Optimal Schedule Research Articles

An optimization model for monthly time-step drilling schedule under planned field production

The field production profile over the yearly horizon is planned for a balance between economy, security, and sustainability of energy. An optimal drilling schedule is required to achieve the planned production profile with minimized drilling frequency and summation. In this study, we treat each possible production process of each well as a dependent time series and the basic unit. Then we ensemble all of them into a tensor. Based on formulated tensor calculation and Lasso regularization, a linear mathematical optimization model for well drilling schedule was developed. The model is aimed at minimizing production profile error while optimizing drilling frequency and summation. Although the model proposed in this work requires more memory consumption to be solved using a computer, it is assured as a linear model and could be numerically globally solved in a stable and efficient way using gradient descent, avoiding complex nonlinear programming problems. Main input data and parameters involved in the model are analyzed in detail to understand the effects of different production parameters on the drilling schedule and production profile. The proposed model in this work can evaluate the manual drilling schedule and automatically generate an optimized drilling schedule for the gas field, significantly reducing development plan formulation time.

Quantitative systems pharmacology-based exploration of relevant anti-amyloid therapy challenges in clinical practice.

Addressing practical challenges in clinical practice after the recent approvals of amyloid antibodies in Alzheimer's disease (AD) will benefit more patients. However, generating these answers using clinical trials or real-world evidence is not practical, nor feasible. Here we use a Quantitative Systems Pharmacology (QSP) computational model of amyloid aggregation dynamics, well validated with clinical data on biomarkers and amyloid-related imaging abnormality-edema (ARIA-E) liability of six amyloid antibodies in clinical trials to explore various clinical practice challenges. Treatment duration to reach amyloid negativity ranges from 12 to 44, 16 to 40, and 6 to 20 months for lecanemab, aducanumab, and donanemab, respectively, for baseline central amyloid values between 50 and 200 Centiloids (CL). Changes in plasma cerebrospinal fluid Aβ42 and the plasma Aβ42/ Aβ40 ratio-fluid biomarkers to detect central amyloid negativity-is greater for lecanemab than for aducanumab and donanemab, indicating that these fluid amyloid biomarkers are only suitable for lecanemab. After reaching amyloid negativity an optimal maintenance schedule consists of a 24-month, 48-month and 64-month interval for 10mg/kg (mpk) lecanemab, 10mpk aducanumab, and 20mpk donanemab, respectively, to keep central amyloid negative for 10 years. Cumulative ARIA-E liability could be reduced to almost half by introducing a drug holiday in the first months. For patients experiencing ARIA-E, restarting treatment with a conservative titration strategy resulted in an additional delay ranging between 3 and 4 months (donanemab), 5 months (lecanemab), and up to 7 months (aducanumab) for reaching amyloid negativity, depending upon the timing of the incident. Clinical trial designs for Down syndrome patients suggested the same rank order for central amyloid reduction, but higher ARIA-E liability especially for donanemab, which can be significantly mitigated by adopting a longer titration period. This QSP platform could support clinical practice challenges to optimize real-world treatment paradigms for new and existing amyloid drugs.

Modeling the subcutaneous pharmacokinetics of antibodies co-administered with rHuPH20.

Predicting the subcutaneous (SC) pharmacokinetics (PK) of antibodies in humans is challenging, with clinical data currently being the only reliable data source for modeling SC absorption and bioavailability. Recombinant human hyaluronidase PH20 (rHuPH20) is an enzyme that facilitates SC delivery of high-dose, high-volume therapeutics. Numerous monoclonal antibodies have been co-administered SC with rHuPH20 in a clinical setting, establishing an extensive PK database. The goal of this work is to demonstrate how aggregated clinical data can be leveraged in a universal modeling framework for characterizing SC antibody PK, resulting in parameterization that can be used in predictive simulations of new antibodies. Data for 10 individual antibodies co-administered SC with rHuPH20 were obtained from publicly available sources. PK modeling of each antibody was conducted using the same model structure, but uniquely parameterized. The model structure consisted of a two-compartment model to capture linear kinetics, plus a target-binding mechanism to accommodate nonlinear kinetics driven by antibody-target complex formation and elimination. The clinical PK profiles for all antibodies were accurately described using the universal modeling framework. The SC PK parameters of absorption and bioavailability were consistent across the range of antibody and target properties evaluated. SC administration with rHuPH20 yielded a 30% increase in absorption rate on average and similar or better bioavailability. These parameter values can serve as initial conditions for model-based PK predictions for new antibodies co-administered SC with rHuPH20 to enable evaluation of optimal SC dose and schedule regimens prior to and during clinical development.

