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This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.

A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?

The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.

Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.

In situations where the crew is reduced, the optimization of crew task allocation and sequencing (CTAS) can significantly enhance the operational efficiency of the man-machine system by rationally distributing workload and minimizing task completion time. Existing related studies exhibit a limited consideration of workload distribution and involve the violation of precedence constraints in the solution process. This study proposes a CTAS method to address these issues.

The method defines visual, auditory, cognitive and psychomotor (VACP) load balancing objectives and integrates them with workload balancing and minimum task completion time to ensure equitable workload distribution and task execution efficiency, and then a multi-objective optimization model for CTAS is constructed. Subsequently, it designs a population initialization strategy and a repair mechanism to maintain sequence feasibility, and utilizes them to improve the non-dominated sorting genetic algorithm III (NSGA-III) for solving the CTAS model.

The CTAS method is validated through a numerical example involving a mission with a specific type of armored vehicle. The results demonstrate that the method achieves equitable workload distribution by integrating VACP load balancing and workload balancing. Moreover, the improved NSGA-III maintains sequence feasibility and thus reduces computation time.

The study can achieve equitable workload distribution and enhance the search efficiency of the optimal CTAS scheme. It provides a novel perspective for task planners in objective determination and solution methodologies for CTAS.

Many hotels allocate guests to specific rooms immediately after reservation. This happens because individual rooms are sold (and there is no concept of room type) or because the assignment is done by hand at reservation or because of a connection with a channel manager, which is immediately fixing the room number after a reservation request. This early allocation is suboptimal, and it causes the unnecessary rejection of some reservations when the hotel has a high occupancy level. The purpose of this paper is to investigate different room allocation algorithms, including an optimal one (called RoomTetris), aiming at higher occupancy levels and profitability.

The methodology is based on theoretical results and experimentation. The optimality or the proposed RoomTetris algorithm is demonstrated. Experiments are executed in different contexts, including realistic ones, through the adoption of a hotel simulator, to measure the improvements in the occupancy rate of the optimal and heuristic strategies with respect to random or sub-optimal assignments of rooms.

The main results are that smart allocation algorithms can greatly reduce the rejection rate (reservation requests which cannot be fit into the hotel room plan) and improve the occupancy level, the percentage of available rooms or beds sold for the various periods.

This analysis can be extended by considering cancellations and overbookings. A second possibility to add flexibility in room allocation for hotels having more than one type of rooms is that the hotel can upgrade and offer a high-price room to the customer, which given an even large flexibility to fix rooms by shifting customers to other compatible types. In addition, more complex integrations with revenue management can also be considered, for cases in which the cost of a room depends on the number of guests.

Given that the difference in occupancy rate of the optimal algorithm is particularly large in high season and high-request periods, periods which are usually associated to higher rates and higher volumes, the proposed algorithm will improve the main financial performance indicators such as revenue per available room by an even bigger multiplier, depending on the hotel pricing policy. Because the room allocation process can be completely automated, the adoption of appropriate smart allocation algorithms represents a low-hanging fruit to be picked by efficient hotel managers.

To the best of the knowledge this is the first proposal of an optimal algorithm (with proof of optimality) for the considered problem.

很多酒店, 特别是私人、家庭经营型、或者精品酒店, 在客人预定后立刻分派指定的房间给客人。这往往是因为独立房间售卖（没有特殊房型概念）或者因为客人在预定时, 工作人员手动指派房间, 亦或者是因为预订系统与渠道管理系统链接, 直接在预定后指派房间号。这种早期的分派程序是不优化的, 往往在酒店住房率高的时候, 会造成一些不必要的房间预定失败, 继而带来的利润损失。本论文旨在研究不同房间指派参数配置, 包括最优系统（RoomTetris）, 使得酒店达到更高住房率的同时产生高利润。

本论文采用理论讨论和实验等研究方法, 并展示了提出的RoomTetris参数的最优性。本论文还将其参数放在不同的情景中做实验, 以显示其提高酒店针对随机或者次优化分派的最佳启发式策略中的住房率。

