Scheduling Tool Research Articles

Application of the DIDAS program for the design and scheduling of drip irrigation

The DIDAS software package assists in drip-irrigation system design and irrigation scheduling. It is based on analytical solutions of the linearized water flow equation. The problem of water flow and uptake is described by superposing solutions for positive sources (on-surface or subsurface emitters) and negative sinks (root systems). Steady water flow is assumed in the design module and unsteady flow in the irrigation-scheduling module. The design tool, based on the relative water-uptake rate (RWUR) criterion, assesses the effects on water-use efficiency of geometrical attributes: distances between emitters along drip lines, separation between drip lines, depth of subsurface emitters, and size and depth of root systems. For design purposes, it is assumed that there is no plant-atmosphere resistance to water uptake, i.e., roots are assumed to apply maximum suction and water-uptake rate depends only on the soil's ability to conduct water from sources to sinks. The RWUR computations require only three parameters describing soil texture, root-zone size, and potential evaporation. The optimizing tool for irrigation scheduling is based on a relative water-uptake volume (RWUV) criterion. The RWUV computations for a given drip-irrigation design require additional information on the diurnal pattern of plant resistance to water uptake and soil hydraulic conductivity. DIDAS also contains a diurnal pattern module to evaluate diurnal water-uptake patterns; it assumes quasi-steady flow, accounts for diurnal variations in plant-atmosphere resistance and evaporation, and serves for fine-tuning the design and in preliminary evaluation of scheduling scenarios. DIDAS was programmed in Delphi, runs on a PC under the Windows operating system, and requires no further software. The drip-irrigation scenario is constructed via a few GUI windows, which also contain a library of the required soil input parameters, and a best-fitting procedure to determine them. The computed RWURs and RWUVs are displayed graphically and the tabulated output results can be exported to, e.g., Microsoft Excel for further processing. An updated DIDAS version can be downloaded freely from http://app.agri.gov.il/didas. From March 2014 through July 2016, 877 people from 94 countries downloaded the first or second DIDAS versions (1.0.1 and 1.1.1, respectively). We briefly introduce drip irrigation and other available programs for its design and scheduling, outline the major DIDAS concepts, and illustrate the operation of the main software modules.

Online Appointment-Scheduling for Optimizing a High Volume of Research Consultations

Librarians at Penn State University piloted the online appointment-scheduling tool YouCanBook.me to determine its effectiveness in facilitating a high number of research consultations and improving an embedded service model. Librarians sought to leverage the tool to work with as many students as possible while balancing other duties. This article details using YouCanBook.me for this specific application, provides data and evaluation of the pilot, and explores possibilities for scaling usage of this scheduling tool in other library settings, both large and small.

Scheduling EURO-k conferences

EURO-k conferences are among the largest Operations Research conferences in the world, typically including more than 2000 presentations. As opposed to many other conferences, EURO-k conferences are hierarchically organized, and the conference schedule should reflect this structure to make navigation easier and more logical. In this article we present a scheduling tool that has been developed during the EURO2015 and EURO2016 conferences to schedule the streams, sessions and talks. A schedule is obtained by solving a number of optimization models, each addressing a specific objective. First, areas are assigned to buildings, making sure that related research areas are located close to each other. Next, the goal is to allocate each stream to only one room, and to ensure that the stream consists of a sequence of consecutive time slots. Finally, we optimize the assignment of room sizes. We illustrate the process by showing results from the scheduling of the EURO2016 conference, which took place in Poznan (Poland), July 3–6, 2016.

Retrospective and predictive optimal scheduling of nitrogen liquefier units and the effect of renewable generation

The construction and application of a multiple nitrogen liquefier unit (NLU) optimal scheduling tool is discussed. Constrained by customer demands and subject to electricity spot market prices over a week-ahead horizon, a retrospective optimiser (RO) determines the minimum scheduling costs. Plant start-up penalties and inter-site optimisation capabilities are incorporated into the optimisation model to emulate realistic operational flexibilities and costs. Using operational data, actual process schedules are compared to the RO results leading to improved process scheduling insights; such as increasing afternoon NLU operation during the spring to utilise lower power pricing caused by high solar generation. The RO is used to output a trackable load management key performance indicator to quantify potential and achieved scheduling improvements. Subsequently, correlations between renewable energy generation and spot market power prices are developed. Forecast pricing is used within a predictive optimiser (PO) to automatically generate an optimal schedule for the week ahead to meet projected customer demands. The RO provides potential hindsight savings of around 11%, and the PO up to 8% (representing significant cost savings for such energy intensive processes).

