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Food, energy, and water (FEW) systems have been recognized as an issue of critical global importance. Understanding the mechanisms that govern the FEW nexus is essential to develop solutions and avoid humanitarian crises of displacement, famine, and disease. The U.S. and China are the world's leading economies. Although the two nations are shaped by fundamentally different political and economic systems, they share FEW trajectories in several complementary ways. These realities place the U.S. and China in unique positions to engage in problem definition, dialogue, actions, and transdisciplinary convergence of research to achieve productive solutions addressing FEW challenges. By comparing the nexus and functions of the FEW systems in the two nations, this perspective aims to facilitate collaborative innovations that reduce disciplinary silos, mitigate FEW challenges, and enhance environmental sustainability. The review of experiences and challenges facing the U.S. and China also offers valuable insights for other nations seeking to achieve sustainable development goals.

Asia-Pacific (APAC) has been the world’s most dynamic emerging area of economic development and trade in recent decades. Here, we reveal the significant and imbalanced environmental and socio-economic effects of the region’s growths during 1995–2015. Owing to the intra-regional trade of goods and services, APAC economies grew increasingly interdependent in each other’s water and energy use, greenhouse gas (GHG) and PM2.5 emissions, and labor and economic productivity, while the environmental and economic disparity widened within the region. Furthermore, our results highlight APAC’s significant role in globalization. By 2015, APAC was engaged in 50–71% of the virtual flows of water, energy, GHG, PM2.5, labor, and value added embodied in international trade. While the region’s final demand and trade grew less resource- and emissions-intensive, predominantly led by China’s transformations, APAC still lags behind global averages after two decades. More joint efforts of APAC economies and attention to sustainable transformation are needed., The environmental and socio-economic implications of the growth in welfare and trade in Asia-Pacific (APAC) remain unclear. Here the authors show that over the past two decades (1995–2015), owing to intraregional trade, the APAC economies have grown increasingly interdependent in natural resource use, air emissions, and labor and economic productivity.

Smart logistics is an effective way to meet the challenges of fast-changing customer expectations, take opportunities brought by new technologies, and facilitate new business models. This review pa...

Although hexagonal boron nitride (h-BN) has recently been identified as a highly efficient catalyst for the oxidative dehydrogenation of propane (ODHP) reaction, the reaction mechanisms, especially regarding radical chemistry of this system, remain elusive. Now, the first direct experimental evidence of gas-phase methyl radicals (CH3 . ) in the ODHP reaction over boron-based catalysts is achieved by using online synchrotron vacuum ultraviolet photoionization mass spectroscopy (SVUV-PIMS), which uncovers the existence of gas-phase radical pathways. Combined with density functional theory (DFT) calculations, the results demonstrate that propene is mainly generated on the catalyst surface from the C-H activation of propane, while C2 and C1 products can be formed via both surface-mediated and gas-phase pathways. These observations provide new insights towards understanding the ODHP reaction mechanisms over boron-based catalysts.

Additive manufacturing (AM) is a technology that creates parts directly from 3D CAD files based on a layer-by-layer manufacturing process. Quality and productivity are the two key concerns in powder-bed AM processes, which are one of the most widely used AM in the industry. Because of the keyholes and porosity formation occurring in the object’s structure during a printing process, quality is considered a difficult factor to manage. On the other side, focusing only improving quality could result in higher building cost and inefficient printing operations. Thus, a trade-off between these two concerns is vital for maintaining competitiveness of AM. To the best of our knowledge, there is no study in the literature that has yet considered this trade-off in a systematic way and can provide optimal results for productivity based on a desired quality level. This study combined equations from previous studies in a systematic way to create an empirical model to optimize major process parameters, including laser power, scan speed, layer thickness, hatch distance, and laser spot size for a trade-off between quality and productivity in powder-bed AM processes. The combined and individual effects of all process parameters were analyzed in a numerical experiment to show their significance for the printing process. The case study also showed that the proposed empirical optimization model yielded lower building costs than those from similar studies in the literature and is effective on maximizing productivity at the desired quality level.

