenience stores.
The analysis process comprises of (a). Problem definition and objective setting; (b). Data source selection;
(c). Data collection; (d). Data preprocessing/preparation; (e). Data attributes selection; (f). Data mining
and model construction; (g). Results analysis and evaluation; (h). Knowledge discovery and dissemination.
The key factors influencing the convenience stores energy consumption and the influence intensity order
can be explored by data attributes selection. The numerical prediction model for energy consumption is
built by applying regression analysis and classification techniques. The optimization thresholds of various
influential factors are obtained. The different cluster data are compared by using clustering analysis to
verify the correlation between the factors influencing the convenience stores energy consumption char
acteristic. The implicit knowledge of energy consumption characteristic obtained by the aforesaid analysis
can be used to (a). Provide the owners with accurate predicted energy consumption performance to opti
mize architectural space, business equipment and operations management mode; (b). The design planners
can obtain the optimum design proposal of Cost Performance Ratio (C/P) by planning the thresholds of
various key factors and the validation of prediction model; (c). Provide decision support for government
energy and environment departments, to make energy saving and carbon emission reduction policies, in
order to estimate and set the energy consumption scenarios of convenience store industry.
Keywords: Convenience store ، Data mining ، Machine learning ، Energy consumption characteristics ، Energy consumption affecting factor |
مقاله انگلیسی |