Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

Thank you for reading optimization in operations research. As you may know, people have search hundreds times for their favorite books like this optimization in operations research, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their laptop. optimization in operations research is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the optimization in operations research is universally compatible with any devices to read.

Optimization In Operations Research

Proceedings of the fifth international conference on Autonomous agents

The growing trend in electrical vehicle (EV) deployment has transformed independent power network and transportation network studies into highly congested interdependent network performance evaluations assessing their impact on power and transportation systems. Electrified transportation is highly capable of intensifying the interdependent correlations across charging service, transportation, and power networks. However, the evaluation of the complex coupled relationship across charging services, transportation, and power networks poses several challenges, including an impact on charging scheduling, traffic congestion, charging loads on the power grid, and high costs. Therefore, this article presents comparative survey analytics of large-scale EV integration’s impact on charging service network scheduling, transportation networks, and power networks. Moreover, price mechanism strategies to determine the charging fares, minimize investment profits, diminish traffic congestion, and reduce power distribution constraints under the influence of various factors were carried out. Additionally, the survey analysis stipulates the interdependent network performance index, ascertaining travel distance, route selection, long-term and short-term planning, and different infrastructure strategies. Finally, the limitations of the proposed study, potential research trends, and critical technologies are demonstrated for future inquiries.

https://www.mdpi.com/1996-1073/13/13/3371/pdf

Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends

Impacts of electric vehicle deployment on the electricity sector in a highly urbanised environment

This paper considers an electric vehicle charging scheduling setting where vehicle users can reserve charging time in advance at a charging station. In this setting, users are allowed to explicitly express their preferences over different start times and the length of charging periods for charging their vehicles. The goal is to compute optimal charging schedules that maximize the social welfare of all users given their time preferences and the state of charge of their vehicles. Assuming that users are self-interested agents who may behave strategically to advance their own benefits rather than the social welfare of all agents, we propose an iterative auction, which computes high-quality schedules and, at the same time, preserves users’ privacy by progressively eliciting their preferences as necessary. We conduct a game theoretical analysis on the proposed iterative auction to prove its individual rationality and the best response for agents. Through extensive experiments, we demonstrate that the iterative auction can achieve high-efficiency solutions with a partial value information. Additionally, we explore the relationship between scheduling efficiency and information revelation in the auction.

Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling

Coordinating patient preferences through automated negotiation: A multiagent systems model for diagnostic services scheduling

The unprecedented growth of demand for charging electric vehicles (EVs) calls for novel expansion solutions to today's charging networks. Riding on the wave of the proliferation of sharing economy, Airbnb-like charger sharing markets open the opportunity to expand the existing charging networks without requiring costly and time-consuming infrastructure investments, yet the successful design of such markets relies on innovations at the interface between game theory, mechanism design, and large scale optimization. In this paper, we propose a price-based iterative double auction for charger sharing markets where charger owners rent out their under-utilized chargers to the charge-needing EV drivers. Charger owners and EV drivers form a two-sided market which is cleared by a price-based double auction. Chargers' locations, availabilities, and unit time service costs as well as drivers' time and location preferences are considered in the allocation and scheduling process. The goal is to compute social welfare maximizing schedules which benefit both charger owners and EV drivers and, in turn, ensure the continuous growth of the market. We prove that the proposed double auction is budget balanced and individually rational. In addition, results from our computational study show that the proposed auction achieves on average 94% efficiency compared with that of the optimal solutions and is suitable for a larger day-ahead charger sharing market setting in terms of running time.

A Price-Based Iterative Double Auction for Charger Sharing Markets

Reinforcement learning has become an important scheduling solution with many successes in markets with dynamic pricing options, e.g., electric vehicle charging in a deregulated electricity market. However, the highly-uncertain requests and partially-unknown individual preferences remain major challenges to effective demand responses in the user-centric environment. For charging stations who aim to maximize the long-term revenue in this fast-growing market, an accurate estimate of user’s sensitivity, or acceptance, of the prices they offered to the potential customers is the key to the success of dynamic pricing. While most existing pricing schemes assume users will consistently follow stable patterns that are observable or inferrable by the charging service provider, it remains crucial to consider how users may be influenced by historic prices they have observed and react strategically to decide optimal charging demands that can maximize their utilities. To overcome this limitation, this paper presents a new framework based on reinforcement mechanism design to determine the optimal charging price in a mechanism design setting, which can optimize the long-term revenue of charging stations as well as the social welfare of users with private utility functions. Specifically, the strategic interaction between the station and users is modelled as a discrete finite Markov decision process, a Q-learning-based dynamic pricing mechanism is proposed to explore how price affects users’ demands over a sequence of time. The experiments demonstrate that our pricing mechanism outperforms the predetermined time-of-use pricing in maximizing the long-term revenue of the charging station.

Reinforcement Mechanism Design for Electric Vehicle Demand Response in Microgrid Charging Stations

This paper addresses surgical scheduling problem in a decentralized environment where the availability of surgeons is considered as their private information. In addition to combinatorial complexities inherited from centralized surgical scheduling models, strategic behaviors of surgeons derived from decentralized game theoretic environments have to be addressed. We propose an iterative auction mechanism, a form of decentralized combinatorial optimization, for solving the surgical scheduling problem. The eligibility restriction of operation rooms is also considered. The objective is to maximize the overall social welfare of patients which is represented by the overall weights of surgeries scheduled. Under the proposed mechanism, we prescribe strategies for surgeons on submitting their availability information to maximize their preference values, and at the same time, minimize the revelation of their private information. We prove that myopic bidding strategy is a weakly dominant strategy for surgeons under the proposed scheduling mechanism and the solution quality is an non-decreasing function of the number of bidding rounds along the bidding process. We also present a nontrivial worked example to illustrate the application of the proposed approach in surgical scheduling setting.

Chun Wang

Papers

Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling

Coordinating patient preferences through automated negotiation: A multiagent systems model for diagnostic services scheduling

A Price-Based Iterative Double Auction for Charger Sharing Markets

Reinforcement Mechanism Design for Electric Vehicle Demand Response in Microgrid Charging Stations

A combinatorial auction mechanism for surgical scheduling considering surgeon's private availability information