Generating a spatial coverage plan for the emergency medical service on a regional scale: Empirical versus random forest modelling approach

Adequate spatial coverage by the emergency medical service and the ability to reach any location in the area of interest in the shortest possible time are crucial for the survival of patients with serious conditions. Knowledge of blind spots (i.e. sites that cannot be reached within the required time) represents key information for improving the service quality and may lead, e.g. to a relocation of bases or to other active interventions. Spatial coverage can be derived from experience based on historical data. Such an approach may be problematic if a larger area is being analysed, especially if data is not available for some parts of such areas or if no data is available. To eliminate such problems, we created a prediction model utilising the random forest ensemble learning method. The model is capable of predicting the travel time based on available historical data on ambulance movements (GPS) and the geometric and construction characteristics of individual road segments. We therefore collaborated with the regional public administration and emergency medical service authorities to deliver a time- and resource-efficient solution for emergency spatial planning practice. The outputs from the newly built model were subsequently validated against data from an empirical model currently used by the regional authorities. The results from both models were compared from the perspective of performance in various seasonal and time-of-day conditions. The prediction of travel times using the new model improved according to all the evaluated validation metrics. The importance and applicability of the foregoing model lies in the fact that it can be incorporated into the current emergency medical service management system in a simple manner in terms of data availability and the required computational resources. We conclude that the dynamic model presented in this paper represents an improvement relative to the reference data, and discuss the possibilities of further improving the proposed model.     

Optimising flight connection times in airline bank structure through Simulated Annealing and Tabu Search algorithms

In hub and spoke airline networks, flight arrivals and departures generally have a bank structure to increase connections among spoke cities through a hub airport in order to provide cheaper service for higher volumes of air traffic. In this study, we introduce the airline bank optimisation problem with a novel mathematical model for improving flight connection times. The mathematical model aims to minimise the total connection times for transfer passengers and generates flight schedules regarding slot capacities in the hub airports. Since the problem is a combinatorial optimisation problem NP-hard and computational complexity increases rapidly for real-world problems, we employ the simulated annealing and the tabu search algorithms to achieve better solutions in a reasonable time. We generate sub-problems using real-world data and investigate the effectiveness of the algorithms. Finally, we present the results of a real case study of a Turkish airline company which has a hub airport connecting the flights between Middle Eastern and European cities.     

A novel model to manage air cargo disruptions caused by global catastrophes such as Covid-19

The outbreak of the COVID-19 pandemic has drastically disrupted the air cargo industry. This disruption has taken many directions, one of which is the demand imbalance which occurs due to the sudden change in the cargo capacity, as well as demand. Therefore, the random change leads to excessive demand in some routes (hot-selling routes), while some other routes suffer from a big shortage of demand (underutilized routes). Routes are substitutable when there are several adjacent airports in the Origin & Destination (O&D) market. In this market, demand imbalance between substitutable routes occurs because of the above reasons. To tackle the demand imbalance problem, a novel model is introduced to estimate the quantity combinations which maintains the balance between underutilized and hot-selling routes. This model is a variant of the classic Cournot model which captures different quantity scenarios in the form of the best response for each route compared to the other. We then cultivate the model by integrating the Puppet Cournot game with the quantity discount policy. The quantity discount policy is an incentive which motivates the freight forwarders to increase their orders in the underutilized routes. After conducting numerical experiments, the results reveal that the profit can increase up to 25% by using the quantity discount. However, the quantity discount model is only applicable when the profit increase in the hot-selling route is greater than the profit decrease in the underutilized route.     

Estimating the sensitivity of small airport catchments to competition from larger airports: A case study in Indiana

An accurate estimation of airport catchment area enables airlines and airport operators to make informed decisions and to target potential markets precisely. This study uses the state of Indiana as a case study to estimate traffic leakage from the local airport, Indianapolis International Airport (IND), to two large hub airports in Illinois, the neighboring state of Indiana, namely Chicago O'Hare International Airport (ORD) and Chicago Midway International Airport (MDW). By using a decision making model that considers flying cost and access cost, this study simulates from local passengers' perspective which origin airport delivers the most cost effective flight itinerary. Using the top 20 routes of IND in 2018 as model inputs, the catchment area of two Chicago based airports in Indiana with variable coverage is plotted for different traveling scenarios. The analysis shows that an airport catchment area is sensitive to location, service level and traffic volume of competing airports nearby, as well as purpose of travel (business or leisure), number of travelers in a group (single, couple, family or multiple), length of trip, destination (domestic or international), preference of airlines (network carrier or budget carrier), and frequent flyer program status (premier member or general member). These findings could be valuable to all three aforementioned airports as well as airlines serving these airports when allocating operational and marketing resources. More importantly, this study creates a generic model that could be used by virtually any airport to estimate scenario-based catchment area using readily available itinerary and spatial data without resorting to expensive passenger surveys.     

