• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.73-82
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• 1988

This research is concerned with production scheduling for FMS (Flexible Manufacturing System) which consists of machine centers served by cycle conveyor. The objective of the research is to develop and evaluate scheduling procedures to minimize the mean flow time. An optimal algorithm called SCTF (Shortest Circle Time First) is proposed when the conveyor runs at minimum possible speed (CS=1) and a heuristic algorithm called SCTJMF (Shortest Cycle Time and Job Matching Algorithm) is suggested when the conveyor runs at double speed (CS=2). The evaluation of the heuristic algorithm was implemented by comparison with the optimal algorithm for 112 experimentations for CS=1 and random schedule. The results showed that the proposed heuristic algorithm provides better solution that can be regarded noticeable when compared with SCTF algorithm and random scheduling.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.7-14
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• 2011

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.813-820
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• 2005

To develop a genetic algorithm about job shop scheduling with alternative routing, we are performed that genetic algorithm efficiency analysis of job shop scheduling with alternative routing, First, we proposed genetic algorithm for job shop scheduling with alternative routing. Second, we applied genetic algorithm to traditional benchmak problem appraise a compatibility of genetic algorithm. Third, we compared with dispatching rule and genetic algorithm result for problem Park[3].

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.4
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• pp.13-22
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• 1993

The aircraft scheduling, which is used to determine flight frequency, departure times and aircraft type assignments, is main problem of airline's planning. This paper proposes a new algorithm for aircraft scheduling that is to maximize airline profits. This paper proposes a column generation algorithm to get an optimal solution of the continous relaxation not using all the feasible variables, but using only a limited number of variables that is generated whenever it is necessary. Using this algorithm, proposes an optimal algorithm to get an optimal integer solution of aircraft scheduling problem efficiently. The effectiveness of the column generation algorithm and the optimal algorithm is illustrated by the computational results obtained from a series of real airline problems.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.18-24
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• 2013

Purpose: This study was conducted to develop an optimal combine scheduling program using Max-Coverage algorithm which derives the maximum efficiency for a specific location in harvest seasons. Methods: The combine scheduling program was operated with information about combine specification and farmland. Four operating types (Max-Coverage algorithm type, Boustrophedon path type, max quality value type, and max area type) were selected to compare quality and working capacity. Result: The working time of Max-Coverage algorithm type was shorter than others, and the total quality value of Max-Coverage algorithm and max quality value type were higher than others. Conclusion: The developed combine scheduling program using Max-Coverage algorithm will provide optimal operation and maximum quality in a limited area and time.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.941-950
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• 2017

The round robin algorithm is regarded as one of the most efficient and effective CPU scheduling techniques in computing. It centres on the processing time required for a CPU to execute available jobs. Although there are other CPU scheduling algorithms based on processing time which use different criteria, the round robin algorithm has gained much popularity due to its optimal time-shared environment. The effectiveness of this algorithm depends strongly on the choice of time quantum. This paper presents a new effective round robin CPU scheduling algorithm. The effectiveness here lies in the fact that the proposed algorithm depends on a dynamically allocated time quantum in each round. Its performance is compared with both traditional and enhanced round robin algorithms, and the findings demonstrate an improved performance in terms of average waiting time, average turnaround time and context switching.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.739-748
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• 2007

The objective of this research study is to develop the permutation-based genetic algorithm for solving the resource-constrained project scheduling problem in construction engineering by incorporating elitism into genetic algorithm. A key aspect of the algorithm was the development of the elitist roulette selection operator to preserve the best individual solution for the next generation so the improved solution can be obtained. Another notable characteristic is the application of the parallel schedule generation scheme to generate a feasible solution to the problem. Case studies with a standard test problem were presented to demonstrate the performance and accuracy of the algorithm. The computational results indicate that the proposed algorithm produces reasonably good solutions for the resource-constrained project scheduling problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2509-2528
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• 2012

Time division multiple access (TDMA) is a widely used media access control (MAC) technique that can provide collision-free and reliable communications, save energy and bound the delay of packets. In TDMA, energy saving is usually achieved by switching the nodes' radio off when such nodes are not engaged. However, the frequent switching of the radio's state not only wastes energy, but also increases end-to-end delay. To achieve high energy efficiency and low delay, as well as to further minimize the number of time slots, a multi-objective TDMA scheduling problem for industrial wireless mesh networks is presented. A hybrid algorithm that combines genetic algorithm (GA) and simulated annealing (SA) algorithm is then proposed to solve the TDMA scheduling problem effectively. A number of critical techniques are also adopted to reduce energy consumption and to shorten end-to-end delay further. Simulation results with different kinds of networks demonstrate that the proposed algorithm outperforms traditional scheduling algorithms in terms of addressing the problems of energy consumption and end-to-end delay, thus satisfying the demands of industrial wireless mesh networks.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.1-11
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• 1986

This research is concerned with n jobs, m parallel identical machines scheduling problem in which all jobs have a common due date. The objective of the research is to develop an optimum scheduling algorithm for determining an optimal job sequence, the optimal value of the common due date and the minimum makespan to minimize total cost. The total cost is based on the common due date cost, the earliness cost, the tardiness cost and the flow time cost of each job in the selected sequence. The optimum scheduling algorithm is developed. A numerical example is given to illustrate the scheduling algorithm.

• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.1-7
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• 1998

High-Level synthesis of digital circuits calls for automatic translation of a behavioral description to a structural design entity represented in terms of components and connection. One of the critical steps in high-level synthesis is to determine a particular scheduling algorithm that will assign behavioral operations to control states. A new scheduling algorithm called Data Availability Scheduling (DAS) for high-level synthesis is presented. It can determine an appropriate scheduling algorithm and minimize the number of states required using data availability and dependency conditions extracted from the behavioral code, taking into account of states required using data availability and dependency conditions extracted from the behavioral code, taking into account resource constraint in each control state. The DAS algorithm is efficient because data availability conditions, and conditional and wait statements break the behavioral code into manageable pieces which are analyzed independently. The output is the number of states in a finite state machine and shows better results than those of previous algorithms.