• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.306-309
• /
• 1996

The job shop scheduling problem has been a major target for many researchers. And, most of the past studies did not consider setup time. In many cases of real manufacturing environment, however, there exists a setup time for each operations. The setup can be divide into two parts, one can be done after job arrival. The setup time based on the latter can be summed together with processing time, but that based on the former can not be. We propose an approximation method based on shifting bottleneck procedure for solving the job shop scheduling problem with sequence independent setup time. It schedules the machines one by one, taking a bottleneck machine among the machines not yet scheduled. Every time after a new machine is scheduled, all schedules previously established are updated. Both the bottlenck search and the schedule updating procedure are based on solving a single machine scheduling problem with ready time, setup time and delivery time iteratively.

• Industrial Engineering and Management Systems
• /
• v.11 no.4
• /
• pp.310-330
• /
• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• Journal of Intelligence and Information Systems
• /
• v.2 no.2
• /
• pp.69-83
• /
• 1996

The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.747-752
• /
• 2004

Researches about scheduling of the distributed real-time systems have been proposed. However, they have some weak points, not scheduling both sporadic and periodic tasks and messages or being unable to guaranteeing the end-to-end constraints due to omitting precedence relations between sporadic tasks. So this paper proposes a new scheduling method for distributed real-time systems consisting of sporadic and periodic tasks with precedence relations and sporadic and periodic messages, guaranteeing end-to-end constraints. The proposed method is based on a binary search-based period assignment algorithm, an end-to-end laxity-based priority assignment algorithm, and three kinds of schedulability analysis, node, network, and end-to-end schedulability analysis. In addition, this paper describes the application model of sporadic tasks with precedence constraints in a distributed real-time system, shows that existing scheduling methods such as Rate Monotonic (RM) scheduling are not proper to be applied to the system having sporadic tasks with precedence constraints, and proposes an end-to-end laxity-based priority assignment algorithm.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.5
• /
• pp.1709-1718
• /
• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• IE interfaces
• /
• v.14 no.3
• /
• pp.218-226
• /
• 2001

Scheduling for shipbuilding processes has many alternative solutions since it has long time horizon and handles many jobs. This requests the scheduling system to generate and search feasible alternative solutions in short period of time. Among shipbuilding schedules, the ship erection schedule in a dock is the most important since the dock is the most critical resource in shipyard. In this research, we model the erection scheduling problem for shipbuilding and develop a new problem solving method using CST(Constraint Satisfaction Technique) and ILOG Scheduler. Experimental results show that the proposed scheduling method outperforms the existing manual scheduling methods in terms of schedule performance and required time.

• Journal of Computing Science and Engineering
• /
• v.9 no.2
• /
• pp.108-117
• /
• 2015

Interrupt handling is generally separated from process scheduling. This can lead to a scheduling anomaly and priority inversion. The processor can interrupt a higher priority process that is currently executing, in order to handle a network packet reception interruption on behalf of its intended lower priority receiver process. We propose a new network interrupt handling scheme that combines interrupt handling with process scheduling and the priority of the process. The proposed scheme employs techniques to identify the intended receiver process of an incoming packet at an earlier phase. We implement a prototype system of the proposed scheme on Linux 2.6, and our experiment results show that the prototype system supports the predictable real-time behavior of higher priority processes even when excessive traffic is sent to lower priority processes.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.6 no.1
• /
• pp.41-47
• /
• 2011

The Pfair scheduling algorithm, which is an optimal scheduling algorithm in the hard real-time multiprocessor environments, propose the necessary and sufficient condition for the schedulability and is based on the fixed quantum size. Recently, several methods that determine the optimal quantum size dynamically were proposed in the mode change environments. But these methods considered only the case in which the period of a task is increased or decreased. In this paper, we also consider the case in which the execution time of a task is increased or decreased, and propose new methods that determine the optimal quantum size dynamically.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.4
• /
• pp.302-307
• /
• 2012

Real-time systems perform periodic tasks and real-time aperiodic tasks such as alarm processing. Especially the periodic tasks included in control systems such as robots have precedence relationships among them. This paper proposes a new scheduling algorithm based on topological sort and residual time. The precedence relationships among periodic tasks are translated to the priorities of the tasks using topological sort algorithm. During the execution of the system the proposed scheduling algorithm decides on whether or not a newly arrived real-time aperiodic task is accepted based on residual time whenever the aperiodic task such as alarm is arrived. The proposed algorithm is validated using examples.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.487-487
• /
• 2000

The scheduling of real-time tasks needs both correctness and timeliness. But it is not easy to schedule real-time tasks having different characteristics in a single system. In this paper we solve the problem through an approach using the performance evaluation of real-time tasks through fuzzy-random variables. Using the performance evaluation through fuzzy-random variable, we can achieve flexible and efficient scheduling for real-time systems.