Solving a static repositioning problem in bike-sharing systems using iterated tabu search

In this paper, we study the static bike repositioning problem where the problem consists of selecting a subset of stations to visit, sequencing them, and determining the pick-up/drop-off quantities (associated with each of the visited stations) under the various operational constraints. The objective is to minimize the total penalties incurred at all the stations. We present an iterated tabu search heuristic to solve the described problem. Experimental results show that this simple heuristic can generate high quality solutions using small computing times.     

Route optimization for energy efficient airport shuttle operations – A case study from Dallas Fort worth International Airport

The objective of this research is to reduce energy consumption from intra airport shuttle operations by optimizing routes and schedules, without compromising on passenger travel experience. To achieve this objective, we propose an optimization model that generates optimal airport shuttle routes for a given set of constraints and a discrete-event simulator that evaluates the optimal shuttle routes in a stochastic environment to understand the tradeoffs between the amount of time passengers wait for shuttles, and shuttle energy consumption. The proposed optimization model and stochastic simulation are tested using shuttle route data provided by the Dallas Fort Worth International Airport. Results indicate that optimized routes can lead to a 20% energy reduction in shuttle operations with a modest 2-min increase in average shuttle wait times. The optimization model and simulator presented here are designed to be generalizable and can be adapted to optimize shuttle operations at any major airport.     

Combining service frequency and vehicle routing for managing supplier shipments

This paper proposes a new approach to designing inbound material collection routes that considers pick-up frequency and spatial design as joint decisions to minimize total logistics (transportation plus inventory) cost. The clustering-based optimization uses an approximation to the actual cost of a routing solution without actual route construction. We show that the problem is analogous to a single-source fixed-charge facility location problem, and near-optimal solutions can be found using an efficient heuristic algorithm. Tests show the effectiveness of how this model is formulated and a case study demonstrates that substantial total cost savings can be achieved in realistic applications.     

A novel modeling approach for the fleet deployment problem within a short-term planning horizon

This paper is concerned with model development for a short-term fleet deployment problem of liner shipping operations. We first present a mixed integer nonlinear programming model in which the optimal vessel speeds for different vessel types on different routes are interpreted as their realistic optimal travel times. We then linearize the proposed nonlinear model and obtain a mixed integer linear programming (MILP) model that can be efficiently solved by a standard mixed integer programming solver such as CPLEX. The MILP model determines the optimal route service frequency pattern and take into account the time window constraints of shipping services. Finally, we report our numerical results and performance of CPLEX on randomly generated instances.     

The maxisum and maximin-maxisum HAZMAT routing problems

We design routes for transportation of hazardous materials (HAZMAT) in urban areas, with multiple origin-destination pairs. First, we introduce the maxisum HAZMAT routing problem, which maximizes the sum of the population-weighted distances from vulnerable centers to their closest point on the routes. Secondly, the maximin-maxisum HAZMAT routing problem trades-off maxisum versus the population-weighted distance from the route to its closest center. We propose efficient IP formulations for both NP-Hard problems, as well as a polynomial heuristic that reaches gaps below 0.54% in a few seconds on the real case in the city of Santiago, Chile.     

Expanding the effect of metro station service coverage by incorporating a public bicycle sharing system

This study examines how metro stations can expand their service coverage for passengers by implementing a public bicycle sharing system (PBSS) in the vicinity. We design a stated preference method questionnaire to determine the choice behavior intention of metro passengers toward PBSS and calibrate the survey data using the mixed logit model. We also use a geographic information system to display this expansion in metro station service coverage following the incorporation of PBSS. We then conduct a cost-benefit analysis of PBSS incorporation into the metro system to determine its cost-effectiveness. The decision criteria for PBSS station allocation strategies as derived in this study can act as useful references for urban planners and PBSS operation agencies when initiating low-carbon and sustainable policies.     

Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership

In this study, we employ spatial regression analysis to empirically investigate the impacts of land use, rail service coverage, and rail station accessibility on rail transit ridership in the city of Seoul and the surrounding metropolitan region. Our analyses suggest that a rail transit service coverage boundary of 500 m provides the best fit for estimating rail transit ridership levels. With regard to land use, our results confirm that density is positively related to rail transit ridership within a 750 m radius of each station. In contrast, land use diversity is not associated with rail transit ridership. We also found that station-level accessibility is as important as land use for explaining rail transit ridership levels. Finally, we conclude that development density and station-level accessibility measures such as the number of station entrances or exits and the number of bus routes at the station are the most important and consistent factors for promoting rail transit ridership.     

Clustered airline flight scheduling: Evidence from airline deregulation in Korea

This paper explores the impacts of competition level on airline scheduling in the Korean domestic short-haul routes where a hub-and-spoke system is not the optimal air transport network strategy. The empirical findings using the Korean airline panel data for the period 2006–2010 suggest that competition leads to less differentiated departure flight times as expected from spatial competition theory. Unlike the previous study on the U.S airline industry, the degree of this tendency for less differentiation differs across the type of routes: the Jeju island routes (leisure type) and the inland routes (business type), in the deregulated period. Following the May 2008 Deregulation Act we find an increasingly clustered pattern of airline scheduling in the Jeju island routes where there have been competitive pressures associated with new low cost entrants. This recent evidence would imply that airline carriers strategically schedule departure flight times and allocate flights between routes as competition increases in the deregulated period.     

