Reducing emissions through speed optimization in supply vessel operations

This paper examines how optimizing sailing speeds can reduce supply vessels emissions in the upstream supply chain to offshore installations. We introduce several speed optimization strategies to be used in construction of periodic vessel schedules. The strategies consider vessel waiting times before the start of service at installations and at supply base. Tests carried out on real instances from Statoil’s activities on the Norwegian continental shelf indicate that a 25% emissions and fuel cost reductions can be achieved without fleet size increase.     

Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

The airline schedule planning problem is defined as the sequence of decisions that need to be made to obtain a fully operational flight schedule. Historically, the airline scheduling problem has been sequentially solved. However, there have already been many attempts in order to obtain airline schedules in an integrated way. But due to tractability issues it is nowadays impossible to determine a fully operative and optimal schedule with an integrated model which accounts for all the key airline related aspects such as competitive effects, stochastic demand figures and uncertain operating conditions. Airlines usually develop base schedules, which are obtained much time in advance to the day of operations and not accounting for all the related uncertainty. This paper proposes a mathematical model in order to update base schedules in terms of timetable and fleet assignments while considering stochastic demand figures and uncertain operating conditions, and where robust itineraries are introduced in order to ameliorate miss-connected passengers. The proposed model leads to a large-scale problem which is difficult to be solved. Therefore, a novel improved and accelerated Benders decomposition approach is proposed. The analytical work is supported with case studies involving the Spanish legacy airline, IBERIA. The presented approach shows that the number of miss-connected passengers may be reduced when robust planning is applied.     

Passenger Perspectives in Railway Timetabling: A Literature Review

When looking at railway planning, a discrepancy exists between planners who focus on the train operations and publish fixed railway schedules, and passengers who look not only at the schedules but also at the entirety of their trip, from access to waiting to on-board travel and egress. Looking into this discrepancy is essential, as assessing railway performances by merely measuring train punctuality would provide an unfair picture of the level of service experienced by passengers. Firstly, passengers’ delays are often significantly larger than the train delays responsible for the passengers to be late. Secondly, trains’ punctuality is often strictly related to too tight schedules that in turn might translate into knock-on delays for longer dwelling times at stations, trip delays for increased risk of missing transfer connections, and uncertain assessment of the level of service experienced, especially with fluctuating passenger demand. A key aspect is the robustness of railway timetables. Empirical evidence indicates that passengers give more importance to travel time certainty than travel time reductions, as passengers associate an inherent disutility with travel time uncertainty. This disutility may be broadly interpreted as an anxiety cost for the need for having contingency plans in case of disruptions, and may be looked at as the motivator for the need for delay-robust railway timetables. Interestingly, passenger-oriented optimisation studies considering robustness in railway planning typically limit their emphasis on passengers to the consideration of transfer maintenance. Clearly, passengers’ travel behaviour is far more complex and multi-faceted and thus several other aspects should be considered, as becoming more and more evident from passenger surveys. The current literature review starts by looking at the parameters that railway optimisation/planning studies are focused on and the key performance indicators that impact railway planning. The attention then turns to the parameters influencing passengers’ perceptions and travel experiences. Finally, the review proposes guidelines on how to reduce the gap between the operators’ railway planning and performance measurement on the one hand and the passengers’ perception of the railway performance on the other hand. Thereby, the conclusions create a foundation for a more passenger-oriented railway timetabling ensuring that passengers are provided with the best service possible with the resources available.     

A stochastic fleet size and mix model for maintenance operations at offshore wind farms

A fleet of vessels and helicopters is needed to support maintenance operations at offshore wind farms. The cost of this fleet constitutes a major part of the total maintenance costs, hence keeping an optimal or near-optimal fleet is essential to reduce the cost of energy. In this paper we study the vessel fleet size and mix problem that arises for the maintenance operations at offshore wind farms, and propose a stochastic three-stage programming model. The stochastic model considers uncertainty in vessel spot rates, weather conditions, electricity prices and failures to the system. The model is tested on realistic-sized problem instances, and the results show that it is valuable to consider uncertainty and that the proposed model can be used to solve instances of a realistic size.     

