Practical Scheduling Method Research Articles

A participatory and practical irrigation scheduling in semiarid areas: the case of Gumselassa irrigation scheme in Northern Ethiopia

Poor irrigation scheduling practices have been quoted as the major challenges for sustainability of small-scale irrigation schemes in Ethiopia due to complexity of scheduling techniques, cost and inaccessibility of soil-water monitoring tools, lack of various local climatic data and soil-water parameters. For local experts to easily schedule irrigation and to promote adoption by farmers, a cheap and simple computation procedure of irrigation scheduling is needed that considers local resources and opinions. So far, there is no such study in the context of Ethiopia. A simple irrigation scheduling method (Practical) was developed based on the FAO procedure (Brouwer et al., 1989), employing Hargreaves ET0 equation and the opinions of local farmers and extension agents. Then, the method was validated on-farm through participatory and close observation of farmers by comparing with CropWat simulated (Sophisticated) and local (Traditional) scheduling practices for 2015and 2015/16 irrigation seasons considering maize as indicator crop. Data on irrigation depths, yield and yield components and soil salinity were collected and analysed. Furthermore, a farmers’ day was arranged to collect opinions on the crop stand and scheduling techniques. In both irrigation seasons, the practical irrigation schedule method resulted in higher grain yield while saving substantial amount of water and in significantly higher water productivity (WP) compared to the other methods. Maximum (0.68 kg m−3 in 2015) and minimum (0.47 kg m−3 in 2015/16) WP were found in the practical and sophisticated approaches, respectively. The average root zone salinities among the alterative irrigation scheduling methods were not significantly different, in both irrigation seasons. Farmers’ and experts’ opinions were in favour of the practical scheduling method. The practical irrigation scheduling method is thus recommended for maize, around Gumselassa area. Further, the presented procedure can be adopted for preparation of irrigation calendars of other cops and in other regions.

Optimal Scheduling for a Service Technician Workforce with Time-varying Work Volume and Technician Availability

A practical scheduling method is developed and implemented to determine the optimal allocation of technicians to candidate tour types and start times in a field service environment. Historical data is aggregated to determine a weekly work volume distribution and technician availability profile. These and other quantitative factors populate a mixed integer programming model for determining the distribution of technician tours that will minimize queueing delay in completing service, subject to side constraints on tour type quantities. The approach has been successfully implemented to schedule installation and maintenance technicians at a major telecommunication service provider and could easily be adapted to other operational contexts.

A practical scheduling method based on workflow management technology

In the literature, most scheduling models are developed using mathematical methods such as programming models or graphic models like Petri net. Although they are efficient in computing, they are difficult to build and understand by industry users. In order to make the scheduling model more capable and practical for industrial use, a new scheduling model based on the workflow management technique (SMWMT) is proposed here. It is a process-oriented compound model that features various process constraints definitions, activity behaviour descriptions, flexible resource definition and mapping, and graphic presentations that offer intuitional understandings to users. The corresponding scheduling system based on workflow simulation is provided. A cost-oriented iterative algorithm is proposed to enhance the scheduling performance. The example of the model and the simulation results show that the proposed model and scheduling method are effective.

Dynamic Job Shop Scheduling Using a Neural Network. 1st Report. A Two-Stage Training Method to Minimize Tardiness Cost.

We consider dynamic job shop scheduling problems with a tardiness cost criterion. The most practical scheduling method in dynamic scheduling environments is using dispatching rules. However, there is no dispatching rule that performs best under all scheduling conditions. In this paper, we apply a multi-layer feed-forward neural network to the scheduling, and propose a two-stage training method to minimize the average tardiness cost of jobs under various scheduling conditions. In the first stage, each neural network for its specified scheduling condition is trained separately by a simulated annealing method. In the second stage, a final neural network for all scheduling conditions is trained by a back-propagation algorithm. Simulation results show that the trained neural network outperforms the best dispatching rules in any levels of due-date tightness and job arrival rate.

A Practical Scheduling Method for Multiclass Production Systems with Setups

Consider a multiclass production system where many job classes share a single server and a setup time is incurred whenever the server changes class. This paper presents a simple method for scheduling these systems that performs well, not only with respect to mean waiting time, but also with respect to waiting-time variance and the outer percentiles of waiting time. The scheduling method is dynamic and uses the ages of items in each queue, as well as the queue statistics, to decide which queue to service next.

A Practical Scheduling Method for Minimizing the Sum of Earliness and Tardiness

A Practical Scheduling Method for Minimizing the Sum of Earliness and Tardiness