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The purpose of this paper is to estimate total emission during idling of vehicles and validate emission results from real-world data.

Motor Vehicle Emission Simulator (MOVES)2010b emission model is customised for developing country like India and a case study of the Ashram intersection in Delhi has been selected in order to measure the emissions of vehicles during idling.

Results show that 3.997 mg/m3 of hydrocarbon, 1.82 mg/m3 of NOx and 17.688 mg/m3 of carbon monoxide is emitted from the cars, trucks and buses, respectively, at Ashram intersection in one day. As there are 600 intersections throughout Delhi, a total of 2,398.055 mg/m3 of hydrocarbon, 1,087.068 mg/m3 of NOx and 10,612.612 mg/m3 of carbon monoxide is emitted from cars, trucks and buses in a day in all of Delhi.

Knowledge of idling emission and fuel loss is very little for Indian traffic condition during delays.

This paper explains the modelling of emission in real world onboard measurement under local driving condition for engine size 1000cc and 600cc for motorcycles in Edinburgh. Impact of instantaneous speed, acceleration on emission have been investigated on the air quality management area (AQMA) in Edinburgh. Emission directly observed from the analyser have been converted from ppm and % unit into gm/sec by using the fuel consumption estimates and carbon mass balance equation Finally average emission factors for CO, HC, and NOX along the corridor have been estimated on time based (gm per second) and distance based (gm/km). Since emissions are primarily affected by speed, therefore a correlation between emission factors and speed have been developed. Onboard emission measurements have advantages to collect the emission data into different driving cycle i.e. vehicle operating modes (idling cruise, acceleration, and deceleration). This has been further investigated by developing the relationship between time spent in these modes and emission. These types of models are suitable, in sustainable development of transportation system, traffic demand management, signal coordination, and environment friendly application for Intelligent Transportation System (ITS).

The dispatching of trucks in earthmoving and like operations is worthy of examination because of potential emission reductions and savings through the appropriate allocation of trucks to excavators and dump sites. The paper aims to discuss this issue.

Truck dispatching is performed through linear programming (LP) and the effect of truck allocation on unit emissions and unit costs established. Number of trucks, unit cost and unit emissions are all considered as objective functions. A cut and fill operation on a road project provides a numerical case study.

It is demonstrated analytically that the minimum unit emissions solution is the same as that for minimum unit cost. Numerical results from the case study, including sensitivity analyses on the underlying parameters, support this conclusion.

The LP dispatching solution, based on minimizing truck numbers and unit costs, accordingly impacts the environment the least in terms of emissions. The paper's results will be of interest to those designing and managing earthmoving and like operations for production, cost and emissions.

While LP has been used by others to examine optimum unit cost dispatching, this paper is original in examining the dispatching or truck allocation based on both unit cost and unit emissions, and showing the relationship between the optima for both.

The purpose of this study is to compare the environmental impacts of two additive manufacturing machines to a traditional computer numerical control (CNC) milling machine to determine which method is the most sustainable.

A life-cycle assessment (LCA) was performed, comparing a Haas VF0 CNC mill to two methods of additive manufacturing: a Dimension 1200BST FDM and an Objet Connex 350 “inkjet”/“polyjet”. The LCA’s functional unit was the manufacturing of two specific parts in acrylonitrile butadiene styrene (ABS) plastic or similar polymer, as required by the machines. The scope was cradle to grave, including embodied impacts, transportation, energy used during manufacturing, energy used while idling and in standby, material used in final parts, waste material generated, cutting fluid for CNC, and disposal. Several scenarios were considered, all scored using the ReCiPe Endpoint H and IMPACT 2002+ methodologies.

Results showed that the sustainability of additive manufacturing vs CNC machining depends primarily on the per cent utilization of each machine. Higher utilization both reduces idling energy use and amortizes the embodied impacts of each machine. For both three-dimensional (3D) printers, electricity use is always the dominant impact, but for CNC at maximum utilization, material waste became dominant, and cutting fluid was roughly on par with electricity use. At both high and low utilization, the fused deposition modeling (FDM) machine had the lowest ecological impacts per part. The inkjet machine sometimes performed better and sometimes worse than CNC, depending on idle time/energy and on process parameters.

The study only compared additive manufacturing in plastic, and did not include other additive manufacturing technologies, such as selective laser sintering or stereolithography. It also does not include post-processing that might bring the surface finish of FDM parts up to the quality of inkjet or CNC parts.

Designers and engineers seeking to minimize the environmental impacts of their prototypes should share high-utilization machines, and are advised to use FDM machines over CNC mills or polyjet machines if they provide sufficient quality of surface finish.

This is the first paper quantitatively comparing the environmental impacts of additive manufacturing with traditional machining. It also provides a more comprehensive measurement of environmental impacts than most studies of either milling or additive manufacturing alone – it includes not merely CO₂ emissions or waste but also acidification, eutrophication, human toxicity, ecotoxicity and other impact categories. Designers, engineers and job shop managers may use the results to guide sourcing or purchasing decisions related to rapid prototyping.

