Pub Date : 2023-12-10DOI: 10.1080/00207543.2023.2290699
Eyüp Ensar Işık, S. Yildiz
{"title":"Integer and constraint programming models for the straight and U-shaped assembly line balancing with hierarchical worker assignment problem","authors":"Eyüp Ensar Işık, S. Yildiz","doi":"10.1080/00207543.2023.2290699","DOIUrl":"https://doi.org/10.1080/00207543.2023.2290699","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138982551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Simulation and process mining in a cross-docking system: a case study","authors":"Sadaf Shams-Shemirani, Reza Tavakkoli-Moghaddam, Alireza Amjadian, Bahar Motamedi-Vafa","doi":"10.1080/00207543.2023.2281665","DOIUrl":"https://doi.org/10.1080/00207543.2023.2281665","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.1080/00207543.2023.2289644
Fengmei Xu, Feifei Shan, Feng Yang, Ting Chen
{"title":"The impacts of gray products and counterfeits in the luxury industry","authors":"Fengmei Xu, Feifei Shan, Feng Yang, Ting Chen","doi":"10.1080/00207543.2023.2289644","DOIUrl":"https://doi.org/10.1080/00207543.2023.2289644","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138586480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-07DOI: 10.1080/00207543.2023.2289643
Rong Wang, Peng Yang, Yeming Gong, Cheng Chen
{"title":"Operational policies and performance analysis for overhead robotic compact warehousing systems with bin reshuffling","authors":"Rong Wang, Peng Yang, Yeming Gong, Cheng Chen","doi":"10.1080/00207543.2023.2289643","DOIUrl":"https://doi.org/10.1080/00207543.2023.2289643","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138590183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-07DOI: 10.1080/00207543.2023.2289073
Jingyan Li, Xiang Ji, Sandun C. Perera
{"title":"Behaviour-based pricing for multi-version information goods","authors":"Jingyan Li, Xiang Ji, Sandun C. Perera","doi":"10.1080/00207543.2023.2289073","DOIUrl":"https://doi.org/10.1080/00207543.2023.2289073","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.1080/00207543.2023.2289076
Gyeongho Kim, Sang Min Yang, S. Kim, Do Young Kim, Jae Gyeong Choi, Hyung Wook Park, Sunghoon Lim
{"title":"A multi-domain mixture density network for tool wear prediction under multiple machining conditions","authors":"Gyeongho Kim, Sang Min Yang, S. Kim, Do Young Kim, Jae Gyeong Choi, Hyung Wook Park, Sunghoon Lim","doi":"10.1080/00207543.2023.2289076","DOIUrl":"https://doi.org/10.1080/00207543.2023.2289076","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-02DOI: 10.1080/00207543.2022.2032860
Jianyu Long, Yibin Chen, Zhe Yang, Yunwei Huang, Chuan Li
Fault diagnosis is an indispensable basis for the collaborative maintenance in prognostic and health management. Most of existing data-driven fault diagnosis approaches are designed in the framework of supervised learning, which requires a large number of labelled samples. In this paper, a novel self-training semi-supervised deep learning (SSDL) approach is proposed to train a fault diagnosis model together with few labelled and abundant unlabelled samples. The addressed SSDL approach is realised by initialising a stacked sparse auto-encoder classifier using the labelled samples, and subsequently updating the classifier via sampling a few candidates with most reliable pseudo labels from the unlabelled samples step by step. Unlike the commonly used static sampling strategy in existing self-training semi-supervised frameworks, a gradually exploiting mechanism is proposed in SSDL to increase the number of selected pseudo-labelled candidates gradually. In addition, instead of using the prediction accuracy as the confidence estimation for pseudo-labels, a distance-based sampling criterion is designed to assign the label for each unlabelled sample by its nearest labelled sample based on their Euclidean distances in the deep feature space. The experimental results show that the proposed SSDL approach can achieve good prediction accuracy compared to other self-training semi-supervised learning algorithms.
{"title":"A novel self-training semi-supervised deep learning approach for machinery fault diagnosis","authors":"Jianyu Long, Yibin Chen, Zhe Yang, Yunwei Huang, Chuan Li","doi":"10.1080/00207543.2022.2032860","DOIUrl":"https://doi.org/10.1080/00207543.2022.2032860","url":null,"abstract":"Fault diagnosis is an indispensable basis for the collaborative maintenance in prognostic and health management. Most of existing data-driven fault diagnosis approaches are designed in the framework of supervised learning, which requires a large number of labelled samples. In this paper, a novel self-training semi-supervised deep learning (SSDL) approach is proposed to train a fault diagnosis model together with few labelled and abundant unlabelled samples. The addressed SSDL approach is realised by initialising a stacked sparse auto-encoder classifier using the labelled samples, and subsequently updating the classifier via sampling a few candidates with most reliable pseudo labels from the unlabelled samples step by step. Unlike the commonly used static sampling strategy in existing self-training semi-supervised frameworks, a gradually exploiting mechanism is proposed in SSDL to increase the number of selected pseudo-labelled candidates gradually. In addition, instead of using the prediction accuracy as the confidence estimation for pseudo-labels, a distance-based sampling criterion is designed to assign the label for each unlabelled sample by its nearest labelled sample based on their Euclidean distances in the deep feature space. The experimental results show that the proposed SSDL approach can achieve good prediction accuracy compared to other self-training semi-supervised learning algorithms.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138606706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1080/00207543.2023.2288866
M. Elyasi, O. Ö. Özener, Ihsan Yanikoglu, Ali Ekici, Alexandre Dolgui
{"title":"A column generation-based approach for the adaptive stochastic blood donation tailoring problem","authors":"M. Elyasi, O. Ö. Özener, Ihsan Yanikoglu, Ali Ekici, Alexandre Dolgui","doi":"10.1080/00207543.2023.2288866","DOIUrl":"https://doi.org/10.1080/00207543.2023.2288866","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138614778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1080/00207543.2023.2285424
Riccardo Aldrighetti, Martina Calzavara, Michele Martignago, I. Zennaro, Daria Battini, Dmitry Ivanov
{"title":"A methodological framework for the design of efficient resilience in supply networks","authors":"Riccardo Aldrighetti, Martina Calzavara, Michele Martignago, I. Zennaro, Daria Battini, Dmitry Ivanov","doi":"10.1080/00207543.2023.2285424","DOIUrl":"https://doi.org/10.1080/00207543.2023.2285424","url":null,"abstract":"","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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