Long-term Scheduling Research Articles

Long-term production scheduling of open pit mines using particle swarm and bat algorithms under grade uncertainty

Long-term production scheduling of open pit mines using particle swarm and bat algorithms under grade uncertainty

A rapid solution adjustment strategy in solving Long-term scheduling of large cascade hydropower stations

Due to the great potential of large cascade hydropower stations on power generation, long-term scheduling of large cascade hydropower stations (LSLCHS) plays an important role in electrical power system. As more and more concentrations focused on the optimal operation of large cascade hydropower stations, the LSLCHS has been taken into a multi-dimensional, non-convex and non-linear optimization problem due to its complicated hydraulic connection relationships and varieties of complex constraints with considering its power generation, shipping and ecological characteristics. In order to solve this problem, a strategy of solution rapid adjustment regarding the principle of monotone principle is proposed accordingly. The simulation results show that the proposed method is an efficient for solving joint optimization dispatch model of cascade hydropower stations with fast convergent rate, strong robustness.

Reservoir-type hydropower equivalent model based on a future cost piecewise approximation

The long-term (LT) scheduling of reservoir-type hydropower plants is a multistage stochastic dynamic problem that has been traditionally solved using the stochastic dual dynamic programming (SDDP) approach. This LT schedule of releases should be met through short-term (ST) scheduling decisions obtained from a hydro-thermal scheduling that considers uncertainties. Both time scales can be linked if the ST problem considers as input the future cost function (FCF) obtained from LT studies. Known the piecewise-linear FCF, the hydro-scheduling can be solved as a one-stage problem. Under certain considerations a single segment of the FCF can be used to solve the schedule. From this formulation an equivalent model for the hydropower plant can be derived and used in ST studies. This model behaves accordingly to LT conditions to be met, and provides a marginal cost for dispatching the plant. A generation company (GENCO) owning a mix of hydro, wind, and thermal power will be the subject of study where the model will be implemented. The GENCO faces the problem of scheduling the hydraulic resource under uncertainties from e.g. wind and load while determining the market bids that maximize its profit under uncertainties from market prices. A two-stage stochastic unit commitment (SUC) for the ST scheduling implementing the equivalent hydro model will be solved.

Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

The authors describe a method for long-term hydro-thermal scheduling allowing treatment of detailed large-scale hydro systems. Decisions for each week are determined by solving a two-stage stochastic linear programming problem considering uncertainty in weather and exogenous market prices. The overall scheduling problem is solved by embedding such two-stage problems in a rolling horizon simulator. The method is verified on data for the Nordic power system, studying the incremental changes in expected socio-economic surplus for expansions in both the transmission and generation systems. Comparisons are made with a widely used existing long-term hydro-thermal scheduling model. The results indicate that the model is well suited to valuate the flexibility of hydropower in systems with a high share of intermittent renewable generation.

Deciphering molecular mechanisms of arginine deiminase-based therapy – Comparative response analysis in paired human primary and recurrent glioblastomas

Arginine auxotrophy constitutes the Achilles' heel for several tumors, among them glioblastoma multiforme (GBM). Hence, arginine-depleting enzymes such as arginine deiminase (ADI) from Streptococcus pyogenes are promising for treatment of primary and maybe even refractory GBM. Based on our previous study in which ADI-susceptibility was shown on a panel of patient-derived GBM cell lines, we here aimed at deciphering underlying molecular mechanisms of ADI-mediated growth inhibition. We found that ADI (35 mU/mL) initially induces a cellular stress-response that is characterized by upregulation of genes primarily belonging to the heat-shock protein family. In addition to autophagocytosis, we show for the first time that senescence constitutes another cellular response mechanism upon ADI-treatment and that this bacterial enzyme is able to act as radiosensitizer (¼ cases). Long-term treatment schedules revealed no resistance development, with treated cells showing morphological signs of cell stress. Next, several combination strategies were employed to optimize ADI-based treatment. Simultaneous and sequential S. pyogenes ADI-based combinations included substances acting at different molecular pathways (curcumin, resveratrol, quinacrine, and sorafenib, 2 × 72 h treatment). Adding drugs to GBM cell lines (n = 4, including a matched pair of primary and recurrent GBM in one case) accelerated and potentiated ADI-mediated cytotoxicity. Autophagy was identified as the main cause of tumor growth inhibition. Of note, residual cells again showed classical signs of senescence in most combinations.Our results suggest an alternative treatment regimen for this fatal cancer type which circumvents many of the traditional barriers. Using the metabolic defect in GBM thus warrants further (pre-) clinical evaluation.

