Integer Linear Programming Framework Research Articles

Optimization of hybrid renewable-diesel power plants considering operational cost, battery degradation, and emissions

Renewable energy sources have immense potential for enhancing environmental sustainability; however, addressing their intermittency and irregularity is vital for optimizing economic benefits within microgrids. Integrating renewable energy systems with energy storage presents a promising solution. This study introduces an innovative energy management system designed for hybrid renewable power stations, incorporating battery energy storage systems and diesel generators. By accounting for battery degradation costs associated with charge depth and lifespan, the study transforms long-term battery expenses into real-time operational costs. The optimization problem is formulated as a mixed integer linear programming framework with the objectives of minimizing operating costs, battery degradation costs, and pollutant gas emissions. Through diverse case studies reflecting various market profiles. The proposed approach demonstrates reductions in overall system costs.

Automated Scheduling of Doppler Exoplanet Observations at Keck Observatory

Precise Doppler studies of extrasolar planets require fine-grained control of observational cadence, i.e., the timing of and spacing between observations. We present a novel framework for scheduling a set of Doppler campaigns with different cadence requirements at the W. M. Keck Observatory. For a set of observing programs and allocated nights on an instrument, our software optimizes the timing and ordering of ∼1000 observations within a given observing semester. We achieve a near-optimal solution in real-time using a hierarchical Integer Linear Programming framework. Our scheduling formulation optimizes over the roughly 103000 possible orderings. A top level optimization finds the most regular sequence of allocated nights by which to observe each host star in the request catalog based on a frequency specified in the request. A second optimization scheme minimizes the slews and downtime of the instrument. We have assessed our algorithms performance with simulated data and with the real suite of Doppler observations of the California Planet Search in 2023.

Data-driven zonotopic approximation for n-dimensional probabilistic geofencing

Advanced air mobility is a promising way of metropolitan air transportation. One critical concern that arises is how to ensure operational safety in high-dense, dynamic, and uncertain airspace environments in real time. To address this challenge, we seek a probabilistic geofence that bounds system states with high confidence. To identify the n-dimensional probabilistic geofence for arbitrary unknown uncertainties not limited to Gaussian ones, we present an online algorithm based on a data-driven approach of kernel density estimator. Considering the irregular shape of the probabilistic geofence, we formulate an optimization framework of integer linear programming whose solution determines a zonotope which provides a convex approximation for the probabilistic geofence. Leveraging this formulation, a heuristic algorithm is developed to find its solution efficiently without losing notable accuracy. This heuristic algorithm is tested on case studies that demonstrate it enjoys efficiency, accuracy, near-optimality, and robustness simultaneously.

A novel multi document summarization with document-elements augmentation for learning materials using concept based ILP and clustering methods

Multi Document Summarization (MDS) is a technique for extracting succinct summaries from groups of related documents. The usage of MDS in the e-learning context is more appealing for providing summaries for learning materials, which helps students and teachers to focus on key concepts of the learning materials. In a learning material, availability of non-textual document-elements such as figures/diagrams, plots, graphs, tables and algorithms can be seen widely, but keeping such document-elements in the summary is not possible as they are non-textual. This proposed work incorporates the text summarization approach with document-elements augmentation to the summary to provide a detailed coverage of information without exceeding the summary length constraint. The key information in the source text is identified by important phrase features and sentence features, and the summaries are generated by selecting important sentences using the Integer Linear Programming (ILP) framework while reducing the redundancy using pre-trained sentence vectors. The relationships between the summary and document-elements are identified through document-element snippet extraction and a Hierarchical Agglomerative Clustering approach. Experimental results of the proposed summary extraction and augmentation on educational dataset (EduSumm) show better performance compared to the state-of-the-art approaches.