IceProd 2 Usage Experience

IceProd is a data processing and management framework developed by the IceCube Neutrino Observatory for processing of Monte Carlo simulations, detector data, and data driven analysis. It runs as a separate layer on top of grid and batch systems. This is accomplished by a set of daemons which process job workflow, maintaining configuration and status information on the job before, during, and after processing. IceProd can also manage complex workflow DAGs across distributed computing grids in order to optimize usage of resources.IceProd has recently been rewritten to increase its scaling capabilities, handle user analysis workflows together with simulation production, and facilitate the integration with 3rd party scheduling tools. IceProd 2, the second generation of IceProd, has been running in production for several months now. We share our experience setting up the system and things we’ve learned along the way.

Evaluation of simulator tools and power‐aware scheduling model for wireless sensor networks

The sharp increase of the wireless sensor networks (WSNs) performance has increased their power requirements. However, with a limited battery lifetime it is more and more difficult to deploy many more sensors with today's solutions. Therefore, the authors need to implement autonomous WSNs without any human intervention or external power supply. To this end, this study proposes an effective strategy to ensure an energy consumption gain that takes into account time constraints through a power-aware model based on the dynamic voltage and frequency scaling and the dynamic power management that are appropriate to the WSNs and on a global Earliest Deadline First scheduler. To select the most suitable simulator to integrate and simulate the developed models, >25 of the existing WSN simulators are outlined and evaluated. On the basis of this comparative study analysis, the authors chose the simulation tool for real-time multiprocessor scheduling (STORM) to validate their work for its multiple advantages.

A model exploring whether the coupled effects of plant water supply and demand affect the interpretation of water potentials and irrigation management

Water potential is a useful predictive tool in irrigation scheduling as it, or a component, is associated with physiological responses to water deficit. Increasing atmospheric demand for water increases transpiration and decreases water potential for the same stomatal conductance. However, based on supply by the soil-plant-atmosphere-continuum, decreasing soil water potential should decrease stomatal conductance and thus transpiration but also decrease water potential. Such contradictory behavior of supply and demand responses, may limit the value of water potential as an indicator of plant water status. This work studied the relationship between plant water potential and transpiration affected by supply (soil moisture) and atmospheric evaporative demand, and has implications for interpretation of water potentials and irrigation management. Results were that plant water potential has a narrow range of sensitivity to variation in supply and demand in hydrated soils, but greatly varying sensitivity in dry soils, limiting interpretation under dry conditions. Loss of soil conductance in dry, coarse soil types affects the trajectory of plant water potential response to supply and demand. Sapflow measurements on almonds indicated that variation in reference evapotranspiration and/or soil moisture deficit led to similar variation in stem water potentials to that predicted by the model. The model indicates hypotheses that with further testing may have important repercussions on the measurement of plant water use and irrigation scheduling.

Efficient scheduling of astronomical observations

Targeted spectroscopic exoplanet surveys face the challenge of maximizing their planet detection rates by means of careful planning. The number of possible observation combinations for a large exoplanet survey, i.e., the sequence of observations night after night, both in total time and amount of targets, is enormous. Sophisticated scheduling tools and the improved understanding of the exoplanet population are employed to investigate an efficient and optimal way to plan the execution of observations. This is applied to the CARMENES instrument, which is an optical and infrared high-resolution spectrograph that has started a survey of about 300 M-dwarf stars in search for terrestrial exoplanets. We use evolutionary computation techniques to create an automatic scheduler that minimizes the idle periods of the telescope and that distributes the observations among all the targets using configurable criteria. We simulate the case of the CARMENES survey with a realistic sample of targets, and we estimate the efficiency of the planning tool both in terms of telescope operations and planet detection. Our scheduling simulations produce plans that use about 99$\%$ of the available telescope time (including overheads) and optimally distribute the observations among the different targets. Under such conditions, and using current planet statistics, the optimized plan using this tool should allow the CARMENES survey to discover about 65$\%$ of the planets with radial-velocity semi-amplitudes greater than 1$~m\thinspace s^{-1}$ when considering only photon noise. The simulations using our scheduling tool show that it is possible to optimize the survey planning by minimizing idle instrument periods and fulfilling the science objectives in an efficient manner to maximize the scientific return.

Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP

Over the last decade, manufacturing companies have identified renewable energy as a promising means to cope with time-varying energy prices and to reduce energy-related greenhouse gas emissions. As a result of this development, global installed capacity of wind power has expanded significantly. To make efficient use of onsite wind power generation facilities in manufacturing, production scheduling tools need to consider the uncertainty attached to wind power generation along with changes in the energy procurement cost and in the products’ environmental footprints. To this end, we propose a solution procedure that first generates a large number of wind power scenarios that characterise the variability in wind power over time. Subsequently, a two-stage stochastic optimisation procedure computes a production schedule and energy supply decisions for a flow shop system. In the first stage, a bi-objective mixed integer linear programme simultaneously minimises the total weighted flow time and the expected energy cost, based on the generated wind power scenarios. In the second stage, energy supply decisions are adjusted based on real-time wind power data. A numerical example is used to illustrate the ability of the developed decision support tool to handle the uncertainty attached to wind power generation and its effectiveness in realising energy-related objectives in manufacturing.

Optimization of irrigation scheduling using ant colony algorithms and an advanced cropping system model

A generic simulation-optimization framework for optimal irrigation and fertilizer scheduling is developed, where the problem is represented in the form of decision-tree graphs, ant colony optimization (ACO) is used as the optimization engine and a process-based crop growth model is applied to evaluate the objective function. Dynamic decision variable option (DDVO) adjustment is used in the framework to reduce the search space size during the generation of trial solutions. The framework is applied for corn production under various levels of water availability and rates of fertilizer application in eastern Colorado, USA. The results indicate that ACO-DDVO is able to identify irrigation and fertilizer schedules that result in better net returns while using less irrigation water and fertilizer than those obtained using the Microsoft Excel spreadsheet-based Colorado Irrigation Scheduler (CIS) tool for annual crops. Another advantage of ACO-DDVO compared to CIS is the identification of both optimal irrigation and fertilizer schedules.

P-6-2 Webベースの需要家設備の最適運用計画ツールの開発

P-6-2 Webベースの需要家設備の最適運用計画ツールの開発

Simulation Model of a Quality Control Laboratory in Pharmaceutical Industry

Laboratories are critical components in drug manufacturing, and inefficiencies in laboratory management can have a major impact on the overall supply chain service level. The aim of this paper is to build a Discrete Event Simulation model of a Quality Control laboratory. To achieve this objective, a generic framework for information treatment and organization was built. In particular, information coming from different databases was organized into a single one that was used as input to a discrete event simulation model. The proposed model represents in detail the work flow of a quality control laboratory and it is intended as a support tool for planning, scheduling and decision making. The model was validated using real data, and it proved to be effective in the estimation of performance parameters such as, system throughput, equipment usage rate, system responsiveness and tasks processing times. Furthermore, the simulation model was tested with an alternative scheduling policy to evaluate how modifications on the system may improve its performance.

Planning and Scheduling of Payloads of AstroSat During Initial and Normal Phase Observations

On 28th September 2015, India launched its first astronomical space observatory AstroSat, successfully. AstroSat carried five astronomy payloads, namely, (i) Cadmium Zinc Telluride Imager (CZTI), (ii) Large Area X-ray Proportional Counter (LAXPC), (iii) Soft X-ray Telescope (SXT), (iv) Ultra Violet Imaging Telescope (UVIT) and (v) Scanning Sky Monitor (SSM) and therefore, has the capability to observe celestial objects in multi-wavelength. Four of the payloads are co-aligned along the positive roll axis of the spacecraft and the remaining one is placed along the positive yaw axis direction. All the payloads are sensitive to bright objects and specifically, require avoiding bright Sun within a safe zone of their bore axes in orbit. Further, there are other operational constraints both from spacecraft side and payloads side which are to be strictly enforced during operations. Even on-orbit spacecraft manoeuvres are constrained to about two of the axes in order to avoid bright Sun within this safe zone and a special constrained manoeuvre is exercised during manoeuvres. The planning and scheduling of the payloads during the Performance Verification (PV) phase was carried out in semi-autonomous/manual mode and a complete automation is exercised for normal phase/Guaranteed Time Observation (GuTO) operations. The process is found to be labour intensive and several operational software tools, encompassing spacecraft sub-systems, on-orbit, domain and environmental constraints, were built-in and interacted with the scheduling tool for appropriate decision-making and science scheduling. The procedural details of the complex scheduling of a multi-wavelength astronomy space observatory and their working in PV phase and in normal/GuTO phases are presented in this paper.

