Scheduling Allocation and Inventory Replenishment Problems Under Uncertainty: Applications in Managing Electric Vehicle and Drone Battery Swap Stations

In this dissertation, motivated by electric vehicle (EV) and drone application growth, we propose novel optimization problems and solution techniques for managing the operations at EV and drone battery swap stations. In Chapter 2, we introduce a novel class of stochastic scheduling allocation and inventory replenishment problems (SAIRP), which determines the recharging, discharging, and replacement decisions at a swap station over time to maximize the expected total profit. We use Markov Decision Process (MDP) to model SAIRPs facing uncertain demands, varying costs, and battery degradation. Considering battery degradation is crucial as it relaxes the assumption that charging/discharging batteries do not deteriorate their quality (capacity). Besides, it ensures customers receive high-quality batteries as we prevent recharging/discharging and swapping when the average capacity of batteries is lower than a predefined threshold. Our MDP has high complexity and dimensions regarding the state space, action space, and transition probabilities; therefore, we can not provide the optimal decision rules (exact solutions) for SAIRPs of increasing size. Thus, we propose high-quality approximate solutions, heuristic and reinforcement learning (RL) methods, for stochastic SAIRPs that provide near-optimal policies for the stations. In Chapter 3, we explore the structure and theoretical findings related to the optimal solution of SAIRP. Notably, we prove the monotonicity properties to develop fast and intelligent algorithms to provide approximate solutions and overcome the curses of dimensionality. We show the existence of monotone optimal decision rules when there is an upper bound on the number of batteries replaced in each period. We demonstrate the monotone structure for the MDP value function when considering the first, second, and both dimensions of the state. We utilize data analytics and regression techniques to provide an intelligent initialization for our monotone approximate dynamic programming (ADP) algorithm. Finally, we provide insights from solving realistic-sized SAIRPs. In Chapter 4, we consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions. Drones are an innovative method with many benefits including low-contact delivery thereby reducing the spread of pandemic and vaccine-preventable diseases. While we focus on medical supply delivery for this work, it is applicable to drone delivery for many other applications, including food, postal items, and e-commerce delivery. In this chapter, our goal is to address drone delivery challenges by optimizing the distribution operations at a drone hub that dispatch drones to different geographic locations generating stochastic demands for medical supplies. By considering different geographic locations, we consider different classes of demand that require different flight ranges, which is directly related to the amount of charge held in a drone battery. We classify the stochastic demands based on their distance from the drone hub, use a Markov decision process to model the problem, and perform computational tests using realistic data representing a prominent drone delivery company. We solve the problem using a reinforcement learning method and show its high performance compared with the exact solution found using dynamic programming. Finally, we analyze the results and provide insights for managing the drone hub operations

Systematic review of image segmentation using complex networks

This review presents various image segmentation methods using complex networks. Image segmentation is one of the important steps in image analysis as it helps analyze and understand complex images. At first, it has been tried to classify complex networks based on how it being used in image segmentation. In computer vision and image processing applications, image segmentation is essential for analyzing complex images with irregular shapes, textures, or overlapping boundaries. Advanced algorithms make use of machine learning, clustering, edge detection, and region-growing techniques. Graph theory principles combined with community detection-based methods allow for more precise analysis and interpretation of complex images. Hybrid approaches combine multiple techniques for comprehensive, robust segmentation, improving results in computer vision and image processing tasks

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

This paper proposes a novel approach to optimize the main energy consumptions of heavy oil refining industries (ORI) in response to electricity price uncertainties. The whole industrial sub-processes of the ORI are modeled mathematically to investigate the joint power-heat flexibility potentials of the industry. To model the refinery processes, an input/output flow-based model is proposed for five main refining units. Moreover, the role of storage tanks capacity in the power system flexibility is investigated. To hedge against the electricity price uncertainty, an uncertain bound for the wholesale electricity price is addressed. To optimize the industrial processes, a dual robust mixed-integer quadratic program (R-MIQP) is adopted; therefore, the ORI’s operational strategies are determined under the worst-case realization of the electricity price uncertainty. Finally, the suggested approach is implemented in the south-west sector of the Iran Energy Market that suffers from a lack of electricity in hot days of summer. The simulation results confirm that the proposed framework ensures industrial demand flexibility to the external grids when a power shortage occurs. The approach not only provides demand flexibility to the power system, but also minimizes the operation cost of the industries

Remote preoperative tonic-clonic seizures do not influence outcome after surgery for temporal lobe epilepsy.

