Complex Scheduling Research Articles

A novel artificial intelligence search algorithm and mathematical model for the hybrid flow shop scheduling problem

Hybrid flow shop (HFS) environments are prevalent in various industries, including glass, steel, paper, and textiles, posing complex scheduling challenges. This paper introduces a novel approach employing Variable Neighborhood Search (VNS) to address the HFS scheduling problem, with a primary focus on minimizing makespan. The fundamental innovation lies in the fusion of VNS with domain-specific strategies, harnessing the adaptability of VNS. Departing significantly from conventional HFS approaches, our methodology incorporates a special encoding that allows jobs to wait strategically, even when free machines are available. This approach trades immediate machine utilization for the potential of improved makespan. Additionally, using this encoding, a proper decomposition of the problem is feasible. This innovative strategy aims to balance machine load while optimizing the overall scheduling performance. Experimental testing demonstrates the effectiveness of the proposed approach in comparison to existing methods.

SAP HCM time management and payroll integration: streamlining payroll processing

The integration of SAP HCM Time Management and Payroll systems has revolutionized workforce management by streamlining operations and enhancing organizational efficiency. This comprehensive examination explores how these integrated systems transform payroll processing through automated calculations, quality assurance mechanisms, and specialized case handling. The implementation benefits span across various sectors, particularly in manufacturing and healthcare, where complex scheduling and compliance requirements demand sophisticated solutions. Modern organizations leveraging cloud-based technologies and artificial intelligence have experienced significant improvements in processing accuracy, regulatory compliance, and cost efficiency. The integration framework demonstrates remarkable capabilities in handling diverse payroll scenarios, from basic time tracking to complex shift differentials and leave management. The emergence of predictive analytics and machine learning further enhances these systems' ability to detect anomalies, prevent fraud, and optimize workforce planning, setting new standards for payroll automation and workforce management.

Metaheuristics for multi-objective scheduling problems in industry 4.0 and 5.0: a state-of-the-arts survey

The advent of Industry 4.0 and the emerging Industry 5.0 have fundamentally transformed manufacturing systems, introducing unprecedented levels of complexity in production scheduling. This complexity is further amplified by the integration of cyber-physical systems, Internet of Things, Artificial Intelligence, and human-centric approaches, necessitating more sophisticated optimization methods. This paper aims to provide a more comprehensive perspective on the application of metaheuristic algorithms in shop scheduling problems within the context of Industry 4.0 and Industry 5.0. Through a systematic review of recent literature (2015–2024), we analyze and categorize various metaheuristic approaches, including Evolutionary Algorithms (EAs), swarm intelligence, and hybrid methods, that have been applied to address complex scheduling challenges in smart manufacturing environments. We specifically examine how these algorithms handle multiple competing objectives such as makespan minimization, energy efficiency, production costs, and human-machine collaboration, which are crucial in modern industrial settings. Our survey reveals several key findings: 1) hybrid metaheuristics demonstrate superior performance in handling multi-objective optimization compared to standalone algorithms; 2) bio-inspired algorithms show promising results in addressing complex scheduling and multi-objective manufacturing environments; 3) tri-objective and higher-order multi-objective optimization problems warrant further in-depth exploration; and 4) there is an emerging trend towards incorporating human factors and sustainability objectives in scheduling optimization, aligned with Industry 5.0 principles. Additionally, we identify research gaps and propose future research directions, particularly in areas such as real-time scheduling adaptation, human-centric optimization, and sustainability-aware scheduling algorithms. This comprehensive review provides insights for researchers and practitioners in the field of industrial scheduling, offering a structured understanding of current methodologies and future challenges in the evolution from Industry 4.0 to 5.0.

Production scheduling with multi-robot task allocation in a real industry 4.0 setting

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems.