Economic and environmental optimisation of mixed plastic waste supply chains in Northern Italy comparing incineration and pyrolysis technologies

In the quest for sustainable plastic waste management, understanding economic and environmental implications enables optimal selection of treatment technologies. This study presents a multi-objective mixed integer linear programming framework to optimise the supply chain for mixed plastic waste in Northern Italy. Two technologies are considered: incineration and pyrolysis. Results offer quantitative insights into economic and environmental performance, balancing trade-offs between maximising gross profit and minimising greenhouse gas (GHG) emissions. Economic optimisation favours incineration for treating mixed plastic waste, resulting in the highest gross profit of 115 M€ per year, and the highest net GHG emissions of about 680 kt CO2eq per year. When the aim is environmental optimisation, pyrolysis is preferred due to its lower GHG emissions of 387 kt of CO2eq per year and yielding a gross profit of 54 M€ per year. Trade-off Pareto optimal solutions were analysed to identify reasonable trade-off configurations between the two objectives.

Drone Stations-Aided Beyond-Battery-Lifetime Flight Planning for Parcel Delivery

This paper considers using drones to conduct the last-mile parcel delivery. To enable the beyond-battery-lifetime flight, drone stations are considered to replace or recharge the battery for drones. We focus on the flight planning problem with the goal of minimizing the total travel time from the depot to a customer, a key indicator of the quality of service. We investigate four typical ways for the drone to get extra energy at drone stations: 1) replacing the battery with a fresh one, 2) recharging the battery to the full capacity, 3) recharging the battery to the optimal level, and 4) recharging the battery to the optimal level accounting for the availability of drone stations (i.e., whether a drone station is occupied by other drones). While the first two scenarios can be formulated following the framework of integer linear programming, the last two scenarios turn into mixed-integer nonlinear programming problems. To address the later problems, we present a framework in which discretized state graphs are constructed first and then the optimal paths are found by graph searching algorithms. We propose a dynamic version of Dijkstra’s algorithm to deal with the unavailability issue of drone stations. The algorithm can quickly find the optimal flight path for a drone, and extensive computer-based experimental results have been presented to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Multi-rotary unmanned aerial vehicles (UAVs), also known as drones, have been regarded as a promising means to reshape future logistics. To save human labour and reduce cost, many giant logistics companies have been dedicated to developing various drones to deliver light and small parcels during the past decade. However, due to the limitation of payload, the battery capacity is constrained, which prevents drones from long-distance flights. Practitioners have tried the drone-vehicle collaboration method, but this still requires human labour to participate. In this paper, we present a framework where drones autonomously conduct long-distance delivery with the assistance of drone stations. It is worth pointing out that such a framework is not to replace the ground delivery method but to serve as an alternative to the ground counterpart for small and light parcels. A particular focus is on the flight planning from the depot to a destination, which includes not only a sequence of drone stations to stop at but also the corresponding rest time to recharge the battery. Several typical scenarios about battery recharging are discussed, and a dynamic version of Dijkstra’s algorithm is presented to deal with the challenging case where drone station resources are limited. The presented approach is able to find out the optimal flight plan quickly.

A robust chance-constrained programming approach for a bi-objective pre-emptive multi-mode resource-constrained project scheduling problem with time crashing

The presented study proposes a novel bi-objective mixed integer linear programming (MILP) framework for the multi-mode resource-constrained project scheduling problem (MRCPSP) with pre-emptive and non-preemptive activities splitting under uncertain conditions. Minimising the project makespan and resource costs are the considered objectives. Renewable and non-renewable resources along with different modes are taken into account for activities implementation. Additionally, some activities can be crashed by consuming additional renewable and non-renewable resources. Model uncertainty is efficiently addressed by utilising a fuzzy chance constrained programming (CPP) method as well as extending two robust possibilistic programming models. The capability of the presented mathematical framework is validated using problem instances from PSPLIB (j10, j14, j20, and j30) and MMLIB (MM50 and MM100). Finally, a detailed computational comparison is presented to assess the performance of the two robust possibilistic programming models.

