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scheduling optimization of a Compact End-to-End Pharmaceutical Manufacturing Line: Design and Experimental Evaluation

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JOURNAL OF PHARMACEUTICAL INNOVATION 2025年第2期20卷 1-21页

作者： Knoll, Sebastian Steinberger, Martin Kuchler, Lisa Azimi, Atabak Tranninger, Markus Sacher, Stephan Horn, Martin Graz Univ Technol Inst Automat & Control Inffeldgasse 21b A-8010 Graz Styria Austria Res Ctr Pharmaceut Engn GmbH RCPE Inffeldgasse 13 A-8010 Graz Styria Austria

In today's competitive landscape, the demand for scheduling optimization to enhance processes and industrial plants is rapidly increasing. To address this need, we introduce a scheduling optimization approach for a compact continuous pharmaceutical manufacturing line by developing a mixed integer linear programming (MILP) model that incorporates all necessary constraints and bounds. The MILP employs a continuous time representation and an extension to a multi-product manufacturing line to improve the capabilities of current equipment and the model itself is shown. To reinforce the ideas and exemplify the overall concept, we present simulation results, demonstrating the optimal scheduling plans for various scenarios. In a case study, we applied the MILP to a manufacturing line and successfully integrated the optimal scheduling plan into the plant. Our results show that the time constraints, expected mass flows, and mass hold-ups align with those of the optimal scheduling plan, confirming that fully automated and integrated operations are feasible.

关键词： scheduling optimization Integration into an operational manufacturing line Fully optimized and automated operation Continuous pharmaceutical manufacturing

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MOSO: multi-objective snake optimizer with density estimation and grid indexing mechanism for edge computing task offloading and scheduling optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-31页

作者： Zhang, Shi-Hui Wang, Jie-Sheng Zhang, Si-Wen Xing, Yu-Xuan Wang, Xiao-Tian Sui, Xiao-Fei Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

The proposal of task offloading and scheduling optimization in edge computing aims to effectively alleviate network congestion caused by a large amount of data in ultra-dense networks (UDN), minimize energy consumption during data transmission, reduce transmission latency, and enhance Quality of Service (QoS). To address this issue, the Power Allocation (PA) problem for mobile users is presented, aiming to minimize energy consumption through the application of Convex optimization Techniques. Subsequently, the Joint Request Offloading and Resource scheduling (JRORS) problem is modeled as a mixed-integer nonlinear programming problem to minimize request response latency and enhance welfare. The JRORS problem can be further divided into two subproblems: the request offloading problem and the computational resource scheduling problem. The optimal solution is sought through mathematical modeling. A multi-objective snake optimizer based on density estimation and grid index mechanism is proposed to solve the multi-objective problem of edge computing task offloading and scheduling optimization. Firstly, applying the grid indexing mechanism transforms the single-objective snake optimizer to a multi-objective algorithm. Secondly, a roulette wheel selection method is utilized to choose the elite solutions, referred to as "Leaders". Density estimation is conducted based on the dominance capability of individuals, employing fast non-dominated sorting and density estimation for dual optimization of the archive. This approach not only yields an optimal Pareto solution set but also excludes similar individuals before the next iteration, ensuring population diversity and enhancing the effectiveness of the multi-objective snake optimizer. The proposed improved algorithm demonstrates outstanding performance across various test functions and conducts multi-objective optimization of energy consumption and efficiency for task offloading under nine different user scales. Comparative resul

关键词： Internet of things Edge computing scheduling optimization Task uninstallation Snake optimizer Multi-objective optimization

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Multi-aircraft scheduling optimization in urban environments

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APPLIED MATHEMATICAL MODELLING 2025年 145卷

作者： Zhang, Jin Qin, Xiaoran Zhang, Ming South China Univ Technol Dept Transportat Engn Guangzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Gen Aviat & Flight Nanjing Peoples R China