SA-Based QoS Aware Workflow Scheduling of Collaborative Tasks in Grid Computing

Scheduling workflow tasks in grid computing is a complex process, especially if it is associated with satisfying the user's requirements to complete tasks within a specified time, with lowest possible cost. This paper presents a proposed Simulated Annealing (SA) based Grid Workflow Tasks Scheduling Approach (SA-GWTSA) that takes into account users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time is within the time constraints (deadline) imposed by the user. In SA-GWTSA, the workflow tasks, which are modeled as a DAG, are divided into task divisions, each of which consists of a set of sequential tasks. Then, the optimal sub-schedules of all task divisions are computed applying SA algorithm, and used to obtain the execution schedule of the entire workflow. In the proposed algorithm, the sub-schedule of each branch division is represented by a vector, in which each element holds the ID of the service provider chosen from a list of service providers capable of executing the corresponding task in the branch. The algorithm uses a fitness function that is formulated as a multi-objective function of time and cost, which gives users the ability to determine their requirements of time against cost, by changing the weighting coefficients in the objective function. The paper also exhibits the experimental results of assessing the performance of SA-GWTSA with workflows samples of different sizes, compared to different scheduling algorithms: Greedy-Time, Greedy-Cost, and Modified Greedy-Cost.

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

Optimizing electricity peak shaving through stochastic programming and probabilistic time series forecasting

This paper proposes a predict-then-optimize framework to optimally schedule the charging and discharging activities of battery energy storage systems (BESS). BESS are used to eliminate peak electricity consumption loads, which pose a significant risk of microgrid system failure and increase electricity costs to end users. A major challenge in BESS lies in determining the battery usage schedule, which must be determined before actual energy consumption materializes. To address this challenge, we propose a two-phase approach. In the first phase, we develop a deep-learning-based probabilistic time-series forecasting model to predict future electricity consumption. In the second phase, the output of the prediction model is used to generate future load scenarios, which are incorporated into a two-stage stochastic programming model that determines the optimal battery usage schedule. Extensive computational experiments on real-world datasets demonstrate the effectiveness of the proposed framework in shaving peak loads and minimizing energy costs. Specifically, our proposed framework reduces daily energy peaks by up to 26%, with the potential for greater improvement as the forecast of future energy loads improves. To the best of our knowledge, this work is the first to investigate the integration of probabilistic forecasting and stochastic optimization to enhance the effectiveness of BESS in managing peak energy loads, leading to more energy-efficient buildings.

Optimising O&M scheduling in offshore wind farms considering weather forecast uncertainty and wake losses

Offshore wind farms, while strategically positioned to maximise wind energy capture, are exposed to harsh environments, impacting their reliability and accessibility. Low reliability and accessibility in turn translate into high Operation and Maintenance (O&M) costs, which represent approximately 30% of the total lifetime costs of the project. One approach to reduce O&M costs is to improve maintenance planning decisions. The present paper presents a novel O&M scheduling methodology for offshore wind farm operations based on wind and wave forecasts. The methodology forecasts farm yield considering farm wake effects to identify optimal operation schedules, vessel routing, and turbine maintenance sequences that minimise costs and revenue losses due to downtime. Weather forecast uncertainty is modelled and integrated into the decision-making process to minimise weather risks. A case study inspired by the WindFloat Atlantic floating wind project was used to demonstrate the optimisation potential. Results suggest that adopting the proposed methodology over business-as-usual scheduling significantly boosts offshore wind farm operational profits during the forecasted period, with increases ranging from 2% to 24%, depending on the scenario considered. It was found that integrating the wind farm wake losses into the scheduling decisions can improve the optimal scheduling solution and further minimise total operation costs.