Risk management and long-term production schedule optimization at the LabMag iron ore deposit in Labrador, Canada

Risk management and long-term production schedule optimization at the LabMag iron ore deposit in Labrador, Canada

Impact of large-scale rooftop solar PV integration: An algorithm for hydrothermal-solar scheduling (HTSS)

Hydrothermal scheduling is a standard problem in the operation of power systems. With the integration of renewable resources, like solar and/or wind to the existing hydro-thermal system, there is a need for a dedicated approach for long-term generation scheduling of the integrated system. Long-term generation scheduling of hydro-thermal-solar systems at the grid level has not been reported so far. This paper discusses a Hydrothermal-Solar Scheduling (HTSS) algorithm for the same. The algorithm is a two-stage formulation using dynamic programming and linear programming techniques. This generic methodology can be used for assessing the impact of large-scale rooftop solar-PV integration with a hydrothermal system, in terms of the electricity energy-mix, generation schedules and the annual generation-costs of the concerned system. It has been illustrated by application to the problem of integration of a large-scale rooftop solar photovoltaic scenario with the existing Mumbai sub-system.The results show a significantly higher annual generation-cost saving, achieved by integrating the large-scale rooftop solar photovoltaic scenario via the proposed HTSS algorithm. The benefits, however, reduce with increasing penetrations of solar in the electricity energy-mix. The annual generation-cost savings have also been used for estimating the upper cap on feed-in tariffs for solar generation, and the financial viability indicators for the large-scale rooftop solar photovoltaic scenario; the corresponding internal rate of return is found to decline from 18.4% to 16.6% as the share of solar-PV in the electricity energy-mix of Mumbai increases from 2.8% to 13.9%.

Probabilistic Multiobjective Transmission Expansion Planning Incorporating Demand Response Resources and Large-Scale Distant Wind Farms

Transmission expansion planning (TEP) is a critical issue, particularly coping with the new challenges of smart grids. Under the smart grid environment, demand response resources (DRRs) are contemplated as virtual power plants in energy policy decisions. DRRs affect the controllability of power systems, ranging from short-term to long-term scheduling. In this paper, a nonlinear economic model of responsive loads is presented based on price elasticity of demand and customers benefit function. In order to investigate the impacts of DRRs on TEP, a probabilistic multiobjective TEP incorporating demand response programs (DRPs) is introduced (PMO-TEP $^\mathrm{DRPs}$ ). IC, congestion costs, risk costs, and total incentive costs for participating in DRPs are considered different objectives of PMO-TEP $^\mathrm{DRPs}$ . Here, a probabilistic analysis technique, the so-called two-point estimation method, is applied to handle the uncertainty of wind-farm generation in the TEP problem. Furthermore, the maximum achievable potential and realistic achievable potential for participating customers in DRPs are derived. Due to the problem's nonconvex formulation, a nondominated sorting genetic algorithm II is utilized to evaluate PMO-TEP $^\mathrm{DRPs}$ . Several analyses are carried out on the IEEE Reliability Test System and Iran 400-kV transmission grid to confirm the capability of the proposed framework.

Agent-Based Microgrid Scheduling: An ICT Perspective

New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of the operation of distributed generators (DG) and renewable energy resources (RES) in microgrids is a problem that requires tough planning and the use of advanced tools to be efficiently addressed. This paper discusses different ICT technologies that can enable microgrid communication for control and management of distributed energy resources (DER). Based on such ICT, we propose a novel agent-based model to address the long-term scheduling of DER in microgrids as a distributed constraint optimization problem (DCOP). However, finding the optimal solution for a DCOP is known to be an NP-Hard problem, making difficult to guarantee optimal solutions even for short optimization periods. Hence, for the long-term scheduling of DER, we propose to split the problem into small time windows that can be effectively solved sequentially by off-the-shelf DCOP algorithms. A particular, but general enough case study is used to compare different DCOP algorithms under the proposed model. Results show that DCOP algorithms can find optimal and near-optimal solutions depending on the window size and the scenario considered.

