Finding optimal vaccination strategies under parameter uncertainty using stochastic programming

We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*) 相似文献   

Fuzzy Multi-Objective Optimization of a Green Supply Chain Network with Risk Management that Includes Environmental Hazards

Among the leading environmental risks, global climate alteration has become one of the most important controversial issues. Greenhouse gas emissions (CO₂, methane, etc.) and air pollution have motivated a need to develop and improve environmental management strategies. As a consequence, environmental sanctions are forcing commercial enterprises to re-consider and re-design supply chain processes in a green way. This article provides a multi-objective model to design a closed-loop supply chain (CLSC) network in a green framework. Our first and second objectives are to minimize all the transportation costs for the supply chain's forward and reverse logistics; the third objective is to minimize total CO₂ emissions; the fourth objective is to encourage customers to use recyclable materials as an environmental practice. To provide more realistic modeling by treating the uncertainty in decision-makers’ objectives, fuzzy modeling is used in this study. The model is explained and tested via fulfilling a numerical example. In scenario analyses, analytic hierarchy process (AHP), fuzzy AHP (F-AHP), and fuzzy TOPSIS (F-TOPSIS) approaches were applied and compared to evaluate different objectives to guide decision-makers.     

Sustainable sourcing of strategic raw materials by integrating recycled materials

In this paper we investigate a manufacturer’s sustainable sourcing strategy that includes recycled materials. To produce a short life-cycle electronic good, strategic raw materials can be bought from virgin material suppliers in advance of the season and via emergency shipments, as well as from a recycler. Hence, we take into account virgin and recycled materials from different sources simultaneously. Recycling makes it possible to integrate raw materials out of steadily increasing waste streams back into production processes. Considering stochastic prices for recycled materials, stochastic supply quantities from the recycler and stochastic demand as well as their potential dependencies, we develop a single-period inventory model to derive the order quantities for virgin and recycled raw materials to determine the related costs and to evaluate the effectiveness of the sourcing strategy. We provide managerial insights into the benefits of such a green sourcing approach with recycling and compare this strategy to standard sourcing without recycling. We conduct a full factorial design and a detailed numerical sensitivity analysis on the key input parameters to evaluate the cost savings potential. Furthermore, we consider the effects of correlations between the stochastic parameters. Green sourcing is especially beneficial in terms of cost savings for high demand variability, high prices of virgin raw material and low expected recycling prices as well as for increasing standard deviation of the recycling price. Besides these advantages it also contributes to environmental sustainability as, compared to sourcing without recycling, it reduces the total quantity ordered and, hence, emissions are reduced.     

Effects of Using Heterogeneous Prices on the Allocation of Impacts from Electricity Use: A Mixed‐Unit Input‐Output Approach

Economic input‐output life cycle assessment (IO‐LCA) models allow for quick estimation of economy‐wide greenhouse gas (GHG) emissions associated with goods and services. IO‐LCA models are usually built using economic accounts and differ from most process‐based models in their use of economic transactions, rather than physical flows, as the drivers of supply‐chain GHG emissions. GHG emissions estimates associated with input supply chains are influenced by the price paid by consumers when the relative prices between individual consumers are different. We investigate the significance of the allocation of GHG emissions based on monetary versus physical units by carrying out a case study of the U.S. electricity sector. We create parallel monetary and mixed‐unit IO‐LCA models using the 2007 Benchmark Accounts of the U.S. economy and sector specific prices for different end users of electricity. This approach is well suited for electricity generation because electricity consumption contributes a significant share of emissions for most processes, and the range of prices paid by electricity consumers allows us to explore the effects of price on allocation of emissions. We find that, in general, monetary input‐output models assign fewer emissions per kilowatt to electricity used by industrial sectors than to electricity used by households and service sectors, attributable to the relatively higher prices paid by households and service sectors. This fact introduces a challenging question of what is the best basis for allocating the emissions from electricity generation given the different uses of electricity by consumers and the wide variability of electricity pricing.     

Rethinking Environmental Performance from a Public Health Perspective: A Comparative Industry Analysis

To date the most common measures of environmental performance used to compare industries, and by extension firms or facilities, have been quantity of pollution emitted or hazardous waste generated. Discharge information, however, does not necessarily capture potential health effects. We propose an alternative environmental performance measure that includes the public health risks of toxic air emissions extended to industry supply chains using economic input-output life-cycle assessment. Cancer risk to the U.S. population was determined by applying a damage function to the Toxic Release Inventory (TRI) as modeled by CalTOX, a multimedia multipathway fate and exposure model. Risks were then translated into social costs using cancer willingness to pay. For a baseline emissions year of 1998, 260 excess cancer cases were calculated for 116 TRI chemicals, dominated by ingestion risk from polycyclic aromatic compounds and dioxins emitted by the primary aluminum and cement industries, respectively. The direct emissions of a small number of industry sectors account for most of the U.S. population cancer risk. For the majority of industry sectors, however, cancer risk per $1 million output is associated with supply chain upstream emissions. Ranking industries by total (direct + upstream) supply chain risk per economic output leads to different conclusions about the relative hazards associated with these industries than a conventional ranking based on emissions per economic output.     

