Recovery of disruptions in rapid transit networks

This paper studies the disruption management problem of rapid transit rail networks. Besides optimizing the timetable and the rolling stock schedules, we explicitly deal with the effects of the disruption on the passenger demand.We propose a two-step approach that combines an integrated optimization model (for the timetable and rolling stock) with a model for the passengers’ behavior.We report our computational tests on realistic problem instances of the Spanish rail operator RENFE. The proposed approach is able to find solutions with a very good balance between various managerial goals within a few minutes.     

Carrying capacity procurement of rail and shipping services for automobile delivery with uncertain demand

The determination of the optimal carrying capacity procurement of rail and shipping services in the automobile intermodal network with unique characteristics is essential to save automobile delivery cost. In this research we develop a two-stage stochastic programming model for the tactical-level decision problem arising in the special automobile intermodal network. Furthermore, we improve the sample average approximation algorithmic procedure to solve the model. We apply the model and solution method to a case study associated with the Shanghai Automobile Industry Corporation. We believe that this study deals with an emerging new research topic with practical significance for the automobile industry.     

A comparison of two exact methods for passenger railway rolling stock (re)scheduling

The assignment of rolling stock units to timetable services in passenger railways is an important optimization problem that has been addressed by many papers in different forms. Solution approaches have been proposed for different planning phases: strategic, tactical, operational, and real-time planning. In this paper we compare two approaches within the operational and real-time planning phase. The first exact approach is based on a known Mixed Integer Linear Program (MILP) which is solved using CPLEX. The second approach is a new method that is an extension of a recently introduced MILP, which is solved using a column and row generation approach. In this paper, we benchmark the performance of the methods on networks of two countries (Denmark and The Netherlands). We use the approaches to make daily schedules and we test their real time applicability by performing tests with different disruption scenarios. The computational experiments demonstrate that both models can be used on both networks and are able to find optimal rolling stock circulations in the different planning phases. Furthermore, the results show that both approaches are sufficiently fast to be used in a real-time setting.     

Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions

In urban logistics, the last-mile delivery from the warehouse to the consumer’s home has become more and more challenging with the continuous growth of E-commerce. It requires elaborate planning and scheduling to minimize the global traveling cost, but often results in unattended delivery as most consumers are away from home. In this paper, we propose an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery. To efficiently solve the model, we formulate it as a network min-cost flow problem and propose various pruning techniques that can dramatically reduce the network size. Comprehensive experiments were conducted with Singapore and Beijing datasets. The results show that our solution can support real-time delivery optimization in the large-scale mobile crowd-sourcing problem.     

A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach

This paper presents a new multi-objective mathematical model to address a Healthcare Inventory Routing Problem (HIRP) for medicinal drug distribution to healthcare facilities. The first part of objective function minimizes total inventory and transportation costs, while satisfaction is maximized by minimizing forecast error which caused by product shortage and the amount of expired drugs; Greenhouse Gas (GHG) emissions are also minimized. A demand forecast approach has been integrated into the mathematical model to decrease drug shortage risk. A hybridized possibilistic method is applied to cope with uncertainty and an interactive fuzzy approach is considered to solve an auxiliary crisp multi-objective model and find optimized solutions.     

Study of emerging air taxi network operation using discrete-event systems simulation approach

This research focuses on understanding the air taxi operations to determine the number of air taxis required to fulfill the demand for urban air mobility in New York City (NYC). We leverage the Define, Measure, Analyze, Design, and Verify (DMADV) framework and integrate it with the systems simulation approach. Upon investigation, we find that all the parameters linearly impact the vehicle utilization, while other measures are robust, specifically with respect to the seating capacity. It is also recommended to operate initially with 70 air taxis in NYC to achieve a trade-off between customer wait time and vehicle utilization. The proposed approach can act as a recommender system for air taxi companies.     

Estimation of traffic density from drone-based delivery in very low level urban airspace

Driven by rising consumer demand, interest is growing in the application of autonomous unmanned aerial vehicles (drones) for the last-mile delivery of small express packages and fast-food meals in cities. To be realised, this would require the Very Low Level (VLL) urban airspace to be able to cope with high traffic densities of commercial delivery drones. The potential benefits of such novel drone-based applications are a reduction of traffic congestion in cities, lower greenhouse gas emissions and more efficient transportation operations. To help realise this concept, programs such as U-Space, the unmanned traffic management system for Europe, are developing important services such as deconfliction management and dynamic capacity management. However, for several of these services, design choices will depend on how, and how extensive they will be used. It therefore becomes important to estimate how many delivery drones would operate in a typical city. This paper aims to provide an estimate by establishing a framework to determine the traffic demand for express drone-based package delivery of five European countries. In addition, a detailed case-study is presented for determining traffic density of express package drone delivery for Paris metropolitan area in order to assess the feasibility from a user's perspective. The paper also discusses the potential of fast-food meal delivery drones compared to traditional delivery modes for Paris. Results suggest that hourly traffic densities culminating from express package and fast-food meal delivery drones will exceed today's global commercial aircraft traffic of 10,000 per day by more than six-fold for just one potential metropolitan city.     