The shuttle-based storage and retrieval system (SBS/RS) is composed of a shuttle sub-system that is responsible for horizontal movements and a lift sub-system that is responsible for vertical movements. As the combination of the two sub-systems yields high flexibility, low operating cost and large storage capacity, SBS/RS is becoming more and more popular, but also raises managerial issue of how to coordinate the shuttle and the lift. Based on its operational processes, this study models the tier-to-tier SBS/RS system as a semi-open queuing network (SOQN). By removing or adding the synchronization nodes, the SOQN is further transformed into two different closed queuing networks (CQNs), and applies the approximate mean value analysis (AMVA) algorithm to solve the model and estimate its performance. The system performance is measured by the utilization of shuttles, the utilization of lift, and the task cycle time, under various design configurations. Simulation is carried out to validate the effectiveness of the analytical model and algorithm. Compared with the simulation results, the established semi-open queuing network can accurately estimate task cycle time for different rack configurations. The proposed solution method can help to identify the optimal number of shuttles and guide the design of the SBS/RS system.

This study explored the national hydrogen refueling infrastructure requirement along major United States (US) interstate highway corridors to support the deployment of fuel cell electric trucks (FCETs) for the national long-haul trucking fleet. Given the long-haul trucking shipment demand in 2025 projected by the Freight Analysis Framework, locations and capacities of hydrogen stations were identified for inter-zone freight flows, and the total daily refueling demand was estimated for intra-zone flows for each FAF zone. Based on the infrastructure deployment results, we conducted an economic feasibility analysis of FCETs by evaluating the total ownership cost. We found that when the FCET penetration is relatively high (e.g., 10% penetration), FCETs become more competitive in terms of fuel cost and idling cost and could be economic viable if the incremental vehicle cost is reduced to meet the near-term FCET technology cost targets and the liquefaction cost is reduced to an optimal case. We also observed that the station cost depends on regional factors, particularly regional demand, which is used to determine station capacity. Thus, one possible strategy for station roll-out is to have early investment in target regions where station costs are expected to be relatively low such as the Pacific and West South Central regions.

Due to the complex outsourcing characteristics of aviation equipment development, one key issue is how to coordinate quality and cost decisions during development. This article focuses on the quali...

This study quantified the significance of assuming heterogeneity in consumer valuation of fuel economy on the estimated compliance with fuel economy standards, with two approaches. One is mathematical derivation on how the heterogeneity affects the estimated market acceptance of high-efficiency vehicles. The other is a consumer choice based simulation method to analyze the heterogeneity impacts on the compliance with the fuel economy standards for the entire light duty vehicle fleet in the United States. Our findings suggest that the direction of the impact of heterogeneity assumption depends on market positions of high-efficiency vehicles: (1) with the “Likely-Accept” condition, where high-efficiency vehicles sell well and higher achieved fuel economy is expected, the heterogeneity assumption can add additional risks to the compliance; and (2) with the “Likely-Reject” condition where the fuel economy standards may have difficulty in compliance, the heterogeneity can reduce the risks and improve the compliance outcome. It is concluded that consideration of fuel economy valuation heterogeneity is important in fuel economy standard analysis. It is hoped that such consideration may provide a framework to reconcile the different views on consumer valuation of fuel economy.

This paper discusses the industrial potential for waste heat recovery (WHR) in harsh environments – defined as a waste heat stream having either a temperature of at least 650 °C or containing reactive constituents that complicate heat recovery. The analysis covers five industries (steel, aluminum, glass, cement, and lime), chosen based on volume of production, discharge of exhaust gases containing components that present harsh environments, possibility of recovering considerably more heat than currently recovered, and current lack of acceptable WHR options. The total potential energy savings identified in harsh environment waste heat streams from these industries is equal to 15.4% (113.6 TWh) of the process heat energy lost in U.S. manufacturing. Existing technologies and materials for these industries are evaluated and the recoverable waste heat from harsh environment gas for each industrial sector is estimated. Finally, an in-depth summary of each waste heat source shows exactly where waste heat can be recovered and what specific issues must be addressed. The most potential lies within steel blast furnaces (46 TWh/year). Other waste heat streams considered include steel electric arc furnaces (14.1 TWh/year), flat glass (3.6 TWh/year), container glass (5.7 TWh/year), glass fiber (1.1 TWh/year), specialty glass (2.2 TWh/year), aluminum melting furnaces (4.7 TWh/year), cement (17.1 TWh/year), and lime (10.5 TWh/year). Although attempts to recover waste heat in harsh environments have been mostly unsuccessful, advances in research and technology could unlock an enormous potential for energy and cost savings.