Long-Term Generation Scheduling of Hydropower System Using Multi-Core Parallelization of Particle Swarm Optimization

A multi-core parallel Particle Swarm Optimization (MPPSO) algorithm is developed to improve computational efficiency for long-term optimal hydropower system operation, in response to rapidly increasing size and complexity of hydropower systems, especially in China. The MPPSO can be implemented in three steps with easily accessible multi-core hardware platforms. First, a multi-group parallel computing strategy is introduced to maintain the diversity of population for finding the global optima. Second, the fork/join framework based on divide-and-conquer strategy is adopted to distribute multiple populations to different CPU cores for parallel calculations to take full advantage of CPU performance. Third, the results generated in different CPUs are merged to achieve an improved acceleration effect on computational time cost and more accurate optimal scheduling solution. Results for a system of twelve hydropower stations in the Guizhou Power Grid in China demonstrate that the proposed algorithm makes full use of multi-core resources, and significantly improves the computational efficiency and accuracy of the optimal solution, in addition to its low parallelization cost and low implementation cost. These suggest that the proposed algorithm has great potential for future optimal operation of hydropower systems.

Strategic forest planning and operational decisions under uncertainty

ABSTRACTA lack of certainty about future forest growth or market conditions may lead to substantial variations in optimal strategic management and projected net value of a commercial forest. Optimal operation decisions, such as decisions to develop a road network or other infrastructure in the immediate future to support expected harvesting or other actions, would also be affected substantially by a lack of certainty about future conditions.This paper describes a practical approach to combining yield models with stochastic variation by forest type and period in a large number of scenarios to produce a distribution of optimal, long-term harvest schedules. The approach allows robust operational decisions to be identified that are appropriate under a wide range of uncertain future conditions. The approach also allows the development of logistic regression models that relate operational decisions to variation in future growth or market conditions. A case study is provided to demonstrate operational decisions on roading and infrastructure development in the immediate or operational period that are robust, and those that would depend on decisions made by a manager who is risk- adverse, risk-neutral or a risk-taker.

송전제약과 등가운전시간을 고려한 장기 예방정비계획 최적화에 관한 연구

송전제약과 등가운전시간을 고려한 장기 예방정비계획 최적화에 관한 연구

Optimal Long-Term Hydro Generation Scheduling of Small Hydropower Plant (SHP) using Metaheuristic Algorithm in Himreen Lake Dam

This study focuses on the improvement of optimization model by applying particle swarm optimization and firefly algorithm methods to get a stable power production utility at its maximum level. Furthermore, it investigates on the minimization of utility loss in power production from the hydropower system, which is done by optimizing the variables of operation control in the hydropower plant at Lake Himreen - Diyala Dam. The variables mentioned are net turbine head, the rate of water flow and power production which had been gathered in the data during a research throughout a 10-year period. The results obtained from these two methods, namely Firefly Algorithm (FA) and Particle Swarm Optimization, (PSO) are compared. The inferences for general comparisons are created through several behavior indicators. The behavior indicators illustrate that FA's performance is better than PSO's performance, in some fields. At the end, the results show the strength of FA, as well as its efficiency and superiority.

Joint Millimeter Wave and Microwave Resources Allocation in Cellular Networks With Dual-Mode Base Stations