Containership routing with time deadlines and simultaneous deliveries and pick-ups

In this paper we seek to determine optimal routes for a containership fleet performing pick-ups and deliveries between a hub and several spoke ports. A capacitated vehicle routing problem with pick-ups, deliveries and time deadlines is formulated and solved using a hybrid genetic algorithm for establishing routes for a dedicated containership fleet. Results on the performance of the algorithm and the feasibility of the approach show that a relatively small fleet of containerships could provide efficient services within deadlines. Moreover, through sensitivity analysis we discuss performance robustness and consistency of the developed algorithm under a variety of problem settings and parameters values.     

A carrier’s optimal bid generation problem in combinatorial auctions for transportation procurement

We consider the carrier’s optimal bid generation problem in combinatorial auctions for transportation procurement. Bidders (carriers) employ vehicle routing models to identify sets of lanes (origin-destination pairs) based on the actual routes that a fleet of trucks will follow in order to maximize profit. Routes are constructed by optimally trading off repositioning costs of vehicles and the rewards associated with servicing lanes. The carrier optimization represents simultaneous generation and selection of routes and can incorporate any existing commitment. We employ both column generation and Lagrangian based techniques for solving the carrier optimization model and present numerical results.     

A short-term flight scheduling model for international express package delivery

A short-term flight scheduling model for air express carriers is developed to determine suitable routes and flight schedules with the objective of minimizing operating costs, subject to related operating constraints. The model is formulated as an integer multiple commodity network flow problem solved using mathematical programming. For evaluation, a case study is used based on the operating data from a major international air express carrier.     

On the formulation and solution of the convoy routing problem

In this work, we will identify important variables that contribute to vehicular movement in an emergency environment. In particular, we formulate and pose the convoy routing problem. We suggest a method for modeling the problem and formulate a precise problem statement that significantly reduces the number of variables under consideration relative to similar previous work; even so, we prove that the decision version of this problem is NP-complete. After devising an algorithm using artificial intelligence techniques, we then empirically analyze this model (via software simulation) to get computational results on a single instance of the problem.     

SARIMA damp trend grey forecasting model for airline industry

The aim of this paper is to propose a new model that improves the Damp Trend Grey Model (DTGM) with a dynamic seasonal damping factor to forecast routes passengers demand (pax) in the air transportation industry. The model is called the SARIMA Damp Trend Grey Forecasting Model (SDTGM). In the DTGM, the damp trend factor is a static smoothing factor because it does not change over time, and therefore, it cannot capture the dynamic behavior of time series data. For this reason, the modification consists in using the trend and seasonality effects of time series data to calculate a dynamic damp trend factor as time grows. The DTGM damping factor is based on the forecasted data obtained by the GM(1,1) model; otherwise, the SDTGM calculates a seasonal damping factor based on historical data using a large amount of data points for short lead-times. The SDTGM has less uncertainty than the DTGM. The simulation results show that the SDTGM captures the seasonality effect and does not allow the forecast to exponentially grow. The SDTGM forecasts more reasonable routes pax for short lead-times when having a large amount of data points than the DTGM. The United States domestic air transport market data are used to compare the performance of the DTGM against the proposed SDTGM.     

The economics and geography of regional airline services in six countries

Do the determinants of service and pricing on “regional” routes – linking towns and smaller cities to main trunk routes and/or to each other – differ from the established results from the literature? We study all flights (about 3000) on all regional routes (about 250) with scheduled airline service from one of about 130 regional towns or cities, in regional airline markets in six countries: Australia, Canada, New Zealand, Norway, Sweden, and a sample of three U.S. states which closely resemble the other regions studied. For each flight we have observations on up to five prices offered at different times before flight date. We also have equipment type and social-economic data. Overall, our results give qualified support to the standard gravity model of the extent of service between city pairs, though with two interesting differences: operators on regional routes have greater flexibility in the size of aircraft they can deploy, which results in a finer-grained variability of service offerings and, the presence of competition on regional routes has a large effect on the total supply of seats. We are able to successfully estimate a well-specified airfare model, which shows strong effects of competition on prices, quite substantial intertemporal price discrimination, and interesting differences between regional and main trunk route pricing.     

Airline alliances and partner firms’ outputs

This paper analyzes the effects of airline alliances on partner airlines’ outputs by comparing traffic changes on alliance routes with those on non-alliance routes. A theoretical model of an airline alliance is developed, and an empirical analysis is then conducted using panel data from four major alliances in North Atlantic markets during the 1992–1994 period. It is found that most of the partners have greater traffic increases on their alliance routes than those on their non-alliance routes, supporting a hypothesis derived from the theoretical model.     

Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost

The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.