Article

Robust Pareto optimal approach to sustainable heavy-duty truck fleet composition

Authors:
  • Connected Wise
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Abstract

Heavy-duty trucks are the main carrier of the most of freight in the United States today. The U.S. Department of Energy’s projections show that, under the reference case, the truck vehicle-miles-travelled (VMT) on the national highway system will further increase in the near future. This outlook with regard to U.S. Class 8 Heavy-Duty Trucks (HDTs) raises concerns regarding environmental, economic, and social impacts of these vehicles and HDT fleets. However, the transition to sustainable trucking is a challenging task for which multiple sustainability objectives must be considered and addressed, such as minimizing the life-cycle costs (LCCs), life-cycle GHGs (LCGHGs), and life-cycle air pollution externality costs (LCAPECs) of trucks while composing a truck fleet. This study proposes a hybrid life-cycle assessment-based robust Pareto optimal approach to developing a HDT fleet mix, accounting for the sector-specific average payloads of 5 U.S. economic sectors. The results of this study indicate that battery-electric, hybrid, and diesel HDTs make up most of the fleet mixes in the studied sectors in order to optimize their environmental, economic, and social impacts. It is therefore concluded that, given the relevant objectives and constraints, the current techno-economic circumstances in the U.S. and current forms of electricity generation should both be improved in order for HDT fleet mixes to achieve greenhouse gas emission reductions of 30% or greater. The findings of the study will support decision-making processes by public and private organizations and help them to develop environmentally, economically, and socially optimized fleet mixes for their operations.

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... SDG1 was the target of 5% of the papers in this corpus. SEN et al. (2019) [25] found that the costs incurred by the health impacts of tailpipe emissions are the major cost component for each HDT, so this was considered as one angle of the discussion of SDG1's instrument "ensure resources for National Policies and Programs". ...
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Purpose The construction industry has considerable impacts on the environment, economy, and society. Although quantifying and analyzing the sustainability implications of the built environment is of great importance, it has not been studied sufficiently. Therefore, the overarching goal of this study is to quantify the overall environmental, economic, and social impacts of the U.S. construction sectors using an economic input–output-based sustainability assessment framework. Methods In this research, the commodity-by-industry supply and use tables published by the U.S. Bureau of Economic Analysis, as part of the International System of National Accounts, are merged with a range of environmental, economic, and social metrics to develop a comprehensive sustainability assessment framework for the U.S. construction industry. After determining these sustainability assessment metrics, the direct and indirect sustainability impacts of U.S construction sectors have been analyzed from a triple bottom-line perspective. Results When analyzing the total sustainability impacts by each construction sector, “Residential Permanent Single and Multi-Family Structures" and "Other Non-residential Structures" are found to have the highest environmental, economic, and social impacts in comparison with other construction sectors. The analysis results also show that indirect suppliers of construction sectors have the largest sustainability impacts compared with on-site activities. For example, for all U.S. construction sectors, on-site construction processes are found to be responsible for less than 5 % of total water consumption, whereas about 95 % of total water use can be attributed to indirect suppliers. In addition, Scope 3 emissions are responsible for the highest carbon emissions compared with Scopes 1 and 2. Therefore, using narrowly defined system boundaries by ignoring supply chain-related impacts can result in underestimation of triple bottom-line sustainability impacts of the U.S. construction industry. Conclusions Life cycle assessment (LCA) studies that consider all dimensions of sustainability impacts of civil infrastructures are still limited, and the current research is an important attempt to analyze the triple bottom-line sustainability impacts of the U.S. construction sectors in a holistic way. We believe that this comprehensive sustainability assessment model will complement previous LCA studies on resource consumption of U.S. construction sectors by evaluating them not only from environmental standpoint, but also from economic and social perspectives.
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Prior design optimization efforts do not capture the impact of battery degradation and replacement on the total cost of ownership, even though the battery is the most expensive and least robust powertrain component. A novel, comprehensive framework is presented for model-based parametric optimization of hybrid electric vehicle powertrains, while accounting for the degradation of the electric battery and its impact on fuel consumption and battery replacement. This is achieved by integrating a powertrain simulation model, an electrochemical battery model capable of predicting degradation, and a lifecycle economic analysis (including net present value, payback period, and internal rate of return). An example design study is presented here to optimize the sizing of the electric motor and battery pack for the North American transit bus application. The results show that the optimal design parameters depend on the metric of interest (i.e. net present value, payback period, etc.). Finally, it is also observed that the fuel consumption increases by up to 10% from “day 1” to the end of battery life. These results highlight the utility of the proposed framework in enabling better design decisions as compared to methods that do not capture the evolution of vehicle performance and fuel consumption as the battery degrades.