研究结果表明智能型分派参数能够大大降低预定失败率（预定需求不能符合酒店房型供给）, 并且提高住房率和利润。住房时间并不是必须的参数, 极具个性化服务, 比如让客人选房间号, 可能导致利润损失（因为最优房间分派无法实现）, 房型的设计也应该参与到最优房间分派的效果中来。

预定取消和超额预定的情况也应该加入到分析中来。第二种对于拥有不止一种房型的酒店来说, 可能增加房间分派的情况在于为客人升级房型, 这样可以将客人转到其他适合房型以解决房间分派问题。此外, 更复杂系统兼容财务管理系统应该被考量, 有的时候, 房间的成本取决于客人的数量。

由于最优算法的住房率区别在于旺季和高预定时段, 也就是高房间价格和高预定量, 本论文提出的最佳算法将提高主要财务指标, 比如RevPAR（平均客房收益）。由于房间分配系统可以完全实现自动化, 那么采用智能分派系统无疑是有效酒店管理中的优质选择。

据作者所知, 此文章是首篇关于此类话题的研究优质算法（且被证实其最佳）。

A growing body of research finds that gig economy platforms use gamification to enhance managerial control. Focusing on technologically mediated forms of gamification, this literature reveals how platforms mobilize gig workers’ work effort by making the labour process resemble a game. This chapter contends that this tech-centric scholarship fails to fully capture the historical continuities between contemporary and much older occurrences of game-playing at work. Informed by interviews and participatory observations at two food delivery platforms in Amsterdam, I document how these platforms’ piece wage system gives rise to a workplace dynamic in which severely underpaid delivery couriers continuously employ game strategies to maximize their gig income. Reminiscent of observations from the early shop floor ethnographies of the manufacturing industry, I show that the game of gig income maximization operates as an indirect modality of control by (re)aligning the interests of couriers with the interests of capital and by individualizing and depoliticizing couriers’ overall low wage level. I argue that the new, algorithmic technologies expand and intensify the much older forms of gamified control by infusing the organizational activities of shift and task allocation with the logic of the piece wage game and by increasing the possibilities for interaction, direct feedback and immersion. My study contributes to the literature on gamification in the gig economy by interweaving it with the classic observations derived from the manufacturing industry and by developing a conceptualization of gamification in which both capital and labour exercise agency.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

This chapter addresses adaptation to dynamic, novel and uncertain military environments. These environments require a shift from a maneuver warfare paradigm to an asymmetric world where shifting alliances, questionable civilian loyalties, opaque cultures, and the requirement to maintain peace one day and combat the next makes for a particularly confusing situation. This new warfare paradigm requires adaptation to an uncertain, complex environment.

The initial section discusses a general cognitive model of visualization called RAVENS and its importance for adaptation developed specifically to address complex military environments. RAVENS posits that humans are inherently flexible decision makers and situation awareness depends on the ability of humans to create narrative visualizations that capture the overall context of complex military environments. Using the framework as a guideline, we will examine two important visualization research programs whose purpose is to allow military operators to rapidly adapt to volatile situations. The first program investigates cognitive effects such as the framing bias and their possible interactions with a variety of display concepts during a series of missile defense simulations. The experimenters presented risk as a spatial representation of uncertainty and target value that emphasized either expected population lost or expected population saved. The second program investigated the feasibility of using visualizations generated from Scheherazade (a coevolutionary algorithm) to aid MI analysts in predicting emergent tactics of terrorist groups during urban operations. Finally, we discuss the value of these approaches for providing coherent narrative understanding as called for in the RAVENS model.

The displaced commercial risk is one of the specific risks in the Islamic finance that creates a serious debate among practitioners and researchers about its management. The purpose of this paper is to assess a new approach to manage this risk using machine learning algorithms.

To attempt this purpose, the authors use several machine learning algorithms applied to a set of financial data related to banks from different regions and consider the deposit variation intensity as an indicator.

Results show acceptable prediction accuracy. The model could be used to optimize the prudential reserves for banks and the incomes distributed to depositors.

However, the model uses several variables as proxies since data are not available for some specific indicators, such as the profit equalization reserves and the investment risk reserves.

Previous studies have analyzed the origin and impact of DCR. To the best of authors’ knowledge, none of them has provided an ex ante management tool for this risk. Furthermore, the authors suggest the use of a new approach based on machine learning algorithms.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.