• Title, Summary, Keyword: Heuristic algorithms

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Heuristic Algorithms for Parallel Machine Scheduling Problems with Dividable Jobs

  • Tsai, Chi-Yang;Chen, You-Ren
    • Industrial Engineering and Management Systems
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    • v.10 no.1
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    • pp.15-23
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    • 2011
  • This research considers scheduling problems with jobs which can be divided into sub-jobs and do not required to be processed immediately following one another. Heuristic algorithms considering how to divide jobs are proposed in an attempt to find near-optimal solutions within reasonable run time. The algorithms contain two phases which are executed recursively. Phase 1 of the algorithm determines how jobs should be divided while phase 2 solves the scheduling problem given the sub-jobs established in phase 1. Simulated annealing and genetic algorithms are applied for the two phases and four heuristic algorithms are established. Numerical experiment is conducted to determine the best parameter values for the heuristic algorithms. Examples with different sizes and levels of complexity are generated. Performance of the proposed algorithms is evaluated. It is shown that the proposed algorithms are able to efficiently and effectively solve the considered problems.

Probabilistic Model for Performance Analysis of a Heuristic with Multi-byte Suffix Matching

  • Choi, Yoon-Ho
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.4
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    • pp.711-725
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    • 2013
  • A heuristic with multi-byte suffix matching plays an important role in real pattern matching algorithms. By skipping many characters at a time in the process of comparing a given pattern with the text, the pattern matching algorithm based on a heuristic with multi-byte suffix matching shows a faster average search time than algorithms based on deterministic finite automata. Based on various experimental results and simulations, the previous works show that the pattern matching algorithms with multi-byte suffix matching performs well. However, there have been limited studies on the mathematical model for analyzing the performance in a standard manner. In this paper, we propose a new probabilistic model, which evaluates the performance of a heuristic with multi-byte suffix matching in an average-case search. When the theoretical analysis results and experimental results were compared, the proposed probabilistic model was found to be sufficient for evaluating the performance of a heuristic with suffix matching in the real pattern matching algorithms.

A Development of Heuristic Algorithms for the Multi-stage Manufacturing Systems with Sequence Dependent Setup Times (준비시간이 종속적인 n/M 스케쥴링 문제의 휴리스틱 알고리듬(I))

  • Choe, Seong-Un;No, In-Gyu
    • Journal of the Korean Society for Quality Management
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    • v.17 no.1
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    • pp.35-47
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    • 1989
  • This paper is concerned with a development and evaluation of heuristic algorithms for the n-job, M-stage flowshop with sequence dependent setup times. Three heuristic algorithms, CAIDAN, DANNEN and PETROV, are proposed. The makespan is taken as a performance measure for the algorithms. The experiment for each algorithm is designed for a $4{\times}3{\times}3$ factorial design with 360 observations. The experimental factors are PS (ratio of processing times to setup times), M (number of machines), and N (number of jobs). The makespan of the proposed heuristic algorithms is compared with the optimal makespan obtained by the complete enumeration method. The result of comparision of performance measure is called a relative error. The mean relative errors of CAIDAN, DANNEN and PETROV algorithms are 4.488%. 6.712% and 7.282%, respectively. The computational results are analysed using SPSS. The experimental results show that the three factors are statistically signiticant at 5% level.

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Scheduling for improving productivity of the automated manufacturing system

  • Choi, Jung-Sang;Jang, Gil-Sang
    • International Journal of Quality Innovation
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    • v.2 no.2
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    • pp.101-120
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    • 2001
  • In this paper jobshop scheduling problem was considered on automated manufacturing systems with the closed loop and unidirectional material handling system. The objective of this research is to develop and evaluate heuristic scheduling procedures to improve productivity by minimizing makespan. Especially travel time of material handling system as well as processing time was considered in the proposed algorithms, A new heuristic algorithms are proposed and illustrates the proposed algorithm. The heuristic algorithms are implemented for various cases. The results show that the proposed algorithms provide better solutions in productivity, frequency, job waiting time and the number of waiting jobs than the random scheduling algorithm.

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Meta-Heuristic Algorithms for a Multi-Product Dynamic Lot-Sizing Problem with a Freight Container Cost

  • Kim, Byung-Soo;Lee, Woon-Seek
    • Industrial Engineering and Management Systems
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    • v.11 no.3
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    • pp.288-298
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    • 2012
  • Lot sizing and shipment scheduling are two interrelated decisions made by a manufacturing plant and a third-party logistics distribution center. This paper analyzes a dynamic inbound ordering problem and shipment problem with a freight container cost, in which the order size of multiple products and single container type are simultaneously considered. In the problem, each ordered product placed in a period is immediately shipped by some freight containers in the period, and the total freight cost is proportional to the number of containers employed. It is assumed that the load size of each product is equal and backlogging is not allowed. The objective of this study is to simultaneously determine the lot-sizes and the shipment schedule that minimize the total costs, which consist of production cost, inventory holding cost, and freight cost. Because the problem is NP-hard, we propose three meta-heuristic algorithms: a simulated annealing algorithm, a genetic algorithm, and a new population-based evolutionary meta-heuristic called self-evolution algorithm. The performance of the meta-heuristic algorithms is compared with a local search heuristic proposed by the previous paper in terms of the average deviation from the optimal solution in small size problems and the average deviation from the best one among the replications of the meta-heuristic algorithms in large size problems.

