[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85621-en":3,"doc-seo-85621-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85621,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Viable Supply Chain Network Design Machine Learning-Derived Chance-Constrained Programming","Investigates a viable two-echelon supply chain network design under unreliable facility disruptions, explicitly modeling cross-echelon disruption effects and the value of capturing interdependencies. Network viability is built using backup reassignment for resilience, mobile facilities for agility, and emission limits for sustainability while maintaining demand satisfaction across both echelons. Two mixed-integer formulations minimize expected fixed and service costs, with probabilistic service handled via machine learning–enhanced chance-constrained programming and 95% confidence linear cuts. Experiments show computational efficiency and solution quality, with fix-and-relax heuristics and SAA enabling medium- and large-scale solutions.","Viable Supply Chain Network Design: Machine Learning-Derived  \nChance-Constrained Programming  \nMohammad Rohaninejada, Behdin Vahedi-Nourib, Elham Jelodari Mamaghanic, Mehdi  \nFoumanid, Olga Battaiae,1*  \na Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague,  \nCzech Republic  \nb School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran c Institute of Sustainable Business and Organisations Sciences and Humanities Confluence Research Center  \nUCLY, ESDES, Lyon, France  \nd Department of Industrial Engineering, Hainan Bielefeld University of Applied Sciences, Hainan, China  \neKEDGE Business School, Bordeaux, France  \nAbstract:  \nThis paper investigates a viable two-echelon supply chain network design problem with unreliable facilities subject to disruptions. Unlike existing studies that consider supply chain echelons in isolation, the proposed models explicitly capture cross-echelon disruptions and quantify the value of incorporating such interdependencies. Network viability is obtained by combining resilience through backup reassignment, agility through mobile facilities, and environmental considerations such as emission limits. Together, these elements help maintain demand satisfaction across both echelons and support long-term network performance. Two mixed-integer programming formulations are developed. The first formulation is a scenariobased formulation and the second is implicit formulation which both minimize expected fixed and service costs. To handle probabilistic service requirements, the implicit formulation integrates a machine learning–enhanced chance-constrained programming approach. In this framework, intractable capacity chance constraints are approximated by learned linear cuts that apply a 95% service confidence level. These cuts are trained using several classification methods, including logistic regression, L1-regularized logistic regression, stochastic gradient descent, the perceptron algorithm, and logistic regression with a regularization parameter of 0.1. The best-performing classifier is then selected as a surrogate model. To improvescalability, two fix-and-relax heuristics are developed for the implicit formulation, while a  \n1* Corresponding author. [E-mail address:](E-mail address: olga.battaia@kedgebs.com)[ olga.battaia@kedgebs.com](E-mail address: olga.battaia@kedgebs.com)(O. Battaia).  \nsample average approximation (SAA) method is used for the scenario-based formulation.  \nComputational experiments demonstrate that the implicit formulation proposes a computationally efficient and high-quality alternative to the scenario-based formulation.  \nFurthermore, the proposed heuristics and SAA approach effectively address medium- and large-scale instances, delivering high-quality solutions within acceptable computational times.  \nKeywords: Supply chain network design; Viable supply chain; Fix and relax heuristic;  \nMachine learning; Chance-constrained programming; Sample average approximation  \n1. Introduction and literature review  \nSupply chain network design problems (SCNDPs) involve determining the optimal locations of a number of facilities, with finite or infinite capacity, among demand points, as well as determining the flow of materials between them. SCNDPs have long been central to supply chain management because they determine how infrastructure supports the flow of goods and services (Celik & Genevois, 2020; Kalczynski et al., 2025) by achieving a balance between first-stage establishment costs, second-stage service and transportation costs. Classical SCNDP formulations usually assume that facilities remain available, clients are always served, and demand is completely satisfied. Although these assumptions make the model easier to handle, they do not capture the reality of supply chains, where disruptions occur frequently (Rohaninejad et al., 2018a; Cheng et al., 2021a; Maliki et al., 2025) .  \nEvery year, nume","cbCaibN7UNpzqwAK","https://ap.wps.com/l/cbCaibN7UNpzqwAK","pdf",1652869,4,1,51,"English","en",105,"# Abstract\n# Keywords\n# 1. 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