Recommended 10-Year Follow-Up Strategy for Small Hepatocellular Carcinoma After Radiofrequency Ablation: A Cost-Effectiveness Evaluation.

An optimal follow-up schedule for small (≤3-cm) hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA) remains unclear in clinical guidelines. We aimed to assess the cost-effectiveness of follow-up strategies in patients with small HCC after RFA. In total, 11,243 patients were collected from global institutions to calculate recurrence rates. Subsequently, a Markov model covering a 10-year period was developed to compare 25 surveillance strategies involving different surveillance techniques (computed tomography [CT], magnetic resonance imaging or ultrasonography [US], and α-fetoprotein [AFP]) and intervals (3 or 6 months). The study endpoint was incremental cost-effectiveness ratio (ICER), which represented additional cost per incremental quality-adjusted life year. Sensitivity analysis was conducted by varying the values of input parameters to observe the ICER. In a base case analysis, the dominant strategy was CT every 3 months during an initial 2 years, followed by semiannual CT, and then switch to biannual the combination of US screening and AFP testing after 5 years (m3_CT-m6_CT-m6_USAFP), with an ICER of $68,570.92 compared with the "not followed" strategy. One-way sensitivity analysis showed the ICER consistently remained below the willingness-to-pay threshold of $100,000.00. In a probabilistic sensitivity analysis, m3_CT-m6_CT-m6_USAFP was the most cost-effective approach in 95.6% of simulated scenarios at a willingness-to-pay threshold. For small HCC after RFA, the recommended follow-up strategy is CT, with scans scheduled every 3 months for the first 2 years, every 6 months thereafter, and transition to biannual the combination of US screening and AFP testing after 5 years.

Skin marker combined with surface-guided auto-positioning for breast DIBH radiotherapy daily initial patient setup: An optimal schedule for both accuracy and efficiency.

By employing three surface-guided radiotherapy (SGRT)-assisted positioning methods, we conducted a prospective study of patients undergoing SGRT-based deep inspiration breath-hold (DIBH) radiotherapy using a Sentine/Catalys system. The aim of this study was to optimize the initial positioning workflow of SGRT-DIBH radiotherapy for breast cancer. A total of 124 patients were divided into three groups to conduct a prospective comparative study of the setup accuracy and efficiency for the daily initial setup of SGRT-DIBH breast radiotherapy. Group A was subjected to skin marker plus SGRT verification, Group B underwent SGRT optical feedback plus auto-positioning, and Group C was subjected to skin marker plus SGRT auto-positioning. We evaluated setup accuracy and efficiency using cone-beam computed tomography (CBCT) verification data and the total setup time. In groups A, B, and C, the mean and standard deviation of the translational setup-error vectors were small, with the highest values of the three directions observed in group A (2.4±1.6, 2.9±1.8, and 2.8±2.1mm). The rotational vectors in group B (1.8±0.7°, 2.1±0.8°, and 1.8±0.7°) were significantly larger than those in groups A and C, and the Group C setup required the shortest amount of time, at 1.5±0.3min, while that of Group B took the longest time, at 2.6±0.9min. SGRT one-key calibration was found to be more suitable when followed by skin marker/tattoo and in-room laser positioning, establishing it as an optimal daily initial set-up protocol for breast DIBH radiotherapy. This modality also proved to be suitable for free-breathing breast cancer radiotherapy, and its widespread clinical use is recommended.