A methodology for separating uncertainty and variability in the life cycle greenhouse gas emissions of coal-fueled power generation in the USA

Purpose

Results of life cycle assessments (LCAs) of power generation technologies are increasingly reported in terms of typical values and possible ranges. Extents of these ranges result from both variability and uncertainty. Uncertainty may be reduced via additional research. However, variability is a characteristic of supply chains as they exist; as such, it cannot be reduced without modifying existing systems. The goal of this study is to separately quantify uncertainty and variability in LCA.

Methods

In this paper, we present a novel method for differentiating uncertainty from variability in life cycle assessments of coal-fueled power generation, with a specific focus on greenhouse gas emissions. Individual coal supply chains were analyzed for 364 US coal power plants. Uncertainty in CO₂ and CH₄ emissions throughout these supply chains was quantified via Monte Carlo simulation. The method may be used to identify key factors that drive the range of life cycle emissions as well as the limits of precision of an LCA.

Results and discussion

Using this method, we statistically characterized the carbon footprint of coal power in the USA in 2009. Our method reveals that the average carbon footprint of coal power (100 year time horizon) ranges from 0.97 to 1.69 kg CO₂eq/kWh of generated electricity (95 % confidence interval), primarily due to variability in plant efficiency. Uncertainty in the carbon footprints of individual plants spans a factor of 1.04 for the least uncertain plant footprint to a factor of 1.2 for the most uncertain plant footprint (95 % uncertainty intervals). The uncertainty in the total carbon footprint of all US coal power plants spans a factor of 1.05.

Conclusions

We have developed and successfully implemented a framework for separating uncertainty and variability in the carbon footprint of coal-fired power plants. Reduction of uncertainty will not substantially reduce the range of predicted emissions. The range can only be reduced via substantial changes to the US coal power infrastructure. The finding that variability is larger than uncertainty can obviously not be generalized to other product systems and impact categories. Our framework can, however, be used to assess the relative influence of uncertainty and variability for a whole range of product systems and environmental impacts.     

Consequential Life Cycle Assessment With Market‐Driven Design

This article describes the development of a consequential life cycle assessment (cLCA) with endogenous market‐driven design (MDD). Incorporation of MDD within cLCA (cLCA‐MDD) is beneficial because design decisions, influenced by market forces, are a major source of environmental emissions and resource consumption in many life cycle systems. cLCA‐MDD captures the environmental impact of these design responses resulting from industrial and policy decisions. We begin by developing the concept of cLCA‐MDD, then present a case study that demonstrates how design responses can be endogenously captured in a cLCA analysis. The case study is in two parts: First, we incorporate endogenous design responses into a cLCA of a mid‐size vehicle and, second, we conduct a policy analysis using a cLCA‐MDD approach. The case study illustrates that cLCA‐MDD can capture multiple “ripple effects” resulting from an industrial decision (e.g., downsizing a vehicle's engine) or a policy decision (e.g., raising gasoline taxes) and that these effects significantly influence results. A key challenge of the approach is appropriately managing and communicating uncertainties associated with the choice of economic parameters or models. We discuss sources of uncertainty in cLCA‐MDD and demonstrate a presentation scheme to facilitate communication of result sensitivity to uncertainties from input parameters, models, and model structure.     

A Procedure for Life-Cycle-Based Solid Waste Management with Consideration of Uncertainty

The development of integrated solid-waste management (SWM) strategies that are efficient with respect to both cost and environmental performance is a complex task. It must incorporate the numerous interrelations among different unit operations in the solid waste system (e.g., collection, recycling, and combustion), and the large number of design parameters that affect estimates of cost and environmental emissions. Uncertainty in design and operational parameters can lead to uncertainty in the estimates of cost and emissions. This article describes an extension of the capability of the Integrated Solid Waste Management Decision Support Tool (ISWM DST) to enable consideration of the effects of uncertainty in input parameters. The uncertainty analysis capability is illustrated using a hypothetical case study of a typical municipality. Results show that increased expenditure does not necessarily result in a reduction in the expected levels of environmental emissions and that some SWM alternatives may be more robust, although deterministic estimates of their expected performances are similar. The uncertainty analysis also facilitates use of the ISWM DST by policy makers responsible for evaluation of the expected effect of SWM practices on, for example, greenhouse-gas emissions.     