Stochastic programming approach to re-designing a warehouse network under uncertainty

The warehouse network re-design problem includes integrating or eliminating existing warehouses and establishing new sites. In this paper, we incorporate variability in product demand and operational costs with a two-stage stochastic modeling approach. We use the Sample Average Approximation (SAA) approach together with Benders decomposition to provide a solution method. Our results indicate not only that the stochastic solution is an improvement over the deterministic solution but also that the solutions’ differences grow with increasing uncertainty. The stochastic solutions show more robustness than the deterministic solutions. The computational results show that a change in the type of probability distribution of the stochastic parameters does not significantly affect the value of the stochastic solutions.     

Choice model based analysis of consumer preference for drone delivery service

It is anticipated that drones will soon be utilized for a range of applications, including delivery service. However, there has been a lack of research on consumer preference between drone delivery service and traditional delivery service. This is the first study to analyze the consumer preference for drone delivery based on a discrete choice model between the drone delivery service and traditional delivery services by truck or motorcycle. The discrete choice model is estimated using a stated preference survey, and potential consumers’ preference is analyzed for representative commodities with different price. The results show that the price and type of commodities influence consumer preference, which also depends on socio-demographic characteristics such as gender, age, and household income. Specifically, it was consistently observed in all cases that the younger the age, the higher the preference for drone delivery service. This study contributes to predicting the consumer preference for drone delivery service before real service offerings and to supporting the establishment of business strategies for companies who prepare for the new market of drone-based delivery.     

A location-inventory-pricing model in a supply chain distribution network with price-sensitive demands and inventory-capacity constraints

This paper presents a location-inventory-pricing model for designing the distribution network of a supply chain with price-sensitive demands and inventory-capacity constraints. The supply chain has market power and uses markup pricing. An efficient Lagrangian relaxation algorithm is proposed to solve the model. Our numerical study shows that by moderately increasing the number of possible values for pricing decisions, the model can be used to find near-optimal solutions of a similar location-inventory-pricing problem with continuous pricing decisions. The approach used here to incorporate pricing decisions can be applied to other supply-chain design and planning problems with price-sensitive demands.     

Delivery leadtime and channel structure decisions for make-to-order duopoly under different game scenarios

We develop game-theoretic models to explore the quoted delivery leadtime, price, and channel structure decisions for a make-to-order duopoly system under three game scenarios. Under the integrated-manufacturer first scenario, we find that (i) decentralization of the supply chain increases quoted leadtime; and (ii) both manufacturers may choose different channel structures under symmetric duopoly. By comparing with the symmetric scenario and the retailer first scenario, we find that a manufacturer facing a decentralized rival adopts decentralization when leadtime sensitivity, leadtime cost, and price elasticity are very small; the effect of decentralization on quoted leadtime largely depends on game scenario.     

Multi-objective design of an organ transplant network under uncertainty

We propose a novel multi-period location–allocation model for the design of an organ transplant transportation network under uncertainty. The model consists of a bi-objective mathematical programming model that minimizes total cost and time, including waiting time in the queue for the transplant operation, while considering organs’ priorities. A fuzzy multi-objective programming based approach is presented to solve the small and medium size problems to optimality. For larger problems, we propose two meta-heuristics based algorithms. Lower bounds, and several numerical examples with managerial insights are discussed. A real case-study is provided, and the existing and the proposed optimal solutions are compared.     

An integrative approach to determine store delivery patterns in grocery retailing

This article focuses on the tactical problem of selecting delivery patterns according to which grocery stores are repetitively supplied with products from different order segments by retail-owned distribution centers. The research environment considered consists of logistics processes in DCs, transportation and instore logistics. We identify dependencies on the delivery patterns selected and specify the relevant costs. These costs are reflected in the objective function of a binary selection model. Implementing and applying the model to the real case of a major European retail company yields substantial cost savings potential of 5.3%, amounting to tens of millions of euros per annum.     

Competition and efficiency in the Italian airport system: new insights from a conditional nonparametric frontier analysis

We analyse the effect of competition on technical efficiency of Italian airports by applying a novel conditional nonparametric frontier analysis for the first time to the airport industry. We find that competition affects mostly the frontier of best performers, whilst airports that are lagging behind are less influenced. A novel two stage approach shows that, on average, competition has a negative impact on technical efficiency. We estimate a measure of pure efficiency, whitened from the main effect of the competition, whose distribution has a bi-modal shape, indicating the existence of two differently managed groups of airports.     

