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

Efficient Emission Reduction Through Dynamic Supply Mode Selection

We study the inbound supply mode and inventory management decision making for a company that sells an assortment of products. Stochastic demand for each product arrives periodically and unmet demand is backlogged. Each product has two distinct supply modes that may be different suppliers or different transport modes from the same supplier. These supply modes differ in terms of their carbon emissions, speed, and costs. The company needs to decide when to ship how much using which supply mode such that total holding, backlog, and procurement costs are minimized while the emissions associated with different supply modes across the assortment remains below a certain target level. Since the optimal policy for this inventory system is highly complex, we assume that shipment decisions for each product are governed by a dual-index policy. This policy dynamically prescribes shipment quantities with both supply modes based on the on-hand inventory, the backlog, and the products that are still in-transit. We formulate this decision problem as a mixed integer linear program that we solve through Dantzig-wolfe decomposition. We benchmark our decision model against two state-of-the-art approaches in a large test-bed based on real-life carbon emissions data. Relative to our decision model, the first benchmark lacks the flexibility to dynamically ship products with two supply modes while the second benchmark makes supply mode decisions for each product individually rather than holistically for the entire assortment. Our computational experiment shows that our decision model can outperform the first and second benchmark by up to 15 and 40 percent, respectively, for realistic targets for carbon emission reduction

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

Copula-Based Multivariate Hydrologic Frequency Analysis

Multivariate frequency distributions are being increasingly recognized for their role in hydrological design and risk management. The conventional multivariate distributions are severely limited in that all constituent marginals have to be from the same distribution family. The copula method is a newly emerging approach for deriving multivariate distributions which overcomes this limitation. Use of copula method in hydrological applications has begun only recently and ascertaining the applicability of different copulas for combinations of various hydrological variables is currently an area of active research. Since there exists a variety of copulas capable of characterizing a broad range of dependence, the selection of appropriate copulas for different hydrological applications becomes a non-trivial task. This study evaluates the relative performance of various copulas and methods of parameter estimation as well as of recently developed statistical inference procedures. Potential copulas for multivariate extreme flow and rainfall processes are then identified. Multivariate hydrological frequency analysis typically utilizes only the concurrent parts of observed data, leaving a lot of non-concurrent information unutilized. Uncertainty in distribution parameter estimates can be reduced by simultaneously including such non-concurrent data in the analysis. A new copula-based “Composite Likelihood Approach” that allows all available multivariate data of varying lengths to be combined and analyzed in an integrated manner has been developed. This approach yields additional information, enhancing the precision of parameter estimates that are otherwise obtained from either purely univariate or purely multivariate considerations. The approach can be advantageously employed in limited hydrological data situations in order to provide significant virtual augmentation of available data lengths by virtue of increased precision of parameter estimates. The effectiveness of a copula selection framework that helps in an a priori short listing of potentially viable copulas on the basis of dependence characteristics has been examined using several case studies pertaining to various extreme flow and rainfall variables. The benefits of the composite likelihood approach in terms of significant improvement in the precision of parameter estimates of commonly used distributions in hydrology, such as normal, Gumbel, gamma, and log-Pearson Type III, have been quantified

