Optimal Schedule Research Articles

Nonlinear Pricing with Underutilization: A Theory of Multi-part Tariffs

We study the nonlinear pricing of goods whose usage generates revenue for the seller and of which buyers can freely dispose. The optimal price schedule is a multi-part tariff, featuring tiers within which buyers pay a marginal price of zero. We apply our model to digital goods, for which advertising, data generation, and network effects make usage valuable, but monitoring legitimate usage is infeasible. Our results rationalize common pricing schemes including free products, free trials, and unlimited subscriptions. The possibility of free disposal harms producer and consumer welfare and makes both less sensitive to changes in usage-based revenue and demand. (JEL D11, D21, D42, L86, M37)

A Biomathematical Model of Tumor Response to Radioimmunotherapy With αPDL1 and αCTLA4.

There is evidence of synergy between radiotherapy and immunotherapy. Radiotherapy can increase liberation of tumor antigens, causing activation of antitumor T-cells. This effect can be boosted with immunotherapy. Radioimmunotherapy has potential to increase tumor control rates. Biomathematical models of response to radioimmunotherapy may help on understanding of the mechanisms affecting response, and assist clinicians on the design of optimal treatment strategies. In this work we present a biomathematical model of tumor response to radioimmunotherapy. The model uses the linear-quadratic response of tumor cells to radiation (or variation of it), and builds on previous developments to include the radiation-induced immune effect. We have focused this study on the combined effect of radiotherapy and αPDL1/ αCTLA4 therapies. The model can fit preclinical data of volume dynamics and control obtained with different dose fractionations and αPDL1/ αCTLA4. A biomathematical study of optimal combination strategies suggests that a good understanding of the involved biological delays, the biokinetics of the immunotherapy drug, and the interplay between them, may be of paramount importance to design optimal radioimmunotherapy schedules. Biomathematical models like the one we present can help to interpret experimental data on the synergy between radiotherapy and immunotherapy, and to assist in the design of more effective treatments.

Reward Shaping-Based Actor–Critic Deep Reinforcement Learning for Residential Energy Management

Residential energy consumption continues to climb steadily, requiring intelligent energy management strategies to reduce power system pressures and residential electricity bills. However, it is challenging to design such strategies due to the random nature of electricity pricing, appliance demand, and user behavior. This article presents a novel reward shaping (RS)-based actor–critic deep reinforcement learning (ACDRL) algorithm to manage the residential energy consumption profile with limited information about the uncertain factors. Specifically, the interaction between the energy management center and various residential loads is modeled as a Markov decision process that provides a fundamental mathematical framework to represent the decision-making in situations where outcomes are partially random and partially influenced by the decision-maker control signals, in which the key elements containing the agent, environment, state, action, and reward are carefully designed, and the electricity price is considered as a stochastic variable. An RS-ACDRL algorithm is then developed, incorporating both the actor and critic network and an RS mechanism, to learn the optimal energy consumption schedules. Several case studies involving real-world data are conducted to evaluate the performance of the proposed algorithm. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art RL methods in terms of learning speed, solution optimality, and cost reduction.

Modeling Traffic Volume Reduction as a Function of Winter Weather Factors for a Cold Region Highway

This research aims to quantify the impact of winter weather conditions (cold temperatures and snowfall) on traffic volume. Traffic data and winter weather data (temperature and snowfall intensities from November to March) were collected for 5 years from a cold region highway in Alberta, Canada. The study results suggest that, overall, traffic volume substantially reduces under extremely cold temperatures and heavy snowfall conditions. Yet, truck traffic was estimated to increase in the combined presence of some weather conditions, which could be due to more trucks being used and/or trucks potentially being rerouted from other routes. Highway agencies may use the proposed methodology to determine (1) the optimal snowplowing schedules, and (2) when to close (or reopen) highways in winter.

PENERAPAN EXTREME PROGRAMMING DALAM PENGEMBANGAN SISTEM INFORMASI PENJADWALAN KULIAH JURUSAN AKUNTANSI POLINES

Scheduling is a routine job that is carried out every semester in the college academic system. There are two schedules that are often found in universities, namely the scheduling of lectures and exams, both theoretical and practical. The scheduling process is a process for implementing events that contain components of courses, lecturers, classes and semesters in time slots that contain time and space components. Many aspects must be considered in order to process optimal class schedules and exams. The problem that occurs at this time is the process of making teaching decrees for lecturers which is automatic from the existing system. Research begins with planning, designing, coding, and testing. With the improvement of the scheduling information system using extreme programming, it is hoped that it can help make teaching decrees in the Polines Accounting department.

