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首页> 外文期刊>Management research review >Optimization of delay time and environmental pollution in scheduling of production and transportation system: a novel multi-society genetic algorithm approach
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Optimization of delay time and environmental pollution in scheduling of production and transportation system: a novel multi-society genetic algorithm approach

机译：生产与运输系统调度中延迟时间和环境污染的优化：一种新型多社会遗传算法方法

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摘要

Purpose - The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental pollution (EP) concurrently. To this, an integrated multi-site manufacturing process using a cumulative transportation system is investigated. Additionally, a novel multi-society genetic algorithm is developed to reach the best answers. Design/methodology/approach - A bi-objective model is proposed to optimize the production and transportation process with the objectives of minimizing DT and EP. This is solved by a social dynamic genetic algorithm (SDGA), which is a novel multi-society genetic algorithm, in scenarios of equal and unequal impacts of each objective. The impacts of each objective are calculated by the analytical hierarchical process (AHP) using experts' opinions. Results are compared by dynamic genetic algorithm and optimum solution results. Findings - Results clearly depict the efficiency of the proposed algorithm and model in the scheduling of production and transportation systems with the objectives of minimizing DT and EP concurrently. Although SDGA's performance is acceptable in all cases, in comparison to other genetic algorithms, it needs more process time which is the cost of reaching better answers. Additionally, SDGA had better performance in variable weights of objectives in comparison to itself and other genetic algorithms. Research limitations/implications - This research is an improvement which allows both society and industry to elevate the levels of their satisfaction while their social responsibilities have been glorified through assuaging the concerns of customers on distribution networks' emission, competing more efficient and effective in the global market and having the ability to make deliberate decisions far from bias. Additionally, implications of the developed genetic algorithm help directly to the organizations engaged with intelligent production and/or transportation planning which society will be merited indirectly from their outcomes. It also could be utilitarian for organizations that are engaged with small, medium and big data analysis in their processes and want to use more effective and more efficient tools. Originality/value - Optimization of EP and DT are considered simultaneously in both model and algorithm in this study. Besides, a novel genetic algorithm, SDGA, is proposed. In this multi-society algorithm, each society is focused on a particular objective; however, in one society all the feasible answers will have been integrated and optimization will have been continued.
机译：目的 - 本研究的目的是调查生产和运输系统调度的优化，同时考虑延迟时间（DT）和环境污染（EP）。为此，研究了使用累积运输系统的集成多站点制造过程。此外，开发了一种新的多社会遗传算法以达到最佳答案。设计/方法/方法 - 建议使用最小化DT和EP的目标优化生产和运输过程。这是由社会动态遗传算法（SDGA）解决的，它是一种新型多社会遗传算法，在每个目标的平等和不平等影响的情况下。每个目标的影响由使用专家意见的分析分层过程（AHP）计算。结果通过动态遗传算法和最佳解决方案结果进行比较。结果 - 结果清楚地描绘了所提出的算法和模型在生产和运输系统调度中的效率，其目的是将DT和EP同时最小化。尽管在所有情况下，SDGA的表现是可接受的，但与其他遗传算法相比，它需要更多的过程时间，这是达到更好答案的成本。此外，与本身和其他遗传算法相比，SDGA在可变的目标中具有更好的性能。研究限制/影响 - 这项研究是一个改进，使社会和行业都能提升他们满意度的水平，而通过抵御客户对分销网络排放的关注，在全球范围内更高效和有效地竞争竞争更加高效和有效的社会责任，才能提升他们的社会责任。市场并有能力使蓄意偏离偏见。此外，发达的遗传算法对与智能生产和/或运输规划进行的组织的影响有助于与其结局间接合并的组织。它还可能是在他们的过程中与小型，中型和大数据分析进行的组织的功利主义，并且想要使用更有效和更有效的工具。本研究中的模型和算法同时考虑原创性/值 - EP和DT的优化。此外，提出了一种新型遗传算法SDGA。在这种多社会算法中，每个社会都专注于特定目标;然而，在一个社会中，所有可行的答案都将被纳入，优化将继续进行。

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