David Rey

A Scenario-Based Evaluation of the Middle East Respiratory Syndrome Coronavirus and the Hajj

Between April 2012 and June 2014, 820 laboratory‐confirmed cases of the Middle East respiratory syndrome coronavirus (MERS‐CoV) have been reported in the Arabian Peninsula, Europe, North Africa, Southeast Asia, the Middle East, and the United States. The observed epidemiology is different to SARS, which showed a classic epidemic curve and was over in eight months. The much longer persistence of MERS‐CoV in the population, with a lower reproductive number, some evidence of human‐to‐human transmission but an otherwise sporadic pattern, is difficult to explain. Using available epidemiological data, we implemented mathematical models to explore the transmission dynamics of MERS‐CoV in the context of mass gatherings such as the Hajj pilgrimage, and found a discrepancy between the observed and expected epidemiology. The fact that no epidemic occurred in returning Hajj pilgrims in either 2012 or 2013 contradicts the long persistence of the virus in human populations. The explanations for this discrepancy include an ongoing, repeated nonhuman/sporadic source, a large proportion of undetected or unreported human‐to‐human cases, or a combination of the two. Furthermore, MERS‐CoV is occurring in a region that is a major global transport hub and hosts significant mass gatherings, making it imperative to understand the source and means of the yet unexplained and puzzling ongoing persistence of the virus in the human population.

A Computational Method for Estimating Travel Frequencies in Site Layout Planning

Optimizing the layout of facilities on a construction site to minimize the material handling costs has been widely investigated in the literature. An integral part of the objective function utilized in the previously proposed optimization models is the frequency parameter, which generally provides a rough estimate of the frequency of travel between each pair of facilities at a specific stage of the project. The majority of travel frequencies deduced in prior studies have been estimated merely based on past experience and may not be an actual representation of movements within the project studied. There is currently a lack of a systematic approach for estimating travel frequencies, required in site layout planning, based on the information available at early stages of the project. Obtaining a reasonable estimate of the travel frequency matrix requires a realistic evaluation of the material transportation quantities at each construction stage. This necessitates the utilization of information on the progress of different activities and their corresponding material needs during the various stages. This paper presents a framework for obtaining travel frequencies at different construction project phases by taking advantage of the information made available by building information models and project schedules. The estimated frequencies are later embedded as parameters in an optimization model to improve site layout planning. The results of a case study are presented to highlight the capabilities of the proposed framework.

A cutting plane algorithm for the site layout planning problem with travel barriers

A novel discrete Construction Site layout planning model (SLP) is proposed, where multiple coverage of locations by facilities is permitted.We contrast the proposed model with the approach commonly adopted in the literature to solve the SLP problem to exact optimality.We quantify the impact of space discretisation on discrete SLP models.We propose a novel cutting plane algorithm to solve large instances of the discrete SLP problem.We provide a comprehensive analysis on the computational effectiveness of the proposed models. Site layout planning is an imperative procedure that may significantly impact the productivity and the efficiency of logistical operations undertaken on a construction site. This paper considers the site layout planning problem (SLPP) which entails the allocation of temporary facilities on a construction site in the presence of travel barriers such that the total transportation cost between facilities is minimised. In order to account for travel barriers, the SLPP is typically solved under the assumption that the available region for facility layout can be discretised. In this paper, we propose a general Mixed Integer Programming (MIP) model to represent the SLPP, accounting for the presence of barriers, and we show how space-discretised formulations can be derived from this model. In particular, we propose a novel MIP model, which permits facilities to cover multiple locations. This is then benchmarked against a commonly adopted MIP model in the literature. We also highlight a systematic procedure to convert the continuous feasible space in SLPP to a set of discretised locations based on the concept of d-visibility, enabling us to approximate the barrier distance function embedded in the objective function. In particular, we focus on presenting a simple space discretisation approach for converting the continuous SLP into a discrete problem for which the discrete SLP models would be applicable. Space-discretised MIP formulations are highly combinatorial and we introduce a cutting plane algorithm to improve their tractability. Specifically, we propose a novel exact location-decomposition algorithm which works from a relaxed MIP formulation and iteratively generates feasibility cuts to converge to an optimal solution. Both space-discretised MIP models and the decomposition algorithm are tested on a large group of instances to analyse their effectiveness in solving the SLPP. Computational results indicate that the proposed location-decomposition algorithm improves on the pure MIP approach and provides a competitive framework to solve realistic SLPP instances.

A Clustering Algorithm for Bi-Criteria Stop Location Design with Elastic Demand

This article proposes a bi-criteria formulation to find the optimal location of light rapid transit stations in a network where demand is elastic and budget is constrained. Our model is composed of two competing objective functions seeking to maximize the total ridership and minimize the total budget allocated. In this research, demand is formulated using the random utility maximization method with variables including access time and travel time. The transit station location problem of this study is formulated using mixed integer programming and we propose a heuristic solution algorithm to solve large-scale instances which is inspired by the problem context. The elastic demand is integrated with the optimization problem in an innovative way which facilitates the solution process. The performance of our model is evaluated on two test problems and we carry out its implementation on a real-world instance. Due to the special shape of the Pareto front function, significant practical policy implications, in particular budget allocation, are discussed to emphasize the fact that the trade-off between cost and benefit may result in large investments with little outcomes and vice versa.

