• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.9-18
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• 2000

This paper considers the problem of two-machine, M-parallel flow shop scheduling to minimize makespan, and proposes a series of heuristic algorithms and a branch and bound algorithm. Two processing times of each job at two machines on each line are identical on any line. Since each flow-shop line consists of two machines, Johnson's sequence is optimal for each flow-shop line. Heuristic algorithms are developed in this paper by combining a "list scheduling" method and a "local search with global evaluation" method. Numerical experiments show that the proposed heuristics can efficiently give optimal or near-optimal schedules with high accuracy. with high accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.23
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• pp.47-55
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• 1991

In automated assembly line, an automatic guided vehicle system(AGVS) represents a mire versatile means of moving materials automatically. In this paper, the vehicles not only provide the transportation medium between workstations but also as mobile workstations. The objective for the developed model is the determination of the appropriate time to control AGV based assembly line in order to minimize production makespan while maximizing the efficient use of vehicles. In this paper, we consider the finished goods of two types which are produced in assembly line. The assembly line is considered with and without queue. Because no buffer are present in case 1. this model seeks to determine the point in time at which vehicles should be launched in the assembly line without experiencing a delay. The case 2 model also seek to determine the vehicle launch times while minimizing production makespan. The assumption in this model is that the maximum queue size cannot exceed 1 at any time.

• Journal of Industrial Convergence
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• v.3 no.1
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• pp.19-31
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• 2005

This paper deals with the sequencing problem in the operation of the manufacturing systems with the constraint of buffer capacity. Some of studies for this theme have been progressed for several years. And then most of them considered only one objective, such as maximum lateness, machine utilization, makespan, mean flowtime and so on. This study deal with two objectives of the delivery for customers and the idle time of machines for producers. For the decision of sequence, the utility function is used. The developed heuristic algorithm presents a good solution. Through a numerical example, the procedures of the job sequencing is explained.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.148-148
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• 2000

• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.99-106
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• 2005

The task scheduling in multiprocessor system Is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost all practical cases, an NP hard problem. Consequently various modern heuristics based algorithms have been proposed for practical reason. Recently, several approaches using Genetic Algorithm (GA) are proposed. However, these algorithms have only one objective such as minimizing cost and makespan. This paper proposes a new task scheduling algorithm using Genetic Algorithm combined simulated annealing (GA+SA) on multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method. the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan and total number of processors used. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of proposed algorithm show better than that of other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2821-2839
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• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• Korean Chemical Engineering Research
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• v.43 no.5
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• pp.571-578
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• 2005

We propose a new optimization framework for the reactive scheduling. The proposed rescheduling scheme is specially focused on how to generate rescheduling results when equipment failure occurs. The approach is based on a continuous-time problem representation that takes into account the schedule in progress, the updated information on the batches still to be processed, the present plant state, the deviations in plant parameters and the time data. To update the predictive scheduling, we used right shift rescheduling and total regeneration when equipment failure occurs. And, a practical solution to the rescheduling problem requires satisfaction of two often confliction measures: the efficiency measure that evaluates the satisfaction of a desired objective function value and the stability measure that evaluates the amount of change between the schedules before and after the disruption. In this paper, the efficiency is measured by the makespan of all jobs in the system. And, the stability is measured by the percentage change in makespan and the modified sequence deviation in the predictive scheduling and rescheduling.

• IE interfaces
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• v.24 no.4
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• pp.281-294
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• 2011

This paper considers the rail crane scheduling problem which is defined as determining the sequence of loading/unloading container on/from a freight train. The objective is to minimize the weighted sum of the range of order completion time and makespan. The range of order completion time implies the difference between the maximum of completion time and minimum of start time of each customer order consisting of jobs. Makespan refers to the time when all the jobs are completed. In a rail freight terminal, logistics firms as a customer wish to reduce the range of their order completion time. To develop a methodology for the crane scheduling, we formulate the problem as a mixed integer program and develop three metaheuristics, namely, genetic algorithm, simulated annealing, and tabu search. To validate the effectiveness of heuristic algorithms, computational experiments are done based on a set of real life data. Results of the experiments show that heuristic algorithms give good solutions for small-size and large-size problems in terms of solution quality and computation time.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.8 s.179
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• pp.2131-2142
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• 2000

As recent production facilities are usually operated with unmanned material-handling system, the development of an efficient schedule with deadlock avoidance becomes a critical problem. Related researches on deadlock avoidance usually focus on real-time control of manufacturing system using deadlock avoidance policy. But little off-line optimization of deadlock-free schedule has been reported. This paper presents an optimization method for deadlock-free scheduling for Job-Shop system with no buffer. The deadlock-free schedule is acquired by the procedure that generates candidate lists of waiting operations, and applies a deadlock avoidance policy. To verify the proposed approach, simulation resultsare presented for minimizing makespan in three problem types. According to the simulation results the effect of each deadlock avoidance policy is dependent on the type of problem. When the proposed LOEM (Last Operation Exclusion Method) is employed, computing time for optimization as well as makespan is reduced.