The use of dual-mode base stations that can jointly exploit millimeter wave (mmW) and microwave ( $\mu \text{W}$ ) resources is a promising solution for overcoming the uncertainty of the mmW environment. In this paper, a novel dual-mode scheduling framework is proposed that jointly performs user applications (UAs) selection and scheduling over $\mu \text{W}$ and mmW bands. The proposed scheduling framework allows multiple UAs to run simultaneously on each user equipment (UE) and utilizes a set of context information , including the channel state information per UE, the delay tolerance and required load per UA, and the uncertainty of mmW channels, to maximize the quality-of-service (QoS) per UA. The dual-mode scheduling problem is then formulated as an optimization problem with minimum unsatisfied relations problem, which is shown to be challenging to solve. Consequently, a long-term scheduling framework, consisting of two stages, is proposed. Within this framework, first, the joint UA selection and scheduling over the $\mu \text{W}$ band is formulated as a one-to-many matching game between the $\mu \text{W}$ resources and UAs. To solve this problem, a novel scheduling algorithm is proposed and shown to yield a two-sided stable resource allocation. Second, over the mmW band, the joint context-aware UA selection and scheduling problem is formulated as a 0-1 Knapsack problem and a novel algorithm that builds on the Q-learning algorithm is proposed to find a suitable mmW scheduling policy while adaptively learning the UEs’ line-of-sight probabilities. Furthermore, it is shown that the proposed scheduling framework can find an effective scheduling solution, over both $\mu \text{W}$ and mmW, in polynomial time. Simulation results show that, compared with conventional scheduling schemes, the proposed approach significantly increases the number of satisfied UAs while improving the statistics of QoS violations and enhancing the overall users’ quality-of-experience.

Cost–benefit analysis of pumped hydro storage using improved probabilistic production simulation method

This study presents an improved probabilistic production simulation method to facilitate the cost–benefit analysis of pumped hydro storage. To capture the coherent feature of power system operation, the traditional form of probabilistic production simulation is strengthened under a three-fold computational framework. First, the chronological sequences of renewable energy generation and imbalanced power are produced according to a set of feature metrics. Then, the imbalanced power series are modified through the long-term scheduling of pumped hydro storage. At last, the regular steps of probabilistic production simulation, including construction of equivalent energy function and convolution operation, are implemented to derive the operational cost and reliability metrics of power system. Case studies on IEEE-RTS79 system demonstrate the effectiveness of the proposed simulation method, which enables the quantitative assessment for cost-benefits of pumped hydro storage towards a high-penetration renewable energy integrated power system.

Implementation of a Schedule II patient agreement for opioids and stimulants in an adult primary care practice

Background:The consumption of controlled substances in North Carolina and the nation has created a health crisis with epidemic levels of medication diversion, abuse, overdose and death. Primary care providers are the principal prescribers of controlled substances and at greatest risk of encountering patients that abuse medications. Guidelines recommend patient agreements with monitoring requirements when prescribing opioids and stimulants. Studies have focused on opioids and excluded stimulants. Adherence to recommended monitoring requirements has not been fully evaluated.Methodology:This was a quality improvement project using the Plan-Do-Check-Act procedure. The following outcome measures were evaluated: signed agreement on file, prescription monitoring program (pmp) checks, urine screens, and prescriptions written without a mandatory visit.Implementation:Who: patients aged 19 and over prescribed a long-term Schedule II medication for the chronic conditions of pain and/or attention deficit hyperactivity disorder. What: implemented a patient agreement and measured fidelity to components of the agreement. When: seven months pre- to seven months post-implementation. Where: in an adult primary care practice with approximately 2,500 patients. How: an agreement was implemented with monthly feedback provided.Results:Post-implementation, 94% of patients meeting criteria had a signed agreement in their medical record. Adherence to urine screening improved from 5.3% to 71.1%. Guideline adherence to pmp checks improved from 11.3% to 99.0%. Guideline deviation for prescriptions written without a visit improved from 20.6% to 0%. All improvements were statistically significant (P < .001).Conclusion:A Schedule II controlled substance patient agreement was successfully implemented in a primary care practice reducing risk for both the patient and provider.

