Significant developments and advancement pertaining to electric vehicle (EV) technologies, such as extreme fast charging (XFC), have been witnessed in the last decade. However, there are still many challenges to the wider deployment of EVs. One of the major barriers is its availability of fast charging stations. A possible solution is to build a fast charging sharing system, by encouraging small business owners or even householders to install and share their fast charging devices, by reselling electricity energy sourced from traditional utility companies or their own solar grid. To incentivize such a system, a smart dynamic pricing scheme is needed to facilitate those growing markets with fast charging stations. The pricing scheme is expected to take into account the dynamics intertwined with pricing, demand, and environment factors, in an effort to maximize the long-term profit with the optimal price. To this end, this paper formulates the problem of dynamic pricing for fast charging as a Markov decision process and accordingly proposes several algorithmic schemes for different applications. Experimental study is conducted with useful and interesting insights.

Problem definition: Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into or right after overbooked slots. That scheduling decision maximizes the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if a racial group is characterized by a higher no-show risk, then the patients belonging to that racial group will be scheduled into or right after overbooked slots disproportionately to the general population. Academic/Practical Relevance: That scheduling decision is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities. Methodology: Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we analytically study racial disparity in this context. Then, we propose new objective functions that minimize both schedule cost and racial disparity and that can be readily adopted by researchers and practitioners. We develop a race-aware objective, which instead of minimizing the waiting times of all patients, minimizes the waiting times of the racial group expected to wait the longest. We also develop race-unaware methodologies that do not consider race explicitly. We validate our findings both on simulated and real-world data. Results: We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Our race-aware methodology achieves both goals of eliminating racial disparity and obtaining a similar schedule cost as that obtained by the state-of-the-art scheduling method, whereas the race-unaware methodologies fail to obtain both efficiency and fairness. Managerial implications: Our work uncovers that the traditional objective of minimizing schedule cost may lead to unintended racial disparities. Both efficiency and fairness can be achieved by adopting a race-aware objective. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Funding: M. Samorani’s work was partly funded by the 2020 Leavey Research Grant. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0999 .

In today's networked world, resource providers and consumers are distributed globally and locally, especially under current cloud computing environment. However, with resource constraints, optimization is necessary to ensure the best possible usage o

Role Based Access Control (RBAC) is accepted as the de facto access control model for organizations of all sizes. However, engineering the right set of roles is crucial to enable the correct deployment of RBAC within an organization. Indeed, discovering an optimal and correct set of roles from existing permission assignments, referred to as the role mining problem (RMP), has gained significant attention in recent years. Role Mining is itself an instantiation of Boolean matrix decomposition – wherein a Boolean matrix is decomposed into two Boolean matrices giving a set of basis vectors and their appropriate combination. In fact, such decompositions are useful in a number of application domains beyond role engineering, including text mining as well as knowledge discovery. While a Boolean matrix can be decomposed in many ways, however, certain decompositions better characterize the semantics associated with the original matrix in a succinct but comprehensive way. Indeed, one can find different decompositions that are optimal with respect to different criteria that may match various semantics. In this paper, we first present a number of variants of the optimal Boolean matrix decomposition problem, including usage RMP, basic RMP, δ-approximate RMP, and edge RMP, that have pragmatic implications in the context of role mining. We then present a unified framework for modeling the optimal Boolean matrix decomposition and its variants using integer linear programming (ILP). Such modeling allows us to directly adopt the huge body of heuristic solutions and tools developed for integer linear programming. We also develop efficient heuristics and solutions for each RMP variant, and validate them by a comprehensive experimental evaluation.

Role mining is to define a role set to implement the role-based access control (RBAC) system and regarded as one of the most important and costliest implementation phases. While various role mining models have been proposed, we find that user experience/perception – one ultimate goal for any information system – is surprisingly ignored by the existing works. One advantage of RBAC is to support multiple role assignments and allow a user to activate the necessary role to perform the tasks at each session. However, frequent role activating and deactivating can be a tendinous thing from the user perspective. A user-friendly RBAC system is expected to assign few roles to every user. So in this paper we propose to incorporate to the role mining process a user-role assignment constraint that mandates the maximum number of roles each user can have. Under this rationale, we formulate user-oriented role mining as the user role mining problem, where all users have the same maximal role assignments, the personalized role mining problem, where users can have different maximal role assignments, and the approximate versions of the two problems, which tolerate a certain amount of deviation from the complete reconstruction. The extra constraint on the maximal role assignments poses a great challenge to role mining, which in general is already a hard problem. We examine some typical existing role mining methods to see their applicability to our problems. In light of their insufficiency, we present a new algorithm, which is based on a novel dynamic candidate role generation strategy, tailored to our problems. Experiments on benchmark data sets demonstrate the effectiveness of our proposed algorithm.