With the increasing development of intelligent transportation systems and advancements in aviation technology, the concept of Advanced Air Mobility (AAM) is gaining attention. This study aims to improve operational safety and service quality within Urban Air Mobility (UAM) through a trajectory-based operation (TBO). A multi-layer operational risk assessment model is introduced to capture the effects of aircraft failure scenarios on critical objects, including ground personnel, vehicles, and in-flight UAVs (unmanned aerial vehicles). Based on this, a single-aircraft track planning model is designed to balance operational risk and transportation cost under the performance constraints of eVTOL (electric Vertical Take-off and Landing) aircraft. A customized track planning algorithm with safety buffer zones is used to identify the most efficient flight paths. Additionally, a multi-aircraft scheduling optimization model is proposed to minimize delays and reduce mid-air collision risks. Experimental results show that the presented approach improves both efficiency and safety, providing practical solutions for UAM operations.

关键词： Urban air mobility eVTOL Operational risk Track planning scheduling optimization

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scheduling optimization of Landing-Site-Candidates Observation on Phobos from Quasi-Satellite-Orbit in MMX Mission

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TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 2025年第2期68卷 84-97页

作者： Takeo, Yosuke Ozawa, Satoru Chujo, Toshihiro Nakanishi, Hiroki Nakamura, Tomoki Matsumoto, Koji Wada, Koji Keda, Hitoshi Tokyo Inst Technol Univ Tokyo 1528552 Japan Japan Aerosp Explorat Agcy Tsukuba Ibaraki 3050047 Japan Tohoku Univ Sendai Miyagi 9808579 Japan Natl Astron Observ Japan Mitaka Tokyo 1818588 Japan Chiba Inst Technol Planetary Explorat Res Ctr Narashino Chiba 2750016 Japan

In the Martian Moons eXploration (MMX) mission aimed at sample return from Phobos, stereo observations at the requested local time are crucial for creating a local shape model of landing site candidates. To optimize the observation scheduling and facilitate as many stereo observations as possible within a limited time, we address the Phobos observation scheduling optimization problem and customize a genetic algorithm previously proposed for Earth observation satellites. Our ingenuity integrates elements related to stereo observations and local time constraints such that they can be applied to observation scheduling for planetary exploration. The scheduling demonstration results illustrate that 38 of the 50 landing site candidates can be observed during stereo viewing. Furthermore, we perform a sensitivity analysis of the number of landing site candidates to be scheduled, which will provide insights for improving the MMX operational planning.

关键词： Phobos Observation scheduling optimization Genetic Algorithm Planetary Mission Observation Planning

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Data-Driven scheduling optimization for SMT Lines Using SMD Reel Commonality

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DATA 2025年第2期10卷 16-16页

作者： Quijano, Jorge Cruz, Nohemi Torres Quijano-Quian, Leslie Poblano-Ojinaga, Eduardo Rafael Morales, Salvador Anacleto Noriega Univ Autonoma Ciudad Juarez Inst Engn & Technol Dept Ind & Mfg Engn Ciudad Juarez 32310 Mexico Univ Texas Paso Coll Sci El Paso TX 79968 USA Inst Tecnol Ciudad Juarez Tecnol Nacl Mexico Ciudad Juarez 32500 Mexico

Optimizing production efficiency in Surface-Mount Technology (SMT) manufacturing is a critical challenge, particularly in high-mix environments where frequent product changeovers can lead to significant downtime. This study presents a scheduling algorithm that minimizes changeover times on SMT lines by leveraging the commonality of Surface-Mount Device (SMD) reel part numbers across product Bills of Materials (BOMs). The algorithm's capabilities were demonstrated through both simulated datasets and practical validation trials, providing a comprehensive evaluation framework. In the practical implementation, the algorithm successfully aligned predicted and measured changeover times, highlighting its applicability and accuracy in operational settings. The proposed approach integrates heuristic and optimization techniques to identify scheduling strategies that not only minimize reel changes but also support production scalability and operational flexibility. This framework offers a robust solution for optimizing SMT workflows, enhancing productivity, and reducing resource inefficiencies in both greenfield projects and established manufacturing environments.

