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It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This paper aims to obtain the evolution law of dynamic performance of CR400BF electric multiple unit (EMU).

Using the dynamic simulation based on field test, stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers were tested. Stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to research the evolution law with running mileage of dynamic index of CR400BF EMU.

The results showed that stiffness and damping coefficient subjected to normal distribution, the mean and variance were computed and the evolution law of stiffness and damping coefficient with running mileage was obtained.

Firstly, based on the field test we found that stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers subjected to normal distribution, and the evolution law of stiffness and damping coefficient with running mileage was proposed. Secondly stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to the research to the evolution law with running mileage of dynamic index of CR400BF EMU.

The purpose of this paper was to assess the economic impact of investment in different animal welfare–enhancing flooring solutions in Swedish dairy farming.

The authors developed a bio-economic model and used stochastic partial budgeting approach to simulate the economic consequences of enhancing solid and slatted concrete floors with soft rubber covering.

The findings highlight that keeping herds on solid and slatted concrete floor surfaces with soft rubber coverings is a profitable solution, compared with keeping herds on solid and slatted concrete floors without a soft covering. The profit per cow when kept on a solid concrete floor with soft rubber covering increased by 13%–16% depending on the breed.

Promoting farm investments such as improvement in flooring solution, which have both economic and animal welfare incentives, is a potential way of promoting sustainable dairy production. Farmers may make investments in improved floors, resulting in enhanced animal welfare and economic outcomes necessary for sustaining dairy production.

This literature review indicated that the economic impact of investment in specific types of floor improvement solutions, investment costs and financial outcomes have received little attention. This study provides insights needed for a more informed decision-making process when selecting optimal flooring solutions for new and renovated barns that improve both animal welfare and ease the burden on farmers and public financial support.

The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.

Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.

The electricity costs of the bus route can be reduced by applying the optimal charging plans.

This paper produces a viable option for transit agencies to reduce their operation costs.

The awarding of terminals to private operators is considered a prime task of landlord port authorities. Yet, terminal concessions in seaports have only recently gained interest in academic circles. The awarding process poses a complex set of managerial challenges to port authorities, one of the key issues being the determination of the duration of the concession.

Despite the importance of the duration of terminal concessions in seaports, the issue has not received much attention in academic circles. Factors impacting on the duration of contracts, leases or concessions have, however, been studied extensively in other research areas, such as agriculture, coal contracts, franchising and natural gas. This paper uses insights from these academic studies to obtain a better understanding of the impact of concession duration on the stakeholders involved and relates them to empirical evidence on concession length in European seaports. The paper then proposes a classification scheme for the exogenous determination of concession duration, based on techniques developed for Public-Private-Partnerships in large infrastructure projects. In the last section the paper discusses the importance of concession durations to various stakeholders in seaports and illustrates these principles using a case study.

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.

Though an accumulating body of study has analysed monetary policy transmission in India, there are few studies examining the differential impact of monetary policy action. Against this backdrop, this study aims to analyse the differential impact of monetary policy on aggregate demand, aggregate supply and their components along with the general price level in India.

The study develops a structural macroeconometric model, which is primarily aggregate and eclectic in nature. The generalized method of movements is used for estimation of behavioural equations, while a Gauss–Seidel algorithm is used for model simulation purposes.

The paper presents the results of two policy simulations from the estimated model that highlight the differential impact of monetary policy. The first one, hike in the policy rate by 5% and second is a reduction in bank credit to the commercial sector by 10%. The results from the first policy simulation experiment reveal that interest hike has a significant negative impact on aggregate demand, aggregate supply and general price level. However, the maximum impact is borne by investment demand and imports followed by private consumption. While as among the components of aggregate supply maximum impact is born by infrastructure output followed by the manufacturing and services sector with the agriculture sector found to be insensitive in nature. The results from the second policy simulation experiment revealed that pure monetary shocks have a significant negative impact on aggregate demand, aggregate supply and general price level. However, the maximum impact is born by private consumption and imports followed by investment demand. While as among components of aggregate supply maximum impact is borne by infrastructure followed by the manufacturing and services sector with the agriculture sector found to be insensitive in nature. From both policy simulation experiments, the study highlighted the relative importance of the income absorption approach as opposed to the expenditure switching effect.

The results obtained in this study provides a strong framework for design the monetary policy framework. The results are in a view of the differential impact of monetary policy action among the components of both aggregate demand and aggregate supply. This reflection of differential impact has immense significance for the macroeconomic stabilization as the central bank will have to weigh the varying repercussion of its actions on different sectors. For instance, the decline in output after monetary tightening might be conceived as mild from an overall perspective, but it can be appreciable for some sectors. This differential influence will have an implication for policy design to care for distributional aspects, which otherwise could be neglected/disregarded. Similarly, the output decline may be as a result of either consumption postponement or a temporary slowdown in investment. However, the one emanating due to investment decline will have lasting growth implications compared to a decline in consumer demand. In addition, the relative strength of expenditure changing or expenditure switching policies of trade balance stabilization may have varying consequences in the aftermath of monetary policy shock. Accordingly information on the relative sensitiveness/insensitiveness of different sectors/ components of aggregate demand towards monetary policy actions furnish valuable insights to monetary authorities in framing appropriate policy.

The work carried out in the present paper is motivated by the fact that although a number of studies have examined the monetary transmission mechanism in India, a very few studies examining the differential impact of monetary policy action. However, to the best of the knowledge, there is no such studies, which have examined the differential impact of monetary policy in the structural macro-econometric framework. The paper will enrich the existing literature by providing a detailed account of the differential impact of monetary policy among the components of both aggregate demand and aggregate supply in response to an interest rate hike, as well as a decrease in the money supply.

This study aims to analyze how different room-committing practices affect the occupancy and profitability of hotels and it critically reviews the role of minimum-length-of-stay (MLOS) requirements given these findings.

The approach uses statistical analysis of simplified contexts to develop understanding, and simulations of more complex situations to confirm the relevance in realistic contexts.

The study demonstrates that proper solutions of the room-committing problem improve occupancy and profitability, in particular, for hotels working in high-season and high-occupancy situations. Smart committing algorithms diminish the role of MLOS requirements. More demand can be accepted without sacrificing late-arriving long reservations.

To the best of the authors’ knowledge, this work, building upon a previous one cited in this paper, is the first to rigorously study the room-committing problem and to demonstrate its relevance in practical situations and its implications on MLOS rules.

This study aims to assess the use of variational quantum imaginary time evolution for solving partial differential equations using real-amplitude ansätze with full circular entangling layers. A graphical mapping technique for encoding impulse functions is also proposed.

The Smoluchowski equation, including the Derjaguin–Landau–Verwey–Overbeek potential energy, is solved to simulate colloidal deposition on a planar wall. The performance of different types of entangling layers and over-parameterization is evaluated.

Colloidal transport can be modelled adequately with variational quantum simulations. Full circular entangling layers with real-amplitude ansätze lead to higher-fidelity solutions. In most cases, the proposed graphical mapping technique requires only a single bit-flip with a parametric gate. Over-parameterization is necessary to satisfy certain physical boundary conditions, and higher-order time-stepping reduces norm errors.

Variational quantum simulation can solve partial differential equations using near-term quantum devices. The proposed graphical mapping technique could potentially aid quantum simulations for certain applications.

This study shows a concrete application of variational quantum simulation methods in solving practically relevant partial differential equations. It also provides insight into the performance of different types of entangling layers and over-parameterization. The proposed graphical mapping technique could be valuable for quantum simulation implementations. The findings contribute to the growing body of research on using variational quantum simulations for solving partial differential equations.