Abstract P4-01-02: Results from a dose escalation phase 1b study of palbociclib and avelumab in advanced breast cancer in the PAveMenT Trial

Abstract Background CDK4/6 inhibitors may trigger anti-tumour immunity, both through tumour cell intrinsic and extrinsic mechanisms, which can be enhanced by immune checkpoint blockade in pre-clinical studies. Although most triple negative breast cancers (TNBC) are resistant to CDK4/6 inhibition, palbociclib (palbo) has activity in pre-clinical models of the luminal androgen receptor (LAR) subtype. Here we report the dose finding phase Ib cohort A of the PAveMenT trial combining avelumab and palbociclib Methods The PAveMenT phase 1b trial consists of a dose finding phase (cohort A), to be followed by an expansion in androgen receptor positive (AR+) TNBC (Cohort B). The phase 1B phase investigated palbo dosing schedules (75mg intermittent dosing (ID), 100mg continuous dosing (CD) and 125mg ID) in combination with the anti-PD-L1 antibody, avelumab 10mg/kg 2-weekly (cohort A) to determine the optimal schedule for cohort B. The CD dose-level was pre-specified as the preferred schedule if tolerated, due to the proliferative nature of TNBC and potential for tumour re-growth with ID. Patients with previously treated advanced breast cancer (ABC) of any histology with measurable disease and adequate organ function, who had not received prior immunotherapy or CDK4/6 inhibitors or more than 2 lines of chemotherapy for ABC, were eligible for cohort A. Dose-limiting toxicities (DLTs) were defined as Grade(G) 4 neutropenia, complicated G3 neutropenia or G4 thrombocytopenia, or ≥G3 immune toxicity during cycle 1. AR staining of ≥10% using the SP107 antibody (Ventana) was considered AR+. PD-L1 was evaluated using CC23 Pharma Dx antibody (DAKO) and PD-L1 positive defined as CPS score of ≥10%. Results Fourteen patients were recruited to Part A, all female, 12 with TNBC, two with ER positive/HER2 negative ABC, median age 53.5 years. Three evaluable patients received palbo 75mg/day ID without DLTs, therefore two further dose levels were opened in parallel. Three evaluable patients received palbo 125mg/day ID without DLTs. Due to a DLT of fever with G3 neutropenia in one of 3 patients receiving palbo 100mg CD, 3 further evaluable patients received 100mg CD without DLTs. Two patients were not evaluable for DLTs having not received a full cycle of treatment so were replaced. One case of grade 3 colitis and one case of grade 2 hypothyroidism (both 100mg CD) occurred outside the DLT period. Amongst 12 patients with TNBC, metastatic tissue was available for 11, archival breast tissue for one. Four of 12 patients were AR+. Five of 10 TNBC patients with sufficient tissue were PD-L1 positive, all of whom had previously tested negative with the SP142 antibody (Ventana). One of the 5 PD-L1+ and 2/5 PD-L1 negative TNBC cases were also AR+. TNBC patients received a median of 2 cycles of treatment (range 1-17). Both ER+ patients were AR+ and PD-L1 negative; one progressed after 4 cycles, the other remains on treatment at 12 cycles. The 100mg CD schedule was selected for cohort B. Conclusions All schedules were tolerable, with activity observed in patients with both ER positive and TNBC cancers. Cohort B of the study is open to recruitment for patients with AR+ TNBC. NCT04360941. Citation Format: Alicia F. Okines, Houman Moghadam, Laura Sparks, Kabir Mohammed, Kathryn Dunne, Ashutosh Nerurkar, Peter Osin, Claire Swift, Ruth Sardinha, Nicholas Turner. Results from a dose escalation phase 1b study of palbociclib and avelumab in advanced breast cancer in the PAveMenT Trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-01-02.

Electric, Thermal and Cooling Energy Management of a Microgrid Incorporating Renewable Energy Hub

Energy hub (EH) is gradually used in microgrid (MG) for local generation, transfer, and storage of several forms of energy. This manuscript suggests bottlenose dolphin optimizer (BDO), HPSO-TVAC and GWO for optimum day-ahead scheduling of grid connected MG based on renewable EH. The power swap between grid and MG is bidirectional. MG consists of small hydro power plants (SHPPs), wind turbine generators (WTGs), solar micro-cogeneration (SMC) units, biomass-fuel-fired micro-cogeneration (BMC) units, battery energy storage system (BESS), plug-in electric vehicles (PEVs), electric chiller (EC), absorption chiller (AC), ice storage conditioner (ISC) and thermal energy storage system (TESS). SMC units and BMC units are incorporated alternately. Demand response program (DRP) is applied for electric, thermal and cooling demands to flatten demand curves and to improve system flexibility and decrease cost.