A simulation optimisation approach for real-time scheduling in an open shop environment using a composite dispatching rule

In the last decades, many researchers have studied open shop scheduling (OSS) problem by considering deterministic parameters using mathematical modelling, heuristics and meta-heuristics. However, it is important to study the problem as close as possible to real world conditions which consists of uncertainty and stochastic parameters. In this study, dispatching rules, as accepted tools for real-time scheduling, are applied for optimising the OSS problem. Since none of conventional dispatching rules performs well for all performance measures, a simulation-based real-time scheduling composite dispatching rule is developed. For this purpose, a multi response optimisation approach based on computer simulation for scheduling a non-preemptive open shop with stochastic ready times is presented in order to minimise the mean waiting time of jobs. The presented approach composed of design of experiments, discrete event simulation, multi-layer perceptron artificial neural network, radial basis function and data envelopment analysis to determine the most efficient dispatching rule for each machine.

\u2018The Healthy Migrant Effect\u2019 for Mental Health in England: Propensity-score Matched Analysis Using the EMPIRIC Survey

Evidence has demonstrated that immigrants have a mental health advantage over the indigenous population of developed countries. However, much of the evidence-base demonstrating this mental health advantage is susceptible to confounding and inadequate adjustment across immigrant and non-immigrant groups preventing a rigorous assessment of a ’healthy migrant effect’. To compare the risk of common mental disorders in the immigrant population compared to the non-immigrant population in ethnic minority groups in England. A propensity-score matched analysis was carried out to adequately balance immigrant and non-immigrant groups for known confounders using the EMPIRIC national survey of Black-Caribbean, Indian, Pakistani and Bangladeshi groups. The mental health of participants was assessed using the validated Revised Clinical Interview Schedule tool. Immigrant participants were significantly less likely to have a common mental disorder than non-immigrant participants; OR = 0.47, (95% CI 0.40, 0.56). The results from this study demonstrate that a mental health advantage exists in ethnic minority immigrants compared to non-immigrants when balancing the two groups for confounding factors. This may be due to immigrants possessing certain personality traits, such as "psychological hardiness", that the migration process may select for.

Development and implementation of an operating room scheduling tool: an action research study

In this study, we report the results of an action research project whose aim was to develop and implement an operating room scheduler at the Meyer University Children’s Hospital. The study offers insights into the features that make an MSS optimisation model and scheduler effective and easy to implement, and shed light on those actions facilitating their introduction and use. Specifically, it suggests that creating an effective operating room scheduler requires clustering patients in homogeneous surgery groups and developing a flexible tool that allows: scheduling surgery groups instead of actual patients, easily adding/removing constraints, changing the objective function(s) and adjusting the planning horizon. In addition, it posits that gaining the commitment of top management by showing credible preliminary results, inferring stakeholder preferences by letting them comment on tentative schedules, introducing changes gradually and involving staff at lower levels of the hospital hierarchy can significantly facilitate the scheduler development and implementation.

Comparing canopy temperature and leaf water potential as irrigation scheduling criteria of potato in water-saving irrigation strategies

Irrigation scheduling is important in irrigation water management. In this study, full (FI),deficit (DI) and partial root drying (PRD) irrigation strategies were applied in Agria and Ramospotato cultivars. Canopy temperature (CT) and leaf water potential (LWP) were assessed as thepotential tools for irrigation scheduling during the vegetative and productive growth stages.LWP varied between ca. -1.4 and -1.6 MPa and was not significantly different between FI, DIand PRD irrigation treatments. The LWP and CT values were not significant between the twopotato cultivars during the measurements, but CT was frequently significant between theirrigation treatments such that the PRD treatments were significantly 5 and 2 oC warmer than FIand DI treatments, respectively. Higher CT in PRD caused significant yield penalty such that DIand FI produced almost two times higher fresh tuber yield. Analysis revealed that CT hadsignificantly higher correlation (r=0.66) with water productivity (weight of fresh tuber yielddivided by volume of applied irrigation water) than LWP, which its correlation was notsignificant (r=0.40). This showed that crop water productivity could be better controlled by CT.Moreover, it was realized that the seven-day irrigation interval was far longer than required andpotatoes in all irrigation treatments were under water stress, especially in PRD, that mightpartially explain the 50% yield penalty of PRD compared to DI. The reason was that due to highatmospheric demand, soil water in the wet part of the root system would be completely depletedearlier by the time of next irrigation event. Conclusively, the CT is recommended as a morereliable crop water status and irrigation scheduling indicator than LWP in response to differentwater-saving irrigation managements.

