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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 194卷

作者： Montes, Daniel Pitarch, Jose Luis de Prada, Cesar Univ Valladolid Dept Syst Engn & Automatic Control C Dr Mergelina s-n Valladolid 47011 Spain Univ Politecn Valencia Inst U Automat Informat Ind ai2 Cno Vera s-n Valencia 46022 Spain Univ Valladolid Inst Sustainable Proc ISP Dr Mergelina S-N Valladolid 47011 Spain

This paper presents a novel decomposition method for two-stage stochastic mixed-integer optimization problems. The algorithm builds upon the idea of similarity between finite sample sets to measure how similar the first-stage decisions are among the uncertainty realization scenarios. Using such a Similarity Index, the non-anticipative constraints are removed from the problem formulation so that the original problem becomes block-separable on a scenario basis. Then, a term for maximizing the Similarity Index is included in all the sub-problems objective functions. Such sub-problems are solved iteratively in parallel so that their solutions are used to update the weighting parameter for maximizing the Similarity Index. The algorithm obtains a feasible solution when full similarity among scenario first stages is reached, that is, when the incumbent solution is non- anticipative. The proposal is tested in four instances of different sizes of an industrial-like scheduling problem. Comparison results show that the Similarity Index Decomposition provides significant speed-ups compared with the monolithic problem formulation, and provides simpler tuning and improved convergence over the Progressive Hedging Algorithm.

关键词： Production planning mathematical programming Uncertainty Progressive hedging Mixed-integer optimization

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A Clustering-Based Optimization Approach for Hospital Miscoding Correction

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第2期21卷 1501-1516页

作者： He, Chen Dalmas, Benjamin Bousquet, Cedric Trombert-Paviot, Beatrice Xie, Xiaolan Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Comp Res Inst Montreal Montreal PQ H3N 1M3 Canada CHU St Etienne Unitof Publ Hlth & Med Informat F-42055 St Etienne France Univ Clermont Auvergne Ctr CIS Mines St Etienne CNRSUMR 6158 LIMOS F-42023 St Etienne France Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200030 Peoples R China

This paper addresses the problem of correcting medical coding errors with respect to some coding recommendations. The problem consists in clustering medical codings and determining for each cluster the set of features to correct in order to maximize the financial benefits subject to coding correction effort constraints. For this purpose, we model the coding recommendation as a disjunction of hypercubes and introduce the concept of correction sets. A mixed integer linear programming model is then proposed to assign medical codes to correction sets in order to maximize the financial benefits. The miscoding is then explained by characterizing optimal clusters with association rules and coding error distribution. A case study on patient stays associated with malnutrition-related ICD codes is presented, and the performance of the proposed methodology is assessed in regard to the current coding staff practice. A significant increase in health services reimbursement is achieved with a limited number of subjects' features *** to Practitioners-Medical miscoding has a significant negative impact on hospitals with a financial loss for under coding and a penalty for over coding. Whether a medical review is necessary for all descriptive features of a miscoded subject? Is it possible to reduce unnecessary medical reviews without compromising the goal of increasing hospital financial benefits? This article attempts to answer these questions with a data-driven optimization approach to determine a limited number of miscoding clusters and the set of features to review for each in order to best balance the financial benefits and the medical review workload. The application to a real-life case study leads to a significant increase in hospital fiscal revenue of nearly 6,992,489.69, while reviewing only a small number of descriptive features (5293 out of 22056 features, or 24% of features). Causes are also provided for each discovered coding error subtype to ameliorate medical

关键词： Encoding Codes Hospitals Medical diagnostic imaging Task analysis Behavioral sciences Optimization Hospital miscoding coding recommendation coding review rationing clustering mathematical programming

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Large neighborhood local search method with MIP techniques for large-scale machining scheduling with many constraints

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12297-12312页

作者： Matsuzaki, Jin Sakakibara, Kazutoshi Nakamura, Masaki Watanabe, Shinya Toyama Prefectural Univ 5180 Kurokawa Imizu Toyama 9390398 Japan Muroran Inst Technol 27-1 Mizumoto Muroran Hokkaido 0508585 Japan

This study addresses the problem of scheduling machining operations in a highly automated manufacturing environment while considering the work styles of workers. In actual manufacturing, many aspects of the operation must be considered, such as constraints related to the works to be machined in the machining schedule and the states of the workers. To derive good solutions for such a large-scale problem with many constraints within a realistic amount of computation time, we develop an optimization technique based on a mixed-integer programming (MIP)-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments on a problem based on actual machining requirements are performed to verify the validity of the proposed method.

