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.

中文翻译：

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生产和运输系统调度中延迟时间和环境污染的优化：一种新的多社会遗传算法方法

目的

本研究的目的是研究在同时考虑延迟时间 (DT) 和环境污染 (EP) 的情况下优化生产和运输系统的调度。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，还开发了一种新颖的多社会遗传算法来获得最佳答案。

设计/方法/方法

提出了一个双目标模型来优化生产和运输过程，目标是最小化 DT 和 EP。这是由社会动态遗传算法 (SDGA) 解决的，SDGA 是一种新颖的多社会遗传算法，在每个目标的影响相等和不相等的情况下。每个目标的影响是通过分析分层过程 (AHP) 使用专家的意见来计算的。结果通过动态遗传算法与最优解结果进行比较。

发现

结果清楚地描述了所提出的算法和模型在以同时最小化 DT 和 EP 为目标的生产和运输系统调度中的效率。尽管 SDGA 的性能在所有情况下都可以接受，但与其他遗传算法相比，它需要更多的处理时间，这是获得更好答案的代价。此外，与自身和其他遗传算法相比，SDGA 在目标的可变权重方面具有更好的性能。

研究限制/影响

这项研究是一项改进，通过减轻客户对配电网排放的担忧，在全球市场上更高效和有效地竞争，并有能力提高社会和行业的满意度，同时他们的社会责任得到了美化做出远离偏见的深思熟虑的决定。此外，开发的遗传算法的影响直接有助于从事智能生产和/或运输规划的组织，社会将从其结果中间接受益。对于在流程中进行小、中和大数据分析并希望使用更有效和更高效工具的组织来说，它也可能是功利的。

原创性/价值

本研究在模型和算法中同时考虑了 EP 和 DT 的优化。此外，提出了一种新的遗传算法SDGA。在这个多社会算法中，每个社会都专注于一个特定的目标；然而，在一个社会中，所有可行的答案都将被整合并继续优化。