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Intelligent search techniques for network-based manufacturing systems: multi-objective formulation and solutions

机译：基于网络的制造系统的智能搜索技术：多目标制定和解决方案

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摘要

Effective and efficient implementation of intelligent and recently emerged networked manufacturing systems requires enterprise-level integration. The first step in this direction is to integrate the manufacturing functions such as process planning and scheduling for multi-jobs in order to generate optimal or near optimal solutions. Addressed in this paper is multi-objective optimisation in the context of a network-based manufacturing system to optimise multiple objectives, i.e. minimisation of makespan and minimisation of variation of workload, simultaneously. This paper introduces a mathematical model for calculating the above-mentioned objectives with consideration of alternative machines, as well as tools and tool approach directions. The authors propose a new modified block-based genetic algorithm (MBBGA) and modified non-dominated sorting genetic algorithm (MNSGA-II) to resolve the above-mentioned complex problem and compare the proposed algorithms' performance and their effectiveness with the non-dominated sorting genetic algorithm (NSGA-II). An illustrative example with complex scenarios is carried out to demonstrate the feasibility of the proposed MBBGA and MNSGA-II. The experimental results presented show that the proposed algorithms perform better in comparison with NSGA-II.
机译：有效，高效地实施智能和新近出现的联网制造系统需要企业级集成。这个方向的第一步是集成制造功能，例如对多个作业进行工艺规划和调度，以生成最佳或接近最佳的解决方案。本文讨论的是基于网络的制造系统中的多目标优化，以优化多个目标，即同时最小化制造时间和最小化工作量变化。本文介绍了一种数学模型，该模型考虑了替代机器以及工具和工具进近方向，从而计算了上述目标。作者提出了一种新的改进的基于块的遗传算法（MBBGA）和改进的非支配排序遗传算法（MNSGA-II），以解决上述复杂问题，并将该算法的性能和有效性与非支配遗传算法进行比较。排序遗传算法（NSGA-II）。进行了具有复杂场景的说明性示例，以证明所提出的MBBGA和MNSGA-II的可行性。实验结果表明，与NSGA-II相比，该算法具有更好的性能。

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