87 research outputs found

    Bicriterion scheduling with equal processing times on a batch processing machine

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    Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

    Single machine scheduling with resource dependent release times and processing times

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    Author name used in this publication: T. C. E. Cheng2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

    ПОСТРОЕНИЕ РАСПИСАНИЙ ДЛЯ ОДНОСТАДИЙНЫХ СИСТЕМ ОБСЛУЖИВАНИЯ

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    Приведен обзор результатов, полученных в лаборатории математической кибернетики ОИПИ НАН наук Беларуси по решению задач теории расписаний для одностадийных детерминированных систем обслуживания

    Machine Loading in Flexible Manufacturing system using Artificial Immune algorithm

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    In this project thesis the FMS loading problem is discussed with the objective to minimize the system unbalance and throughput by the use of Artificial Immune system. Manufacturing technology focuses primarily on flexibility and productivity. With the product variety and product life being the characterizing standards it is important that the flexibility of the job shop is maintained as its efficiency is increases. The complexity of a basic Machine loading problem in FMS is very high due to the different flexibility criteria as Part selection, Operation allocation and the various constraints involved. This dissertation proposes a soft computing technique with constraints on tool capacity and workload of the machine. The aim of using this algorithm is to reach an optimal solution and to ease the tedious computations in large problems involving loading which are NP hard problems. Immune algorithm is a very suitable method due to its self learning and memory acquisition abilities. First some sample machine loading problems are collected from the literature and the optimal system unbalance of the machine is calculated using LINGO optimization software. This project improves some issues inherent in existing techniques and proposes an effective Immune algorithm with reduced memory requirements and reduced computational complexity. The proposed Algorithm is tested on 3 problems adopted from literatures and the results reveal substantial improvement in solution quality over the existing basic mathematical approaches

    Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

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    In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems

    Resource Management in Machine Scheduling Problems: A Survey

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    The paper is a survey devoted to job scheduling problems with resource allocation. We present the results available in the scientific literature for commonly used models of job processing times and job release dates, i.e., the models in which the job processing time or the job release date is given as a linear or convex function dependent on the amount of the additional resource allotted to the job. The scheduling models with resource dependent processing times or resource dependent release dates extend the classical scheduling models to reflect more precisely scheduling problems that appear in real life. Thus, in this paper we present the computational complexity results and solution algorithms that have been developed for this kind of problems

    Branch and Bound Method to Solve Multi Objectives Function

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    This paper  presents  a  branch  and  bound  algorithm  for  sequencing  a  set  of  n independent  jobs  on  a single  machine  to  minimize sum of the discounted total weighted completion time and maximum lateness,  this problems is NP-hard. Two lower bounds were proposed and heuristic method to get an upper bound. Some special cases were  proved and some dominance rules were suggested and proved, the problem solved with up to 50 jobs
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