Accounting for Emissions and Sinks from the Biogeochemical Cycle of Carbon in the U.S. Economic Input‐Output Model

Biogeochemical cycles are essential ecosystem services that continue to degrade as a result of human activities, but are not fully considered in efforts toward sustainable engineering. This article develops a model that integrates the carbon cycle with economic activities in the 2002 U.S. economy. Data about the carbon cycle, including emissions and sequestration flows, is obtained from the greenhouse gas inventory of the U.S. Environmental Protection Agency. Economic activities are captured by the economic input‐output model available from the Bureau of Economic Analysis. The resulting model is more comprehensive in its accounting for the carbon cycle than existing methods for carbon footprint (CF) calculations. Examples of unique flows in this model include the effect of land‐use and land‐cover change on carbon dioxide flow within the U.S. national boundary, carbon sequestration in urban trees, and emissions resulting from liming. This model is used to gain unique insight into the carbon profile of U.S. economic sectors by providing the life cycle emissions and sequestration in each sector. Such insight may be used to support policies, manage supply chains, and be used for more comprehensive CF calculations.     

Manufacturing capacity planning and the value of multi-stage stochastic programming under Markovian demand

Capacity planning is a crucial part of global manufacturing strategies in the automotive industry, especially in the presence of volatile markets with high demand uncertainty. Capacity adjustments in machining intensive areas, e.g. body shop, paint shop, or aggregate machining face lead times exceeding a year, making an elaborated decision support indispensable. In this regard, two-stage stochastic programming is a frequently used framework to support capacity and flexibility decisions under uncertainty. However, it does not anticipate future capacity adjustment opportunities in response to market demand developments. Motivated by empirical findings from the automotive industry, we develop a multi-stage stochastic dynamic programming approach where the evolution of demand is represented by a Markov demand model. An efficient multi-stage solution algorithm is proposed and the benefits compared to a rolling horizon application of a two-stage approach are illustrated for different generic manufacturing networks. Especially network structures with limited flexibility might significantly benefit from applying a multi-stage framework.     

An approach to assess logistics and ecological supply chain performance using postponement strategies

This paper presents a methodology that helps managers evaluate how to assess the impact of postponement on supply chain performance considering logistics and ecological criteria. We consider a green supply chain design that considers CO₂ transport emissions under different postponement strategy scenarios using a simulation tool. The paper focuses on a relevant extension of postponement theory by including green considerations into the evaluation of postponement strategies in green supply chain design. Moreover, it provides some insight on how to measure and evaluate the impact of postponement regarding supply chain transport performance, considering different transport mode (container ocean ship and truck) using the European Platform on Life-Cycle Assessment (EPLCA) of ELCD – European Life-Cycle Database. The study has demonstrated that logistics and packing postponement strategies can improve the performance of logistics (total inventory and order lead-time) and, at the same time contribute to reducing the environmental impact of CO₂ emissions from transportation process.     

The effect of variability in the food supply on the daily singing routines of European robins: a test of a stochastic dynamic programming model

A stochastic dynamic programming (SDP) model offers a general explanation of daily singing routines in birds, but remains almost untested empirically. I examined a central prediction of the SDP model, that a more variable food supply decreases the bird's song output at dawn, relative to its song output at dusk. I provided supplementary food to make the food supply more or less variable over 2-week periods in the territories of free-living European robins Erithacus rubecula. Robins sang relatively less at dawn than at dusk after weeks in which their supplementary food supply was variable, and more at dawn than at dusk after weeks in which their food supplementation was constant. These results provide strong support for the prediction of the SDP model. Copyright 1999 The Association for the Study of Animal Behaviour.     

Incorporating environmental aspects in an inventory routing problem. A case study from the petrochemical industry

Carbon emissions from transport activities in a supply chain are extensively contributing to global warming. However, the focus of literature in the field of sustainable supply chain management is mainly on production processes or network design decisions, considering transportation as a “necessary evil”. Consequently, transport activities are often accounted for in a rather simplified way in the analyses and are rarely analyzed on a high level of detail. Thereby the actual impacts transport processes have on the economic and environmental performance of a company are distorted. Our work focuses solely on the analysis of transport processes and shows the economic and environmental effects of routing decisions in a supply chain with vertical collaboration, for instance through vendor-managed inventory. We propose an Inventory Routing Model and apply it to a case study from the petrochemical industry. The outcome from this detailed transport analysis is then compared with results from former studies, where the main focus was on facility location decisions rather than on transportation decisions. The results point out the importance of detailed transport process analyses in order to get accurate results and suggest a potential for achieving pareto-improvements by reducing at the same time both costs and carbon emissions in a supply chain with vertical collaboration.     