A hybrid manufacturing/remanufacturing system (HMRS) is an effective tool to address the global challenge of resource depletion and environmental deterioration. This paper aims to make an optimal production plan for a stochastic HMRS with demand substitution. To achieve the above objective, a multi-period mixed integer programming model was first constructed. An ant colony system algorithm with random sampling method (ACS-RSM) was proposed to minimize the total expected cost of the stochastic HMRS. Finally, the proposed model and ACS-RSM algorithm were applied to an auto alternator case. The effects of the used product recovery rate and batch sizes of new and remanufactured products on the total expected cost were analyzed. The research results showed that the ACS-RSM algorithm performed well regarding computational efficiency and solution quality. There were two major findings through the practical case study. The first finding was that with increase of recovery rate of used product, total expected cost of the HMRS declined dramatically until a certain point. When the recovery rate was greater than 91%, the total expected cost kept almost constant. The second finding was that when the batch sizes of the new product and remanufactured product rose, the total expected cost had an obvious increase and the running time of the ACS-RSM algorithm decreased monotonically. The study yields an effective decision-making tool for optimizing the production plan of the stochastic HMRS with demand substitution.

Transportation planners often need to justify transportation related investments to public officials. Besides efficiency measures, the environment protection and sustainability are gaining more and more importance. In order to improve freight flow efficiency in a sustainable way, it is necessary to have a systematic tool to study the freight flow over all three major surface modes and their connections and, in turn, to help public officials identify the best way to improve freight transportation. We built a system-level intermodal simulation model that includes highways, railways, and waterways because all three modes, working together, play significant roles in freight flows. We evaluated the performance of the Louisiana freight network under different scenarios and measured the benefits of mode changes evaluating the tradeoff between environmental goals and other performance measures.

Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N–65°N, during 1971–2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0–10 cm SSI during August–November, and 0–100 cm during September–April. We further develop and apply an emergent constraint method on the future SSI’s signal-to-noise (S/N) ratios and trends under the Shared Socioeconomic Pathway 5-8.5. The results show continued significant presence of human forcings and more rapid drying in 0–10 cm than 0–100 cm. Our findings highlight the predominant human contributions to spatiotemporally heterogenous terrestrial aridification, providing a basis for drought and flood risk management.

Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

International audience; This paper studies a Robotic Mobile Fulfilment System (RMFS), featured by a number of robots lifting and transporting movables storage shelves from storage grids to order pickers. In such systems, online retailers often classify their customers by two major classes ‘expedited shipping’ and ‘standard shipping’. We build high-dimension Markov models to describe this system with customer classes, calculate the throughput of this system given the number of robots and provide design rules to determine the optimal number of robots and their capacities considering the trade-off between capacities of picker stations and robots. We verify the analytic results of Markov models with simulations. We further consider multiple-picker RMFS and study its optimal design.

While many supply chains use vendor-managed inventory (VMI) to handle deteriorating products, they employ special facilities (e.g., cold warehouses), which produce significant carbon emissions. This new operational practice motivates us to investigate the effects of carbon emission reduction on a supply chain with one manufacturer and two competing retailers for deteriorating products under VMI, which has not been fully studied in the existing literature. Carbon cap-and-trade regulation and investment in green technologies are used to curb carbon emissions generated by production and inventory holding in this system. To bench mark the performance of the decentralized system, an optimization model for the centralized system is formulated to show that there exists an upper bound on the profit penalty for decentralization and the carbon emissions of the centralized system may be lower than those of the decentralized system. A revenue-sharing contract is then proposed to improve the profit and emissions of the decentralized system. The results show that the supply chain can be coordinated perfectly when the demand depends on the manufacturer's green technology level and two competing retailers' selling prices. Numerical examples with sensitivity analysis are further provided to test the robustness of the supply chain's operational decisions.

Salary administration research mostly examines the compensation guides in a subjective manner and lacks quantitative approaches. Recent failure instances of salary administration demonstrate the ex...

Energy modeling and efficiency analysis are considered the foundation of manufacturing process optimization to improve quality and efficiency and reduce energy consumption and carbon emissions during aluminum die-casting processes. This paper proposed an energy modeling method to connect gas and electric energy consumption with production rate of aluminum die-casting processes based on data collected at workshops with various combination of machines and products. The detailed modeling process involved the development of a data-acquiring system and the comparison of various kinds of nonlinear regression methods. The resulting models were validated with actual production data and were further used to improve production scheduling. It was found that if the modeling results are reasonably used and production is accordingly well-scheduled, 10 to 15% of energy savings could be realized without sacrificing profits.