6-3-3 オープンソースソフトウェアを用いた負荷スケジューリングツールの開発(Session 6-3 エネルギーマネージメントII)

6-3-3 オープンソースソフトウェアを用いた負荷スケジューリングツールの開発(Session 6-3 エネルギーマネージメントII)

Production planning and scheduling with material handling using modelling and simulation

Increase flexibility of manufacturing requires implementation of effective planning IT tools. Planning and scheduling problems traditionally consider the production resources as the only constraints. Since the raw material and intermediate transferring times are relevant in relation to the production times, it is required to model the material handling equipment and its activities. The problem of scheduling subject to manufacturing and logistical constraints based on data-driven models generation method is presented. A KbRS and FlexSim systems are used as a scheduling tool.

Cotton Irrigation Scheduling Using a Crop Growth Model and FAO-56 Methods: Field and Simulation Studies

Abstract. Crop growth simulation models can address a variety of agricultural problems, but their use to directly assist in-season irrigation management decisions is less common. Confidence in model reliability can be increased if models are shown to provide improved in-season management recommendations, which are explicitly tested in the field. The objective of this study was to compare the CSM-CROPGRO-Cotton model (with recently updated ET routines) to a well-tested FAO-56 irrigation scheduling spreadsheet by (1) using both tools to schedule cotton irrigation during 2014 and 2015 in central Arizona and (2) conducting a post-hoc simulation study to further compare outputs from these tools. Two replications of each irrigation scheduling treatment and a water-stressed treatment were established on a 2.6 ha field. Irrigation schedules were developed on a weekly basis and administered via an overhead lateral-move sprinkler irrigation system. Neutron moisture meters were used weekly to estimate soil moisture status and crop water use, and destructive plant samples were routinely collected to estimate cotton leaf area index (LAI) and canopy weight. Cotton yield was estimated using two mechanical cotton pickers with differing capabilities: (1) a two-row picker that facilitated manual collection of yield samples from 32 m2 areas and (2) a four-row picker equipped with a sensor-based cotton yield monitoring system. In addition to statistical testing of field data via mixed models, the data were used for post-hoc reparameterization and fine-tuning of the irrigation scheduling tools. Post-hoc simulations were conducted to compare measured and simulated evapotranspiration, crop coefficients, root zone soil moisture depletion, cotton growth metrics, and yield for each irrigation treatment. While total seasonal irrigation amounts were similar among the two scheduling tools, the crop model recommended more water during anthesis and less during the early season, which led to higher cotton fiber yield in both seasons (p < 0.05). The tools calculated cumulative evapotranspiration similarly, with root mean squared errors (RMSEs) less than 13%; however, FAO-56 crop coefficient (Kc) plots demonstrated subtle differences in daily evapotranspiration calculations. Root zone soil moisture depletion was better calculated by CSM-CROPGRO-Cotton, perhaps due to its more complex soil profile simulation; however, RMSEs for depletion always exceeded 20% for both tools and reached 149% for the FAO-56 spreadsheet in 2014. CSM-CROPGRO-Cotton simulated cotton LAI, canopy weight, canopy height, and yield with RMSEs less than 21%, while the FAO-56 spreadsheet had no capability for such outputs. Through field verification and thorough post-hoc data analysis, the results demonstrated that the CSM-CROPGRO-Cotton model with updated FAO-56 ET routines could match or exceed the accuracy and capability of an FAO-56 spreadsheet tool for cotton water use calculations and irrigation scheduling. Keywords: Cottonseed, Crop coefficient, Decision support, Depletion, Evapotranspiration, Fiber, Management, Simulation, Soil moisture, Yield.