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This research models urban freight distribution services lifecycle CO2e emissions. A lifecycle emissions minimization model for the fleet size and composition problem is presented and applied to a real-world case study. The model explicitly incorporates parking and idling emissions which are significant in multi-stop urban distribution routes. Lifecycle emission elasticities as well as the impact of logistics constraints such as route duration and vehicle cargo capacity are estimated and analyzed. Policy implications and tradeoffs between electric tricycles and conventional diesel vans are discussed.
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In this study, a comparative life cycle assessment of internal combustion engine-based vehicles fueled by various fuels, ranging from hydrogen to gasoline, is conducted in addition to electric and hybrid electric vehicles. Three types of vehicles are considered, namely; internal combustion engine vehicles using gasoline, diesel, liquefied petroleum gas, methanol, compressed natural gas, hydrogen and ammonia; hybrid electric vehicles using 50% gasoline and 50% electricity; and electric only vehicles for comprehensive comparison and environmental impact assessment. The processes are analyzed from raw material extraction to vehicle disposal using process-based life cycle assessment methodology. In order to reflect the sustainability of the vehicles, seven different environmental impact categories are considered: abiotic depletion, acidification, eutrophication, global warming, human toxicity, ozone layer depletion and terrestrial ecotoxicity. The primary energy resources are selected based on currently utilized options to indicate the actual performances of the vehicles. The results show that electric and plug-in hybrid electric vehicles result in higher human toxicity, terrestrial ecotoxicity and acidification values because of manufacturing and maintenance phases. In contrast, hydrogen vehicles yield the most environmentally benign option because of high energy density and low fuel consumption during operation.
Article
We analyse the comparative investigation into truncation vs. aggregation errors of process-based and hybrid LCA by Yang et al. 2017. We analyse the validity of their findings when the hypothetical five-sector economy is altered to account for realistic technological and sectoral interdependencies. We show that in such cases the truncation error of process-based LCA outweighs the aggregation error of hybrid LCA. To this end, we compare the dominant eigenvalue of our alternative economy with that of real economies, showing good agreement. The same validity check does not hold for the system used by Yang and colleagues. Additionally, we demonstrate that even simple process systems can have higher dominant eigenvalues, provided they are based on realistic data.
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A sustainable society index (SSI) has been designed to measure the sustainability of different countries in terms of economic, environmental and human well-being dimensions. These areas were subdivided into eight categories and 24 indicators. The paper shows that the SSI could be considered as a decision-making problem with multiple objectives. We propose a decision-analysis approach for estimation of the weights of indicators, categories and dimensions of well-being, with the target being a construction of overall scores for countries. A questionnaire was sent to experts from several countries, and the weights were estimated from their individual answers. The SSI 2010 results for different countries were recalculated through utilization of the estimated weights. There were some differences in the rankings compared with the earlier results with equal weights. Potential biases of the SSI approach used are critically evaluated, and opportunities to develop the SSI described. Copyright © 2016 John Wiley & Sons, Ltd and ERP Environment
Article
The risk of accelerated electric vehicle battery degradation is commonly cited as a concern inhibiting the implementation of vehicle-to-grid (V2G) technology. However, little quantitative evidence exists in prior literature to refute or substantiate these concerns for different grid services that vehicles may offer. In this paper, a methodology is proposed to quantify electric vehicle (EV) battery degradation from driving only vs. driving and several vehicle-grid services, based on a semi-empirical lithium-ion battery capacity fade model. A detailed EV battery pack thermal model and EV powertrain model are utilized to capture the time-varying battery temperature and working parameters including current, internal resistance and state-of-charge (SOC), while an EV is driving and offering various grid services. We use the proposed method to simulate the battery degradation impacts from multiple vehicle-grid services including peak load shaving, frequency regulation and net load shaping. The degradation impact of these grid services is compared against baseline cases for driving and uncontrolled charging only, for several different cases of vehicle itineraries, driving distances, and climate conditions. Over the lifetime of a vehicle, our results show that battery wear is indeed increased when vehicles offer V2G grid services. However, the increased wear from V2G is inconsequential compared with naturally occurring battery wear (i.e. from driving and calendar ageing) when V2G services are offered only on days of the greatest grid need (20 days/year in our study). In the case of frequency regulation and peak load shaving V2G grid services offered 2 hours each day, battery wear remains minimal even if this grid service is offered every day over the vehicle lifetime. Our results suggest that an attractive tradeoff exists where vehicles can offer grid services on the highest value days for the grid with minimal impact on vehicle battery life.