Simulating VLBI observations to BeiDou and Galileo satellites in L-band for frame ties

Abstract Using the very long baseline interferometry (VLBI) technique to observe Earth-orbiting satellites is a topic of increasing interest for the establishment of frame ties. We present a simulation study on VLBI observations to BeiDou and Galileo satellites in L-band to investigate the accuracy of inter-technique frame ties between VLBI and global navigation satellite system (GNSS). We employ a global network of 16 antennas equipped with dedicated L-band receivers capable of observing BeiDou’s B1 and B3 navigation signals and Galileo’s E1 and E6 navigation signals. Through extensive Monte Carlo simulations, we simulate 24-h sessions to determine the optimal ratio of satellite to quasar scans. The optimal schedule uses about 80–90% satellite sources. We find that the 10–20% observations of quasar sources improve the estimation of the troposphere and, consequently, the estimation of the antenna position. Combining the normal equations from seven 24-h sessions, we derive a 7-day solution. The average antenna position repeatability is 7.3 mm. We identify the limitations of the results as the tropospheric turbulence, inaccuracies in the satellite orbit determination, and the lack of a more homogeneously distributed global network. This simulation study supports the understanding in the topic of building a frame tie using VLBI observations to GNSS satellites.

Abstract 5707: Preclinical evaluation of KRLS-017, a potent, highly selective and reversible CDK7 inhibitor with broad antitumor effect in preclinical models, in preparation for a Phase 1 clinical trial in advanced solid tumor malignancies

Abstract KRLS-017 is a potent, selective, and reversible inhibitor of Cyclin Dependent Kinase 7 (CDK7). Its molecular formula is C24H35N5O2Si and its molecular weight is 453.65 Daltons. KRLS-017 is being developed for the treatment of patients with advanced solid tumor malignancies. To differentiate KRLS-017 from other CDK7 inhibitors, we created a reversible inhibitor to allow for treatment schedule optimization and maximization of therapeutic index in clinic. High selectivity of KRLS-017 for CDK7 was confirmed using a panel of 313 recombinant human kinases. The IC50 of KRLS-017 against recombinant human CDK7 was determined to be 16 nM, and no other kinases in the panel showed IC50 values less than 300 nM. Antiproliferative activity of KRLS-017 was investigated in vitro using a broad panel of human tumor cell lines. KRLS-017 showed potent antiproliferative effect across a wide variety of both solid and hematologic tumor types, producing GI50 values <200 nM in 258/302 human tumorcell lines tested. Antitumor activity of KRLS-017 was investigated in vivo using multiple mouse xenograft tumormodels at dose levels ranging from 25 mg/kg to 200 mg/kg using both continuous (daily) andintermittent dosing schedules. Statistically significant tumor growth inhibition was seen at doselevels of 50 mg/kg and higher, with tumor regression observed at 100 mg/kg and 200 mg/kg. Better in vivo tolerability at efficacious doses was observed using intermittent dosing schedules in mouse xenograft models, which led to adoption of an intermittent schedule in the proposed first-in-human phase 1 trial. KRLS-017 is orally bioavailable in mice, rats, and dogs and plasma drug exposure increased in a proportional manner with dose. Comparing first dose and 28th dose in the dog, minimal plasma accumulation was observed, suggesting once daily dosing in humans would be initially tested. Repeat dose 28 day GLP toxicology studies in rat and dog showed adverse effects in highly proliferative bone marrow and gastrointestinal tissues which is consistent with the known mechanism of action for CDK7 and is considered to be monitorable and manageable in the setting of oncology. The clinical development plan for KRLS-017 includes an initial Phase 1 dose escalation study (KRLS-017-101) in patients with refractory solid tumor malignancies to assess pharmacokinetics/pharmacodynamics and to determine an optimal dose level and treatment schedule to test in expansion cohorts. Given the potential of CDK7 inhibitors in the treatment of hormone receptor positive breast cancer, combination work with other agents (such as selective estrogen receptor degraders) is on-going. Citation Format: Alison L. Hannah, Steve Ritland, Thomas Steele, Steven Smith, Ashwin Ram, BT Slingsby, Kapil Dhingra, James Chrisman, Rajesh Dua, Yasunori Tokunaga, Shigeyuki Kono, Yasuhiro Aga. Preclinical evaluation of KRLS-017, a potent, highly selective and reversible CDK7 inhibitor with broad antitumor effect in preclinical models, in preparation for a Phase 1 clinical trial in advanced solid tumor malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5707.

