Shippers’ opportunities to increase load factor: managing imbalances between required and available capacity

Controlling micro-CHP systems to modulate electrical load profiles

This paper considers logistics decision support system which deals with transportation mode selection considering transportation and carbon emission cost. Transportation and carbon emission costs vary with the choice of transportation modes and to become competitive companies need to find proper transportation modes for their logistics services. However, due to the restricted capacity of transportation modes, it is difficult to balance transportation and carbon emission costs when designing logistics network including transportation mode choice for each service. Therefore this paper aims to analyze the trade-off relationship between transportation and carbon emission cost in mode selection of intermodal transportation and to provide optimal green logistics strategy. In this paper, the logistics decision support system is designed based on mixed integer programming model. To understand the trade-off relationship of transportation and carbon emission cost, the system is tested with various scenarios including transportation of containers between Seoul and Busan. The analysis results show that, even though sea transportation combined with trucking is competitive in carbon emission per unit distance travelled, the total cost of carbon emission and transportation for the sea transportation may not have competitive advantage over other transportation modes including rail and truck transportation modes. The sea-based intermodal logistics service may induce detours which have negative impacts on the overall carbon emission. The proposed logistics decision support system is expected to play key role in green logistics and supply chain management.

An empirical assessment of the CO2-sensitive productivity of European airlines from 2000 to 2010

In the present scenario, in order to meet the growing electricity demand, demand side management (DSM) is one among the various approaches used in smart grid. By applying DSM at the customer side, various benefits such as reduction in electricity cost, reduction in peak demand, and improvement in load factor can be achieved. This paper proposes a new architecture, namely, Intelligent Universal Load Management System (IULMS) to implement DSM based on load shifting approach for a residential building in the presence of different classes of loads. In this work, the objective is to minimize the cost of electricity consumption and there are several constraints imposed by the device types operating in the system and it is solved by Binary Grey Wolf Optimization algorithm. The objective function is non-linear in nature because the price of electricity varies each hour. A real-time system involving residential building users of Agrini Apartment in Madurai, Tamilnadu, India is discussed. Further, the potential of DSM is analyzed. The minimization of electricity consumption cost, peak demand, and Peak to Average Ratio (PAR) are validated for each test case.

A model for development of optimized feeder routes and coordinated schedules—A genetic algorithms approach

Arab carriers besieged by growing international competition are operating in a turbulent environment. Now with the plummeting oil revenues and the levelling off in infrastruetural development, their load factor is declining. Besides, the prevailing winds of deregulation that swept the entire airline industry coupled with escalating regional tension have compounded the felony and depressed air traffic even further. Consequently Arab carriers are feeling the pinch and fighting a battle for survival as their direct pipeline the to state treasury is ruptured. After identifying the common structural features of Arab carriers and the key environmental forces threatening their survival, the authors have explored several strategies aimed at lowering operating costs and improving load factors without hurting their service image. They then offer some specific alternative proposals geared to help Arab carriers in implementing new strategies and adapting themselves successfully to the demands of their turbulent environment. The authors conclude by arguing that the performance of state-owned enterprises should be made by reference to their stated objectives rather than to the commonly accepted economic criteria.

‘Sustainability’ and ‘going green’ have become buzzwords in today’s business world. In logistics, greening up in a sustainable way in the future suggests the need to identify innovative measures that go beyond merely reducing carbon emissions. The purpose of this article was to explore managerial perceptions on green logistics barriers, applications and the importance of green logistics in Zimbabwean small and medium-sized enterprises (SMEs). A qualitative research approach that utilised semi-structured interviews to collect data from five SME managers and one government official was used. Using a thematic deductive line-by-line content analysis approach, this article principally found that internal barriers such as costs, lack of resources and knowledge are major barriers impeding the adoption and application of green logistics in Zimbabwean SMEs. Poor infrastructure and roads were reported to be the common external barriers of green logistics adoption in SMEs. The article also found that most of the participant SMEs in Zimbabwe were not yet applying green logistics, although some were striving towards practising packaging optimisation, warehousing, inventory management optimisation, along with transport optimisation and efficiency (particularly, route optimisation and fuel efficiency). From the themes, the study found that green logistics is important to the few environmentally conscious SMEs. The importance of green logistics identified were the creation of brand loyalty, good brand image, the possibility of attracting government support, cost saving, as well as improving the firm’s profitability in the long run. Conclusions were drawn and recommendations to both firms and government were made.

