Scheduling Simulation Model Research Articles

GISAREG—A GIS based irrigation scheduling simulation model to support improved water use

To support improved irrigation scheduling in the Syr Darya basin, Uzbekistan, the ISAREG model was selected due to previous applications in similar climates. It is a conceptual non-distributed water balance model for simulating crop irrigation schedules at field level and to compute irrigation requirements under optimal and/or water stressed conditions. To provide for the use of the model at the project scale, a new version of the model, integrated with a Geographical Information System (GIS), was developed. This GIS based application is aimed at supporting the implementation of improved farm irrigation management and, in a later phase, to also help project management. The integration concerns the creation of spatial and weather GIS databases usable by ISAREG, the models operation for different water management scenarios, and the production of crop irrigation maps and time dependent irrigation depths at selected aggregation modes, including the farm scale. The resulting information on alternative irrigation schedules is, therefore, spatially distributed and shall be used both to support irrigation scheduling advising and to help in the identification of practices that may lead to water saving and provide for salinity control. The paper includes brief descriptions of the models, the databases and the integration of the models, as well as main features of the GISAREG application. Results relative to the comparative analysis of irrigation scheduling scenarios show the capabilities of the model to support the selection of water saving alternatives. The appropriateness of implementing 15–20-day time intervals between irrigations is shown. In addition, results relative to compare net irrigation requirements for wet and dry years show the usefulness of the model in handling time series of weather data.

Resource constrained scheduling simulation model for alternative stochastic network projects

The paper presents a heuristic for resource constrained network project scheduling. A network project comprising both alternative deterministic decision nodes and alternative branching nodes with probabilistic outcomes is considered. Several renewable activity related resources, such as machines and manpower, are imbedded in the model. Each type of resource is in limited supply with a resource limit that is fixed at the same level throughout the project duration. Each activity in the project requires resources of various types with fixed capacities. The activity duration is a random variable with given density function. The problem is to minimize the expected project duration by determining for each activity, which will be realized within the project’s realization, its starting time (decision variable), i.e. the time of feeding-in resources. The resource delivery schedule is not calculated in advance and is based on decision-making in the course of monitoring the project. The suggested heuristic algorithm is performed in real time via simulation. Decision-making is carried out: • at alternative deterministic decision nodes, to single out all the alternative sub-networks (joint variants) in order to choose the one with the minimal average duration; • at other essential moments when at least one activity is ready to be operated but the available amount of resources is limited. A competition among those activities is carried out to determine the subset of activities which have to be operated first and can be supplied by available resources. Such a competition is realized by a combination of a knapsack resource reallocation model and a subsidiary simulation algorithm. A numerical example to illustrate the heuristic algorithm is presented.

A genetic algorithm-based optimal resource-constrained scheduling simulation model

Resources for construction activities are limited in the real construction world so that scheduling must include resource allocation. Furthermore, activity duration is uncertain due to the variation in the outside environment, such as resource availabilities, weather, space congestion, etc. A new optimal resource-constrained construction scheduling simulation model is proposed in this paper, in which the effects of both uncertain activity duration and resource constraints are taken into account. Probability distribution is used to model the uncertainties of activity duration. An optimal schedule simulation model is then established in which a genetic algorithm-based search technique is adopted to search for the probabilistic optimal project duration under resource constraints. The model can effectively provide the optimal averaged project duration, cumulative project completion probabilities and the impact of influence factors on the probabilistic resource-constrained scheduling problem.

A Computer Simulation Model for Scheduling and Optimisation of a Hot Steel Mill

A computer simulation model supporting production scheduling and optimisation of a hot steel mill has been developed based on the framework of hybrid systems in which continuous dynamics coexists with discrete events. The aim of this simulation model is to support scheduling and optimisation under different production and order situations. The computer simulation software was built under the G2 environment. Many complicated aspects of the process can be easily investigated using the computer simulation, which is impossible based on any analytical approach. Another benefit is that new scheduling and optimisation policies and their best combinations can be tested conveniently, which greatly reduces the risk introduced to implement such strategies.