Cost allocation and risk management in renewable electricity networks

As part of the efforts to mitigate climate change, there has been rapidly increasing share of renewable power generation in the European electricity system. In the interest of bridging the gap between corporate and academic research interests, this PhD project presents a research collaboration on renewable electricity systems between Aarhus University and the energy trading company Danske Commodities.The first part of this dissertation has the perspective of a central planner exploring the optimal system design based on simplified fundamental models of the European electricity system. The aim is to determine the optimal locations and capacities of renewable generation sources while keeping the system reliable and cost-efficient. A subsequent step is to allocate the costs associated with the investments needed for the optimal electricity system of the future. I apply power flow tracing techniques for allocation of transmission system usage, cost allocation of generation capacities as well as consumption-based carbon accounting.In the second part, the perspective is changed to that of individual investors in renewable generation technologies, specifically wind turbines. I apply econometric models in the form of copulas to jointly model wind power production and power spot price. The goal is for an energy trading company to minimize the risk associated with long-term wind power purchase agreements, which, in turn, minimizes the risk of investors in these wind turbines. This provides additional incentives for similar investments and thereby increasing the share of renewable power generation in the European electricity system.Applying physical and financial models to different aspects of the European electricity system has led to insights on the differences between the two modeling perspectives. The central planning perspective is useful when exploring pragmatic solutions to the overall design of the European electricity system of the future, but provides no guidance for the individual actors in the system. In contrast, an investor in renewable generating assets focuses on a set of business goals with little regard to their impact on the overall electricity system.The link between the two perspectives is the policy makers, who regulate the electricity system. The results from system models using the central planning perspective can be used by the policy makers as guidelines to provide the right incentives for investors, and other actors in the system, such that the current European electricity system develops towards the optimal and sustainable system of the future

Cross-trained workforce planning models

Cross-training has emerged as an effective method for increasing workforce flexibility in the face of uncertain demand. Despite recently receiving substantial attention in workforce planning literature, a number of challenges towards making the best use of cross-training remain. Most notably, approaches to automating the allocation of workers to their skills are typically not scalable to industrial sized problems. Secondly, insights into the nature of valuable cross-training actions are restricted to a small set of predefined structures. This thesis develops a multi-period cross-trained workforce planning model with temporal demand flexibility. Temporal demand flexibility enables the flow of incomplete work (or carryover ) across the planning horizon to be modelled, as well as an the option to utilise spare capacity by completing some work early. Set in a proposed Aggregate Planning stage, the model permits the planning of large and complex workforces over a horizon of many months and provides a bridge between the traditional Tactical and Operational stages of workforce planning. The performance of the different levels of planning flexibility the model offers is evaluated in an industry motivated case study. An extensive numerical study, under various supply and demand characteristics, leads to an evaluation of the value of cross-training as a supply strategy in this domain. The problem of effectively staffing a pre-fixed training structure (such as the modified chain or block) is an aspect of cross-training which has been extensively studied in the literature. In this thesis, we attempt to address the more frequently faced problem of ‘how should we train our existing workforce to improve demand coverage?’. We propose a two-stage stochastic programming model which extends existing literature by allowing the structure of cross-training to vary freely. The benefit of the resulting targeted training solutions are shown in application using a case study provided by BT. A wider numerical study highlights ‘rules-of-thumb’ for effective training solutions under a variety of characteristics for uncertain demand

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Essays on Climate Change Computable General Equilibrium Models