This paper explores both random and class-based storage policies and three shuttle dispatching rules, random, distance-based, and demand-rate based, for a tier-to-tier shuttle-based storage and retrieval system (SBS/RS). Modeling the system as a discrete-time Markov Chain, this study derived the shuttle distribution under each policy combination, and further developed the expected travel time models for both single-command (SC) and dual-command (DC) operations. The developed models can help a warehouse designer or manager to comprehensively make four decisions for SBS/RS, whether SC or DC operations should be adopted, the most appropriate storage policy, the best shuttle dispatching rule, and the number of shuttles. The travel time models can evaluate each possible policy combination quickly through multiple criteria, such as the capacity, customer responsiveness, and costs of shuttles. Numerical experiments showed significant impacts of the policy combination on the expected travel time. Class-based storage, if implementable, is always better than random storage under each combination of shuttle dispatching rule and operational mode (i.e., SC or DC). Under random storage and SC operations, the distance-based shuttle dispatching rule is always better than the random rule, and their difference is concave in the number of shuttles. When classed-based storage is adopted and the number of shuttles is small, the demand-rate-based rule is the best under SC operations. Its benefit becomes more obvious when demand rates become more heterogeneous until some point but decreases as the system has more shuttles. Under DC Operations, depending on equipment speeds, the distance-based shuttle dispatching rule can be worse than the random rule under random storage. Under class-based storage and DC operations, the demand-rate-based rule is still the best when demand rates are heterogeneous. The differences between SC and DC operations are more significant when demand rates are more homogenous. When a system wants to be more responsive to customers, it should be run under SC operations. If the overall capacity is of interest, DC operations are much better.

Remanufacturing is a critical tool for solving resource depletion and environmental deterioration by reducing raw material and embedded energy consumption. The Chinese government, like many other countries, has been keenly interested in remanufacturing during the past ten years, because of its potential material and energy usage reductions. However, the development of the remanufacturing industry has been unsatisfactory because of little knowledge of its key industry indices. Many potential customers of remanufactured products do not trust the quality of remanufactured products. The major objectives of this paper are to classify the key industrial indices of the remanufacturing industry and to suggest future directions for improvements. To achieve these objectives, this paper used a combined method of grey incidence analysis and grey clustering, which are suitable for distinguishing critical industry indices that can measure the status of development situation of the remanufacturing industry. This approach was successfully applied to a case in Jiangsu Province, China, and consequently developed useful governmental and industrial policy recommendations for remanufacturing enterprises to promote that industry. The research results identified the “total number of employees with a master's degree or above”, the “total number of engineering and technical staff” and the “number of intermediate technicians” as the most appropriate industry indices to reflect the development situation of the remanufacturing industry in China. There are two more important findings. The first is that remanufacturing talents are the most important internal impetus of the remanufacturing industry and should be paid more attention to. The second is that investment in the remanufacturing industry does not have a strong positive correlation with industry characteristics. The “annual revenue of the remanufacturing industry” and huge investment have not played an effective role in promoting this industry so far. The main contribution of this paper is to provide an effective decision-making tool for defining key industry indices that have an important effect on the remanufacturing industry. The tool provides decision-makers a scientific basis for developing and implementing appropriate industry policies and improvement measures to expand the size and scope of the remanufacturing industry in the future.

Advanced manufacturing uses emerging technologies to critically enhance not only the economic competitiveness of individual manufacturers but also the sustainability of the whole industrial sector. New materials and technologies require new manufacturing processes and novel analytical models for process controls and parameter optimization regarding cost, reliability, quality, product flexibility, energy consumption, and fossil carbon emissions. The successful adoption of advanced manufacturing for sustainability can only be realized by following a systematic approach from concept development, product design and manufacturing to product delivery and service as well as in forward and reverse supply chain management. This special volume reports on progress of advanced manufacturing on sustainability improvements along the whole life cycle and covers the six themes: 1. Design theory and methodology for sustainability with advanced manufacturing; 2. Energy efficiency assessment and control of mechanical manufacturing systems; 3. Parameter optimization for advanced manufacturing and remanufacturing; 4. Low fossil-carbon process planning and production scheduling; 5. Integration of supply-chain innovations and advanced manufacturing; 6. Sustainable innovation for product-service systems. In addition, this SV introductory article, highlights future research directions, such as the need for energy consumption and emission data for advanced manufacturing processes, optimization models and control schemes, supply chain innovations, and product-service integration.