Using Lagrangian Relaxation to Solve Ready Mixed Concrete Dispatching Problems

The logistics and planning problem of delivering ready mixed concrete (RMC) to a set of demand customers from multiple depots is addressed. The RMC dispatching problem (RMCDP) is closely related to the vehicle routing problem, with the difference that a truck may visit demand nodes in the RMCDP more than once. This class of routing problems can be represented by using mixed-integer programming (MIP) and is known to be NP-hard. Solving RMC delivery problems is often achieved through heuristics and metaheuristic-based methods as exact solution approaches are often unable to find optimal solutions efficiently, in particular when multiple depots are represented in the model. Although a variety of methods are available to solve MIP models, in this paper an attempt is made to solve the RMCDP by using a Lagrangian relaxation technique. Namely, a solution algorithm based on Lagrangian relaxation is derived to reduce the complexity of the initial MIP model and show that the proposed relaxation is able to provide promising computation results on a realistic data set representative of an active RMCDP in the region of Adelaide, Australia.

A mixed-integer nonlinear programming model for minimising construction site noise levels through site layout optimisation

Activities undertaken on a construction site are often accompanied with high levels of noise. Addressing the issue of noise pollution in construction is gaining significance with the growing awareness about the social and environmental components of sustainable construction and the increasing numbers of projects being undertaken in congested urban areas. The documented methods for reducing noise pollution in construction include controlling (1) the noise produced at the source; (2) noise levels reaching a receptor; (3) noise propagated along the transmission path. Methods addressing the latter points use the fact that attenuation of noise increases as the transmission path gets longer. Thus the efficiency of such methods can be improved considerably through optimising the arrangement of temporary facilities on construction sites, with respect to a receptor, making use of noise attenuation due to distancing noisy facilities away from noise-sensitive receivers. The building under construction can also be used as a barrier to the noise transmission path, where obstruction of particular facilities from a given receiver can help in producing lower levels of sound as measured at the receptor. The available literature on site layout planning is extensive but limited to only achieving traditional construction project objectives (travel and material handling cost, safety, etc.). This paper presents a mixed integer non-linear programming (MINLP) model that optimises the location of temporary facilities on site in order to minimise the sound levels measured at a pre-defined receptor. The present model is expressed in three stages: (1) defining the noise objective function; (2) implementing model constraints; and (3) application of COUENNE to solve the MINLP for a case study.

Fair allocation and cost-effective routing models for food rescue and redistribution

ABSTRACT The not-for-profit food rescue organizations play a vital role in alleviating hunger in many developing and developed countries. They rescue surplus food from the business sector and redistribute to welfare agencies supporting different forms of food relief. Routing and allocation decisions are critical in food rescue operations, in particular when there is a significant gap between supply and demand. However, there is a gap in the literature with regards to models that account for fairness in the allocation of limited rescued food along with efficient routing. We present three objective functions: utilitarian, egalitarian, and deviation-based for efficient and fair food allocation, and present a goal programming–based formulation combining cost-effective routing and allocation objectives to obtain balanced solutions. We propose and implement a heuristic solution algorithm for this food relief logistics problem and report numerical results from realistic food rescue instances.

Maximizing the number of conflict-free aircraft using mixed-integer nonlinear programming

We address the conflict detection and resolution problem in air traffic control, where an aircraft conflict is a loss of separation between aircraft trajectories. Conflict avoidance is crucial to ensure flight safety and remains a challenging traffic control problem. We focus on speed control to separate aircraft and consider two approaches: (i) maximize the number of conflicts resolved and (ii) identify the largest set of conflict-free aircraft. Both problems are modeled using mixed-integer nonlinear programming and a tailored greedy algorithm is proposed for the latter. Computational efficiency is improved through a pre-processing algorithm which attempts to reduce the size of the conflict resolution models by detecting the existence of pairwise potential conflicts. Numerical results are provided after implementing the proposed models and algorithms on benchmark conflict resolution instances. The results highlight the benefits of using the proposed pre-processing step as well as the versatility and the efficiency of the proposed models.

A Three-stage Framework for Motorway Travel Time Prediction

This paper presents a framework for short-term travel time prediction in a motorway with a three-stage architecture: traffic flow forecasting, traffic flow generation and travel time extraction. Traffic flow forecasting reads the historical traffic data and utilizes a forecasting model - Autoregressive Integrated Moving Average (ARIMA) to predict short-term traffic flow. The traffic flow generation utilizes the Cell Transmission Model (CTM) to generate outgoing flow of a road of interest based on the predicted incoming flow from ARIMA. Predicted short term travel times can then be obtained through N-Curve Analysis. Compared to most studies, this paper presents a historical data-driven framework for travel time prediction that can be trained based on specific profiles of routes and cities. The motorway M4 in Sydney, Australia was used to test this framework. It is shown that the predicted travel times can be used to anticipate congestion episodes at the network level.

Bilevel Optimization Model for the Development of Real-Time Strategies to Minimize Epidemic Spreading Risk in Air Traffic Networks

An understanding is needed of how epidemics spread to new regions via the global air traffic network so that effective strategies for outbreak control can be developed. Various studies have focused on predicting epidemic spread via the complex air traffic network. However, there is a gap in the literature demanding real-time predictive models that exploit the heterogeneous nature of the air travel pattern to optimize decision making among a set of potential control strategies. A bilevel optimization model is proposed to solve the resource allocation problem for an ongoing epidemic spreading via the air traffic network. The upper-level objective is to optimize the distribution of limited resources for epidemic control, and the lower-level simulation model computes the risk posed to the network under possible scenarios. Results from a demonstration network highlight the advantages of this model. A case study evaluates the risk posed by Ebola to the United States through the domestic air traffic network. The results demonstrate the ability of the model to develop real-time strategies that account for the heterogeneous nature of the air traffic network and the complex dynamics of epidemic spread.