Minimising the total cost of tardiness and overtime in a resumable capacitated job shop scheduling problem by using an efficient hybrid algorithm

This paper investigates a resumable capacitated job shop scheduling problem (CJSSP) that can have considerable applications in heavy industries where the job processing times are prolonged. Hence, this type of scheduling is somewhat classified as medium and long-term scheduling problem, in which the limited capacity of machineries cannot be overlooked. In order to formulate the problem a new mixed-integer linear programming (MILP) model with the objective of minimising the total cost of tardiness and overtime is presented. Since the problem is a Non-deterministic Polynomial-time hard (NP-Hard) problem, an effective hybrid meta-heuristic based on the genetic and firefly algorithms are developed to tackle its complexity in a reasonable time. In addition, two heuristic algorithms rooted in the shifting and Lagrangean procedures are proposed to guide the search process toward the feasible points of a given problem. For the sake of obtaining better and more robust solutions, the Taguchi method is also used to calibrate the parameters of the algorithm. Furthermore, numerical experiments are provided for evaluating the performance and effectiveness of the solution method. Regarding to the computational result, the efficiency of the proposed hybrid metaheuristic is evident, especially on solving medium and large-sized problems.

Joint Scheduling and Sensing Allocation in Energy Harvesting Sensor Networks With Fusion Centers

Energy harvesting wireless networks have become a reality in recent years. Designing policies that store and utilize the harvested energy, for achieving the desired network performance, remains one of the key challenges in such networks. To this end, based on the quality of monitoring , we formulate a long-term time-averaged joint scheduling and sensing allocation problem in wireless sensor networks with finite energy and data buffers, subject to certain data and battery quality-of-service constraints. Relaxing the finite energy buffer assumption, using virtual queues and techniques from Lyapunov optimization, we obtain the JSSA algorithm. We show that by appropriately choosing the control parameter of the JSSA algorithm, we can achieve a performance gap that decays inversely proportional to the battery capacity. The implementation overhead of the JSSA algorithm scales as $O(n)$ . Therefore, we also present a low-complexity distributed version of this algorithm whose implementation overhead scales as $O(\log n)$ .

89 - Preclinical validation of a novel compound targeting p70S6 kinase in breast cancer

UNLABELLED Breast cancer is a frequent and treatable disease. However, when recurrent, breast cancer often becomes refractory to therapy and progresses into metastatic forms that are typically incurable. Thus, understanding and targeting the critical pathways underlying breast cancer recurrence is urgently needed to eradicate primary disease and achieve better prognosis. Recently, we have demonstrated that the ribosomal protein p70S6K is activated in residual breast cancer cells as a result of post-surgical inflammation and that interfering with its activity in the peri-operative setting strongly suppresses recurrence in a mouse model. In order to develop clinically-exploitable treatments targeting p70S6K, we have tested a newly generated compound, called FS-115. FS-115 potently inhibited p70S6K1 (IC50 35nM) with high selectivity over other AGC kinases or PI3K pathway kinases. In vitro, treatment with FS-115 efficiently blocked p70S6K activity in breast cancer cell lines and impaired colony formation and anchorage independent growth. Pharmacokinetic profiling showed that FS-115 exhibited high oral bioavailability, optimal plasma distribution and high brain penetrance. In nude mice, FS-115 strongly suppressed tumor take-rate and primary tumor growth. Oral dosing with FS-115 in a peri-operative schedule was effective in decreasing local recurrence of breast cancer and a long-term treatment schedule was well tolerated and efficiently suppressed distant metastasis formation. Altogether, we propose that FS-115 might be a good candidate for the treatment of breast cancer patients at high risk to relapse. SUMMARY STATEMENT Our results confirm that inhibition of p70S6K represents a valuable opportunity for restraining loco-regional relapse and metastasis in breast cancer and identify in FS-115 a promising candidate-inhibitor to move from preclinical to clinical treatments.

Approaches to training in companies

Abstract The need to address generational change and the challenges of a global market in terms of maintaining productivity require small and medium enterprises, mainly of an artisanal nature, to rethink training. The challenges mainly concern production capacity, which is increasingly problematic given that demand does not allow for long-term schedules and enhancement of human resources. There are many tensions and just as many needs for improvement, and training is therefore the space in which to collect and rework in order to restore a new perspective of sustainable and quality change. This study presents a process of collaboration between universities and businesses, in which the former provide the framework for the activation of mechanisms that make use of work analysis, while the latter provide the appropriate support (time and opportunities for discussion) to allow for a review of the proposed training model and trainer functions in a dynamic and complex production environment.