Remanufacturing is widely considered a solution to environmental pollution and resource shortage but its progression is influenced by Original Equipment Manufacturers (OEMs), remanufacturers, consumers and public policy makers. This research studies the optimal price and production quantity of new and remanufactured products with a two-stage model under both scenarios under which the remanufacturing is conducted by the OEM or by the third-party remanufacturer (TPR). A non-cooperative game model is used at the second stage when the TPR conducts remanufacturing. The models consider both the impact of the product life cycle through the market growth factor at the second stage and the influence of consumers’ perceived value of new and remanufactured products, which depends on the identity of the remanufacturer. Both theoretical and numerical analyses show that both the life cycle phase and consumers’ perception have an impact on the OEM’s decision whether to allow the TPR in the remanufacturing business. The results also reveal that the market may not be efficient for maximizing the environmental sustainability and societal equity. Governments may need to interfere with the market in order to promote the TPR manufacturing, especially when the product is at the mature or declining phases of its life cycle.

Cutting parameter optimization of machining processes is crucial for green manufacturing and needs to take energy consumption, cutting force and processing time into consideration. This paper presents a method to optimize machining parameters considering the trade-off between environmental concerns and economic objectives. The model for all three objectives of energy consumption, cutting force, processing time and their relationships with machining parameters is established based on theoretical analysis, experiment design, and statistical regression to obtain Pareto fronts. Various algorithms determining strategies, including sharing function approach, VEGA, NSGA-Ⅱ and MOEA/D, are used to study the Pareto front. Examples of a cylindrical turning and a face milling are used to conduct relative validation experiments to evaluate the proposed method and the computational performance of all algorithms. All of the experiments were conducted on a CK6153i lathe and an XHK-714F CNC machining center cutting C45E4 carbon steels. Results demonstrate that the proposed method is effective in finding trade-off among the three objectives and obtaining reasonable application ranges of machining parameters from Pareto fronts.

Reasonable and effective use of renewable resources can reduce dependence on traditional fossil-based energy sources and reduce carbon emissions. This study mapped the spatial potentials of renewable resources (i.e., solar radiation, precipitation, wind, and geothermal resources) in China. The results showed that China's most abundant renewable resources are located in the southwestern regions, which are significantly different from the spatial distribution patterns of population and economic development. Four southwestern provinces (Tibet, Qinghai, Sichuan, and Yunnan) make up only 7% of the national gross domestic product (GDP) and 30% of the national land area but possess 58% of the renewable resources. We found a weak to moderate degree of negative correlation between the emergy density of renewable resources and GDP per capita on the administrative levels of the prefecture-level cities for the whole country and in its eastern, central, and western regions. This means that the socioeconomically underdeveloped Midwest has more abundant renewable resources. A distributed energy-economic system may help to bridge the regional gaps of renewable sources in China. These findings can support policy decisions for the better development and use of renewable resources in China.

International audience; We consider supply chain (SC) contracts in a new setting, the medical equipment industry, where concern for patient benefits is essential and quality efforts are critical for profits compared with supply chains (SCs) in other industries. It remains unclear how quality efforts and patient concern levels affect SC performance and how medical equipment manufacturers’ quality effort levels are linked to their patient concern levels. This study focuses on the impact of a manufacturer's and a retailer's patient concern levels on optimal pricing and quality decisions in an SC consisting of a manufacturer facing quality effort-dependent demand and a retailer in the medical equipment industry. We use the Stackelberg game to characterize and determine the optimal operational decisions in five scenarios and address the effects of patient concern levels under above five scenarios. A real case is studied and shows that optimized quality efforts can improve SC profits. The parameters settings are derived from the real data. Our findings bridge the gap between SC quality management and patient benefits and help to understand contract design in relation to patient concerns in different SC structures. This paper is among the earliest to investigate quality efforts for SC contract design in relation to patient concerns and to study SC contract design in the medical equipment industry. Our managerial insights are expected to help manufacturers move toward better quality effort decisions considering patient benefits and are also applicable to other SCs with effort-dependent demand and the effect of altruistic preferences.