Dynamic road lane management study: A Smart City application

Our Smart City contribution is transportation-oriented in that it proposes a dynamic road lane management system in order to share appropriately the space devoted to traffic. After a historical view of a series of solutions from physical to ICT supported, we present our proposal extensively supported by up-to-date ICT. Following a main presentation, we describe the system architecture and its working conditions. Then, we present the proposed simulator designed to study operating and driver’s conditions with respect to the new traffic signs proposed. We also describe a Mock-up technology validation and give preliminary information on in-the-field deployment.     

Hedging jet fuel price risk: The case of U.S. passenger airlines

Jet fuel accounts for a large portion of passenger airlines' operating costs, and airlines' earnings are susceptible to swings in the price of jet fuel. This study uses daily data over the past two decades to determine the minimum variance hedge ratio for airlines wishing to hedge jet fuel price risk with futures, while also establishing the best cross hedging asset. Airlines hedging with futures would create the most effective hedge by using heating oil futures contracts with a 3-month maturity. We also find that beyond the 3-month veil, increased time to maturity makes heating oil less effective as a cross hedge proxy for jet fuel. However, both in-sample analysis and Monte Carlo simulation results with daily data show that none of the 4 cross hedge proxies, including heating oil, can be considered highly effective.     

A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling,with consideration of fleet assignment problem

One of the most important airline's products is to determine the aircraft routing and scheduling and fleet assignment. The key input data of this problem is the traffic forecasting and allocation that forecasts traffic on each flight leg. The complexity of this problem is to define the connecting flights when passengers should change the aircraft to reach the final destination. Moreover, as there exists various types of uncertainties during the flights, finding a solution which is able to absorb these uncertainties is invaluable. In this paper, a new robust mixed integer mathematical model for the integrated aircraft routing and scheduling, with consideration of fleet assignment problem is proposed. Then to find good solutions for large-scale problems in a rational amount of time, a heuristic algorithm based on the Simulated Annealing (SA) is introduced. In addition, some examples are randomly generated and the proposed heuristic algorithm is validated by comparing the results with the optimum solutions. The effects of robust vs non-robust solutions are examined, and finally, a hybrid algorithm is generated which results in more effective solution in comparison with SA, and Particle Swarm Optimization (PSO).     

An approximation approach to a trade-off among efficiency,efficacy, and balance for relief pre-positioning in disaster management

This work develops a multi-objective, two-stage stochastic, non-linear, and mixed-integer mathematical model for relief pre-positioning in disaster management. Improved imbalance and efficacy measures are incorporated into the model based on a new utility level of the delivered relief commodities. This model considers the usage possibility of a set of alternative routes for each of the applied transportation modes and consequently improves the network reliability. An integrated separable programming-augmented ε-constraint approach is proposed to address the problem. The best Pareto-optimal solution is selected by PROMETHEE-II. The theoretical improvements of the presented approach are validated by experiments and a real case study.     

Multi-period hub location problems in transportation

Many transport service providers operate on hub-and-spoke network structures. Major operators may have several dedicated hub facilities that are leased for a time horizon rather than being owned or constructed. For a given discrete planning horizon, service providers must decide on the location of the hub ports (i.e. terminals), the period when the lease contract starts, the period when the existing contracts must be terminated and the flow routing over the entire planning horizon so as to minimize the total operational cost. Thus, we propose a mathematical model for a Multi-period Uncapacitated Multiple Allocation Hub Location Problem with Budget Constraint. The proposed model incorporates several features of practice, particularly from maritime and land transport practices. We also propose a meta-heuristic solution algorithm that produces high-quality solutions in a reasonable amount of time. By exploiting the decomposable structure of the model, we extended a Benders decomposition approach by proposing several improvements. Extensive computational experiments confirm the efficiency of the proposed methods and also show its limitations.     

A matheuristic approach to the air-cargo recovery problem under demand disruption

Air cargo transport is subject to unpredictable changes in expected demand, necessitating adjustments to itinerary planning to recover from such disruptions. We study a flight rescheduling problem to react to cargo demand disruptions in the short run. To increase flexibility, we consider two different cargo assignment policies. We propose a matheuristic approach to solve the problem that provides high-quality solutions in a short computational time, based on column generation in which each subproblem is solved using an ad-hoc heuristic. The approach is tested on demand disruption instances containing up to 75 air cargo orders with different penalty levels. The results show that the proposed method improves profit by 54% over the solution generated by a commercial MIP solver within a 1-h time limit, and by 15% over the solution with the routes fixed as in the original flight planning that only allows cargo to be re-routed. We also show that there exist incremental benefits in the range of 3–5% by allowing cargo for a given order to be transported by various aircraft.