Regional analysis of irrigation water requirements using kriging: Application to potato crop ( Solanum tuberosum L.) at Trás-os-Montes

Aiming at the analysis of the regional variation of potato crop irrigation water requirements over the Trás-os-Montes region, data from 106 rainfall stations and eight weather stations were utilized in an irrigation scheduling simulation model to estimate net irrigation water requirements of the potato crop. The simulation model was first validated using a field experiment which allows to derive the required crop data to be used in the simulations. The reference evapotranspiration (ET 0) was estimated using the FAO Penman–Monteith method. The model was applied to all 106 locations, each with a data set spanning a 19-year period. As a result of this application, series of the net irrigation water requirements for a 19-year period were obtained for each location. The resulting 106 point values of the net water requirements of the potato crop have been treated as a regionalized variable. The respective semivariograms have been computed and the kriging method then applied to estimate the spatial distribution of the water requirements in the region. Contour lines of this regionalized variable have been drawn using a GIS system. Results show an estimation error averaging 5% for the entire region.

A computational study on design and performance issues of multi-agent intelligent systems for dynamic scheduling environments

Multi-Agent Intelligent Systems (MAIS) are loosely-coupled network of problem solving systems that, whenever needed, work together with each other to dynamically solve problems that none of the system can individually solve. Among the advantages of the MAIS, when compared to the centralized systems, are increased reliability, faster problem solving, decreased communication, and more flexibility. Learning to coordinate the actions is one of the most important task in MAIS. In the current research, we use a widely reported dynamic job shop scheduling simulation model that uses distributed genetic learning of job scheduling strategies (Pendharkar, P.C., 1997. Doctoral Dissertation, Graduate School, Southern Illinois University at Carbondale; Pendharkar, P.C., 1998. Distributed learning of objectives for adaptive scheduling (in review); Pendharkar, P.C., Bhattacharyya, S., 1997. Multi-agent learning in distributed artificial intelligence. Proc. 2nd INFORMS Conference on Information Systems and Technology. San Diego, CA, p.156–163; Bhattacharyya, S., Koehler, G.J., 1997. Learning by objectives for adaptive shop-floor learning. Decision Sciences (to appear). Aytug, H., Koehler, G.J., Snowdon, J.L., 1994. Genetic learning of dynamic scheduling within a simulation environment, Computers and Operations Research, 21 (8), 909–925; Aytug, H., Bhattacharyya, S., Koehler, G.J., Snowdon, J.L., 1994. A review of machine learning in scheduling, IEEE Transactions on Engineering Management 41 (2) ) and study the performance and design issues in multi-agents information systems for dynamic scheduling in manufacturing. Among the design issue and performance issues considered in this research are coordination between agents, number of agents, and frequency of learning. Our results indicate that coordination between agents, and learning frequency play a significant role in the performance of multi-agent intelligent systems.

Specification of a job shop scheduling simulation model and some properties of its internal transition function

We specify a job shop scheduling system according to DEVS formalism introduced by Zeigler. Our specification defines a set of state variables and an internal transition function that moves the system from state to state at event times. Some properties of the internal transition function are presented.

A real-time water allocation model for large irrigation systems

In this study a simulation model for real-time irrigation scheduling of water deliveries at the tertiary and secondary canal levels of large irrigation systems has been developed. The model is responsive to current season changes in weather and other variables. The irrigation scheduling of the subsequent week is found out at the end of each week by updating the status of the system with real time data up to that week and then by solving the model for the new conditions. The model is based on water balance approach for lowland paddy and a soil moisture simulation approach for determining the irrigation requirements of upland crops. Expected rainfall at different probability levels during the irrigation season was used based on past rain fall data and Leaky law. The model was applied to an irrigation system in Thailand for determining the required irrigation deliveries. Result of the application indicate that the model can be used for determining water deliveries in a real-time basis.

Simulation of irrigation demand hydrographs at sector level

An appropriate estimation of demand at turnouts supplying the distribution network of the sectors constituting an irrigation project is an essential tool to improve delivery scheduling in an upstream controlled irrigation system. This paper reports on studies developed at Sorraia irrigation system, Portugal, where main crops are paddy rice and furrow irrigated row crops. The project covers 15,000 ha and deserves near 2,000 farmers, the delivery being organized through near 20 sectors. Records of supply data by farmer and outlet are available for several years and have been utilized to calibrate the models. The model ISAREG has been adopted for the simulation of irrigation demand for row crops. This is an irrigation scheduling simulation model, with several options including the evaluation of delivery schedules. Adopting and calibrating theETa/ETm ratio as irrigation threshold, the model could be calibrated for the main crops. Aggregated results for several irrigation sectors closely follow the recorded ones. The model IRRICEP has been developed for simulating paddy rice irrigation. Continuous flow deliver is adopted and the model computes the flooding time and the corresponding irrigation units. The irrigation requirements are those required to keep the target water depths in the basins. The aggregated results also satisfactorily follow the recorded supplies. Models ISAREG and IRRICEP are coupled when both rice and row crops are planted in the same irrigation sector.