OBJECTIVES: Tonic-clonic seizures are associated with greater chance of seizure relapse after anterior temporal lobectomy. We investigated whether the interval between the last preoperative tonic-clonic seizure and surgery relates to seizure outcome in patients with drug-resistant mesial temporal lobe epilepsy (MTLE). METHODS: In this retrospective study, patients were prospectively registered in a database from 1986 through 2014. Postsurgical outcome was categorized as seizure freedom or relapse. The relationship between surgical outcome and the interval between the last preoperative tonic-clonic seizure and surgery was investigated. RESULTS: One-hundred seventy-one patients were studied. Seventy nine (46.2%) patients experienced tonic-clonic seizures before surgery. Receiver operating characteristic curve of timing of the last preoperative tonic-clonic seizure was a moderate indicator to anticipate surgery failure (area under the curve: 0.657, significance; 0.016). The best cutoff that maximizes sensitivity and specificity was 27months; with a sensitivity of 0.76 and specificity of 0.60. Cox-Mantel analysis confirmed that the chance of becoming free of seizures after surgery in patients with no or remote history of preoperative tonic-clonic seizures was significantly higher compared with patients with a recent history (i.e., in 27months before surgery) (p=0.0001). CONCLUSIONS: The more remote the occurrence of preoperative tonic-clonic seizures, the better the postsurgical seizure outcome, with at least a two year gap being more favorable. A recent history of tonic-clonic seizures in a patient with MTLE may reflect more widespread epileptogenicity extending beyond the borders of mesial temporal structures

A CRISP-DM-based Methodology for Assessing Agent-based Simulation Models using Process Mining

Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven techniques offer sophisticated capabilities for analyzing the outcomes of ABS models. One such technique is process mining, which encompasses a range of methods for discovering, monitoring, and enhancing processes by extracting knowledge from event logs. However, applying process mining to event logs derived from ABSs is not trivial, and deriving meaningful insights from the resulting process models adds an additional layer of complexity. Although process mining is invaluable in extracting insights from ABS models, there is a lack of comprehensive methodological guidance for its application in ABS evaluation in the research landscape. In this paper, we propose a methodology, based on the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, to assess ABS models using process mining techniques. We incorporate process mining techniques into the stages of the CRISP-DM methodology, facilitating the analysis of ABS model behaviors and their underlying processes. We demonstrate our methodology using an established agent-based model, Schelling model of segregation. Our results show that our proposed methodology can effectively assess ABS models through produced event logs, potentially paving the way for enhanced agent-based model validity and more insightful decision-making

A CRISP-DM-based Methodology for Assessing Agent-based Simulation Models using Process Mining

Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven techniques offer sophisticated capabilities for analyzing the outcomes of ABS models. One such technique is process mining, which encompasses a range of methods for discovering, monitoring, and enhancing processes by extracting knowledge from event logs. However, applying process mining to event logs derived from ABSs is not trivial, and deriving meaningful insights from the resulting process models adds an additional layer of complexity. Although process mining is invaluable in extracting insights from ABS models, there is a lack of comprehensive methodological guidance for its application in ABS evaluation in the research landscape. In this paper, we propose a methodology, based on the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, to assess ABS models using process mining techniques. We incorporate process mining techniques into the stages of the CRISP-DM methodology, facilitating the analysis of ABS model behaviors and their underlying processes. We demonstrate our methodology using an established agent-based model, Schelling model of segregation. Our results show that our proposed methodology can effectively assess ABS models through produced event logs, potentially paving the way for enhanced agent-based model validity and more insightful decision-making

Ictal verbal help-seeking: Occurrence and the underlying etiology.

PURPOSE: Ictal verbal help-seeking has never been systematically studied before. In this study, we evaluated a series of patients with ictal verbal help-seeking to characterize its frequency and underlying etiology. METHODS: We retrospectively reviewed all the long-term video-EEG reports from Jefferson Comprehensive Epilepsy Center over a 12-year period (2004-2015) for the occurrence of the term help in the text body. All the extracted reports were reviewed and patients with at least one episode of documented ictal verbal help-seeking in epilepsy monitoring unit (EMU) were studied. For each patient, the data were reviewed from the electronic medical records, EMU report, and neuroimaging records. RESULTS: During the study period, 5133 patients were investigated in our EMU. Twelve patients (0.23%) had at least one episode of documented ictal verbal help-seeking. Nine patients (six women and three men) had epilepsy and three patients (two women and one man) had psychogenic nonepileptic seizures (PNES). Seven out of nine patients with epilepsy had temporal lobe epilepsy; six patients had right temporal lobe epilepsy. CONCLUSION: Ictal verbal help-seeking is a rare finding among patients evaluated in epilepsy monitoring units. Ictal verbal help-seeking may suggest that seizures arise in or propagate to the right temporal lobe