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Since the 1970s, Brazil has carried out the most successful world program of commercial biomass for use and production of energy by stimulating its sugarcane industry and promoting the large-scale production of ethanol nationwide in response to the first oil shock. Today, the technologies behind ethanol production are well established. Brazil is the world's largest sugarcane supplier, producing its ethanol at a competitive price. If other sugarcane producing countries decided to join Brazil's move toward the production of this biofuel, what impacts might there be for each country's economy and employment? This is what we investigate in this paper. Prime candidates for ethanol production include Australia, China, Colombia, India, Indonesia, Mexico, Pakistan, the Philippines, Thailand, and the United States of America. We evaluated the potential socioeconomic impacts of developing this promising industry by using an input–output approach. More specifically, we adapted the Brazilian method of producing ethanol to these countries' distinct economies. We augmented the input–output table of each country, inserting a new ethanol industry based on the Brazilian ethanol production model. We also augmented their new ethanol industry's sales following a hypothetical hydrous and anhydrous ethanol consumption scenario. Thereafter, we reconcile the national accounts, concluding our analysis by quantifying and comparing the different net effects of this new industry for each of the assessed countries for the year of 2009. We demonstrate which industries would be expected to be positively or negatively impacted by this substitution in each country; and find that not all of the countries we assessed would experience positive socioeconomic results from emulating Brazil's production of ethanol. [ABSTRACT FROM AUTHOR]

The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. [ABSTRACT FROM AUTHOR]

Research on the development of sustainable supply chain models is highly active nowadays. Merging the concept of supply chain management with sustainable development goals, leads to simultaneous consideration of all economic, environmental and social factors. This paper addresses the design of a sustainable closed-loop supply chain including suppliers, manufacturers, distribution centers, customer zones, and disposal centers considering the consumption of energy. In addition, the distribution centers play the roles of warehouse and collection centers. The problem involves three choices of remanufacturing, recycling, and disposing the returned items. The objectives are including the total profit, energy consumption and the number of created job opportunities. As far as we know, these objectives are rarely considered in a sustainable closed-loop supply chain model. The proposed model also responds to the customer demand and also addresses the real-life constraints for location, allocation and inventory decisions in a closed-loop supply chain framework. Another novelty of this research is to develop a set of efficient Lagrangian relaxation reformulations and fast heuristics for solving a real-world numerical example. The results have revealed that the obtained solution is feasible and the developed solution algorithm is highly efficient for solving supply chain models. Finally, a comprehensive discussion is provided to highlight our findings and managerial insights from our results. [ABSTRACT FROM AUTHOR]