关键词： Machining process Job-shop scheduling mathematical programming Meta-heuristics Local search

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Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions

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IEEE ACCESS 2025年 13卷 5203-5218页

作者： Abdullah, Norizal Ayob, Masri Chun Lam, Meng Sabar, Nasser R. Kendall, Graham Khairulamirin Md Razali, Mohamad Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol CAIT Data Min & Optimizat Lab Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol CAIT Mixed Real & Pervas Comp Lab Bangi 43600 Selangor Malaysia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia MILA Univ Sch Engn & Comp Nilai 71800 Negeri Sembilan Malaysia Univ Nottingham Sch Comp Sci Univ Pk Nottingham NG7 2RD Notts England Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia

The Physician Scheduling Problem (PSP) has emerged as a critical challenge in healthcare management, directly relevant to Sustainable Development Goal 3 (SDG 3) - Good Health and Well-being. Driven by physician shortages, rising operational costs, and the need for efficient workforce planning, PSP affects the quality of patient care, staff satisfaction, and the overall efficiency of the healthcare system. While previous reviews have addressed PSP, they are lacking in a comprehensive analysis of recent optimization methodologies and their effectiveness. This work aims to bridge this gap by analyzing 60 research studies which addressed PSP, published between January 2014 and June 2024. Our study also extends the problem definition, constraints, evaluation functions, and the variants of PSP. We examine a wide range of optimization methodologies, including mathematical programming, heuristics, matheuristics, and machine learning, highlighting their strengths and limitations in addressing the multifaceted nature of PSP. This review also analyzes the datasets used in PSP research, noting the lack of standardized benchmarks. Key findings reveal the prevalence of mathematical optimization methods, the growing importance of multi-objective optimization and robustness, as well as the potential of machine learning and data-driven approaches. Future research directions are outlined, emphasizing the need for more scalable algorithms, real-time scheduling capabilities, improved user interfaces, and comprehensive validation studies. This review contributes to the advancement of PSP optimization, aiming to enhance healthcare workforce management, improve patient care, and ultimately address the pressing challenges faced by healthcare systems worldwide, thus supporting the achievement of SDG 3 and promoting universal health coverage.

关键词： Medical services Optimization Market research Machine learning Databases Sustainable development Search problems Personnel mathematical programming Physician scheduling personnel scheduling systematic literature review combinatorial optimization operational research sustainable development goals sustainable development goals

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Improved Free-of-CPP ADMM-Based Iterative Decoding Algorithm of Binary LDPC Codes

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 211-215页

作者： Bai, Jing An, Zedong Chi, Yuhao Song, Guanghui Yuen, Chau Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang 050043 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 487372 Singapore

Iterative decoding algorithms based on the alternating direction method of multipliers (ADMM) decoding of low density parity check (LDPC) codes has emerged as an alternating decoding method and bringed a boom of research on drawing upon mathematical optimization to LDPC decoding. Improving error-correcting performance is a key issue to enhance the superiority of ADMM decoding. In this letter, we investigate an efficient ADMM-based iterative decoder for binary LDPC codes. First, we build an mathematical programming equivalence of the maximum likelihood (ML) decoding problem by transforming parity-check constraints to multiple equivalent linear constraints and eliminating check-polytope projection (CPP). Then, an iterative algorithm based on ADMM technique is developed to solve this free-of-CPP (FCPP) equivalence and each ADMM update can be computed efficiently. Moreover, the proposed ADMM-FCPP decoding algorithm is analyzed to display a linear complexity to the length of the LDPC code at each iteration. Finally, simulation results demonstrate the superiority of the proposed decoder in error-correcting performance compared with the state-of-the-art ADMM-based decoders.

关键词： Maximum likelihood decoding Iterative decoding Convex functions Vectors Signal processing algorithms Optimization Complexity theory Simulation programming profession Low-density parity-check (LDPC) codes alternating direction method of multipliers (ADMM) check- polytope projection (CPP) mathematical programming mathematical programming

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Transportation scheduling for modules used in modular integrated construction

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第11期62卷 3918-3931页

作者： Wang, Huiwen Liao, Liang Yi, Wen Zhen, Lu Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Hong Kong Peoples R China Guangdong REMAC IOC Technol Co Ltd Foshan Peoples R China Shanghai Univ Sch Management Shanghai Peoples R China Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Hong Kong 999077 Peoples R China

Modular integrated construction (MiC) is an innovative construction method that is widely used in practice, but high transportation cost is an obstacle to its wider implementation. This paper is motivated by the real-world need for a scientific approach for MiC transport scheduling, and addresses the practical problem of how to achieve on-time door-to-door MiC delivery in an economical manner. An innovative integer programming model is formulated with the objective of minimising the total transportation costs, including trailer rental, fuel costs, and worker salaries. A case study based on Hong Kong data validates that the developed model can be efficiently solved for problems of practical size. The results also demonstrate that the derived MiC schedule plan can reduce transportation costs by 58.7% compared with a just-in-time (JIT) strategy, thus bringing great economic benefits to off-site construction practitioners. Useful managerial implications related to on-site MiC storage areas are derived through sensitivity analysis. Overall, this paper sets the ground for the application of mathematical programming methodology to the off-site construction field. Future research may be devoted to MiC transport scheduling under uncertainty.