Batteries in offshore support vessels – Pollution,climate impact and economics

This paper assesses the pros and the cons of installing batteries on offshore support vessels. These vessels are specially designed to provide services to oil and gas operations, such as anchor handling, supply and subsea operations. They have multiple engines and advanced dynamic positioning systems to ensure that they can perform their duties with high reliability at nearly any sea state. Combined with high safety requirements, this has resulted in general operational patterns with vessels running multiple combustion engines even at calm water conditions. For emissions, low engine loads yield high emissions of exhaust gases such as nitrogen oxides (NOx) and aerosols such as black carbon (BC), due to less favorable combustion conditions. The high span for these vessels between low loads and high, and their great need for potential power at short notice, motivate our examination of hybrid setups with electric: the vessel segment should be more favorable than many. We find that combining batteries with combustion engines reduces local pollution and climate impact, while the economics with current battery cost and fuel prices is good enough for new vessels, but not good enough for retrofits.     

Storage space allocation in container terminals

Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important in reducing transportation costs and keeping shipping schedules. In this paper, we study the storage space allocation problem in the storage yards of terminals. This problem is related to all the resources in terminal operations, including quay cranes, yard cranes, storage space, and internal trucks. We solve the problem using a rolling-horizon approach. For each planning horizon, the problem is decomposed into two levels and each level is formulated as a mathematical programming model. At the first level, the total number of containers to be placed in each storage block in each time period of the planning horizon is set to balance two types of workloads among blocks. The second level determines the number of containers associated with each vessel that constitutes the total number of containers in each block in each period, in order to minimize the total distance to transport the containers between their storage blocks and the vessel berthing locations. Numerical runs show that with short computation time the method significantly reduces the workload imbalance in the yard, avoiding possible bottlenecks in terminal operations.     

Modal split in offshore supply network under the objective of emissions minimization

We study modal split under the objective of emissions minimization in the transportation of cargo from centralized vendors in the oil and gas industry to decentralized supply bases on the Norwegian coast. The supply network includes direct road transport and a sea route along the coast. To gain insight into modal split decisions between road and sea transport from the shipper’s perspective multi-period mixed integer optimization models are formulated. Particularly the models give possibilities to examine how weekly demand patterns at supply bases, cargo commitments to sea transport, storage possibilities at supply bases, and shipper’s responsibility for a certain share of vessel capacity may effect the emissions and the modal split. Experiments on real data from an oil and gas company operating offshore show that the size of the share of vessel capacity and the possibility for storage at supply bases are the major determinants for a larger shift to environmentally friendly sea transport. The models can be used as means for making decisions regarding how a shipper can commit to sea transport to achieve less emissions.     

Weather impact on containership routing in closed seas: A chance-constraint optimization approach

Weather conditions have a strong effect on the operation of vessels and unavoidably influence total time at sea and associated transportation costs. The velocity and direction of the wind in particular may considerably affect travel speed of vessels and therefore the reliability of scheduled maritime services. This paper considers weather effects in containership routing; a stochastic model is developed for determining optimal routes for a homogeneous fleet performing pick-ups and deliveries of containers between a hub and several spoke ports, while incorporating travel time uncertainties attributed to the weather. The problem is originally formulated as a chance-constrained variant of the vehicle routing problem with simultaneous pick-ups and deliveries and time constraints and solved using a genetic algorithm. The model is implemented to a network of island ports of the Aegean Sea. Results on the application of algorithm reveal that a small fleet is sufficient enough to serve network’s islands, under the influence of minor delays. A sensitivity analysis based on alternative scenarios in the problem’s parameters, leads to encouraging conclusions with respect to the efficiency and robustness of the algorithm.     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

A survey on planning semi-flexible transit systems: Methodological issues and a unifying framework