New model to streamline customer order scheduling

The Customer Order Scheduling (COS) problem involves organizing a set of orders, each characterized by a release time, a due date, and a processing time, with the objective of minimizing the costs associated with delivery delays. This study introduces an innovative approach, the Streamline COS, which integrates order scheduling optimization with improved resource utilization and a reduction in production inefficiencies. We present a model aimed at minimizing an objective function that encompasses delay penalties and the optimization of resource usage. Additionally, we introduce an optimized COS algorithm, named SCOS (Streamline COS), designed to find the optimal solution. The efficacy of this approach is validated through experiments with several datasets of varying sizes and instances, demonstrating that the Streamline COS method significantly enhances resource management, reduces delays, and optimizes order scheduling, while offering flexible priority management. This approach provides a practical and effective solution to complex scheduling problems and opens new research avenues, particularly in integrating dynamic constraints and leveraging optimization and machine learning techniques for further performance improvements.

Collaborative Healthcare Data Management Framework using Parallel Computing and the Internet of Things

Healthcare data management has become a critical research area, primarily driven by the widespread adoption of personal health monitoring systems and applications. These systems generate an immense volume of data, necessitating efficient and reliable management solutions for lossless sharing. This article introduces a Collaborative Data Management Framework (CDMF) that leverages the combined strengths of parallel computing and federated learning. The proposed CDMF is designed to achieve two primary objectives: reducing computational complexity in data handling and ensuring high sharing accuracy, regardless of the data generation rate. The framework employs parallel computing to streamline the scheduling and processing of data acquired at various intervals. This approach minimizes processing delays by operating on a less complex scheduling algorithm, making it suitable for handling high-frequency data generation. Federated learning, on the other hand, plays a pivotal role in verifying data distribution and maintaining sharing accuracy. By enabling decentralized learning, federated learning ensures that data remains on local devices while sharing only the necessary model updates. This approach enhances privacy and security, a critical consideration in healthcare data management. It ensures that data distribution and sharing are verified based on appropriate requests while avoiding latency issues. By decentralizing the learning process, federated learning enhances privacy and security, as raw data does not leave the local systems. This cooperative interaction between parallel computing and federated learning operates in a cyclic manner, allowing the framework to adapt dynamically to increasing monitoring intervals and varying data rates. The performance of the CDMF is validated through improvements in two key metrics. First, the framework achieves a 15.08% enhancement in sharing accuracy, which is vital for maintaining data integrity and reliability during transfers. Second, it reduces computation complexity by 9.48%, even when handling maximum data rates. These results highlight the framework’s potential to revolutionize healthcare data management by addressing the dual challenges of scalability and accuracy.

Optimization of Sports Event Operations Through Algorithmic Scheduling and Management

The operation of sports events requires a highly systematic and refined approach, involving comprehensive planning, scheduling, and resource allocation to meet the needs of participants and spectators while achieving economic and social benefits. For large-scale sports games, the method of diagonal line pass distribution is employed to ensure fairness by preventing athletes from the same unit or with similar performance levels from competing in the same group. This paper proposes an automated arrangement management system that combines the diagonal line pass method with Genetic Algorithms (GA) to optimize the scheduling of track and field events. The algorithm is designed to efficiently manage resources such as tracks, rounds, and competition formats, and it transforms the objective function into a fitness function for optimization. Experimental results demonstrate the improved algorithm's superior performance compared to traditional GA in solving complex scheduling problems, making it a valuable tool for enhancing the efficiency and fairness of sports event management.

From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling Problems

Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and their effectiveness in optimising complex scheduling requirements in higher education institutions. We analysed 95 integer programming-based models developed for solving university timetabling problems, covering relevant research from 1990 to 2023. The goal is to provide insights into the evolution of these algorithms and their impact on improving university scheduling. We identify that the implementation rate of models using integer programming is 98%, which is much higher than 34% implementation rates using meta-heuristics algorithms from the existing review. The integer programming models are analysed by the problem types, solutions, tools, and datasets. For three types of timetabling problems including course timetabling, class timetabling, and exam timetabling, we dive deeper into the commercial solvers CPLEX (47), Gurobi (11), Lingo (5), Open Solver (4), C++ GLPK (4), AIMMS (2), GAMS (2), XPRESS (2), CELCAT (1), AMPL (1), and Google OR-Tools CP-SAT (1) and identify that CPLEX is the most frequently used integer programming solver. We explored the uses of machine learning algorithms and the hybrid solutions of combining the integer programming and machine learning algorithms in higher education timetabling solutions. We also identify areas for future work, which includes an emphasis on using integer programming algorithms in other industrial areas, and using machine learning models for university timetabling to allow data-driven solutions.