Database driven simulation/simulation-based scheduling of a job-shop

The peculiar nature of simulation-based scheduling and the advanced features of modern relational databases present an interesting possibility to improve the simulation modeling process. In this paper we describe a new concept called “direct database-simulation” which uses the database data model as the simulation model for simulation-based scheduling in a job-shop environment. A case study on a large machine shop of 200 machining centres shows that the concept is feasible and performance measures such as tardiness and machine utilisation can be improved by up to 20%.

A knowledge‐based simulation model for job shop scheduling

Constructs a knowledge‐based simulation model for job shop scheduling simulation techniques to build the initial status of the knowledge base. Uses the simulation experiments to study the effect of technological factors, such as machine loading conditions and criteria of scheduling performance requirements, on the selection of scheduling priority rules. Implements the model with real workshop data to test its validity to cater for real‐world situations such as machine group centres, more than one machine and the availability of operators. The proposed model yields satisfactory results.

Direct database-simulation of a job-shop

This paper introduces a new concept called ‘direct database-simulation’ which uses relational database data as the simulation model for simulation-based scheduling in a job-shop environment. The application is in a large machine shop of 200 machining centres producing precision machine tools. The direct database-simulation scheduler was developed on Progress (an application development environment centred on a relational database management system) to address the problem of finite capacity scheduling of the machine shop. It is shown that the concept is feasible and performance measures such as tardiness and machine utilisation are improved by an order of 20%.

Petri Net Based Methodology for Task Scheduling on Multiprocessor Architectures

This paper proposes a Petri Net based methodology for task scheduling on multiproces sor architectures. The aim is to build automati cally a stochastic model (markovian model or stochastic simulation model) for both perfor mance evaluation and task scheduling. The modelling process that we use consists in the consecutive elaboration of two models: the knowledge model and the action model. The knowledge model is a formalization of the structure and the working of the system, using Petri Nets. This knowledge model is made of three parts: a logical subsystem (precedence constraints between tasks), a physical subsystem (multiprocessor architecture) and a decision subsystem (scheduling method). The action model is a translation of the previous knowledge model in a programming language or in a mathematical formalism. We propose in this paper two kinds of action models: a stochastic simulation model and a markovian model. These models are dedicated to dynamic task allocation between identical processors without communi cation cost between tasks. It is to be remarked that these action models are automatically deduced from the corresponding knowledge models. Finally, we report some results obtained from these two action models. In particular, we highlight that these two independent models return identical results and so validate them selves.

A Simulation Model for Irrigation Scheduling Under Variable Rainfall

ABSTRACT Asimulation model was developed by coupling a simple daily rainfall generator to a water balance model that determines irrigation dates and amounts. The rainfall model uses average monthly data to generate daily precipitation and the water balance uses average ETQ values and estimates separately evaporation and transpiration as well as allowable depletion. The results of 50 years of simulating irrigation schedules for corn and sugarbeets at Cordoba and Badajoz, Spain and Davis, CA were analyzed to evaluate the effects of planting date, soil storage capacity and irrigation system constraints. Probability distribution functions of the date of first irrigation were developed for ideal and practical irrigation situations. A method is proposed based on the simulation to develop a predictive irrigation schedule at selected probability levels for optimal and suboptimal irrigation dates.

The Transformation of Opinions, Beliefs, and Heuristic Rules into a Scheduling Simulation Model.

This paper describes the development and use of a scheduling model and system which relied heavily on the diagnostic skill of a specific person whose responsibility was to schedule production facil...

The case of the closed loop conveyor in production: a scheduling simulation model : Bloomberg AP (1972) MSc. thesis, Lyon Playfair Library, Imperial College of Science and Technology, London SW7 2AZ, England

The case of the closed loop conveyor in production: a scheduling simulation model : Bloomberg AP (1972) MSc. thesis, Lyon Playfair Library, Imperial College of Science and Technology, London SW7 2AZ, England