An adaptive model for predicting !Kung reproductive performance: A stochastic dynamic programming approach

A stochastic dynamic programming model is presented that supports and extends work on the reproductive performance of the !Kung Bushmen (Lee 1972; Blurton Jones and Sibly 1978; Blurton Jones 1986), proposing that !Kung women and their reproductive systems may be maximizing reproductive success. The stochastic dynamic programming approach allows the construction of a whole-life model where the physical/environmental constraints along with the uncertainty about future events !Kung women face when making reproductive choices can be explicitly built in. The model makes quantitative predictions for the optimal reproductive strategy assuming !Kung women are maximizing expected lifetime reproduction (ELR) given the physical parameters of !Kung life.The model relies on data gathered from the works cited above and some considerations from simple probability theory. The model predictions for optimal birth spacing match the !Kung reproductive data very well and support earlier findings (Blurton Jones and Sibly; Blurton Jones 1986). The utility of the dynamic modeling approach is illustrated when the effects of varying certain model parameters are investigated.By including the effect of the mother's mortality, which was not included in the Blurton Jones and Sibly (1978) analysis, the model allows for further exploration of the application of an adaptive approach to human reproductive performance. By adding some considerations about the risks of childbirth for the mother the model not only predicts optimal birth spacing, which is site specific, but also predicts the optimal time for a woman to begin and cease having children. These predictions coincide with menarche and menopause and shed light on their possible adaptive value.     

Sustainable development of global supply chains—part 1: sustainability optimization framework

In global industry supply chains, environmental sustainability optimization addresses the overall consumption of resources and energy, the reduction of carbon emissions and generated waste to name a few. In this paper, we propose a holistic sustainability optimization framework for strategic network design of industry supply chains under consideration of economic, social as well as ecologic objectives. The framework is flexible to incorporate multiple sustainability indicators, alternative sustainability optimization strategies as well as a variety of internal and external industry-specific factors which impact the sustainability of the entire industry supply chain in the long-term. The core of the framework is an end-to-end closed-loop value chain model consisting of process, transport and product-in-use modules. For the first time, the product-in-use impact (“use” vs. “make”) is integrated in one network design approach. In addition, the model fully closes the loop from sourcing of raw materials via manufacturing towards reverse value chain steps such as disposal and recycling. Finally, we propose the minimize-time-to-sustainability approach as new optimization strategy for long-term network design problems focusing on minimizing the time, industry supply chain structures need to transform into sustainability steady states for all defined sustainability indicators such as CO₂e emissions, costs or social indicators based on defined target values. In part 2 of this paper the application of the optimization framework to the European automotive industry is shown.     

Analyzing Economic and Environmental Performance of Switchgrass Biofuel Supply Chains

This study optimized the net present value (NPV) of profit of various switchgrass-based ethanol supply chains and estimated associated greenhouse gas (GHG) emissions in west Tennessee. Three configurations of feedstock harvesting and storage, including a large round baler system, a large square baler system, and a chopping/densification system, were evaluated. A mixed-integer mathematical programming model incorporating high-resolution spatial data was used to determine the optimal locations and capacities of cellulosic ethanol plants and feedstock preprocessing facilities, and associated feedstock-draw areas by maximizing the NPV of profit over 20 years. The optimized outputs were then used to estimate the GHG emissions produced in the biofuel supply chain (BSC) per year. The study shows that BSC configurations have important implications for the economic and environmental performance of the system. The harvest and storage configurations affect the locations of conversion and preprocessing facilities, and associated feedstock-draw areas, hence impacting the cost and emissions of both feedstock and biofuels transportation. The findings suggest the BSC system that harvests feedstock with forage choppers and utilizes stretch-wrap balers to increase feedstock density has the highest NPV of profit. The BSC system that uses large square balers for harvest and storage emits the lowest amount of GHGs per year. In addition, the sensitivity analysis suggests that biofuel price and scaling factor of facility capital was influential to the economics of BSC systems. The breakeven price of biofuel for the three BSCs was around $0.97 L^?1.     