A data-driven approach to solve the RT scheduling problem

IntroductionThere is an increase in demand for Radiotherapy (RT) and it is a time critical treatment with a complex scheduling process. RT workflow is inter-dependent and involves various steps including pre-treatment and treatment-related tasks which adds to these challenges. Globally, scheduling delays are reported as one of the most common issues in RT. We aim to create and evaluate an automated strategy which generates a patient allocation list to assist the scheduling staff to create an efficient scheduling process. Methods and MaterialsWe used historical data from a large RT department in Sweden from January to December 2022 with 11–13 operational linear accelerators. The algorithm was developed in C# language. It utilizes patient and treatment-related characteristics including the patient timeline (referral date, preferred treatment start dates), booking category, diagnosis group and intent. Based on this, the algorithm assigns patient priority individually. ResultsThe algorithm’s output resulted in a scheduling list sorted by high to low patient priority per week. We evaluated the algorithm with historical manual allocations from the same year. The comparison between manual and algorithm allocations showed that the number of delayed patients reduced by 10 % in the algorithm suggestion with an average delay reduction of 2 weeks. Furthermore, the focus on patient-related characteristics resulted in diagnosis groups being better balanced. ConclusionThe algorithm’s ability to produce quick results may save significant time that the scheduling staff otherwise need to assess individual patient profiles. RT departments can incorporate such algorithms to accelerate their scheduling decisions and enhance their overall scheduling performance before going through major organizational changes.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production.

ReLU surrogates in mixed-integer MPC for irrigation scheduling

Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problem in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing Rectified Linear Unit (ReLU) surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times — by up to 99.5% — while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach achieves enhanced performance in terms of irrigation water savings and IWUE compared to the triggered approach.

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

Self-Planning Code Generation with Large Language Models

Although large language models (LLMs) have demonstrated impressive ability in code generation, they are still struggling to address the complicated intent provided by humans. It is widely acknowledged that humans typically employ planning to decompose complex problems and schedule solution steps prior to implementation. To this end, we introduce planning into code generation to help the model understand complex intent and reduce the difficulty of problem-solving. This paper proposes a self-planning code generation approach with large language models, which consists of two phases, namely planning phase and implementation phase. Specifically, in the planning phase, LLM plans out concise solution steps from the intent combined with few-shot prompting. Subsequently, in the implementation phase, the model generates code step by step, guided by the preceding solution steps. We conduct extensive experiments on various code-generation benchmarks across multiple programming languages. Experimental results show that self-planning code generation achieves a relative improvement of up to 25.4% in Pass@1 compared to direct code generation, and up to 11.9% compared to Chain-of-Thought of code generation. Moreover, our self-planning approach also enhances the quality of the generated code with respect to correctness, readability, and robustness, as assessed by humans.

Scheduling of memory chips for final testing on parallel machines considering power constraints and deteriorating effects

This paper delves into the intricate scheduling strategies crucial for the final testing phase of memory chip manufacturing within the semiconductor industry and other related sectors. It specifically addresses the complex serial-batching scheduling problem, where memory chips are tested on parallel machines under power constraints and chip deterioration effects. The processing time for each chip is significantly influenced by both the cumulative processing time of preceding chips and the power requirements for testing. We formulate this real-world optimization problem using a mixed integer nonlinear programming model. Exact solutions for small instances are obtained using a commercial solver. However, due to the model's complexity, we also develop heuristic solutions to efficiently handle larger instances. Based on the derivation of structural properties, we develop two tailored heuristic algorithms to determine the schedule for the final testing of memory chips. Additionally, we propose a refined Variable Neighborhood Search algorithm (VNS-H) that seamlessly integrates five local search strategies with two supplementary heuristic algorithms, dynamically alternating between them to ensure a balance between computational efficiency and the quality of the solutions obtained. Additionally, we establish a lower bound to validate the effectiveness of these solutions, particularly for large-scale instances. To validate the efficacy and robustness of our proposed metaheuristic algorithm, we conduct a rigorous comparison of the VNS-H algorithm with five other metaheuristic algorithms that have promising performance in various optimization problems. The results highlight the superior performance of the VNS-H algorithm. In small-scale instances, our VNS-H algorithm achieves an average makespan that is 5.52% lower compared to the original VNS. For large-scale instances, the VNS-H algorithm reduces the average makespan by 13.46% compared to VNS. Finally, we discuss the managerial implications of our findings, providing insights specifically tailored to semiconductor manufacturing enterprises based on the outcomes of this study.