A cloud database route scheduling method using a hybrid optimization algorithm

SummaryCloud computing has appeared as a technology allowing a company to employ computing resources such as applications, software, and hardware to calculate over the Internet. Scholars have paid great attention to cloud computing because of its cutting‐edge availability, cost decrement, and boundless applications. A cloud database is a data storage site on the web where the optimal path is spotted to access the needed database. So, placing the ideal path to a database is crucial. The cloud database defined the scheduling problem to choose the perfect route. Cloud database path scheduling is a multifaceted procedure consisting of congestion control, routing list, and network flow distribution. It has a postponement in searching for the needed source route from the cloud database. Offering numerous infinite resources with the growing database workload is an NP‐Hard optimization problem where the query request needs optimal schedules to respond to the required services. So, we have used a hybrid cuckoo search (CS) and genetic algorithm (GA), motivated by a social bird's phenomenon, to solve this problem. Integrating genetic operators has dramatically enhanced the balance between the capability of searching and utilization.

On the Effects of Optimal Implementation of Variable Rotor Speed and Power Management on Hybrid-Electric Rotorcraft

AbstractThe concept of variable rotor speed (VRS) has been recognized as an efficient means to improve rotorcraft operational performance and environmental impact, with electrification being a potential technology to further contribute to that. This paper explores the impact of optimal implementation and scheduling of VRS and power management strategy for conventional and hybrid-electric rotorcraft on energy, fuel, and emissions footprint. A multidisciplinary simulation framework for rotorcraft performance combined with models for engine performance and gaseous emissions estimation is deployed. A holistic optimization approach is developed for the derivation of globally optimal schedules for combined rotor speed and power split targeting minimum energy consumption. Application of the derived optimal schedules at mission level resulted to a 6% improvement in range capability for the VRS tilt-rotor relative to its conventional counterpart. For the hybrid-electric tilt-rotor, combined optimization of VRS and power management leads to an increase in range to 18.4% at 40% and 25% reduced payload for current (250 Wh/kg) and future (450 Wh/kg) battery technology, respectively. For representative urban air mobility (UAM) scenarios, it is demonstrated that the VRS concept resulted in up to 10% and 14% reductions in fuel burn and NOx relative to the nominal rotor speed case, respectively. The utilization of the combined optimum VRS and power split schedules can boost performance with reductions of the order of 20% and 25% in mission fuel/CO2 and NOx at a reduced payload relative to the conventional tilt-rotor.

The schedule of ATR inhibitor AZD6738 can potentiate or abolish antitumor immune responses to radiotherapy.

Inhibitors of the DNA damage signaling kinase ATR increase tumor cell killing by chemotherapies that target DNA replication forks but also kill rapidly proliferating immune cells including activated T cells. Nevertheless, ATR inhibitor (ATRi) and radiotherapy (RT) can be combined to generate CD8+ T cell-dependent antitumor responses in mouse models. To determine the optimal schedule of ATRi and RT, we determined the impact of short-course versus prolonged daily treatment with AZD6738 (ATRi) on responses to RT (days 1-2). Short-course ATRi (days 1-3) plus RT caused expansion of tumor antigen-specific, effector CD8+ T cells in the tumor-draining lymph node (DLN) at 1 week after RT. This was preceded by acute decreases in proliferating tumor-infiltrating and peripheral T cells and a rapid proliferative rebound after ATRi cessation, increased inflammatory signaling (IFN-β, chemokines, particularly CXCL10) in tumors, and an accumulation of inflammatory cells in the DLN. In contrast, prolonged ATRi (days 1-9) prevented the expansion of tumor antigen-specific, effector CD8+ T cells in the DLN, and entirely abolished the therapeutic benefit of short-course ATRi with RT and anti-PD-L1. Our data argue that ATRi cessation is essential to allow CD8+ T cell responses to both RT and immune checkpoint inhibitors.