To address the gap in energy performance studies regarding the role of information entropy in feedback processes between input and output slacks, the paper develops a novel Stochastic-Entropic Analysis for Ideal Solutions (SEA-IS) model. The non-linear stochastic optimization model is then applied to assess the potential information gains that may arise from energy slacks minimization given the different optimal reduction quantiles in US states. The model relies on Beta distributed priors to model the odds-ratio of learning feedback and takes advantages of numerous strengths present in DEA and TOPSIS approaches for performance management. Machine learning methods are also employed to predict information gains in terms of contextual variables. We find that California is the only U.S. state that has indicate strong mutual information feedback and continuous improvements in efficiency. The results indicate there is ample scope for harnessing the power of information gains in improving energy efficiency, particularly in 37 U.S. states, which indicates scope for a public–private partnership to achieve this goal. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the temporal dependence in energy prices and demand using daily data of Portugal and Spain over the period 2007–2017. The methodology used is based on a stochastic Hidden Markov Model and the results indicate first that all significant relationships between energy prices and demands were found to be positive; second, spot prices are only time dependent on future prices and spot energy, while future energy is solely time dependent on spot energy behavior; third, future prices are not only autocorrelated but also time-dependent with spot energy and future energy demands level; and finally, spot energy is autocorrelated and time-dependent with future prices and future energy. Policy implications of the results obtained are presented at the end of the article. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is an analytical method for measuring, benchmarking, and improving efficiency performance. While DEA has traditionally been a deterministic ex-post approach, when operational data are not available, it can be positioned to represent an ex-ante perspective, if it is feasible to simulate production behaviors. However, there are two limitations of ex-ante DEA. First, is the representation of uncertainties associated with the contextual conditions and their impact on production. Second, is the inability to incorporate risk preferences that relate to the organizations' attitude towards production uncertainty. This paper addresses these two gaps through a method that integrates Monte-Carlo simulations, the Subjective Expected Utility Theory, and ex-ante DEA. Our method allows to explicitly model and incorporate the uncertainties associated with the contextual variables and the production behavior, while facilitating the representation of risk preferences. We demonstrate our method through a case study that is framed around a hypothetical community evaluating Micro-Grid (MG) design alternatives. We utilize the National Renewable Energy Lab data to model the stakeholder energy demand and environmental uncertainties; and use the engineering specifications for the MG technology to populate the production possibility set. We then investigate the distribution of efficiency scores by using various stakeholder risk attitudes and find that DEA rankings are insensitive to risk preferences. The proposed method and the documented risk insensitivity are significant contributions as they allow to reposition ex-ante DEA as a nonparametric alternative to multi-criteria decision-making, which can be generalized across different application areas, including uncommon DEA applications such as engineering. [ABSTRACT FROM AUTHOR]

Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community. [ABSTRACT FROM AUTHOR]

The fiercely competitive business environment may require the manufacturing layouts to be modified across the entire planning horizon owing to addition or deletion of new/existing products, machines or processes. The existing layout may not be appropriate for the next time periods as product combination and part demand tend to vary under multi-time scenario. Also the increased awareness of environmental concerns and paucity of vital resources like electric energy has led organizations to rethink about their manufacturing strategies and design layouts which are both cost effective as well as environmentally sustainable. An appropriately planned layout not only helps in reduction of material handling distance but can also greatly contribute to enhancement of the energy efficiency of manufacturing systems and contribute to resource productivity and sustainable value creation. To address these issues in the facility layout design, this paper models a dynamic cellular facility layout problem incorporating the sustainability aspect by considering the minimization of net electric energy consumption along with material handling and rearrangement costs. The model presented in this work is a mixed integer non-linear program. The model aims to minimize the aggregated cost of overall material handling for both the inter and intra-cell movements simultaneously. Additionally the model also minimizes the net electrical energy consumption across the entire time horizon. Twenty five data sets corresponding to varying combinations of machines, time periods and cells have been taken from extant literature to validate the proposed model. LINGO 10 optimization software has been used to solve the proposed model. However, due to NP-Hard nature of cellular facility layout problem, the proposed model is computationally difficult to be solved in reasonable time using LINGO 10, particularly, for layouts pertaining to larger dimensions. To overcome these complexities, a meta-heuristic based on simulated annealing (SA) is also employed to solve the model. It is discerned from the experimental results that LINGO is not able to optimally solve the model whereas the SA optimally solves the model for larger dimensions as well in reasonable computational time. [ABSTRACT FROM AUTHOR]

China has launched energy quota right trading system, as one of the sustainable operations, since 2016 to cap the total energy consumption, however the economic and energy saving effect of this new policy is unclear. Based on the input and output panel data of China's 29 manufacturing sub-industries, this paper constructs the non-parametric optimization model under the command-and-control and the energy quota right trading scenario, to compare their potential economic gains and energy savings of these two polices for Chinese manufacturing industries. Results show that, whether at overall level or at the sub-industries level, the economic potential and energy-saving potential from energy quota right trading are higher than those from command-and-control. However, a part of energy-saving potential will be squeezed out due to the negative externality of the energy market trading. We suggest the well integration of both command-and-control and the market mechanism to achieve a Win–Win development. [ABSTRACT FROM AUTHOR]