Heuristic Algorithms for Optimization of Energy Consumption in Wireless Access Networks

  • Lorincz, Josip;Capone, Antonio;Begusic, Dinko
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.5 no.4
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    • pp.626-648
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    • 2011
  • Energy consumption of wireless access networks is in permanent increase, which necessitates development of more energy-efficient network management approaches. Such management schemes must result with adaptation of network energy consumption in accordance with daily variations in user activity. In this paper, we consider possible energy savings of wireless local area networks (WLANs) through development of a few integer linear programming (ILP) models. Effectiveness of ILP models providing energy-efficient management of network resources have been tested on several WLAN instances of different sizes. To cope with the problem of high computational time characteristic for some ILP models, we further develop several heuristic algorithms that are based on greedy methods and local search. Although heuristics obtains somewhat higher results of energy consumption in comparison with the ones of corresponding ILP models, heuristic algorithms ensures minimization of network energy consumption in an amount of time that is acceptable for practical implementations. This confirms that network management algorithms will play a significant role in practical realization of future energy-efficient network management systems.

A Hybrid of Evolutionary Search and Local Heuristic Search for Combinatorial Optimization Problems

  • Park, Lae-Jeong;Park, Cheol-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.1 no.1
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    • pp.6-12
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    • 2001
  • Evolutionary algorithms(EAs) have been successfully applied to many combinatorial optimization problems of various engineering fields. Recently, some comparative studies of EAs with other stochastic search algorithms have, however, shown that they are similar to, or even are not comparable to other heuristic search. In this paper, a new hybrid evolutionary algorithm utilizing a new local heuristic search, for combinatorial optimization problems, is presented. The new intelligent local heuristic search is described, and the behavior of the hybrid search algorithm is investigated on two well-known problems: traveling salesman problems (TSPs), and quadratic assignment problems(QAPs). The results indicate that the proposed hybrid is able to produce solutions of high quality compared with some of evolutionary and simulated annealing.

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Heuristics for Motion Planning Based on Learning in Similar Environments

  • Ogay, Dmitriy;Kim, Eun-Gyung
    • Journal of information and communication convergence engineering
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    • v.12 no.2
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    • pp.116-121
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    • 2014
  • This paper discusses computer-generated heuristics for motion planning. Planning with many degrees of freedom is a challenging task, because the complexity of most planning algorithms grows exponentially with the number of dimensions of the problem. A well-designed heuristic may greatly improve the performance of a planning algorithm in terms of the computation time. However, in recent years, with increasingly challenging high-dimensional planning problems, the design of good heuristics has itself become a complicated task. In this paper, we present an approach to algorithmically develop a heuristic for motion planning, which increases the efficiency of a planner in similar environments. To implement the idea, we generalize modern motion planning algorithms to an extent, where a heuristic is represented as a set of random variables. Distributions of the variables are then analyzed with computer learning methods. The analysis results are then utilized to generate a heuristic. During the experiments, the proposed approach is applied to several planning tasks with different algorithms and is shown to improve performance.

Non-Identical Parallel Machine Scheduling with Sequence and Machine Dependent Setup Times Using Meta-Heuristic Algorithms

  • Joo, Cheol-Min;Kim, Byung-Soo
    • Industrial Engineering and Management Systems
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    • v.11 no.1
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    • pp.114-122
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    • 2012
  • This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.

Performance Analysis of Heuristic Algorithms for Consolidated Transportation with Weight and Volume Constraints (무게와 부피를 고려하는 경험적 공동수송 흔적 알고리듬의 성능분석)

  • Rim, Suk-Chul;Yoo, Youngjin
    • Journal of Korean Institute of Industrial Engineers
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    • v.28 no.1
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    • pp.105-111
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    • 2002
  • When transporting multiple items by trucks of various size, one needs to assign a group of items to each truck so as to minimize the total cost, while meeting the weight and volume constraint of each truck. In this paper, we formulate the problem as an integer programming problem and propose four heuristic algorithms for the problem. Computer simulation is used to evaluate the average performance of the four heuristic algorithms for the consolidated transportation problem.