Abstract 7180: Tumor growth modeling to support dose justification of Dato-DXd in hormone receptor positive, HER2 negative breast cancer (HR+HER2-BC) subjects

Abstract BACKGROUND: Dato-DXd, a TROP2-directed antibody-drug conjugate, has demonstrated efficacy and a manageable safety profile in multiple tumor types. A dosing regimen of 6 mg/kg every three weeks (Q3W) has been proposed as the optimal schedule based on phase I data in patients with non-small cell lung cancer (NSCLC)1. Dato-DXd demonstrated statistically significant and clinically meaningful improvement in PFS compared with ICC in patients with HR+HER2-BC in TROPION-Breast012. A tumor growth inhibition (TGI) model was developed to support dose justification at 6 mg/kg in HR+HER2-BC subjects. METHODS: A tumor growth inhibition (TGI) model was developed based on tumor size data from HR+HER2-BC patients received 6 mg/kg Dato-DXd Q3W in a Phase I study (N = 39, Data cutoff July 2022). The TGI model described the tumor size as the result of an exponential tumor growth, a tumor shrinkage that was driven by Dato-DXd cycle-specific AUC, and a resistance term suggesting that the shrinkage effect by Dato-DXd decreases over time. Cycle-specific Dato-DXd AUC was derived based on the AUC and time at each cycle for each individual. Simulations were conducted using the TGI model to compare tumor size change from baseline at 4, 6, and 8 mg/kg in HR+HER2-BC patients, with consideration of dropouts. RESULTS: The TGI model well-described tumor size data with reasonable parameter estimations. Simulated time-courses of tumor sizes at Dato-DXd 4, 6, and 8 mg/kg suggested that Dato-DXd at 6 mg/kg may lead to greater tumor shrinkage effect than that at 4 mg/kg in HR+/HER2-BC patients. At approximately 180 days post first treatment, tumor shrinkage at 6 mg/kg was almost 2-times greater than at 4 mg/kg (-51% vs. -25%, respectively). CONCLUSION: Simulations based on the TGI model suggested greater tumor size reduction at Dato-DXd 6 mg/kg compared to 4 mg/kg in HR+HER2-BC patient population. Reference: 1 ACoP13 2022 2 ESMO 2023. Citation Format: Zoey Tang, Yu Jiang, Haitao Yang, David Dai, Chang Gong, Linda Irons, Yuzhuo Pan, Ying Hong, Shuang Liang, Song Ren, Alex Phipps, Diansong Zhou. Tumor growth modeling to support dose justification of Dato-DXd in hormone receptor positive, HER2 negative breast cancer (HR+HER2-BC) subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7180.

Investigating the compatibility of an irrigation decision support system with water rights and allocation in a selected irrigation network

AbstractIt is necessary to use different planning models, including decision support systems (DSSs), to allocate water resources. For this purpose, in this study, an irrigation decision support system (IDSS) was developed to improve irrigation management in the farming fields of Mahabad Plain located to the south‐east of Lake Urmia. Next, the compatibility of the IDSS with the conditions of the Mahabad irrigation and drainage network, water and soil resources, meteorological data and soil moisture (SM) were investigated. The statistical indices of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (EF) and Wilmot agreement (d) were used to evaluate the adaptability of the IDSS. The results showed that the IDSS has reasonable compatibility with soil and water resources, crop yield and meteorological data. Irrigation scheduling provided by the IDSS led to a 13.9% reduction in water consumption and a 6.7% increase in crop yield. The IDSS estimated minimum and maximum temperature and sunshine hours to a satisfactory degree and relative humidity with an acceptable degree (NRMSE = 0.72–0.77) compared to regional synoptic station data. The performance of the IDSS in simulating SM is ranked from good to well (NRMSE = 0.75–0.83). The results indicate that the IDSS has a sufficient performance in estimating meteorological and soil moisture data with R2 = 0.90, RMSE = 4.65, NRMSE = 0.78, EF = 0.76 and d = 0.80. In addition, the IDSS provides the optimal irrigation schedule by considering the ability to deliver water from the irrigation and drainage network to the third‐grade canal and agricultural fields as the upstream condition.