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This chapter describes how to optimise energy systems considering their economic and environmental performance simultaneously. To this end, we follow an approach that combines life cycle assessment with multi-objective optimisation. We illustrate how to apply such a framework to the strategic design and planning of biofuel supply chains. The problem is formulated in mathematical terms as a multi-objective mixed-integer linear programme. The aim of the design/planning task is to maximise the net present value while the environmental impact is minimised simultaneously. Eco-indicator 99 is the life cycle assessment methodology incorporated in the model to quantify the environmental damage. The implementation of the algorithm in a case study based on the Argentine industry reveals the conflictive trade-off between economic and environmental objectives. The proposed framework provides valuable insight into the incidence of key operational features in the optimal biofuel supply chain network.
Article
Parcel delivery trucks, due to their intensive stop-and-go operational patterns, have relative lower fuel efficiency and higher environmental impacts to urban areas. The adoption of alternative fuel trucks may mitigate the environmental impacts, however, the first cost of these trucks are higher than those of traditional diesel trucks. To this end, based on environmental, social, and economic indicators, a model that provides optimized solutions for a fleet consists of 30 commercial delivery trucks is studied in this paper. An economic input-output based hybrid life cycle assessment is performed in conjunction with Multi-Objective Linear Programming to evaluate various delivery truck fleet combinations and to provide a comprehensive analysis of fleet performance. Furthermore, six specific scenarios, representing different utilization levels and fuel economy levels, have been taken into consideration to reflect the sensitivity of fleet performance with respect to real word operation. The performances of the vehicles are evaluated based on three criteria: economic aspects, environmental concerns, and public health impacts. Furthermore, the results are considered from two perspectives, the first being a case in which no constraints are taken into account, and the second case being considered under tailpipe emission constraints. The results indicate that when fuel economy is high and annual mileage is low, current diesel trucks are able to fulfill the requirement in both cases with reasonably low costs. Conversely, in scenarios with low fuel economy and high utilization levels, hybrid vehicles are preferred. However, the optimization model selects more electric trucks when tailpipe emission constraints are accounted.
Article
The current waste management literature lacks a comprehensive LCA of the recycling of construction materials that considers both process and supply chain-related impacts as a whole. Furthermore, an optimization-based decision support framework has not been also addressed in any work, which provides a quantifiable understanding about the potential savings and implications associated with recycling of construction materials from a life cycle perspective. The aim of this research is to present a multi-criteria optimization model, which is developed to propose economically-sound and environmentally-benign construction waste management strategies for a LEED-certified university building. First, an economic input-output-based hybrid life cycle assessment model is built to quantify the total environmental impacts of various waste management options: recycling, conventional landfilling and incineration. After quantifying the net environmental pressures associated with these waste treatment alternatives, a compromise programming model is utilized to determine the optimal recycling strategy considering environmental and economic impacts, simultaneously. The analysis results show that recycling of ferrous and non-ferrous metals significantly contributed to reductions in the total carbon footprint of waste management. On the other hand, recycling of asphalt and concrete increased the overall carbon footprint due to high fuel consumption and emissions during the crushing process. Based on the multi-criteria optimization results, 100% recycling of ferrous and non-ferrous metals, cardboard, plastic and glass is suggested to maximize the environmental and economic savings, simultaneously. We believe that the results of this research will facilitate better decision making in treating construction and debris waste for LEED-certified green buildings by combining the results of environmental LCA with multi-objective optimization modeling.
Article
By combining life cycle assessment (LCA) with multi-objective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bio-ethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bio-ethanol production would be the most environmentally preferable choice, while the mixed use of wheat and straw as feedstocks would be the most cost-effective one.