Optimized design of filterless horseshoe networks exploiting point-to-multipoint coherent transceivers

The horseshoe topology is widely used to realize metro-aggregation networks, since it provides a natural fit to the hub-and-spoke traffic pattern present in the majority of these deployments, while enabling survivability against single link and hub failures. A filterless architecture can also be adopted to further reduce capital expenditure (CapEx) by replacing active elements, such as reconfigurable add/drop multiplexers, with simpler and passive splitters/combiners. Such an architecture can effectively host coherent-based point-to-multipoint (P2MP) transceivers enabled by digital subcarrier multiplexing (DSCM). Importantly, by carefully optimizing the deployment of amplifiers (location and gain) and splitters/combiners (type), it may be possible to reduce the total number of optical amplifiers required, further decreasing CapEx. This paper proposes an integer linear programming framework to optimize metro-aggregation filterless horseshoe networks, taking into account the specific requirements of DSCM-based P2MP coherent transceivers. The results indicate that a considerable reduction in amplifier count is possible while ensuring that end-to-end performance thresholds are met, which include the minimum required input power at the receivers, a maximum subcarrier input power difference at the hub’s receivers, and the minimum optical-signal-to-noise ratio.

FTSC: Fault-tolerant scheduling and control co-design for distributed real-time system

Ensuring control stability is essential for safety-critical systems. Task redundancy is one commonly employed technique to enhance control performance in applications afflicted by intermittent faults. While increasing the number of replicas can improve control performance, it also introduces challenges in terms of scheduling and mapping. Prior research primarily focuses on controller design to enhance control performance, with limited consideration given to fault tolerance and scheduling in a combined manner. In this paper, we propose a co-design scheme of Fault-Tolerant Scheduling and Control (FTSC), which aims to provide stable control performance along with feasible mapping and scheduling solutions. We present a comprehensive formulation that encompasses mapping, scheduling, and fault tolerance, optimizing control performance into a Mixed Integer Linear Programming (MILP) framework. The problem of task redundancy can either be addressed through a heuristic algorithm efficiently or be solved in a MILP-based approach with a better solution. A case study is conducted to demonstrate the effectiveness of our proposed scheme, highlighting its ability to reduce system control costs while meeting real-time, reliability, and schedulability constraints.

Multi-period planning in metro-aggregation networks exploiting point-to-multipoint coherent transceivers

This paper proposes an integer linear programming framework to optimize the multi-period planning of metro-aggregation networks exploiting point-to-multipoint coherent pluggable transceivers and a filterless line system architecture. We investigate several planning strategies to minimize capital expenditure caused by transceiver cost based on the predictability horizon of traffic and availability of transceivers during planning while considering operational expenses (OpEx) as well. The results obtained by evaluating a deployed reference network provide evidence of reductions in transceiver cost between 20% and 32% compared to that in the point-to-point approach, and the OpEx can be reduced by around 30% at the expense of a 10% increase in transceiver cost over a five-year planning period.

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public network infrastructures that allow clients to access computational resources. Virtual Machines (VMs) are supplied on requests and launched without interactions from service providers. Intruders can target these servers and establish malicious connections on VMs for carrying out attacks on other clustered VMs. The existing system has issues with execution time and false-positive rates. Hence, the overall system performance is degraded considerably. The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target server beyond a certain point. Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server’s position. Allocation of SECURITY Resources accepts a safety game in a simple format as input and finds the best coverage vector for the opponent using a Stackelberg Equilibrium (SSE) technique. A Mixed Integer Linear Programming (MILP) framework is used in the algorithm. The VM challenge is reduced by a firewall-based controlling mechanism combining behavior-based detection and signature-based virus detection. The proposed method is focused on detecting malware attacks effectively and providing better security for the VMs. Finally, the experimental results indicate that the proposed security method is efficient. It consumes minimum execution time, better false positive rate, accuracy, and memory usage than the conventional approach.

Innovative Unit Commitment Modeling Approach Considering Multi-infeed VSC-HVDC Links

This paper introduces an improved optimization model for the unit commitment (UC) problem of AC power systems considering multi-infeed VSC-HVDC links. The proposed formulation is based on the linear modeling of the AC network and point-to-point HVDC links, where power losses are properly considered by piecewise linearization of AC lines, DC links and VSC units. This UC model is featured by a mixed integer linear programming (MILP) framework, thus enabling effective calculations of the optimal hourly generation scheduling for multi-infeed VSC-HVDC power grids. These sophisticated power grid studies are associated with day-ahead electricity markets carried out by power system engineers at control centers. To demonstrate the applicability of the proposed MILP-based UC model, the IEEE RTS 24-bus test system is used to carry out two case studies. The first incorporates three point-to-point HVDC links where an in-depth analysis is carried by comparing its outcomes, with and without HVDC links. The second case study features two interconnected areas having three-infeed HVDC links and several tens of generation units. Both systems are analyzed for a 24-hr planning horizon. It is confirmed that the UC model described in this paper permits to study practical power grids with multi-infeed VSC-HVDC links.