Fast Algorithm for High-Throughput Screening Scheduling Based on the PERT/CPM Project Management Technique

High-throughput screening systems are robotic cells that automatically scan and analyze thousands of biochemical samples and reagents in real time. The problem under consideration is to find an optimal cyclic schedule of robot moves that ensures maximum cell performance. To address this issue, we proposed a new efficient version of the parametric PERT/CPM project management method that works in conjunction with a combinatorial subalgorithm capable of rejecting unfeasible schedules. The main result obtained is that the new fast PERT/CPM method finds optimal robust schedules for solving large size problems in strongly polynomial time, which cannot be achieved using existing algorithms.

Exploratory Study of the Phase IV Immunization Schedule of Quadrivalent Influenza Split-Virion Vaccine in Children Aged 3-8 Years.

This study explored the optimum immunization schedule for the quadrivalent influenza split-virion vaccine containing influenza A strains (H1N1 and H3N2) and B lineage strains (Yamagata and Victoria) in children aged 3-8 years. The 652 participants enrolled were divided into two groups based on a history of influenza immunization (IH group) or no history of influenza immunization (NH group). The groups were administered a two-dose immunization schedule on days 0 and 30. In the NH group, on day 30 after the first dose, the positive rates of influenza hemagglutination-inhibition antibodies of strains H1N1, H3N2, BV, and BY were 85.85%, 71.70%, 65.27% and 60.45%, respectively. The positive rates of BV and BY failed to meet the absolute criteria for evaluating the immunogenicity of influenza vaccine in the population aged 3-60 years (for each strain antibody). On day 30 after the second dose, HI antibodies to strains H1N1, H3N2, BV, and BY met the immunogenicity acceptable criteria. In the IH group, on day 30 after the first dose, HI antibodies to strains H1N1, H3N2, BV, and BY met the acceptable criteria for immunogenicity. The incidence rates of adverse reactions (vaccine-related adverse events) from the first dose up until 30 days after the second dose were 20.80% in the IH group and 19.50% in the NH group. Only two Grade 3 adverse reactions (fever: temperature ≥ 39.5 °C, swelling: area ≥ 50% of the injection site area) occurred in the IH group, and no Grade 3 adverse reactions occurred in the NH group. No serious adverse reactions occurred in either group. We conclude that for the NH group, two doses of quadrivalent influenza vaccine should be administered, and for the IH group, a one-dose regimen is acceptable.

Rescheduling with New Orders Under Bounded Disruption

Rescheduling problems arise when unpredicted events occur, such as the arrival of new orders. These new jobs should be integrated in a proper way in the existing schedule of the so-called old jobs, with the aim of minimizing an objective function for the joint set of jobs. To avoid a major disruption of the original schedule, each old job is not allowed to deviate from its original completion time by more than a certain threshold. Filling a gap in the existing literature, we consider the minimization of the total weighted completion time. The resulting rescheduling problem is shown to be weakly NP-hard and several properties of the structure of an optimal schedule are derived. These can be used for the construction of an exact dynamic programming algorithm with pseudo-polynomial running time. A fully polynomial time approximation scheme is obtained from the dynamic program by three different scaling and reduction steps. Finally, for the minimization of the number of late jobs a strong NP-hardness result is derived. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was partially supported by the Ministero dell’Istruzione, dell’Università e della Ricerca [Award TESUN-83486178370409 finanziamento dipartimenti di eccellenza CAP. 1694 TIT. 232 ART. 6]. U. Pferschy acknowledges support by the Field of Excellence COLIBRI at the University of Graz.

