[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128318-en":3,"doc-seo-128318-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128318,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","Machine Learning and Constraint Programming for Efficient Healthcare Scheduling","Solving combinatorial optimization requires satisfying hard constraints while optimizing objectives, typically balancing exact methods’ exponential runtimes against approximate methods’ reduced solution quality. This work addresses the Nurse Scheduling Problem by proposing implicit learning-based methods from historical solutions and an explicit framework using CSP modeling. It evaluates implicit quality via Frobenius Norm error, and compensates feasibility uncertainty through stochastic local search and a constraint-propagation-enhanced branch and bound algorithm, plus a passive data-driven learned constraint network.","arXiv :2409 .07547v1 [ cs .AI] 11 Sep 2024  \nMachine Learning and Constraint Programming for Efficient Healthcare Scheduling  \nAymen Ben Said and Malek Mouhoub  \nDepartment of Computer Science, University of Regina, SK, Canada,{aymenbensaid,[mouhoubm}@uregina.ca](mouhoubm}@uregina.ca)  \nAbstract. Solving combinatorial optimization problems involve satisfying a set of hard constraints while optimizing some objectives. In this context, exact or approximate methods can be used. While exact methods guarantee the optimal solution, they often come with an exponential running time as opposed to approximate methods that trade the solution’s quality for a better running time. In this context, we tackle the Nurse Scheduling Problem (NSP) . The NSP consist in assigning nurses to daily shifts within a planning horizon such that workload constraints are satisfied while hospital’s costs and nurses’ preferences are optimized.  \nTo solve the NSP, we propose implicit and explicit approaches. In the implicit solving approach, we rely on Machine Learning methods using historical data to learn and generate new solutions through the constraints and objectives that may be embedded in the learned patterns. To quantify the quality of using our implicit approach in capturing the embedded constraints and objectives, we rely on the Frobenius Norm, a quality measure used to compute the average error between the generated solutionsand historical data. To compensate for the uncertainty related to the implicit approach given that the constraints and objectives may not be concretely visible in the produced solutions, we propose an alternative explicit approach where we first model the NSP using the Constraint Satisfaction Problem (CSP) framework. Then we develop Stochastic Local Search methods and a new Branch and Bound algorithm enhanced with constraint propagation techniques and variables/values ordering heuristics. Since our implicit approach may not guarantee the feasibility or optimality of the generated solution, we propose a data-driven approach to passively learn the NSP as a constraint network. The learned constraint network, formulated as a CSP, will then be solved using the methods we listed earlier.  \nKeywords: Nurse Scheduling Problem · Constraint Programming · Machine Learning · Combinatorial Optimization  \n1 Introduction  \nCombinatorial optimization problems play a significant role in various industry applications. Solving these problems involves finding the optimal solution among feasible solutions for many real-world problems. Leveraging combinatorial optimization methods in applications like scheduling can effectively optimize  \n2 A. Ben Said et al.  \nresource management costs by efficient personnel scheduling and improve the overall decision-making processes. In this context, we tackle the Nurse Scheduling Problem (NSP) . Solving the NSP consists of assigning nurses to appropriate shifts satisfying a set of constraints while optimizing hospital costs and/or nurses’preferences that may be obtained from the nurses over a given planning horizon. Many methods and approaches from the areas of Constraint Programming (CP) and Operation Research (OR) were proposed to solve combinatorial optimization problems ranging from exact and approximate methods. While exact methods are able to find the optimal solution for a given problem, they often suffer from their exponential running time cost, especially for large-size problem instances with respect to the number of variables and domain size [1] .  \nApproximate methods such as metaheuristic and Stochastic Local Search (SLS) may be considered in this regard as they are known to relatively trade the quality of the solution over the execution running time. Most of the metaheuristic methods start by randomly generating a population of candidate solutionsand then try to improve the solutions by transitioning between exploration and exploitation using some type of parameters/heuristics and relying on a fitnes","cbCaik8Kba8o3a9b","https://ap.wps.com/l/cbCaik8Kba8o3a9b","pdf",1382979,1,37,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"What optimization problem is targeted in the document?\",\"answer\":\"The document focuses on the Nurse Scheduling Problem (NSP), assigning nurses to daily shifts across a planning horizon while satisfying workload constraints.\"},{\"question\":\"How do the proposed implicit approaches work?\",\"answer\":\"Implicit methods use machine learning on historical data to learn patterns, then generate solutions that embed constraints and objectives represented through those patterns.\"},{\"question\":\"What is the role of explicit constraint programming methods?\",\"answer\":\"Explicit methods model NSP as a Constraint Satisfaction Problem (CSP) and solve it using stochastic local search and a branch-and-bound algorithm enhanced with constraint propagation and variable/value ordering heuristics.\"}]","Machine Learning and Constraint Programming for Efficient Healthcare Scheduling | 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optimization problem is targeted in the document?","Question",{"text":76,"@type":77},"The document focuses on the Nurse Scheduling Problem (NSP), assigning nurses to daily shifts across a planning horizon while satisfying workload constraints.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do the proposed implicit approaches work?",{"text":81,"@type":77},"Implicit methods use machine learning on historical data to learn patterns, then generate solutions that embed constraints and objectives represented through those patterns.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the role of explicit constraint programming methods?",{"text":85,"@type":77},"Explicit methods model NSP as a Constraint Satisfaction Problem (CSP) and solve it using stochastic local search and a branch-and-bound algorithm enhanced with constraint propagation and variable/value ordering 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