Chinese industry has developed greatly since China implemented its 'reform and opening-up' policy in 1978. With the rapid development of industry, the problems of growing energy consumption and environmental pollution are drawing increasing attention from government managers and scholars. This paper divides industrial systems into two stages, an energy utilization stage and a pollution treatment stage, for accurately evaluating the total-factor energy efficiency as well as the overall efficiency. We build a new two-stage data envelopment analysis model with shared inputs to open the 'black box' of efficiency measurement in traditional energy efficiency methods. Applying the model to data for Chinese regions, we can display the advantages and disadvantages of these two stages of industry. The results show that (1) the performance of Chinese industry improved during the years 2006-2010; (2) the energy utilization stage performance was better than that of the pollution treatment stage, but the gaps reduced year by year; and (3) energy efficiency increased during this period. Based on these results, some policy recommendations are given. [ABSTRACT FROM AUTHOR]

Recovery energy has been one of important components of energy consumption structure in China. Recovery energy is a type of secondary energy, which is generated from various sources. Since the estimation of recovery energy from some sources such as coke oven gas and other coal gas contains some uncertainties, recovery energy can be represented as an interval variable. The extant studies in the literature ignore recovery energy in the evaluation of energy efficiency or carbon dioxide ( $$\text {CO}_{2}$$ ) emissions efficiency. As a part of energy supply in China, recovery energy may influence regional primary energy consumption and $$\text {CO}_{2}$$ emissions significantly. In this context, this paper proposes an interval slacks-based measure approach to evaluate energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in the presence of recovery energy in China. In the described approach, the indicator of $$\text {CO}_{2}$$ emissions is incorporated based on the weak disposability assumption, and recovery energy is modeled as a dual factor. The optimal exact data on recovery energy of each region can be obtained. Based on the proposed approach, the measures of energy saving potential and $$\text {CO}_{2}$$ emissions reduction potential are derived. Findings resulting from the model application show that there are great disparities in regional efficiencies, and the inefficiency of Chinese regions is largely driven by those inefficient regions in the central and western areas. Notably, the development of recovery energy can help to improve regional energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in China. [ABSTRACT FROM AUTHOR]

Total-factor energy efficiency (TFEE) assessment has received increasing attention in both operations research and energy economics communities. Earlier TFEE studies implicitly assume that production activity lies in the economic area, which precludes the possibility that the production activity lies in the non-economic area and thus suffers from congestion. This paper contributes to develop TFEE index by taking into account congestion effect. It starts with the definition of congested production technology, based on which several data envelopment analysis models are proposed to construct TFEE index with congestion. The models for quantifying energy inefficiency caused by congestion effect are also developed. We apply the proposed index to evaluate the energy efficiency performance of Chinese industrial sectors at province level in 2010-2012. It is found that TFEE with congestion can yield useful insights about the choice of proper ways to achieve energy efficiency improvement. A comparison with the empirical results under congestion-free production technology indicates that ignoring congestion effect may lead to significantly different TFEE scores when congestion effect does exist. [ABSTRACT FROM AUTHOR]

