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Intelligent apparel production planning for optimizing manual operations using fuzzy set theory and evolutionary algorithms

机译:使用模糊集理论和进化算法优化手动操作的智能服装生产计划

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Effective and accurate production planning is essential for garment manufacturers to survive in today's competitive apparel industry. Varying customer demands, shorter lifecycles and changing fashion trends are amongst the factors that make accurate production planning important. Manufacturers strive to fulfil requirements such as on-time completion, short production lead time and effective allocation of job order to specific production lines. However, effective production planning is difficult to achieve because the apparel manufacturing environment is fuzzy and dynamic. This paper suggests the use of intelligent production planning algorithms, based on fuzzy set theory, genetic algorithms (GA) and multi-objective genetic algorithms (MOGA), to achieve optimal solutions for apparel production planning.
机译:有效和准确的生产计划对于服装制造商在当今竞争激烈的服装行业中生存至关重要。不断变化的客户需求,更短的生命周期和不断变化的流行趋势是使准确的生产计划变得重要的因素。制造商努力满足准时完成,缩短生产提前期以及将工作订单有效分配到特定生产线等要求。然而,由于服装制造环境是模糊且动态的,因此难以实现有效的生产计划。本文建议基于模糊集理论,遗传算法(GA)和多目标遗传算法(MOGA)的智能生产计划算法的使用,以实现服装生产计划的最佳解决方案。

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