Classification yards play a significant role in railroad freight transportation and are often considered bottlenecks for railroad networks. Based on a generic yard simulation model, the model in the presented study fits the Bureau of Public Roads function, which is widely used in highway capacity to represent the volume–dwell time relationship. The proposed analytical model incorporates major features of rail yards, such as the number and capacity of tracks in each area, the number of engines and humps, the humping speed, and the assemble rate. The model is validated by historical data from 16 classification yards of Class I railroads in the United States. The results show that the proposed model can generate precise capacity data of rail yard, as well as the dwell time of rail cars in yards. The dwell time increases sharply when the volume is greater than the capacity of a rail yard. The identified relationship may help a railroad analyze its network at the macro level and therefore improve the systemwide capacity and efficiency.

This paper investigates dynamic railcar planning in railroad classification yards. The plan decides the assignment of railcars from inbound trains to outbound trains under various size limitations of outbound trains and allows dynamic sequencing of inbound train classification and outbound train assembly. A mixed-integer program is presented for the problem along with a heuristic algorithm based on the harmony search strategy. The numerical results based on both a real-world case and randomly created instances show that dynamic sequencing can reduce total dwell time compared to static sequencing, especially when more trains are classified at a yard. However, the savings on average dwell time for each railcar become smaller when the traffic volume goes up. Once the volume through a yard is close to its capacity, the dwell time per rail car goes up very quickly and the benefit of dynamic sequencing diminishes. It implies that the benefit of dynamic sequencing is more significant for yards with lower traffic...

Automatic warehouses need to balance speed, cost, flexibility, and energy consumption to support the responsiveness, efficiency, and sustainability of modern supply chains. This paper explores environmentally friendly velocity and acceleration configurations of the Shuttle-Based Storage and Retrieval Systems (SBS/RS) under throughput requirement. This study develops an energy consumption model for the SBS/RS with cross shuttles operating on a dual-command-cycle basis and estimates the maximum throughput, travel time and energy consumption. Numerical experiments explore the optimal velocity profiles with the least energy consumption under different throughput requirements. The results show that the lift velocity and acceleration are the leading factors influencing energy consumption. Increasing the accelerations of aisle and cross shuttles can moderately reduce travel times and improve storage efficiency, without hurting energy consumption. Besides, this paper also identifies the relationship between throughput capacity and energy consumption. Overall, this study provides insights on the SBS/RS configurations under given throughput requirements and environmental performance and builds a green decision-making basis for automatic warehouse designers and managers.

Research on manufacturing scheduling has historically emphasized production efficiency. With rising environmental consciousness, manufacturing companies are paying increasing attention to energy efficiency on the shop floor. Manufacturing consumes a large amount of electricity globally. The mismatch between electric supply and demand has been a huge problem, which even becomes worse when renewable energy becomes more popular. The time-of-use (TOU) pricing policy is a widely used demand response (DR) approach, trying to align demand to supply. This paper considers energy-efficient scheduling of a single batch processing machine with non-identical job sizes and release times under a TOU electric tariff so as to simultaneously minimize total electricity cost, a criterion of environmental and energy sustainability, and makespan, a criterion of productivity. A mathematical formulation is developed to optimize electricity cost and makespan. Due to computational complexity, a hybrid multi-objective meta-heuristic algorithm is developed to find the Pareto front. Two constructive heuristics are presented to group jobs into batches. Two different approaches are presented to improve total electricity costs. The performance of the proposed model and algorithms is evaluated through extensive numerical experiments. Production managers can use the model and algorithms provided in this work to make a trade-off between productivity and sustainability.