학위논문 (박사)-- 서울대학교 환경대학원 : 환경계획학과, 2013. 8. 홍종호.This research reviews the problems of conventional computable general equilibrium (CGE) models which are widely used for climate change policy analysis. To solve the problems, it proposes multivariate distribution approach as an alternative way of representing the production activities in model structures and assesses the possibility of its practical employment. In the first part of this research, the basic characteristics of three well known global CGE models are reviewed and production function structures are pointed out as the main sources of the differences in carbon emission projections among models. Two experiments are introduced regarding the effects of changes in production function structures. In one experiment, the nested structure of constant elasticity substitution (CES) functions is substituting with alternative nesting structures. In another experiment, fixed input structures are partly applied for incorporating bottom-up approach with top-down mechanism of CGE models. The results show that these structural changes cause a considerable impact on the prediction results of greenhouse gas emissions and carbon prices. Also, the experiments are extended to the comparison of GDP losses among different model structures. Simulations for the case of Korea reveal that the estimations of GDP loss differs among model structures, raising some issues on applying them into practical policy making. In the second part, the performance of a global CGE model is analyzed in marginal abatement cost estimation when data disaggregation is applied. Extraordinary carbon prices are reported for the case of relatively large share of capital in the economies of a few developing countries. Empirical evidence indicates that the abnormal phenomenon is accounted for by the proportional relationship between capital intensity and carbon price. The analysis is extended to CES functions with a numerical analysis, concluding that the unusual phenomena may be connected to distribution parameters of CES functional forms which are most widely used in CGE models. In the last part, multivariate distribution approach is applied for an alternative description of energy related production activities. Applying theories on the microfoundations of aggregate production functions, it is shown that a set of bottom-up microscopic information can converge to specific aggregate production functions if assumptions are imposed on the statistical distribution of local production technologies. The actual characteristics of statistical distributions were reviewed for a real dataset of energy intensive manufacturing sector of Korea. To facilitate simulations and conveniently reproduce the relationships embedded in multivariate joint distribution maps, a statistical tool called copulas is introduced in advance. After the basic theory of copulas is briefly introduced, the performance of a copula model is investigated, revealing that a copula model is successful in describing heterogeneous microscopic information. After the introduction of copulas, a new type of CGE model is applied, in which an aggregation of local Leontief production functions takes over the role of conventional global production functions. A pilot model is composed to apply this scheme to a CGE model and it is shown that this new approach has some advantages: it eliminates the effect of the past time data and improves the precision of projection results.Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i I. Overall introduction . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview and outline . . . . . . . . . . . . . . . . . . . . . 3 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6 II. Structural differences between global climate change CGE models. . . . . 9 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Reviews on global CGE models . . . . . . . . . . . . . . . 14 2.2.1 Models . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.2 Static structure . . . . . . . . . . . . . . . . . . . . 18 2.2.3 Dynamic process . . . . . . . . . . . . . . . . . . . 25 2.3 Model structure analysis . . . . . . . . . . . . . . . . . . . 27 2.3.1 Change in energy-capital bundle structures . . . . . 29 2.3.2 Replacement with fixed input structures . . . . . . . 36 2.4 Policy implications . . . . . . . . . . . . . . . . . . . . . . 43 2.4.1 Carbon price . . . . . . . . . . . . . . . . . . . . . 43 2.4.2 Estimation of GDP change . . . . . . . . . . . . . . 46 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 48 III. Carbon prices and parameter calibration in CES function structures. . . 51 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.2 Problems in regional disaggregation . . . . . . . . . . . . . 54 3.2.1 Derivation of MACC using the EPPA model . . . . . 54 3.2.2 Regional deviations in carbon price . . . . . . . . . 61 3.3 Mathematical analysis . . . . . . . . . . . . . . . . . . . . 67 3.3.1 Ratio of capital intensity . . . . . . . . . . . . . . . 67 3.3.2 Extensions to the CES function . . . . . . . . . . . 74 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 78 IV. The statistical distribution approach for a description of production activities . . 81 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.2 Functional forms and data distribution . . . . . . . . . . . . 87 4.2.1 Microfoundations of production functions . . . . . . 87 4.2.2 Data analysis . . . . . . . . . . . . . . . . . . . . . 96 4.2.3 Dependence representation of the CES function . . . 103 4.3 The copula model . . . . . . . . . . . . . . . . . . . . . . . 106 4.3.1 Copula theory . . . . . . . . . . . . . . . . . . . . . 107 4.3.2 Construction of a copula model . . . . . . . . . . . 109 4.3.3 Performance of the copula model . . . . . . . . . . 113 4.3.4 The copula model with data disaggregation . . . . . 119 4.4 The statistical distribution approach . . . . . . . . . . . . . 128 4.4.1 Set of firms . . . . . . . . . . . . . . . . . . . . . . 128 4.4.2 Properties of cost functions . . . . . . . . . . . . . . 131 4.4.3 Elasticity of substitution . . . . . . . . . . . . . . . 137 4.5 Application of the distribution approach to CGE models . . 144 4.5.1 The pilot CGE model . . . . . . . . . . . . . . . . . 144 4.5.2 Projection results . . . . . . . . . . . . . . . . . . . 148 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 I. The structure of the pilot CGE model . . . . . . . . . . . . . 171 II. Source code . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185Docto

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio