An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition,planning and cost

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment.Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain.Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value.A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.     

Deconstructing delay: A non-parametric approach to analyzing delay changes in single server queuing systems

This paper introduces an empirically driven, non-parametric method to isolate and estimate the effects that changes in demand and changes in throughput have on delay – in particular, arrival and departure flight delay at airport runways. Classic queuing concepts were used to develop a method by which an intermediate, or counterfactual, queuing scenario could be constructed, to isolate the delay effects due to shifts in demand and throughput. This method includes the development of a stochastic throughput function that is based entirely on data and has three key features. Firstly, the function relies on non-parametric, empirically-based probability distributions of throughput counts. Secondly, facility capacity needs not be explicitly defined, as it is implicitly included in the probability distributions of throughput. Thirdly, the throughput performance function preserves the effect of factors that cause capacity (and, therefore, throughput) to fluctuate over a given period. Temporal sequences of high, moderate, and low capacity are maintained between the observed and counterfactual scenarios. The method was applied to a case study of the three major New York area airports of LaGuardia (LGA), Newark Liberty (EWR), and John F. Kennedy (JFK), using operational data extracted from the Federal Aviation Administration’s (FAA’s) Aviation System Performance Metrics (ASPM) database. The focus was on the peak summer travel seasons of 2006 and 2007, as these airports experienced record levels of delay in 2007. The results indicate that decreases in both demand and throughput were experienced at LGA and EWR, although the decreases in throughput had more significant effects on operational delays as they increased overall at these airports. At JFK, the increase in departure throughput was not sufficient to offset the increase in departure demands. For arrivals, demand increased and throughput decreased. These trends caused a significant growth in delay at JFK between 2006 and 2007.     

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.     

Designing optimal autonomous vehicle sharing and reservation systems: A linear programming approach

Autonomous vehicle (AV) technology holds great promise for improving the efficiency of traditional vehicle sharing systems. In this paper, we investigate a new vehicle sharing system using AVs, referred to as autonomous vehicle sharing and reservation (AVSR). In such a system, travelers can request AV trips ahead of time and the AVSR system operator will optimally arrange AV pickup and delivery schedules and AV trip chains based on these requests. A linear programming model is proposed to efficiently solve for optimal solutions for AV trip chains and required fleet size through constructed AVSR networks. Case studies show that AVSR can significantly increase vehicle use rate (VUR) and consequentially reduce vehicle ownership significantly. In the meantime, it is found that the actual vehicle miles traveled (VMT) in AVSR systems is not significantly more than that of conventional taxis, despite inevitable empty hauls for vehicle relocation in AVSR systems. The results imply huge potential benefits from AVSR systems on improving mobility and sustainability of our current transportation systems.     

Modeling the cost sensitivity of intermodal inland waterway terminals: A scenario based approach

Cost characteristics of differently sized inland waterway terminals (IWTs) have not received much scientific attention. This observation is remarkable given the importance of costs in transportation decision-making. Classification of differently sized IWTs and their cost structure will lead to more insight into the container cost per terminal. Therefore, the goal of our research was to determine both the characteristics of the cost structure associated with different inland waterway (IWW) container terminal types and the sensitivity of the system to cost/TEU changes in input and operational conditions. We show that terminals with a higher container throughput encounter fewer costs, and can therefore charge a lower price. Assumed delays of 2 h per day on the waterside cause a 4.7–6.6% cost increase per container, mainly caused by extra labor costs. It is also assumed that the changing climate will influence terminal operations and results in extreme water levels (lasting two weeks occurring four times a year) causing a cost increase of 1.0–3.4%. Subsidies can cause cost reductions of 0.3–10.4% depending on the exact form, with the smaller terminals benefiting more because their investment costs are higher relative to operational costs. A subsidy can lower costs by up to 10.4%, but it is questionable whether small and medium terminals will have a lower cost price than the market price, showing that it is important for small and medium terminals to quickly grow in size.     

Distance and time in intermodal goods transport networks in Europe: A generic approach

This paper is about distance and time as factors of competitiveness of intermodal transport. It reviews the relevance of the factors, evaluates time models in practice, compares network distances and times in alternative bundling networks with geometrically varied layouts, and points out how these networks perform in terms of vehicle scale, frequency and door-to-door time. The analysis focuses on intermodal transport in Europe, especially intermodal rail transport, but is in search for generic conclusions. The paper does not incorporate the distance and time results in cost models, and draws conclusions for transport innovation, wherever this is possible without cost modelling. For instance, the feature vehicle scale, an important factor of transport costs, is analysed and discussed.Distance and time are important factors of competitiveness of intermodal transport. They generate (direct) vehicle costs and – via transport quality – indirect costs to the customers. Clearly direct costs/prices are the most important performance of the intermodal transport system. The relevance of quality performances is less clarified. Customers emphasise the importance of a good match between the transport and the logistic system. In this framework (time) reliability is valued high. Often transport time, arrival and departure times, and frequency have a lower priority. But such conclusions can hardy be generalised. The range of valuations reflects the heterogeneity of situations. Some lack of clarity is obviously due to overlapping definitions of different performance types.The following parts of the paper are about two central fields of network design, which have a large impact on transport costs and quality, namely the design of vehicle roundtrips (and acceleration of transport speed) and the choice of bundling type: do vehicles provide direct services or run in what we call complex bundling networks? An example is the hub-and-spoke network. The objective of complex bundling is to increase vehicle scale and/or transport frequency even if network volumes are restricted. Complex bundling requires intermediate nodes for the exchange of load units. Examples of complex bundling networks are the hub-and-spoke network or the line network.Roundtrip and bundling design are interrelated policy fields: an acceleration of the roundtrip speed, often desirable from the cost point of view, can often only be carried out customer friendly, if the transport frequency is increased. But often the flow size is not sufficient for a higher frequency. Then a change of bundling model can be an outcome.Complex bundling networks are known to have longer average distances and times, the latter also due to the presence of additional intermediate exchange nodes. However, this disadvantage is – inside the limits of maximal vehicle sizes – overruled by the advantage of a restricted number of network links. Therefore generally, complex bundling networks have shorter total vehicle distances and times. This expression of economies of scale implies lower vehicle costs per load unit.The last part of the paper presents door-to-door times of load units of complex bundling networks and compares them with unimodal road transport. The times of complex bundling networks are larger than that of networks with direct connections, but nevertheless competitive with unimodal road transport, except for short distances.