Main objectives of this research are to investigate the effect of different power structures and product dual differences, quality difference among used products and price difference between new ad remanufactured products, on pricing policies of a multi-echelon closed loop supply chain (MCLSC) and to propose future improvement strategies. To achieve these objectives, centralized and decentralized pricing models for the MCLSC were constructed to explore optimal member pricing and optimal profit under different power structures: centralized, Manufacturer-led Stackelberg (MS), Retailer-led Stackelberg (RS) and Collector-led Stackelberg (CS) pricing models and to analyze the impact of three key parameters, collection rate, cross-price elasticity coefficient, and quality grade ratio of used products. By comprehensive theory comparisons and a case study of auto alternators, the results of theoretic analysis indicated three insights. (1) The centralized pricing model always achieves the maximum total profit of the MCLSC, but the CS model obtained a minimum total profit. (2) For three decentralized pricing models, the optimal wholesale price and optimal retail price for new product and remanufactured product all increase with the collection rate and cross-price elasticity coefficient. For MS model and RS model, however, optimal acquisition price for used product decrease with the two parameters. (3) The optimal wholesale price and optimal retail price for new product rise up with the quality grade ratio in each decentralized pricing model. There are also three important findings from the case study. (1) When the collection rate is increased, the optimal total profit shows a U- shaped curve, with a profit trap around a certain collection rate. (2) The optimal acquisition price drops slightly with the cross-price elasticity coefficient, but the optimal wholesale price and optimal retail price increase sharply. This causes a benefit conflict between MCLSC members and consumers. It is recommended that the cross-price elasticity coefficient should be carefully controlled under a certain value to relieve the conflict. (3) When the quality grade ratio increases, optimal profits for all member and the system all increase and have a shell shape. They reach approximate maximum when remanufacturing ratio was raised to 95%. Major contributions of this paper are an effective decision tool for optimal pricing decisions of the MCLSC and insights for appropriate industrial policies and enterprise strategies to enhance the performance of the MCLSC.

In this study, a detailed investigation was conducted to collect the concentrations of heavy metals—including cadmium, copper, nickel, lead and zinc—from five geographic areas to reflect the different land use and traffic conditions in Zhaoyuan, China. Analytical results of heavy metal concentrations showed that the mean and median values of these elements in soils were clearly higher than the corresponding background values, indicating pollution from the urban area. To generate a continuous surface from the collected discrete samples, Kriging interpolation was introduced to determine the spatial distribution patterns of heavy metal contamination. As a major source of urban heavy metal contamination, the traffic area was carefully analyzed. Interelemental relationships showed that most heavy metals in the traffic area had similar anthropogenic origins. In addition, the research team explored the relationships between heavy metals and traffic characteristics. The results indicated that heavy metal concentrations in roadside soils not only decreased with rising altitude and increasing distance from the trunk and branch roads on both sides but also could be affected by traffic volume. Finally, three evaluation methods—including the geoaccumulation index, the pollution index, and the improved pollution index—were introduced to assess heavy metal contamination levels fully in the traffic area. The pollution index evaluation method produced results indicating more severe levels of contamination than the other two evaluation methods.

The manufacturing industry has been focusing on calculating energy consumption because of increased sustainability and environmental consciousness, resulting in a large number of calculation models. However, evaluation on these existing models is missing, so that practitioners often have trouble selecting the right energy models. To identify the most appropriate, cost-effective, and easy-to-implement energy models, three tasks were conducted in this paper. First, various experimental or empirical energy consumption models of spindle acceleration, spindle rotation, feed and material removal, and those of machine tool during material removal processes are reviewed. Then, five evaluation criteria, applicability, accuracy, computational efforts, complexity of fitting coefficients, and ease of data collection, are proposed. These criteria are identified based on the general steps to run the calculation models. Finally, the evaluation criteria are applied with the supporting experimental data of external turning to rank the models. This work is expected to assist practitioners in selecting models to calculate energy consumption in metal cutting processes for a particular situation and purpose.

While auction is typically considered a price-determination mechanism, it could also have a significant impact on supply chain performance. This paper analyzes possible auction and market mechanisms and their expected payoffs in a two-supplier, one-buyer system with asymmetric information, where the players׳ costs are private information and each supplier׳s cost is known only probabilistically by other players. The results show that the auction mechanism could have a significant impact on the payoffs received by the players and on system efficiency. Channel coordination can be achieved with a two-part contract auction where the buyer announces a price-sensitive order function and the suppliers compete in a reverse English auction on the side payment. To achieve the coordination, a market intermediary must prevent the buyer from manipulating the order function by imposing a restriction on the retail price. Thus, the buyer truthfully transfers the market demand function after recovering her unit handling cost to the suppliers. Insights from the analysis allow us to rank different market mechanisms by their impact on expected channel efficiency and the expected payoffs for the buyer and the suppliers.

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.