{"title":"分布式制造平台的位置-生产-路径问题:神经遗传算法求解方法","authors":"Behrang Bootaki, Guoqing Zhang","doi":"10.1016/j.ijpe.2024.109325","DOIUrl":null,"url":null,"abstract":"<div><p>Additive Manufacturing (AM) enhances the flexibility of manufacturing networks. In this paper, we present a Location-Production-Routing (LPR) problem designed for a distributed manufacturing platform, where the manufacturing facilities are distributed in different locations with the support of AM technologies. The proposed LPR problem encompasses three different types of decisions: location-allocation, production planning, and product delivery routing decisions. This is one of the first studies that analyzes integrated logistics and manufacturing optimization under distributed and resilient manufacturing platforms. To efficiently solve the complex problem, we design a novel solution method called the Neural Genetic Algorithm (NGA). The numerical experiments show that the proposed method can attain near-optimal solutions, achieving an average gap of 3% with a standard deviation of 1.4% and a 99% improvement in computational time compared to the CPLEX solver. The sensitivity analysis illustrates the high impact of the unit shortage cost on the customer service level and on the distribution of the AM facilities. Moreover, our results for a given instance show that through the periodic reconfiguration of AM supply chains using the proposed LPR model, we can achieve an average cost reduction of up to 25% in the supply network.</p></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"275 ","pages":"Article 109325"},"PeriodicalIF":9.8000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology\",\"authors\":\"Behrang Bootaki, Guoqing Zhang\",\"doi\":\"10.1016/j.ijpe.2024.109325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Additive Manufacturing (AM) enhances the flexibility of manufacturing networks. In this paper, we present a Location-Production-Routing (LPR) problem designed for a distributed manufacturing platform, where the manufacturing facilities are distributed in different locations with the support of AM technologies. The proposed LPR problem encompasses three different types of decisions: location-allocation, production planning, and product delivery routing decisions. This is one of the first studies that analyzes integrated logistics and manufacturing optimization under distributed and resilient manufacturing platforms. To efficiently solve the complex problem, we design a novel solution method called the Neural Genetic Algorithm (NGA). The numerical experiments show that the proposed method can attain near-optimal solutions, achieving an average gap of 3% with a standard deviation of 1.4% and a 99% improvement in computational time compared to the CPLEX solver. The sensitivity analysis illustrates the high impact of the unit shortage cost on the customer service level and on the distribution of the AM facilities. Moreover, our results for a given instance show that through the periodic reconfiguration of AM supply chains using the proposed LPR model, we can achieve an average cost reduction of up to 25% in the supply network.</p></div>\",\"PeriodicalId\":14287,\"journal\":{\"name\":\"International Journal of Production Economics\",\"volume\":\"275 \",\"pages\":\"Article 109325\"},\"PeriodicalIF\":9.8000,\"publicationDate\":\"2024-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Production Economics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925527324001828\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Production Economics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925527324001828","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
摘要
快速成型制造(AM)提高了制造网络的灵活性。在本文中,我们提出了一个位置-生产-路由(LPR)问题,该问题是为分布式制造平台设计的,在该平台中,制造设施在 AM 技术的支持下分布在不同的位置。提出的 LPR 问题包括三种不同类型的决策:位置分配、生产规划和产品交付路由决策。这是首次对分布式弹性制造平台下的综合物流和制造优化进行分析的研究之一。为了有效解决这一复杂问题,我们设计了一种名为神经遗传算法(NGA)的新型求解方法。数值实验表明,与 CPLEX 求解器相比,所提出的方法可以获得接近最优的解决方案,平均差距为 3%,标准偏差为 1.4%,计算时间缩短了 99%。敏感性分析表明,单位短缺成本对客户服务水平和 AM 设施分布的影响很大。此外,我们对给定实例的结果表明,通过使用所提出的 LPR 模型对 AM 供应链进行周期性重新配置,我们可以在供应网络中实现高达 25% 的平均成本降低。
A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology
Additive Manufacturing (AM) enhances the flexibility of manufacturing networks. In this paper, we present a Location-Production-Routing (LPR) problem designed for a distributed manufacturing platform, where the manufacturing facilities are distributed in different locations with the support of AM technologies. The proposed LPR problem encompasses three different types of decisions: location-allocation, production planning, and product delivery routing decisions. This is one of the first studies that analyzes integrated logistics and manufacturing optimization under distributed and resilient manufacturing platforms. To efficiently solve the complex problem, we design a novel solution method called the Neural Genetic Algorithm (NGA). The numerical experiments show that the proposed method can attain near-optimal solutions, achieving an average gap of 3% with a standard deviation of 1.4% and a 99% improvement in computational time compared to the CPLEX solver. The sensitivity analysis illustrates the high impact of the unit shortage cost on the customer service level and on the distribution of the AM facilities. Moreover, our results for a given instance show that through the periodic reconfiguration of AM supply chains using the proposed LPR model, we can achieve an average cost reduction of up to 25% in the supply network.
期刊介绍:
The International Journal of Production Economics focuses on the interface between engineering and management. It covers all aspects of manufacturing and process industries, as well as production in general. The journal is interdisciplinary, considering activities throughout the product life cycle and material flow cycle. It aims to disseminate knowledge for improving industrial practice and strengthening the theoretical base for decision making. The journal serves as a forum for exchanging ideas and presenting new developments in theory and application, combining academic standards with practical value for industrial applications.