A mixed integer linear programming framework for improving cortical vision prosthesis designs

Although cortical vision prosthesis design has been an active area of research since the early 70s, progress in performance remains quite limited. This paper aims to develop a rigorous mathematical framework, based on mixed integer programming, for the analysis of the cortical vision prosthesis design problem and the optimal estimation of a prosthesis setup parameters. The simulated examples illustrate the unique capabilities of the proposed strategy in testing the feasibility of the goals of a prosthesis under a set of specified design constraints. Investigation of the results of the simulated examples suggest that a number of electrodes much larger than the number of cortical locations that are to be controlled is needed to achieve an improved vision experience, approaching the quality of normal vision (to reach a 300 pixels/inch resolution a million-electrode implantation is required). Thus, future research should focus on how these electrodes can be safely implanted. Once this is possible, the proposed framework can be applied to decide their optimal locations and excitation currents.

A stochastic mixed integer linear programming framework for oil sands mine planning and waste management in the presence of grade uncertainty

ABSTRACT The primary purpose of oil sands mine planning and waste management is to provide ore from the mine pit to the processing plant while containing the tailings in an efficient manner in-pit. Incorporating waste management in the mine plan is essential to maximize the economic potential of the mineral reserve and minimize waste management costs. However, spatial variability such as grade uncertainty results in ore tonnage variations, which leads to fluctuations in the quantity of ore to be processed and waste to be managed. This paper investigates the application of a stochastic mixed integer linear programming (SMILP) on oil sands mine planning to integrate bitumen grade uncertainty and waste management. Sequential Gaussian simulation is employed to quantitatively model the spatial variability of bitumen grade in the oil sands deposit. Multiple simulated orebody models are used as inputs for the SMILP model to generate optimal mine plans in the presence of grade uncertainty. The results demonstrate that the SMILP schedule generates 14% and 17% improvements in net present value compared to the E-type and ordinary kriging schedules, respectively. These results indicate that the SMILP model is a robust tool for optimizing stochastic integrated oil sands production schedules and waste management.

Influence of Inherent Characteristic of PV Plants in Risk-Based Stochastic Dynamic Substation Expansion Planning Under MILP Framework

A suitable probabilistic scenario set of load demand and natural characteristics of renewable energy is becoming a crucial issue in power system planning studies. Properly addressing the impact of potentially thousands of residential PV plants on the resilience and reliability needs of substations necessitates the representation of inherent relations between photovoltaics and the load throughout the long-term planning period. The optimal planning of substation expansions is achievable through proper modeling of input parameters which describes the characteristics of the service areas. In this paper, the co-existence of PV plants and the load in a service area under three different states such as daytime with clear-sky and no-fault, daytime with abnormal events, and nighttime are incorporated into the stochastic dynamic optimization problem by using scenario-based approach. The scenario tree of the problem is branched from three different bases simultaneously instead of only one as in conventional approach. This paper also combines the risk-constrained stochastic dynamic SEP problem and Mixed Integer Linear Programming (MILP) framework under one roof. The comparison between integrating inherent characteristics of PV plants with and without considering abnormal events into the optimization is performed to show the impact of suitable probabilistic model on dynamic nature of investment decisions.

A Mixed Integer Linear Programming Framework for Improving Cortical Vision Prosthesis Designs

A Mixed Integer Linear Programming Framework for Improving Cortical Vision Prosthesis Designs

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.

Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. Supplementary data are available at Bioinformatics online.