Due to considerable contributions of the construction industry to the global carbon emissions, a great deal of attention is placed on possible incorporation of carbon footprint minimization as an important objective in the planning of construction operations. The purpose of this paper is to present a framework to estimate and minimize the carbon emissions of the concrete placing operation through identifying the optimal number of pumps and the inter-arrival time of truck mixers.

The proposed framework integrates discrete event simulation and multi-objective optimization to estimate and minimize the carbon emission, costs and production rate of the concrete placing operation. An actual construction project is used to demonstrate the application of the proposed framework. Furthermore, a sensitivity analysis is performed to investigate the sensitivity of the results to variations in modeling parameters including the ratio of idle to non-idle emission rates of equipment and the activity duration distributions.

The results of the case study highlight that variations in the number of pumps and inter-arrival time of truck mixers significantly affect the carbon emissions, cost and production rate of the concrete placing operation. Furthermore, the results of the sensitivity analysis show that variations in the ratio of idle to non-idle emission rates for pumps and truck mixers have little effects on the selected setting for the project. This is contrary to the effect of uncertainty in the activity duration distributions, which was found to be significant.

Results of this study provide an insight into the trade-off between carbon emissions, cost and production rate of the concrete placing operation.

The purpose of this paper is to study the impacts of time segment modeling approach for berth allocation and quay crane (QC) assignment on container terminal operations efficiency.

The authors model the small time segment modeling approach, based on minutes, which can be a minute, 15 min, etc. Moreover, the authors divided the problem into three sub-problems and proposed a novel three-level genetic algorithm (3LGA) with QC shifting heuristics to deal with the problem. The objective function here is to minimize the total service time by using different time segments for comparison and analysis.

First, the study shows that by reducing the time segment, the complexity of the problem increases dramatically. Traditional meta-heuristic, such as genetic algorithm, simulated annealing, etc., becomes not very promising. Second, the proposed 3LGA with QC shifting heuristics outperforms the traditional ones. In addition, by using a smaller time segment, the idling time of berth and QC can be reduced significantly. This greatly benefits the container terminal operations efficiency, and customer service level.

Nowadays, transshipment becomes the main business to many container terminals, especially in Southeast Asia (e.g. Hong Kong and Singapore). In these terminals, vessel arrivals are usually very frequent with small handling volume and very short staying time, e.g. 1.5 h. Therefore, a traditional hourly based modeling approach may cause significant berth and QC idling, and consequently cannot meet their practical needs. In this connection, a small time segment modeling approach is requested by industrial practitioners.

In the existing literature, berth allocation and QC assignment are usually in an hourly based approach. However, such modeling induces much idling time and consequently causes low utilization and poor service quality level. Therefore, a novel small time segment modeling approach is proposed with a novel optimization algorithm.

The purpose of this paper is to study the unit train make-up scheme for loaded direction in the heavy haul railway.

A 0-1 nonlinear integer programming model with the aim of minimizing the idling period between actual train arrival time and expected train arrival time for all loaded unit trains are proposed.

The proposed model is applied into a case study based on Daqin heavy haul railway. Results show that the proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway.

The proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway.

The case focuses on truck high idle time, truck detention, and enroute challenges faced by Nagpur Golden Transport Company, a trucking company with a fleet size of 150 trucks. Being in a highly competitive market, every single order not fulfilled by the transporter is an opportunity lost. How should NGTC use GPS data, MIS, and other freight transport technological capabilities in reducing truck idling times, and increasing trip revenues?

The purpose of this paper is an investigation of driving behaviour and impacts on emissions at two traffic junctions.

A signalised junction and a roundabout in Edinburgh have been selected. An instrumented car has been used and a GPS to monitor driving activities as well as a gas analyser to monitor the vehicle's emissions during the evening peak hour.

Vehicles’ emissions are affected by a large number of factors including characteristics of the engine and the vehicle, characteristics of the road, the fuel used and driving behaviour.

Different methods and approaches have been used to investigate the behaviour of vehicles at various traffic junctions. The main aim, however, has mostly been to reduce travel times as well as traffic delays and queues at the junction. Consideration of environmental impacts has also been made, but often as a by‐product of congestion reduction and not as a main aim.

The purpose of this paper is to present a method for the environmental impact analysis of machine-tool cutting, which enables the detailed analysis of inventory data on resource consumption and waste emissions, as well as the quantitative evaluation of environmental impact.

The proposed environmental impact analysis method is based on the life cycle assessment (LCA) methodology. In this method, the system boundary of the cutting unit is first defined, and inventory data on energy and material consumptions are analyzed. Subsequently, through classification, five important environmental impact categories are proposed, namely, primary energy demand, global warming potential, acidification potential, eutrophication potential and photochemical ozone creation potential. Finally, the environmental impact results are obtained through characterization and normalization.

This method is applied on a case study involving a machine-tool turning unit. Results show that primary energy demand and global warming potential exert the serious environmental impact in the turning unit. Suggestions for improving the environmental performance of the machine-tool turning are proposed.

The environmental impact analysis method is applicable to different machine tools and cutting-unit processes. Moreover, it can guide and support the development of green manufacturing by machinery manufacturers.