In-vitro evaluation of selected chalcones for antioxidant activity

Synthetic chalcones (SCs) having different side chains on the 1-(2-Hydroxy-3-(2-hydroxy-cyclohexyl)-4,6 dimethoxy-phenyl(-methanone structure were examined in-vitro for their antioxidant abilities by DPPH (2,2-diphenyl-1-picryl hydrazine) radical scavenging activity, reducing ability, OH radical scavenging activity, inhibition of polyphenol oxidase (PPO) and formation of diene conjugates. Overall, with few exceptions, all the SCs showed moderate biological activity in all the parameters examined. The SCs were found to be reactive towards DPPH radical and had considerable reducing ability. With few exceptions, all the test compounds under study were found to possess moderate to poor OH radical scavenging activity and inhibited PPO significantly and all were found to be effective inhibitors of hydroperoxide formation. These findings suggest that these SCs can be considered as potential antioxidant agents which might be further explored for the design of lead antioxidant drug candidates.     

Range and uncertainties in estimating delays in greenhouse gas mitigation potential of forest bioenergy sourced from Canadian forests

Accurately assessing the delay before the substitution of fossil fuel by forest bioenergy starts having a net beneficial impact on atmospheric CO₂ is becoming important as the cost of delaying GHG emission reductions is increasingly being recognized. We documented the time to carbon (C) parity of forest bioenergy sourced from different feedstocks (harvest residues, salvaged trees, and green trees), typical of forest biomass production in Canada, used to replace three fossil fuel types (coal, oil, and natural gas) in heating or power generation. The time to C parity is defined as the time needed for the newly established bioenergy system to reach the cumulative C emissions of a fossil fuel, counterfactual system. Furthermore, we estimated an uncertainty period derived from the difference in C parity time between predefined best‐ and worst‐case scenarios, in which parameter values related to the supply chain and forest dynamics varied. The results indicate short‐to‐long ranking of C parity times for residues < salvaged trees < green trees and for substituting the less energy‐dense fossil fuels (coal < oil < natural gas). A sensitivity analysis indicated that silviculture and enhanced conversion efficiency, when occurring only in the bioenergy system, help reduce time to C parity. The uncertainty around the estimate of C parity time is generally small and inconsequential in the case of harvest residues but is generally large for the other feedstocks, indicating that meeting specific C parity time using feedstock other than residues is possible, but would require very specific conditions. Overall, the use of single parity time values to evaluate the performance of a particular feedstock in mitigating GHG emissions should be questioned given the importance of uncertainty as an inherent component of any bioenergy project.     

Using Bayesian networks to incorporate uncertainty in habitat suitability index models

Habitat suitability index (HSI) models rarely characterize the uncertainty associated with their estimates of habitat quality despite the fact that uncertainty can have important management implications. The purpose of this paper was to explore the use of Bayesian belief networks (BBNs) for representing and propagating 3 types of uncertainty in HSI models—uncertainty in the suitability index relationships, the parameters of the HSI equation, and measurement of habitat variables (i.e., model inputs). I constructed a BBN–HSI model, based on an existing HSI model, using Netica™ software. I parameterized the BBN's conditional probability tables via Monte Carlo methods, and developed a discretization scheme that met specifications for numerical error. I applied the model to both real and dummy sites in order to demonstrate the utility of the BBN–HSI model for 1) determining whether sites with different habitat types had statistically significant differences in HSI, and 2) making decisions based on rules that reflect different attitudes toward risk—maximum expected value, maximin, and maximax. I also examined effects of uncertainty in the habitat variables on the model's output. Some sites with different habitat types had different values for E[HSI], the expected value of HSI, but habitat suitability was not significantly different based on the overlap of 90% confidence intervals for E[HSI]. The different decision rules resulted in different rankings of sites, and hence, different decisions based on risk. As measurement uncertainty in habitat variables increased, sites with significantly different (α = 0.1) E[HSI] became statistically more similar. Incorporating uncertainty in HSI models enables explicit consideration of risk and more robust habitat management decisions. © 2012 The Wildlife Society.     

In-vitro evaluation of selected chalcones for antioxidant activity

Synthetic chalcones (SCs) having different side chains on the 1-(2-Hydroxy-3-(2-hydroxy-cyclohexyl)-4,6 dimethoxy-phenyl(-methanone structure were examined in-vitro for their antioxidant abilities by DPPH (2,2-diphenyl-1-picryl hydrazine) radical scavenging activity, reducing ability, OH radical scavenging activity, inhibition of polyphenol oxidase (PPO) and formation of diene conjugates. Overall, with few exceptions, all the SCs showed moderate biological activity in all the parameters examined. The SCs were found to be reactive towards DPPH radical and had considerable reducing ability. With few exceptions, all the test compounds under study were found to possess moderate to poor OH radical scavenging activity and inhibited PPO significantly and all were found to be effective inhibitors of hydroperoxide formation. These findings suggest that these SCs can be considered as potential antioxidant agents which might be further explored for the design of lead antioxidant drug candidates.