Article
This research involves two novel elements to advance the body of knowledge in existing sustainability assessment frameworks for alternative vehicle technologies. First, we developed an input–output based hybrid life cycle sustainability assessment model using several macro-level social, economic, and environmental indicators, taking into consideration the manufacturing of vehicles and batteries, operation, and end-of-life phases. Second, the results of a hybrid life cycle sustainability assessment for different conventional and alternative vehicles technologies (internal combustion electric vehicles, hybrid electric vehicles, plug-in-hybrid electric vehicles, and battery electric vehicles) are incorporated into the Technique for Order-Preference by Similarity to Ideal Solution and Intuitionistic Fuzzy Sets. Two policy scenarios are considered in this analysis, with Scenario 1 being based on existing electric power infrastructure in the U.S. with no additional infrastructure requirements, while Scenario 2 is an extreme scenario in which the electricity to power electric vehicles is generated exclusively via solar charging stations. The Intuitionistic Fuzzy Multi-Criteria Decision Making and Technique for Order Preference by Similarity to Ideal Solution methods are then utilized to rank the life cycle sustainability performance of alternative passenger vehicles. Furthermore, since expert judgments play an important role in determining the relative performance of alternative vehicle technologies, a sustainability triangle analysis is also presented to show how the weighting applied to each dimension affects the selection of different alternatives. The results indicate that hybrid and plug-in hybrid electric vehicles are the best alternatives for both Scenarios 1 and 2 when all of the indicators are considered. On the other hand, the ranking of vehicles changes significantly when each of the environmental, economic, and social indicators are evaluated individually. This proposed method can be a useful decision making platform for decision-makers to develop more effective policies and guide the offering of incentives to the right domains for sustainable transportation.
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External or social costs of freight transportation have received increased attention recently in development strategies because of sustainability issues. A widely accepted definition of sustainable development is “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” Strategies for sustainable transportation consider economic development, environmental preservation, and social development. But as the negative impacts of freight transportation increase, achieving sustainability goals becomes ever more challenging. Freight transportation is a primary component of all supply-chain and logistics systems. However, the cost of moving commodities between cities and countries is borne not only by direct stakeholders but also by other members of society who may not benefit directly from these movements. In economics literature, this is referred to as the external cost of an activity. Air, noise, and water pollution; vegetation and wildlife destruction; and road accidents are some of the negative impacts of freight transportation. Freight movements and their associated negative impacts have been steadily increasing over the last few decades in most parts of the world. Negative environmental effects of freight transportation are a serious concern, because of the associated long-term direct and indirect impacts such as increasing greenhouse gases (GHGs) and global warming.
Article
The increased use of forest and wood residues for the production of bioenergy, biofuels, and other bioproducts is essential to enhance the economic performance of forest products industries and reduce environmental impacts. Bi-objective optimization models have been developed recently to support the optimum design of either bioenergy or biofuels supply chains considering economic as well as environmental impacts. In an integrated bioenergy and biofuels supply chain where biofuel producers are also users of the generated energy, the energy flows among co-located supply chain entities affect the environmental and economic objective functions and consequently the optimal design of the supply chain, therefore, the energy flows have to be considered in the optimization model. This type of bi-objective problem has not been modeled in previous studies. In this paper, a bi-objective biorefinery supply chain optimization model for the production of bioenergy and biofuels using forest and wood residues is developed. The model considers energy flows among co-located technologies and is formulated as a multi-period mixed integer program (MIP) that calculates the net present value (NPV) and the life cycle greenhouse gas (GHG) emission savings associated with the biorefinery supply chain. The applicability of the proposed model is illustrated through a case study in British Columbia, Canada.
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The life cycle cost and environmental impacts of electric vehicles are very uncertain, but extremely important for making policy decisions. This study presents a new model, called the Electric Vehicles Regional Optimizer, to model this uncertainty and predict the optimal combination of drivetrains in different U.S. regions for the year 2030. First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different Electric Vehicle types (gasoline plug-in hybrid electric vehicles, gasoline extended range electric vehicle, and all-electric vehicle) are evaluated considering their inherent uncertainties. Then, the environmental damage costs and the water footprint of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties in the life cycle cost, environmental damage cost, and water footprint of studied vehicle types are modeled for different U.S. electricity grid regions. Finally, an optimization model is coupled with Exploratory Modeling and Analysis to find the ideal combination of different vehicle types in each U.S. region for the year 2030. The findings of this research will help policy makers and transportation planners to prepare our nation's transportation system for the influx of electric vehicles.
Article
The eco-design of conventional potable water production processes is impeded by the high variability of operating conditions as a function of the inlet and the quality of outlet water, and by the large diversity of feasible technical solutions and treatment processes. Based on an already developed library of unit process modules which generates water treatment process inventories with integrated Life Cycle Assessment (EVALEAU tool), this work presents a new tool to combine Process Modeling, Life Cycle Assessment and MultiObjective Optimization (PM-LCA-MOO tool) for conventional potable water production processes, in order to solve the challenges of interconnecting LCA results and other conflicting process objectives.