Increasing environmental considerations force the logistics sector to develop more environmentally friendly approaches. Thus, the concept of "green logistics" is becoming more crucial. The use of electric vehicles, which become prominent with their low energy consumption, less cost and environmentally friendly features, is a part of green logistics. Moreover, taking into consideration of multi-echelon distribution networks can also be effective in reducing the negative outcomes of urban logistics. In this context, the Two-Echelon Electric Vehicle Routing Problem with Battery Swapping Station (2E-E-VRP-BSS) is considered in this study. A Mixed Integer Programming (MIP) model is presented for the problem in which the total cost consisting of fixed cost, transportation cost, battery swapping cost and handling cost is minimized. To test the validity of the model, three different data sets, each of different sizes, simulating a realistic distribution network were created. In addition, sensitivity analyses were carried out in order to examine the impact of changes in customer demand and battery capacities of electric vehicles on optimal solutions. The analyses indicate the applicability of the model. The findings suggest that fluctuations in customer demand directly impact the total cost, with higher demand leading to increased costs and lower demand resulting in cost reductions compared to the base case scenario. Furthermore, there is a negative correlation between the battery capacity of electric vehicles and the total cost, meaning that as battery capacity increases, the overall cost tends to decrease.

The insufficient loading of vehicles, which leads to a low logistics load factor is a common problem in the logistics industry. This study addresses this issue by utilizing actual shipment data from an automotive company. An effective method has been proposed to improve the company’s logistics efficiency through a scenario-based approach. Two real- world scenarios were developed to enhance vehicle loading performance. Machine learning algorithms were employed to evaluate the shipment performance of these scenarios. For the study, a dataset was generated from the company’s ERP system and real-world shipment data. Since this is a real-world problem, the dataset consisted of unlabeled data. To solve this problem, both supervised and unsupervised learning algorithms were applied. First, unsupervised clustering algorithms were used to group the shipment performance based on similarities. Then, supervised learning algorithms were utilized to classify the data within each group. The average cost was used to evaluate the clusters obtained through the unsupervised methods, while classification performance was measured using supervised machine learning techniques. The scenario-based approach has significantly improved the performance of the shipments as it shows the changes in load factor more clearly. In the actual case, only %25.7 of shipments were high performing, while this percentage gradually increased to %98.4 in the scenarios. The results show that optimizing the load factor makes the transports more efficient and balanced.

The oil and gas industry in Canada is a prosperous industry that involves the rehabilitation and/or installation of new pipelines and facilities, which results in an increase of greenhouse gas (GHG) emissions. This paper compares the amount of GHG emissions of pilot-tube micro tunneling (PTMT) and hand tunneling through a case study that involved replacing an old pipeline and installing a new pipeline. The project site was located in the northeast of Edmonton, Alberta, Canada in which PTMT and hand tunneling were used. In the calculation, the GHG emissions were classified as two parts: construction and transportation emissions. The indices used to specify the GHG emissions are the estimated masses of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxide (NOx), particulate matter (PM), and sulfur dioxide (SO2). The construction emissions calculations are functions of construction hours, type of equipment, loading factor, and emission factor. The transportation emissions calculations...