关键词： SMT scheduling optimization SMD reels changeover time production efficiency

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scheduling optimization of electric ready mixed concrete vehicles using an improved model-based reinforcement learning

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AUTOMATION IN CONSTRUCTION 2024年 160卷

作者： Chen, Zhengyi Wang, Hao Wang, Boyu Yang, Liu Song, Changhao Zhang, Xiao Lin, Fangzhou Cheng, Jack C. P. Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Guangdong Prov Key Lab Modern Civil Engn Technol Guangzhou 510641 Peoples R China Southeast Univ Sch Civil Engn Nanjing Peoples R China China State Construct Sci & Technol Ltd Hong Kong Peoples R China

The decarbonization of the construction industry has emerged as a pressing global priority, and the electric machine is the driving force towards achieving this goal. This study focuses on developing an operation scheduling optimization methodology for electric ready -mixed concrete vehicles (ERVs), which is an innovative and environmental solution for the construction industry. Firstly, we propose a systematic problem definition for the ERV operation process that takes into account the unique properties of electric vehicles and RMC delivery tasks. Subsequently, the entire process is formulated as a Markov decision problem (MDP), which facilitates efficient sequential decision -making. Finally, we develop an improved model -based reinforcement learning method, named parallel -masked -decaying Monte Carlo Tree Search (PMD-MCTS), to solve the MDP efficiently. The entire system is validated in a real case study, and the performance of PMD-MCTS is compared with state-ofthe-art benchmarks. The results have shown that the proposed MDP formulation is well -suited for addressing RMC delivery tasks. The PMD-MCTS algorithm and one of its ablation algorithms (PM-MCTS) have outperformed the other benchmarks in terms of either minimizing costs or delay, and PMD-MCTS consumes 32% less computation time than PM-MCTS.

关键词： Electric vehicle ready-mixed concrete delivery scheduling optimization Model-based reinforcement learning Monte Carlo tree search

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scheduling optimization for production of prefabricated components with parallel work of serial machines

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AUTOMATION IN CONSTRUCTION 2023年第1期148卷

作者： Liu, Wenli Tao, Xingyu Mao, Chao He, Wenjian Chongqing Univ Sch Management Sci & Real Estate Chongqing Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Clear Water Bay Hong Kong Peoples R China Chongqing Univ Coll Comp Sci Chongqing Peoples R China

scheduling optimization of manufacturing prefabricated components is critical for construction in enhancing efficiency and productivity. However, problems of low formulation efficiency and low resource utilization still exist, impeding productivity and increasing costs. Although numerous efforts have been devoted to scheduling optimization, limited research has considered the parallel work of serial machines in production lines, leading to a deviation between actual situations and expectations. Therefore, a new optimization method is proposed with two major contributions. First, a Prefabricated Components Production scheduling (PCPS) model considering the parallel work of serial machines is established, containing 3 new constraint conditions. a Position Algorithm (PA) is also designed to facilitate a constraint related to the locations of component groups. Second, a genetic algorithm-based method is designed to seek potential optimal scheduling schemes. The model is demonstrated and evaluated in an actual case. The results show that: (1) Optimal schedules calculated by the PCPS model can reduce penalty cost by 28.6% and total completion time by 14.9% compared to the case without considering the parallel work of serial machines. (2) Optimal schedules can effectively reduce penalty cost by 63.9% and total completion time by 34.4% compared to the empirical method of ascending order by the due date. The optimal schedules can provide a solution for more detailed and practical scheduling. For future improvement, other factors affecting productivity, such as operator proficiency, will be added to the model constraints.