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We present a parametric analysis of factors that can influence advanced fuel and technology deployments in U.S. Class 7–8 trucks through 2050. The analysis focuses on the competition between traditional diesel trucks, natural gas vehicles (NGVs), and ultra-efficient powertrains. Underlying the study is a vehicle choice and stock model of the U.S. heavy-duty vehicle market. The model is segmented by vehicle class, body type, powertrain, fleet size, and operational type. We find that conventional diesel trucks will dominate the market through 2050, but NGVs could have significant market penetration depending on key technological and economic uncertainties. Compressed natural gas trucks conducting urban trips in fleets that can support private infrastructure are economically viable now and will continue to gain market share. Ultra-efficient diesel trucks, exemplified by the U.S. Department of Energy's SuperTruck program, are the preferred alternative in the long haul segment, but could compete with liquefied natural gas (LNG) trucks if the fuel price differential between LNG and diesel increases. However, the greatest impact in reducing petroleum consumption and pollutant emissions is had by investing in efficiency technologies that benefit all powertrains, especially the conventional diesels that comprise the majority of the stock, instead of incentivizing specific alternatives.
Article
We use longitudinal dynamics and simulation models to study the feasibility of deploying electric buses in place of conventional ones. The longitudinal dynamics model estimates energy use by an electric bus operating on different lines consisting of a mixture of urban and suburban driving. The simulation model is used to study the effect of the type and number of chargers deployed and the queuing policy used on queuing and charging times when buses must recharge their batteries. We use a case study based on the bus service operated on The Ohio State University campus and focus on six of the seven lines which operate around the center of campus. We demonstrate that all 22 of the buses on these lines can be made electric and that one 500 kW or two 250 kW chargers are sufficient to maintain reasonable service frequencies.
Article
In this work we present a systematic tool for the optimal retrofit of buildings that considers several economic and environmental criteria simultaneously at the design stage. Our approach is based on a rigorous mixed-integer linear program (MILP) that identifies in a systematic manner the best alternatives for reducing the environmental impact of buildings. These include the use of different insulation materials and windows as well as the installation of solar panels. Environmental concerns are explicitly accounted for in this MILP by means of Life Cycle Assessment (LCA) principles, which allow evaluating the impact of each alternative being assessed considering all the stages in its life cycle. We illustrate the capabilities of our approach using a case study that considers weather data for Central Portugal.
Article
Purpose In the USA, several studies have been conducted to analyze the energy consumption and atmospheric emissions of Warm-mix Asphalt (WMA) pavements. However, the direct and indirect environmental, economic, and social impacts, termed as Triple-Bottom-Line (TBL), were not addressed sufficiently. Hence, the aim of this study is to develop TBL-oriented sustainability assessment model to evaluate the environmental and socio-economic impacts of pavements constructed with different types of WMA mixtures and compare them to a conventional Hot-mix Asphalt (HMA). The types of WMA technologies investigated in this research include Asphamin® WMA, Evotherm™ WMA, and Sasobit® WMA. Methods To achieve this goal, supply and use tables published by the U.S. Bureau of Economic Analysis were merged with 16 macro-level sustainability metrics. A hybrid TBL-LCA model was built to evaluate the life-cycle sustainability performance of using WMA technologies in construction of asphalt pavements. The impacts on the sustainability were calculated in terms of socio-economic (import, income, gross operating surplus, government tax, work-related injuries, and employment) and environmental (water withdrawal, energy use, carbon footprint, hazardous waste generation, toxic releases into air, and land use). A stochastic compromise programming model was then developed for finding the optimal allocation of different pavement types for the U.S. highways. Results and discussion WMAs did not perform better in terms of environmental impacts compared to HMA. Asphamin® WMA was found to have the highest environmental and socio-economic impacts compared to other pavement types. Material extractions and processing phase had the highest contribution to all environmental impact indicators that shows the importance of cleaner production strategies for pavement materials. Based on stochastic compromised programming results, in a balanced weighting situation, Sasobit® WMA had the highest percentage of allocation (61 %); while only socio-economic aspects matter, Asphamin® WMA had the largest share (57 %) among the asphalt pavements. The optimization results also supported the significance of an increased WMA use in the U.S. highways. Conclusions This research complemented previous LCA studies by evaluating pavements not only from environmental emissions and energy consumption standpoint, but also from socio-economic perspectives. Multi-objective optimization results also provided important insights for decision makers when finding the optimum allocation of pavement alternatives based on different environmental and socio-economic priorities. Consequently, this study aimed to increase awareness of the inherent benefits of economic input–output analysis and multi-criteria decision making through application to emerging sustainable pavement practices.