When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, due to its fixed structure. New transportation alternatives are therefore increasingly proposed, combining on-demand service adjustment capabilities to the regular route and schedule characteristics of traditional transit. Such so-called semi-flexible systems require careful planning, but no formalization of the corresponding decisions problems, nor any comprehensive methodology has been proposed yet. This paper aims at contributing to fill this gap by presenting a comprehensive literature review, and a general and unifying modeling framework for representing and planning semi-flexible systems. The latter takes the form of the Demand Adaptive Systems, which generalizes the semi-flexible systems described in the literature, and also offers a number of advanced features, the scheduling mechanism, in particular. The paper then provides a classification of planning decisions, which is used to structure a comprehensive and comparative literature review of the field of semi-flexible systems, including methodological contributions as well as a number of particularly significant practical experiences.     

Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

Reprint of “Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event”

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

Assessing greenhouse gas emissions from port vessel operations at the Port of Incheon

This paper measures greenhouse gas emissions from port vessel operations by considering the case of Korea’s Port of Incheon. It provides estimates of greenhouse gas emissions based on the type and the movement of a vessel from the moment of its arrival, to its docking, cargo handling, and departure. Taking a bottom-up approach based on individual vessels’ characteristics and using data on vessels processed by the port in 2012 estimate emissions. The results indicate that the level of emissions is five times higher than that estimated through the top-down approach. Among various types of vessels, international car ferries are the heaviest emitters, followed by full container vessels and car carriers. A vessel’s passage through lock gates and maneuver to approach the dock accounts for 96% of its emissions. Docking for cargo handling shows the lowest level of GHG emissions.     

Exploratory analysis of Zipf’s universal power law in activity schedules

People’s behavior is governed by extremely complex, multidimensional processes. This fact is well-established in the transportation research community, which has been working on travel behavior (travel demand) models for many years. The number of degrees of freedom in a person’s activity schedule is enormous. However, the frequency of occurrence of day-long activity schedules obeys a remarkably simple, scale-free distribution. This particular distribution has been observed in many natural and social processes and is commonly referred to as Zipf’s law, a power law distribution. This research provides evidence that activity schedules from various study areas exhibit a universal power law distribution. To this end, an elaborate analysis using 13 household travel surveys from diverse study areas discusses the effect of proportional outlier removal on the power law’s exponent value. Statistical evidence is provided for the hypothesis that activity schedules in all these datasets exhibit a power law distribution with a common exponent value. The study proposes that a Zipf power law could be used as an additional dimension within a travel demand model’s validation process. Contrary to other validation methods, no new data is required. The observation of a Zipf power law distribution in the generated schedules appears to be a necessary condition. Additionally, the universal activity schedule distribution might enable the full integration of activity schedules in models based on universal mobility patterns.

     

Design and development of interactive trip planning for web-based transit information systems

This article presents a Web-based transit information system design that uses Internet Geographic Information Systems (GIS) technologies to integrate Web serving, GIS processing, network analysis and database management. A path finding algorithm for transit network is proposed to handle the special characteristics of transit networks, e.g., time-dependent services, common bus lines on the same street, and non-symmetric routing with respect to an origin/destination pair. The algorithm takes into account the overall level of services and service schedule on a route to determine the shortest path and transfer points. A framework is created to categorize the development of transit information systems on the basis of content and functionality, from simple static schedule display to more sophisticated real time transit information systems. A unique feature of the reported Web-based transit information system is the Internet-GIS based system with an interactive map interface. This enables the user to interact with information on transit routes, schedules, and trip itinerary planning. Some map rendering, querying, and network analysis functions are also provided.     

Reliable logistics networks design with facility disruptions

This paper studies a strategic supply chain management problem to design reliable networks that perform as well as possible under normal conditions, while also performing relatively well when disruptions strike. We present a mixed-integer programming model whose objective is to minimize the nominal cost (the cost when no disruptions occur) while reducing the disruption risk using the p-robustness criterion (which bounds the cost in disruption scenarios). We propose a hybrid metaheuristic algorithm that is based on genetic algorithms, local improvement, and the shortest augmenting path method. Numerical tests show that the heuristic greatly outperforms CPLEX in terms of solution speed, while still delivering excellent solution quality. We demonstrate the tradeoff between the nominal cost and system reliability, showing that substantial improvements in reliability are often possible with minimal increases in cost. We also show that our model produces solutions that are less conservative than those generated by common robustness measures.     