The energy white paper proposes a number of measures that will challenge the traditional role of electricity distribution networks. These include facilitating competition in sustainable, clean and efficient electricity production, which in turn will mean accommodating significantly higher levels of decentralised generation and renewable energy resources. At both the commercial and domestic level, demand-side participation facilitated by cogeneration and micro-generation could play an increasing role in optimising not only electrical energy production (in terms of carbon emissions) but also network cyclic demand patterns. For example, flattening load profiles by 'shifting' peak demands would not only improve load factor and reduce network I2R losses, it might also improve overall network utilisation and enable the deferral of 'load-related' capital investment. Smart Metering has a potentially key role to play in providing the means by which the necessary financial incentives to encourage cogeneration and micro-generation (e.g. in terms of export reward) and effective demand-side management (e.g. in terms of time-of-day pricing) can be created. As well as the obvious benefits to Suppliers of 'responsive' demand, given the necessary attention to regulatory and commercial frameworks, smart metering and its associated data management technologies might also create a number of opportunities for Distributors and benefits for their connected customers; these include. (1)more accurate DUoS billing (2)improved super-customer profiling and hence more accurate settlement (3) reduced technical and non-technical losses (4) identifying 'idle' services (i.e. no unit consumption) or, conversely, overloaded services (5) potential load and voltage-profile monitoring at a highly disaggregated (i.e. LV) level which could in turn identify (6) potential local network thermal overloads; - potential statutory voltage transgressions (7) and - new and emerging demand profiles that might in turn influence economic network design criteria (8)through improved localised network utilisation data (9) enable networks to be more highly utilised (10) and - enable a more rapid response to customers' requests for additional demand and new connections (including generation (11) through an inherent power outage detection capability (12)easier identification of 'worst-served' customers (13) ability to identify vulnerable customers affected by power failures;potential income from providing power line carrier communication services (14) potentially marketable ancillary 'DSO' services (i.e. system balancing.The presentation will outline some of the above opportunities and hopefully provide 'food for thought' for the discussion forum. (18 pages)

9 - History of nuclear energy

Improving Load Factors Can Increase Profits

Bridge cranes at work, due to the large, car plus (minus) speed and improve load factors, will cause the load to swing. This not only increases the likelihood of accidents, but also will seriously affect the efficiency of the production. Under the prerequisite of ensuring the crane operating efficiency, to prevent the swinging of load for bridge crane has great meaning. Based on the physical characteristics of bridge crane inSimulinkestablished with inverter, motor and crane model. According to simulations a mathematical model is proposed based on fuzzy control and batch control of overhead traveling crane anti-sway control method. In actual experiments, using load and the angle of tilt sensor the mass of load is measured as a feedback signal. And, ultimately, the crane's operating efficiency, angle control in the ideal range is ensured.

Controlled Low Strength Material (CLSM), as a structural material for bedding vitrified clay pipe, is a mix consisting of Portland cement, fine aggregate, coarse aggregate, water, entrained air, and chemical admixtures to accelerate cure time. CLSM is gaining popularity as a bedding material for vitrified clay pipe (VCP) due to simplification of pipe installation, reduction in labor and inspection costs, and the benefit of a 2.8 load factor. Computation of predicted backfill load on rigid pipe is performed utilizing the Marston Equation originally developed by Professor Anson Marston in 1913 and 1930. The variables of the Marston Equation consist of the depth of cover, the unit weight and type of the backfill soil and the width of the trench measured at the top of pipe. This design theory is still used today for rigid pipe design even though bedding system standards were yet to be adopted when it was developed. At that time, rigid pipe were generally laid on a hand-shaped trench bottom and imported bedding was rarely used. Professor Marston theorized that the material at the sides of rigid pipe was so loose compared to the rigidity of the pipe that the support of any backfill load by the sidefill would be negligible resulting in a conservative design. The presumed inability of the sidefills to carry a significant share of the backfill load is not applicable when CLSM bedding is used since it neither settles nor compacts or shrinks significantly. CLSM will support a large portion of the backfill load, which would otherwise be carried by the pipe. Thus, utilizing the Marston Equation in its conventional form has proven to be inappropriate on CLSM projects. The growing and successful use of CLSM as a bedding material led the clay pipe industry to conduct research on actual loads received by the pipe both in field installations and laboratory environments. This research demonstrates a load transfer mechanism in which the load on the pipe can be reduced by a ratio of the outside diameter of the pipe to the width of the trench at the top of the pipe (Bc / Bd) resulting in the Modified Marston equation.