关键词： Prefabrication components production scheduling optimization Serial machine Parallel machine Genetic algorithm

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scheduling optimization of flexible manufacturing system using cuckoo search-based approach

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第5-8期64卷 951-959页

作者： Burnwal, Shashikant Deb, Sankha Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

In the present work, a cuckoo search (CS)-based approach has been developed for scheduling optimization of a flexible manufacturing system by minimizing the penalty cost due to delay in manufacturing and maximizing the machine utilization time. To demonstrate the application of cuckoo search (CS)-based scheme to find the optimum job, the proposed scheme has been applied with slight modification in its Levy flight operator because of the discrete nature of the solution on a standard FMS scheduling problem containing 43 jobs and 16 machines taken from literature. The CS scheme has been implemented using Matlab, and results have been compared with other soft computing-based optimization approaches like genetic algorithm (GA) and particle swarm optimization found in the literature. The results shown by CS-based approach have been found to outperform the results of existing heuristic algorithms such as GA for the given problem.

关键词： scheduling optimization Flexible manufacturing system Cuckoo search (CS)

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scheduling optimization of Multiple Hybrid-Propulsive Spacecraft for Geostationary Space Debris Removal Missions

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第3期58卷 2304-2326页

作者： Wei, Zhao Long, Teng Shi, Renhe Wu, Yufei Ye, Nianhui Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Minist Educ Key Lab Dynam & Control Flight Vehicle Beijing 100081 Peoples R China

To efficiently implement space debris removal missions on geostationary Earth orbit (GEO), a scheduling optimization scheme of multiple hybrid-propulsive servicing spacecraft (SSc) is proposed. In this scheme, a many-to-many task allocation coding model is established to formulate the removal sequences of debris targets with respect to different SSc. And a hybrid-propulsive orbit transfer strategy is developed to implement the debris removal maneuvers to save the propellant consumption and removal time simultaneously via using both electric and chemical propulsion systems. Then, the multiple GEO space debris removal scheduling optimization problem is formulated to minimize the total propellant consumption by exploring the optimal removal sequences and orbit maneuver parameters, subject to the available velocity increment, removal time, and many-to-many task allocation constraints. To effectively solve the scheduling optimization problem involving discrete-continuous mixed variables, a clustering based adaptive differential evolution algorithm with potential individual reservation (CADE-PIR) is proposed. In CADE-PIR, the k-means clustering method and potential individual reservation mutation strategy are employed to promote the convergence rate and performance of exploration and exploitation. In the end, a real-world GEO space debris removal mission with multiple SSc is investigated to demonstrate the effectiveness and practicality of the proposed optimization scheme.

关键词： Space debris Propulsion Orbits optimization Space vehicles Aerodynamics Vehicle dynamics Geostationary missions multiple hybrid-propulsive spacecraft scheduling optimization space debris removal

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scheduling optimization in coupling independent services as a Grid transaction

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2008年第6期68卷 840-854页

作者： Yang, Haitao Wang, Zhenghua Deng, Qinghua Grad Univ Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

Due to the dynamic properties of autonomous resource providers, the coupling of independent services as a Grid transaction may abort with inconsistency. In many situations people would resort to compensation actions to regain consistency;consequently there comes the issue of compensation-cost. To handle such an issue, for the first time we set up a costing model for the all-or nothing transaction of Grid services, and introduce the ECC metric to evaluate related service scheduling. The analysis of ECC estimation is based on the so-called CC-PreC commit pattern, which is an abstract of a category of common use cases of commit handling. Our analysis theoretically illustrates the high degree of computational complexity of scheduling optimization with respect to the cost labeling, timing and order of requests. Under certain typical conditions we prove that infinite possible schemes of scheduling can be reduced down to a finite set of candidates of scheduling. Especially based on the ECC metric, the caution scheduling is thoroughly investigated, which as a basic policy could be employed in certain common scenarios, and under which the intuitive product-first or cost-first schemes are justified in several typical situations. (c) 2008 Elsevier Inc. All rights reserved.

关键词： Grid services composite scheduling optimization expected compensation-cost all-or-nothing transaction CC-PreC commit pattern costing model timeout

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