A Hybrid Heuristic Algorithm for Energy Management in Electricity Market with Demand Response and Distributed Generators

Optimal energy management trends are indispensable in improving the power grid’s reliability. However, power usage scheduling for energy management (EM) poses several challenges on a practical and technical level. This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal EM and overcome the major challenge in demand response (DR) implementation. This work aims to solve the power usage scheduling problem for EM to optimize utility bill, peak energy demand, and pollution emission while considering the varying pricing signal, distributed generators (DGs), household load, energy storage batteries, users, and EUC constraints. The ECS is based on a stochastic algorithm (genetic wind-driven optimization (GWDO) algorithm) because generation, DGs, demand, and energy price are stochastic and uncertain. The ECS based on the GWDO algorithm determines the optimal operation schedule of household appliances and batteries charge/discharge for a day time horizon. The developed model is analyzed by conducting simulations for two cases: home is not equipped with DGs, and home is equipped DGs in terms of utility bill, peak energy demand, and pollution emission. The simulation results validated the proposed model’s applicability to EM problems.

Two-stage energy scheduling framework for multi-microgrid system in market environment

This paper focuses on the day-ahead and real-time transactive energy markets in the multi-microgrid system. The main purpose is to obtain an optimal schedule for the energy management of microgrids in energy markets. The proposed approach is modeled in a time frame including offline and online classes using a bi-level optimization structure. The first stage considers the day-ahead scheduling of the multi-microgrid system using game theory, where a retail market is established for transactive energy exchange between the microgrids and the microgrid community on an hourly time scale. The second stage focuses on managing the fluctuations of renewables and electricity demand on a shorter time scale and forming a real-time market in the multi-microgrid. The purpose of the second stage implementation is real-time dispatch to minimize the imbalance cost of the microgrids and the microgrid community. The simulation results show that in a cooperative space between neighboring microgrids, the total operating cost is reduced by $61.26. Moreover, this cost reduction reaches $44.05 by moving the battery energy storage systems to the level of microgrids.

Scheduling distributed multiway spatial join queries: optimization models and algorithms

Multiway spatial joins are a commonly occurring and fundamental type of query for spatial data processing. This article presents models and algorithms to schedule this type of query in distributed database systems while attempting to strike a balance between makespan and communication costs. We propose three algorithms based on combinatorial optimization methods: the well-known linear relaxation technique of rounding a solution generated by linear programming (LP), a more sophisticated Lagrangian Relaxation method (LR), as well as a greedy heuristic (GR) for baseline comparison. Our evaluation shows that a schedule built using GR consumes, on average, 22% more processing and communication resources than a more elaborate schedule constructed via the LR method, when scheduling a query for 64 machines. The schedule provided by LR is also, on average, an order of magnitude closer to the optimal schedule for a query compared to GR. We show that scheduling Gigabyte-size multiway queries before execution can reduce its processing time by an order of magnitude compared to state-of-the-art frameworks for spatial data processing that do not have this capability, and can significantly reduce the amount of shuffled data in the network.

Dynamical Properties of Discrete-Time HTLV-I and HIV-1 within-Host Coinfection Model

Infection with human immunodeficiency virus type 1 (HIV-1) or human T-lymphotropic virus type I (HTLV-I) or both can lead to mortality. CD4+T cells are the target for both HTLV-I and HIV-1. In addition, HIV-1 can infect macrophages. CD4+T cells and macrophages play important roles in the immune system response. This article develops and analyzes a discrete-time HTLV-I and HIV-1 co-infection model. The model depicts the within-host interaction of six compartments: uninfected CD4+T cells, HIV-1-infected CD4+T cells, uninfected macrophages, HIV-1-infected macrophages, free HIV-1 particles and HTLV-I-infected CD4+T cells. The discrete-time model is obtained by discretizing the continuous-time model via the nonstandard finite difference (NSFD) approach. We show that NSFD preserves the positivity and boundedness of the model’s solutions. We deduce four threshold parameters that control the existence and stability of the four equilibria of the model. The Lyapunov method is used to examine the global stability of all equilibria. The analytical findings are supported via numerical simulation. The model can be useful when one seeks to design optimal treatment schedules using optimal control theory.

Model-Based Approach for Treated Wastewater Reuse Strategies Focusing on Water and Its Nitrogen Content “A Case Study for Olive Growing Farms in Peri-Urban Areas of Sousse, Tunisia”