Optimal household appliances scheduling for smart energy management considering inclining block rate tariff and net-metering system

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

MODELLING AND SOLVING COURSE SCHEDULING PROBLEMS (CASE STUDY: MATHEMATICS STUDY PROGRAM, INSTITUT TEKNOLOGI SUMATERA)

Every educational institution has a standard process for scheduling courses. In scheduling, there are a lot of constraints that must be fulfilled, including hard and soft constraints. Hard constraints are constraints that must be met and cannot be disregarded; a lecturer, for instance, is limited to teaching one course in one room at a time. Soft constraints, on the other hand, are a type of restriction that can be broken, but breaks are minimized to the greatest extent possible. The aim of this research is to produce an optimal lecture schedule and to provide alternative solutions. The case study taken is the even semester at Institut Teknologi Sumatera (ITERA) Mathematics Study Program. The problem is modelled using Goal Programming and solved using LINGO. The result shows that the scheduling can fulfill every requirement.

Large-volume LEO satellite imaging data networked transmission scheduling problem: Model and algorithm

With the current miniaturization and cost reduction trend, many small imaging satellites are deployed in low Earth orbit (LEO). Efficient scheduling of transmission links is crucial for handling the large volume of imaging data in the time-varying inter-satellite broadband communication networks. This ensures optimal utilization of inter-satellite link resources and gives rise to a novel problem known as the Large-volume LEO satellite Imaging Data Networked Transmission Scheduling Problem (LLSIDNTSP). This problem requires the integrity of the imaging data received on the ground station. A Specific Time-Evolving Graph (STEG) model is introduced to formulate this problem, while a highly effective Sequential Two-Phased Heuristic Algorithm (STPHA) is proposed to solve it. STPHA determines an optimized Contact Plan (CP) with a Contact-Fast Construction Algorithm (CFCA) in the first phase. With this CP as input, it produces an optimal transmission schedule with Linear Programming in the second phase. The CFCA incorporates a novel heuristic called Mission Residual Volume Factor(MRVF), which guides the selection of a proper inter-satellite and satellite-to-ground link at each construction step. Extensive simulation results demonstrate the effectiveness of the proposed STPHA. In particular, it can achieve the optimal solution for small-sized scenarios with one-hundredth of the computing time used by the exact solver CPLEX. Also, it is consistently better than other heuristic algorithms adapted from the literature for large-sized scenarios. Additional experimental analysis is conducted to showcase the effectiveness of the innovative components of STPHA.

Sustainable optimizing WMN performance through meta-heuristic TDMA link scheduling and routing

Wireless mesh networks (WMNs) have become a popular solution for expanding internet service and communication in both urban and rural areas. However, the performance of WMNs depends on generating optimized time-division multiple access (TDMA) schedules, which distribute time into a list of slots called superframes. This study proposes novel meta-heuristic algorithms to generate TDMA link schedules in WMNs using two different interference/constraint models: multi-transmit-receive (MTR) and full-duplex (FD). The objectives of this study are to optimize the TDMA frame for packet transmission, satisfy the constraints, and minimize the end-to-end delay. The significant contributions of this study are: (1) proposing effective and efficient heuristic solutions to solve the NP-complete problem of generating optimal TDMA link schedules in WMNs; (2) investigating the new FD interference model to improve the network capacity above the physical layer. To achieve these objectives and contributions, the study uses two popular meta-heuristics, the artificial bee colony (ABC) and/or genetic algorithm (GA), to solve the known NP-complete problems of joint scheduling, power control, and rate control. The results of this study show that the proposed algorithms can generate optimized TDMA link schedules for both MTR and FD models. The joint routing and scheduling approach further minimizes end-to-end delay while maintaining the schedule's minimum length and/or maximum capacity. The proposed solution outperforms the existing solutions in terms of the number of active links, end-to-end delay, and network capacity. The research aims to improve the efficiency and effectiveness of WMNs in most applications that require high throughput and fast response time.