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Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon.

The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems.

The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs.

On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.

In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.

Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions.

It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO.

This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment.

The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach.

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

The purpose of this paper is to review the utilization of game theory in the entrepreneurship literature. Game theory can potentially be employed to assess strategies incentivizing productive entrepreneurial activities and subsequent economic development. Therefore, the author reviews entrepreneurship articles and explores the application of game-theoretic models and concepts in the literature.

First, the author provides an overview of the entrepreneurship ecosystem concept, highlighting key challenges in its study. The author also briefly highlights successful applications of game theory in the innovation literature. Second, the author systematically reviews and synthesizes entrepreneurship research employing game-theoretic models and concepts. The author's objective is to provide a state-of-the-art overview of the use of game theory in entrepreneurship.

Broadly, the author categorizes entrepreneurship-game theory articles into three groups based on their scope and purpose: entrepreneurial policy applications, inter-firm applications and entrepreneurship theory applications. Entrepreneurial policy applications include entrepreneurs and the government or policy as the main players in a game. Inter-firm applications encompass games between entrepreneurs and other private entities. Entrepreneurship theory applications include articles that utilize game theory to advance the author's understanding of entrepreneurial behavior and/or mechanisms in the market.

To the best of the author's knowledge, no previous paper has reviewed the use of game-theoretic approaches and models in entrepreneurship literature. This study addresses this research gap.

This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs).

The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control.

It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies.

In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.

Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.

The new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.

The new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.

The new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

Food banks play an increasingly important role in society by mitigating hunger and helping needy people; however, research aimed at improving food bank operations is limited.

This systematic review used Web of Science and Scopus as search engines, which are extensive databases in Operations Research and Management Science. Ninety-five articles regarding food bank operations were deeply analyzed to contribute to this literature review.

Through a systematic literature review, this paper identifies the challenges faced by food banks from an operations management perspective and positions the scientific contributions proposed to address these challenges.

This study makes three main contributions to the current literature. First, this study provides new researchers with an overview of the key features of food bank operations. Second, this study identifies and classifies the proposed optimization models to support food bank managers with decision-making. Finally, this study discusses the challenges of food bank operations and proposes promising future research avenues.

The literature review aims to facilitate a broader understanding of on-demand service platform operations management and proposes potential research directions for scholars.

This study searches four databases for relevant literature on on-demand service platform operations management and selects 72 papers for this review. According to the research context, the literature can be divided into research on “a single platform” and research on “multiple platforms”. According to the research methods, the literature can be classified into “Mathematical Models”, “Empirical Studies”, “Multiple Methods” and “Literature Review”. Through comparative analysis, we identify research gaps and propose five future research agendas.

This paper proposes five research agendas for future research on on-demand service platform operations management. First, research can be done to combine classic research problems in the field of operations management with platform characteristics. Second, both the dynamic and steady-state issues of on-demand service platforms can be further explored. Third, research employing mathematical models and empirical analysis simultaneously can be more fruitful. Fourth, more research efforts on the various interactions among two or more platforms can be pursued. Last but not least, it is worthwhile to examine new models and paths that have emerged during the latest development of the platform economy.

Through categorizing the literature into two research contexts as well as classifying it according to four research methods, this article clearly shows the research progresses made so far in on-demand service platform operations management and provides future research directions.