An optimal aircraft fleet management decision model under uncertainty

Decision planning for an efficient fleet management is crucial for airlines to ensure a profit while maintaining a good level of service. Fleet management involves acquisition and leasing of aircraft to meet travelers' demand. Accordingly, the methods used in modeling travelers' demand are crucial as they could affect the robustness and accuracy of the solutions. Compared with most of the existing studies that consider deterministic demand, this study proposes a new methodology to find optimal solutions for a fleet management decision model by considering stochastic demand. The proposed methodology comes in threefold. First, a five‐step modeling framework, which is incorporated with a stochastic demand index (SDI), is proposed to capture the occurrence of uncertain events that could affect the travelers' demand. Second, a probabilistic dynamic programming model is developed to optimize the fleet management model. Third, a probable phenomenon indicator is defined to capture the targeted level of service that could be achieved satisfactorily by the airlines under uncertainty. An illustrative case study is presented to evaluate the applicability of the proposed methodology. The results show that it is viable to provide optimal solutions for the aircraft fleet management model. Copyright © 2013 John Wiley & Sons, Ltd.     

川东北高含硫气田压力容器设计

高含硫气田中的压力容器腐蚀应重点考虑应力腐蚀开裂。详细分析了高含硫气田压力容器设计中应着重考虑材料因素、环境因素和加工因素。为了确保产品的质量,基于设计中应重点考虑因素与国内外成功经验,针对罗家寨高含硫气田压力容器的设计,明确提出了材料化学成分限制与探伤、制造等特殊的附加要求。另外,还应进一步提高高含硫压力容器设计水平。     

Optimization Of Headway,Vehicle Size and Route Choice for Minimum Cost Feeder Service

ABSTRACT

Many people use public transportation systems to reach their destination, while others use personal vehicles. Poor transportation systems do not attract ridership. Therefore, the usage of passenger cars increases, and traffic and environmental conditions deteriorate. Efficient public transportation has been recognized as one of the potential ways of mitigating air pollution, reducing energy consumption, improving mobility and alleviating traffic congestion. The objective of this study is to optimize a bus feeder service that provides the shuttle service between a recreation center (e.g. Sandy Hook, NJ) and a major public transportation facility, subject to site-specific constraints such as vehicle schedules, bus availability, service capacity and budget. The decision variables include bus headway, vehicle size and route choice. The solution methodology integrating both analytical and numerical techniques is developed, which optimizes the decision variables. Finally, the proposed solution methodology is applied to a case study. Numerical results, including optimal solutions and sensitivity analyses, are presented while the level of coordination between the feeder service and a major transportation service is discussed.     

Implications of climate change for thermal discomfort on underground railways

Hot weather events, ventilation assets, changing passenger demand and service expectations have all caused increased attention on thermal comfort on London’s Tube. This study provides estimates of the future number of days when passengers travelling on sections of the Tube could be subjected to thermal discomfort under future scenarios of climate change, and the potential number of passengers dissatisfied. A risk based methodology is presented, integrating a spatial weather generator modified for urban areas and a thermal comfort model. The study provides an initial assessment of adaptation options by considering the implications of lowering train temperatures by 2 °C and 4 °C to represent saloon cooling. Median results under a 2050 high scenario indicate that all Tube lines assessed could experience near-complete passenger dissatisfaction with the thermal environment in trains in the unlikely event that nothing else were to change. Adaptation aimed at lowering train temperatures has the potential to provide tangible improvements in thermal comfort. However, this was not projected to be sufficient to maintain comfortable thermal conditions for many of the lines in the 2050s under high emission scenarios, requiring a combination of other infrastructure cooling measures to be implemented in parallel.