Technology of autonomous vehicles (AVs) is becoming mature, and many AVs will appear on roads in the near future. AVs become connected with the support of various vehicular communication ...technologies, and they possess a high degree of control to respond to instantaneous situations cooperatively with high efficiency and flexibility. In this paper, we propose a new public transportation system based on AVs. It manages a fleet of AVs to accommodate transportation requests, offering point-to-point services with ride sharing. We focus on the two major problems of the system: scheduling and admission control. The former is to configure the most economical schedules and routes for the AVs to satisfy the admissible requests, whereas the latter is to determine the set of admissible requests among all requests to produce maximum profit. The scheduling problem is formulated as a mixed-integer linear program, and the admission control problem is cast as a bilevel optimization, which embeds the scheduling problem as the major constraint. By utilizing the analytical properties of the problem, we develop an effective genetic-algorithm-based method to tackle the admission control problem. We validate the performance of the algorithm with real-world transportation service data.
We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. ...However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches.
A smart city provides its people with high standard of living through advanced technologies, and transport is one of the major foci. With the advent of autonomous vehicles (AVs), an AV-based public ...transportation system has been proposed recently, which is capable of providing new forms of transportation services with high efficiency, high flexibility, and low cost. For the benefit of passengers, multi-tenancy can increase market competition leading to lower service charge and higher quality of service. In this paper, we study the pricing issue of the multi-tenant AV public transportation system and three types of services are defined. The pricing process for each service type is modeled as a combinatorial auction, in which the service providers, as bidders, compete for offering transportation services. The winners of the auction are determined through an integer linear program. To prevent the bidders from raising their bids for higher returns, we propose a strategy-proof Vickrey-Clarke-Groves-based charging mechanism, which can maximize the social welfare, to settle the final charges for the customers. We perform extensive simulations to verify the analytical results and evaluate the performance of the charging mechanism.
Due to various green initiatives, renewable energy will be massively incorporated into the future smart grid. However, the intermittency of the renewables may result in power imbalance, thus ...adversely affecting the stability of a power system. Frequency regulation may be used to maintain the power balance at all times. As electric vehicles (EVs) become popular, they may be connected to the grid to form a vehicle-to-grid (V2G) system. An aggregation of EVs can be coordinated to provide frequency regulation services. However, V2G is a dynamic system where the participating EVs come and go independently. Thus, it is not easy to estimate the regulation capacities for V2G. In a preliminary study, we modeled an aggregation of EVs with a queueing network, whose structure allows us to estimate the capacities for regulation-up and regulation-down separately. The estimated capacities from the V2G system can be used for establishing a regulation contract between an aggregator and the grid operator, and facilitating a new business model for V2G. In this paper, we extend our previous development by designing a smart charging mechanism that can adapt to given characteristics of the EVs and make the performance of the actual system follow the analytical model.
To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans, so the number of electric vehicles (EVs) running in a city will ...grow significantly. There are many ways to recharge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being recharged. Based on these new perspectives, we formulate the EV charging station placement problem (EVCSPP) in this paper. We prove that the problem is nondeterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP, and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.
Real-Coded Chemical Reaction Optimization Lam, A. Y. S.; Li, V. O. K.; Yu, J. J. Q.
IEEE transactions on evolutionary computation,
06/2012, Letnik:
16, Številka:
3
Journal Article
Recenzirano
Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical ...reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain.
Patterns of hepatitis B virus (HBV) reactivation in hepatitis B surface antigen (HBsAg) -negative, antihepatitis B core antigen antibody (anti-HBc) -positive patients with lymphoma receiving ...rituximab-containing chemotherapy have not been well described.
HBsAg-negative, anti-HBc-positive Chinese patients with undetectable serum HBV DNA (< 10 IU/mL), diagnosed with hematologic malignancies and receiving rituximab-containing chemotherapy, were prospectively monitored every 4 weeks for up to 2 years. Entecavir was started when HBV reactivation (defined as detectable HBV DNA) was encountered.
Among 260 patients receiving rituximab-containing chemotherapy, 63 patients (24.2%) who were HBsAg negative and anti-HBc positive underwent follow-up for a median of 70 weeks (range, 6 to 104 weeks). The 2-year cumulative rate of HBV reactivation was 41.5%, occurring at a median of 23 weeks (range, 4 to 100 weeks) after rituximab treatment. The median HBV DNA level at reactivation was 43 IU/mL (range, 14 to 920 IU/mL). A baseline undetectable antibody to HBsAg (anti-HBs; < 10 mIU/mL) was the only significant risk factor that was positively associated with HBV reactivation (hazard ratio, 3.51; 95% CI, 1.37 to 8.98; P = .009). Patients with negative baseline anti-HBs, compared with those with positive anti-HBs, had a significantly higher 2-year cumulative rate of HBV reactivation (68.3% v 34.4%; P = .012). At HBV reactivation, all patients had normal ALT, and all patients but one were HBsAg negative. Entecavir successfully controlled HBV reactivation in all patients.
A high rate of HBV reactivation was observed in HBsAg-negative, anti-HBc-positive patients undergoing rituximab-containing chemotherapy, with the risk of reactivation significantly higher in anti-HBs-negative patients. Periodic HBV DNA monitoring was an effective strategy in preventing HBV-related complications.
Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for ...power grid, stimulates new ideas in current smart grid research. When coordinating a large number of EVs distributed in different geographical locations, a single aggregator is not sufficient to oversee the whole system and a hierarchical V2G system is required. Therefore, how to design a hierarchical V2G system and how to coordinate large-scale EVs to provide ancillary services become critical issues. In this paper, a generic hierarchical framework for a V2G system, which aims to provide frequency regulation services, is proposed to address the issues. Smart V2G aggregators (SVAs) are designed and employed to control the V2G system in a tree-like manner. A multi-level online V2G (MLOV) algorithm is devised for hierarchical V2G scheduling and it requires no forecasting information on regulation signals. It can also deal with the scalability issue encountered by the centralized algorithms and incast issue arising in the distributed algorithms. The simulation results show that the proposed algorithm outperforms the existing methods for the tradeoff between the quality of frequency regulation services and computational time. Through the computational study of the proposed algorithm, we also find that the computational time of the MLOV algorithm can be reduced exponentially by employing more SVAs and distributing the computational burden to the SVAs, with slight sacrifice on the smoothing quality.
Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in ...microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.
The smart city embraces gradual adoption of autonomous vehicles (AVs) into the intelligent transportation system. Contributed by their full-fledged controllability, AVs can respond to instantaneous ...situations with high efficiency and flexibility. In this paper, we propose a novel AV logistic system (AVLS) to accommodate logistic demands for smart cities. We focus on determining the optimal routes for the governed AVs in consideration of various requirements imposed by the vehicles, logistic requests, renewable generations, and the underlying transportation system. By coordinating their routes and charging schedules, the system can effectively utilize the renewable energy generated by distributed generations. We formulate the joint routing and charging problem in the form of quadratic-constrained mixed integer linear program. To improve its scalability, we develop a distributed solution method via dual decomposition. We conduct extensive simulations to evaluate the performance of proposed system and solution methods. The results show that AVLS can effectively utilize excessive renewable energy while accomplishing all logistic requests. The distributed solution can develop near-optimal solutions with compelling improvement in computational speed.
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