A stochastic machine learning based approach for observability enhancement of automated smart grids

This paper develops a machine learning aggregated integer linear programming approach for the full observability of the automated smart grids by positioning of micro-synchrophasor units, taking into account the reconfigurable structure of the distribution systems. The proposed stochastic approach presents a strategy occurring in several stages to micro-synchrophasor unit positioning based on the load level and demand in the system and based on the pre-determined sectionalizing and tie switches. Such a technique can also deploy the zero-injection limitations of the model and reduce the search space of the problem. Moreover, a novel method based on whale optimization method (WOM) is introduced to simultaneously enhance the reliability indices in order to specify the optimum topology for each phase and reduce the costs of power losses and customer interruptions. Although the problem of micro-synchrophasor placement is formulated in an integer linear programming framework, the restructuring technique is resolved on the basis of the WOM heuristic approach. Considering the uncertainty due to the metering devices or forecast errors, a stochastic framework based on point estimation is deployed to handle the uncertainty effects. The simulation and numerical results on a real system verify that the proposed method assures visibility of the distribution network pre and post reconfiguration in the time horizon of the planning. Furthermore, the results show that the system observability can be guaranteed at different load levels even though the system experiences different reconfiguration and topologies.

Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study.

BackgroundIn clinical diagnostic interviews, mental health professionals (MHPs) implement a care practice that involves asking open questions (eg, “What do you want from your life?” “What have you tried before to bring change in your life?”) while listening empathetically to patients. During these interviews, MHPs attempted to build a trusting human-centered relationship while collecting data necessary for professional medical and psychiatric care. Often, because of the social stigma of mental health disorders, patient discomfort in discussing their presenting problem may add additional complexities and nuances to the language they use, that is, hidden signals among noisy content. Therefore, a focused, well-formed, and elaborative summary of clinical interviews is critical to MHPs in making informed decisions by enabling a more profound exploration of a patient’s behavior, especially when it endangers life.ObjectiveThe aim of this study is to propose an unsupervised, knowledge-infused abstractive summarization (KiAS) approach that generates summaries to enable MHPs to perform a well-informed follow-up with patients to improve the existing summarization methods built on frequency heuristics by creating more informative summaries.MethodsOur approach incorporated domain knowledge from the Patient Health Questionnaire-9 lexicon into an integer linear programming framework that optimizes linguistic quality and informativeness. We used 3 baseline approaches: extractive summarization using the SumBasic algorithm, abstractive summarization using integer linear programming without the infusion of knowledge, and abstraction over extractive summarization to evaluate the performance of KiAS. The capability of KiAS on the Distress Analysis Interview Corpus-Wizard of Oz data set was demonstrated through interpretable qualitative and quantitative evaluations.ResultsKiAS generates summaries (7 sentences on average) that capture informative questions and responses exchanged during long (58 sentences on average), ambiguous, and sparse clinical diagnostic interviews. The summaries generated using KiAS improved upon the 3 baselines by 23.3%, 4.4%, 2.5%, and 2.2% for thematic overlap, Flesch Reading Ease, contextual similarity, and Jensen Shannon divergence, respectively. On the Recall-Oriented Understudy for Gisting Evaluation-2 and Recall-Oriented Understudy for Gisting Evaluation-L metrics, KiAS showed an improvement of 61% and 49%, respectively. We validated the quality of the generated summaries through visual inspection and substantial interrater agreement from MHPs.ConclusionsOur collaborator MHPs observed the potential utility and significant impact of KiAS in leveraging valuable but voluminous communications that take place outside of normally scheduled clinical appointments. This study shows promise in generating semantically relevant summaries that will help MHPs make informed decisions about patient status.

Risk-averse optimal bidding strategy for a wind energy portfolio manager including EV parking lots for imbalance mitigation

In this study, an optimal bidding strategy for a wind energy portfolio manager (WEPM) including electricvehicle parking lots (EVPLs) that aims to maximize profits by trading in the day-ahead (DA) market and balancingmarket (BM) and through bilateral contracts, taking into account line capacities and risk management is proposed. Thementioned structure is modeled in mixed integer linear programming (MILP) framework, and the uncertainties regardingelectric vehicle (EV) behavior, electricity market data, wind power generation are captured via a stochastic approach. Todemonstrate the effectiveness of the model, several case studies are carried out considering Sweden and Turkey electricitymarket prices with different risk aversion factors and bilateral contract situations. In this manner, the important resultsas well as useful findings regarding the economic impacts of the proposed concept are analyzed in detail.