In data envelopment analysis (DEA), it is usually assumed that all data are continuous and not restricted by upper and/or lower bounds. However, there are situations where data are discrete and/or bounded, and where projections arising from DEA models are required to fall within those bounds. Such situations can be found, for example, in cases where percentage data are present and where projected percentages must not exceed the requisite 100 % limit. Other examples include Likert scale data. Using existing integer DEA approaches as a backdrop, the current paper presents models for dealing with bounded and discrete data. Our proposed models address the issue of constraining DEA projections to fall within imposed bounds. It is shown that Likert scale data can be modeled using the proposed approach. The proposed DEA models are used to evaluate the energy efficiency of 29 provinces in China. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are autonomous wireless systems consists of a variety of collaborative sensor nodes forming a self-configuring network with or without any pre-defined infrastructure. The common challenges of a WSN are network connectivity, node mobility, energy consumption, data computation and aggregation at sensor nodes. In this paper we focus on intermittency in network connectivity due to mobility of sensor nodes. We propose a new mathematical model to capture a given entire WSN as is with intermittency introduced between the communication links due to mobility. The model involves open GI/ G/1/ N queuing networks whereby intermittency durations in communication links are captured in terms of mobility models. The analytical formulas for the performance measures such as average end-to-end delay, packet loss probability, throughput, and average number of hops are derived using the queuing network analyzer and expansion method for models with infinite- and finite-buffer nodes, respectively. For models with 2-state intermittency, we analyze the performance measures by classifying these models into three types: namely, model with intermittent reception, model with intermittent transmission and/or reception, and model with intermittent transmission. We extend the analysis to multi-state intermittency models. We demonstrate the gained insight of WSNs through extensive numerical results. [ABSTRACT FROM AUTHOR]

China becomes the largest energy consumer in 2010 but its energy productivity is well below the world average. To meet China's fast growing energy using, energy efficiency should be especially emphasized under China's energy policy. This paper focuses on the regional level of energy efficiency change in China. And we analyze total factor energy efficiency for 30 Chinese provinces over the period 1998-2009 using Malmquist index method and Tobit analysis. The Malmquist estimation results suggest there is a dropping change trend of energy productivity growth. Chinese energy efficiency still faces with huge regional disparity, but the energy technical efficiency reflects convergence in the nationwide and west region. As a result of Tobit regression, we find that industrial structure, energy consumption structure and institutional factor have different influences on energy efficiency. [ABSTRACT FROM AUTHOR]

The contradiction between economic development, resources and environment is becoming more and more important every day. Based on the proposed energy conservation, this paper will study whether reducing energy consumption hinders economic growth. This article considers the spatial heterogeneity factors and the different energy consumption levels between different areas. Through empirical analysis, we discuss the quantitative relationship between energy consumption and economic growth from different areas. Finally, we propose long- and short-term strategic arrangements for energy consumption in order to facilitate sustainable development. [ABSTRACT FROM AUTHOR]

In this paper, we review the energy efficiency in China, and the following topics are discussed. (i) Some useful empirical analyses applied on the energy efficiency based on some expanded models, (ii) the relationship among environment, energy and economic growth, (iii) some important environmental efficiency evaluation indexes, and (iv) some latest researches on the emission system. Finally, some policy recommendations are made. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of a large number of unattended sensors with limited storage, battery power, computation, and communication capabilities, where battery power (or energy) is the most crucial resource for sensor nodes. The information sensed by sensors needs to be transmitted to sink quickly especially for the applications with delay restriction. However, it is difficult to achieve optimal energy efficiency and source-to-sink delay simultaneously. So it is very necessary to find a power control solution based tradeoff between energy and delay. In this paper, a cooperative differential game model is proposed, and a power solution is obtained which determines a fair distribution of the total cooperative cost among sources. [ABSTRACT FROM AUTHOR]

We consider a problem where different classes of customers can book different types of services in advance and the service company has to respond immediately to the booking request confirming or rejecting it. Due to the possibility of cancellations before the day of service, or no-shows at the day of service, overbooking the given capacity is a viable decision. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as the cost of overtime. For the calculation of the latter, information of the underlying appointment schedule is required. Throughout the paper we will relate the problem to capacity allocation in radiology services. Drawing upon ideas from revenue management, overbooking, and appointment scheduling we model the problem as a Markov decision process in discrete time which due to proper aggregation can be optimally solved with an iterative stochastic dynamic programming approach. In an experimental study we successfully apply the approach to a real world problem with data from the radiology department of a hospital. Furthermore, we compare the optimal policy to four heuristic policies, of whom one is currently in use. We can show that the optimal policy significantly improves the currently used policy and that a nested booking limit type policy closely approximates the optimal policy and is thus recommended for use in practice. [ABSTRACT FROM AUTHOR]