关键词： Transportation optimisation mathematical programming prefabricated construction modular integrated construction just-in-time delivery

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Programmable Filterless Optical Networks: Architecture, Design, and Resource Allocation

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第2期32卷 1096-1109页

作者： Etezadi, Ehsan Natalino, Carlos Tremblay, Christine Wosinska, Lena Furdek, Marija Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Ecole Technol Super Montreal PQ H3C 1K3 Canada

Filterless optical networks (FONs) are a cost-effective optical networking technology that replaces reconfigurable optical add-drop multiplexers, used in conventional, wavelength-switched optical networks (WSONs), by passive optical splitters and couplers. FONs follow the drop-and-waste transmission scheme, i.e., broadcast signals without filtering, which generates spectrum waste. Programmable filterless optical networks (PFONs) reduce this waste by equipping network nodes with programmable optical white box switches that support arbitrary interconnections of passive elements. Cost-efficient PFON solutions require optimal routing, modulation format and spectrum assignment (RMSA) to connection requests, as well as optimal design of the node architecture. This paper presents an optimization framework for PFONs. We formulate the RMSA problem in PFONs as a single-step integer linear program (ILP) that jointly minimizes the total spectrum and optical component usage. As RMSA is an NP-complete problem, we propose a two-step ILP formulation that addresses the RMSA sub-problems separately and seeks sub-optimal solutions to larger problem instances in acceptable time. Simulation results indicate a beneficial trade-off between component usage and spectrum consumption in proposed PFON solutions. They use up to 64% less spectrum than FONs, up to 84% fewer active switching elements than WSONs, and up to 81% fewer optical amplifiers at network nodes than FONs or WSONs.

关键词： Filterless optical networks coherent elastic transmission optical white box wavelength routing mathematical programming

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Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm

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JOURNAL OF SUPERCOMPUTING 2024年第17期80卷 24835-24870页

作者： Abdi, Somayeh Ashjaei, Mohammad Mubeen, Saad Islamic Azad Univ Dept Comp Engn Eslam Abad E Gharb Branch Eslam Abad E Gharb Iran Malardalen Univ Dept Networked & Embedded Syst Vasteras Sweden

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

关键词： Workflow offloading Edge-cloud computing mathematical programming Genetic algorithm Cost minimization Security-aware

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Optimizing LPG production under quality specifications by preprocessing MILP with Pareto dominance and DEA

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第24期62卷 8609-8632页

作者： Eirinakis, Pavlos Koronakos, Gregory Plitsos, Stathis Univ Piraeus Dept Ind Management & Technol Piraeus 18534 Greece Univ Piraeus Dept Informat Piraeus Greece

Liquified Petroleum Gas (LPG) is an oil refinery product that must adhere to quality specifications with respect to certain impurities. Refineries apply an LPG purification process that consists of a flow network with several process units (PUs). Current methods focus on optimising the performance of each PU separately;there exists no known approach for identifying the whole process optimum. In this paper, we present an approach for optimising the LPG purification process as a whole. We utilise operational scenarios to model the non-linear transformations of each PU. These scenarios enable us to devise a Mixed Integer Linear Program (MILP) that minimises energy consumption. The obtained solution is an approximation of the optimum but offers actionable support to refinery engineers. To enable the applicability of our approach at large scale, we propose two filters based on Pareto dominance and Data Envelopment Analysis (DEA) to identify the Pareto optimal and the Best Practice set of scenarios, respectively. By filtering out the rest, we reduce the solution space of the corresponding MILP. Further, we provide computational evidence that the application of these filters vastly improves solution time and enables applicability under real production conditions.

关键词： LPG production industrial process operational conditions mathematical programming pareto optimality data envelopment analysis

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An Interactive Preference Disaggregation Method for Renewable Energy Project Sorting With Multiple Experts

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 15569-15584页

作者： Zhang, Zhiying Liao, Huchang Zhang, Chonghui Zeng, Xiao-Jun Sichuan Normal Univ Sch Engn Chengdu 610101 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Zhejiang Gongshang Univ Coll Stat & Math Hangzhou 310018 Peoples R China Univ Manchester Dept Comp Sci Manchester M13 9PL England

To mitigate power supply and demand imbalances and achieve carbon reduction goals, provinces in China have recently been promoting the development of renewable energy (RE) projects. Multiple criteria analysis is commonly employed for RE project assessment as energy considerations from multiple perspectives are required. To support governments' reserve work on RE projects and facilitate their smooth development, this study introduces an interactive preference disaggregation approach for RE project sorting. The sorting model accounts for the influence of different experts. To resolve contradictory preferences among experts, mathematical programming models that can ensure the reliability of the decision-making results are constructed for conflicting analysis and adjustment strategy recommendations. The proposed approach achieves a certain form of "cognitive convergence" through iterative processes and finally derives sorting results aligning with the perceptions of all experts. In addition, to promote the high-quality development of RE projects, an optimization model is given to generate improvement strategies for inferior projects based on the sorting results. An illustrative example of solar photovoltaic project sorting is provided to validate the applicability of the proposed approach.

关键词： Sorting Government Decision making Renewable energy sources Optimization models Investment Stakeholders Reliability mathematical programming MCDM Conflicting analysis multiple criteria analysis preference disaggregation project evaluation renewable energy project

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