One of Tunisia’s main challenges is to conserve and protect water resources for current and future generations. Using non-conventional water in agriculture, such as treated wastewater, can be a sustainable water-saving solution. Therefore, the objectives of this study are (i) to analyze the value chain of treated wastewater for olive growing farms production and (ii) to apply mathematical modeling to maximize the olive production in optimizing irrigation distribution and nitrogen amendment in olive growing farms. The work is carried out in a peri-urban irrigated perimeter of Msaken, Sousse, which is mainly occupied by olive trees and irrigated by treated wastewater. A SWOT analysis is also applied to identify the strengths and weaknesses of reuse in this study area. Moreover, mathematical models are used to determine the optimal schedule for fertigation with treated wastewater. In this process, data on rainfall, soil, water quality and olive production were collected from local farmers, local decision makers, field and laboratory experiments. SWOT results determine farmers’ perceptions of reusing treated wastewater for irrigation. The viability analysis, in terms of soil humidity and nitrogen, shows that the nitrogen stress is not a limiting factor for olive biomass production, but water stress is. This analysis provides numerical values for the maximum irrigation rate and total amount of irrigation water to ensure maximum olive production. It was found that the maximum irrigation could be 5.77 m3/day/ha and the total annual water requirement is 1240 m3/ha. Modeling appears to be an important tool to help local decision makers and to support and encourage local farmers to reuse treated wastewater under safe conditions and without environmental risks.

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory’s (NREL’s) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

A note on exact algorithms and heuristics for generation of V- and [formula omitted]-shaped sequences

We present two groups of algorithms for generation of V- and Λ-shaped sequences, and discuss their applications in scheduling. The first group includes two recursive exact algorithms which generate all V- and Λ-shaped sequences composed of the elements of a given set. The second group includes two heuristics which generate suboptimal V-shaped schedules for a single machine time-dependent scheduling problem with deteriorating jobs. The results of extensive computational experiments with C++, C# and Python implementations of these algorithms show that the exact algorithms can be used to solve single machine scheduling problems with V- or Λ-shaped optimal schedules, while the heuristics generate very good suboptimal schedules for the time-dependent scheduling problem.

Facility planning and schedule design in the pandemic: Eliminating contacts at construction workplace

The construction industry has been severely affected by the COVID-19 pandemic and the associated restrictions on person-to-person contacts issued by the government. A construction site usually has a high number of workers working at the same time; therefore, the question of how to ensure their safety during the pandemic—that is, how to protect them from getting infected—has become an urgent problem. In this study, we propose a bi-objective integer programming model to establish the optimal schedule plan under COVID-19 regulations. We develop a solution method and conduct numerical experiments to solve and validate our model. The optimal schedule plan can avoid contacts between workers of different groups while minimizing the total costs of complying with government policy. Our proposed model can be applied in practice to help project managers establish a reasonable and cost-effective schedule plan. This study contributes to reducing the operating costs of contractors and protecting the health of construction workers.

On the complexity of scheduling unrelated parallel machines with limited preemptions

We consider the problem of finding a minimum-length preemptive schedule for n jobs on m parallel machines. The problem is solvable in polynomial time, whether the machines are identical, uniform or unrelated. For identical or uniform machines, it is easy to obtain an optimal schedule in which the portion of a job that is assigned to a single machine is processed without interruption. We show that imposing this condition in the case of unrelated machines makes the problem NP-hard.

Hypofractionated Postmastectomy Radiotherapy (HF-PMRT): What did We Learn from COVID-19 Era?

Objectives: In breast cancer (BC) patients receiving mastectomy, postmastectomy radiotherapy (PMRT) improves long-term outcomes by decreasing local failure and cancer mortality. However, the optimal PMRT schedule is still under investigation. The present review aims to discuss the evidence regarding hypofractionated (HF) PMRT in BC patients in order to identify the optimal treatment approach. Additional purpose is to highlight what we have learned from COVID-19 era regarding HF schedules for PMRT in BC patients. Mechanism: Between February and November 2021, literature and database research were conducted. Key references were detected from a PubMed query. Range of publication date was between 2000 and 2021. Selection criteria included English language publications in humans. Hand searching included meeting proceedings of the European Society for Radiotherapy and Oncology (ESTRO), European Society of Medical Oncology (ESMO), American Society of Clinical Oncology (ASCO) and American Society for Radiation Oncology (ASTRO). The website clinicaltrials.gov was also searched. Randomized controlled trials evaluating HF-PMRT were included. Findings in brief: Our research returned 87 published papers. Fourteen trials were included in our final analysis. The comparisons of several different schedules of HF-PMRT with conventional fractionated PMRT provided similar results in terms of locoregional disease control without increasing toxicity. Particularly, an acute skin toxicity incidence grade 2 or higher ranged between 10 and 25% among the studies we analyzed. Conclusions: The present paper suggests that safety and efficacy of HF-PMRT is comparable with conventional schedules and standard practice guidelines are already available. COVID-19 pandemic has emphasised the need for increasingly tailored treatment protocols. Modern HF regimens should continue to be the standard of treatment in BC patients who receive PMRT also in the post-COVID-19 era.