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This paper provides an overview of developments in robust optimization since 2007. It seeks to give a representative picture of the research topics most explored in recent years, highlight common themes in the investigations of independent research teams and highlight the contributions of rising as well as established researchers both to the theory of robust optimization and its practice. With respect to the theory of robust optimization, this paper reviews recent results on the cases without and with recourse, i.e., the static and dynamic settings, as well as the connection with stochastic optimization and risk theory, the concept of distributionally robust optimization, and findings in robust nonlinear optimization. With respect to the practice of robust optimization, we consider a broad spectrum of applications, in particular inventory and logistics, finance, revenue management, but also queueing networks, machine learning, energy systems and the public good. Key developments in the period from 2007 to present include: (i) an extensive body of work on robust decision-making under uncertainty with uncertain distributions, i.e., “robustifying” stochastic optimization, (ii) a greater connection with decision sciences by linking uncertainty sets to risk theory, (iii) further results on nonlinear optimization and sequential decision-making and (iv) besides more work on established families of examples such as robust inventory and revenue management, the addition to the robust optimization literature of new application areas, especially energy systems and the public good.
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â–º We analyze various scenarios to examine supply chain results of reducing freight truck transport. â–º There are no individual sectors with a majority burden of freight transport energy and emissions. â–º Increasing truck efficiency 10% or reducing truck in the top 20% sectors reduces emissions 6%. â–º Policies encouraging higher efficiency in freight trucks may be a sufficient short term goal. â–º Reducing truck freight transport through sector specific policies may be a better long term goal.
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This article investigates how environmental trade-offs are handled in life-cycle assessment (LCA) studies in some Nordic companies. Through interviews, the use and understanding of weighting methods in decision making was studied. The analysis shows that the decision makers require methods with which to aggregate and help interpret the complex information from life-cycle inventories. They agreed that it was not their own values that should be reflected in such methods, but they were found to have different opinions concerning the value basis that should be used. The analysis also investigates the difficulties arising from using such methods. The decision makers seemed to give a broader meaning to the term weighting, and were more concerned with the comparison between environmental and other aspects than the weighting of different environmental impacts. A conclusion is that decision makers need to be more involved in modeling and interpretation. The role of the analyst should be to interpret the information needs of the decision maker, and help him or her make methodological choices that are consistent with these needs and relevant from his or her point of view. To achieve this, it is important that decision makers do not view LCA as a highly standardized calculation tool, but as a flexible process of collecting, organizing, and interpreting environmental information. Such an approach to LCA increases the chances that the results will be regarded as relevant and useful.
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This work discusses robustness assessment during multi-objective optimization with a Multi-Objective Evolutionary Algorithm (MOEA) using a combination of two types of robustness measures. Expectation quantifies simultaneously fitness and robustness, while variance assesses the deviation of the original fitness in the neighborhood of the solution. Possible equations for each type are assessed via application to several benchmark problems and the selection of the most adequate is carried out. Diverse combinations of expectation and variance measures are then linked to a specific MOEA proposed by the authors, their selection being done on the basis of the results produced for various multi-objective benchmark problems. Finally, the combination preferred plus the same MOEA are used successfully to obtain the fittest and most robust Pareto optimal frontiers for a few more complex multi-criteria optimization problems.
Article
It is generally recognised that the valuation in LCA requires political, ideological and/or ethical values (hence the term). These values, however, are seldom discussed, and this paper may he seen as an early attempt. One result is that not only the valuation weighting factors, but also the choice of valuation methodology and the choice of using a valuation weighting method at all, are influenced by fundamental ethical and ideological valuations. Since there is no societal consensus on these fundamental values, and never will be one in an open democratic society, there is no reason to expect consensus either on valuation weighting factors, or on the valuation method or even on the choice of using a valuation weighting method at all. Another result of the discussion on values is that the ethical and ideological valuations are often made implicitly in the choice of method, data, etc., thus making it difficult to discuss the values and the implications of different standpoints. Although this paper focus on the valuation methods within LCA, it is expected that much of the discussion and the conclusions are of relevance for other environmental management tools, e.g. Environmental Impact Assessment.