A Novel Set-Based Discrete Particle Swarm Optimization for Wastewater Treatment Process Effluent Scheduling.

With the escalating severity of environmental pollution caused by effluent, the wastewater treatment process (WWTP) has gained significant attention. The wastewater treatment efficiency and effluent quality are significantly impacted by effluent scheduling that adjusts the hydraulic retention time. However, the sequential batch and continuous nature of the effluent pose challenges, resulting in complex scheduling models with strong constraints that are difficult to tackle using existing scheduling methods. To optimize maximum completion time and effluent quality simultaneously, this article proposes a restructured set-based discrete particle swarm optimization (RS-DPSO) algorithm to address the WWTP effluent scheduling problem (WWTP-ESP). First, an effective encoding and decoding method is designed to effectively map solutions to feasible schedules using temporal and spatial information. Second, a restructured set-based discrete particle swarm algorithm is introduced to enhance the searching ability in discrete solution space via restructuring the solution set. Third, a constraint handling strategy based on violation degree ranking is designed to reduce the waste of computational resources. Fourth, a Sobel filter based local search is proposed to guide particle search direction to enhance search efficiency ability. The RS-DPSO provides a novel method for solving WWTP-ESP problems with complex discrete solution space. The comparative experiments indicate that the novel designs are effective and the proposed algorithm has superior performance over existing algorithms in solving the WWTP-ESP.

Metaverse Framework Designing for Energy Scheduling in Energy Internet of Things Considering Emergence

The traditional scheduling approach, which primarily considers energy oligarchs like large-scale loads and large-scale generators, is challenged by the rapid rise of distributed energy resources (DERs) in energy internet of things (EIoT). Metaverse is emerging as one of the most promising technologies considering emergence from DERs in EIoT. Our work, from a macro perspective in the virtual space, provides a metaverse framework to harness the swarm intelligence that emerges from the aggregation behavior of massive diverse DERs in EIoT. The presented framework is built upon virtual twins, data science, systems theory, and 4th-Paradigm (data-intensive scientific discovery paradigm), enabling a novel energy scheduling mode. Our goal is to achieve data empowerment and intelligence improvement through data connectivity, virtual and real interaction, which will ultimately result in a new theory on complex system scheduling.

Solving the resource constrained project scheduling problem with quantum annealing

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research.

The marriage of operations research and reinforcement learning: Integration of NEH into Q-learning algorithm for the permutation flowshop scheduling problem

The permutation flowshop scheduling problem (PFSP) attracted much interest from the operations research (OR) community, resulting in various heuristic and metaheuristic methods over the past half-century. However, given the hard nature of the PFSP, efficient algorithms rely on the configuration of initial solutions and sophisticated heuristics. Combining OR and artificial intelligence (AI), this paper investigates the marriage of OR and reinforcement learning for the PFSP. A novel method integrating NEH into the Q-learning algorithm is proposed for solving the PFSP with makespan criterion. With the refinement of action selection by strengthening good actions and weakening bad actions, the proposed Q-NEH algorithm shows a better learning and convergence rate, and outperforms the Q-learning algorithm for all 120 instances in Taillard’s benchmark dataset, with a significantly decreased average relative learning error (RLE) from 13.05 % to 1.73 %. Furthermore, by comparing the performances of the Q-NEH algorithm with related state-of-art benchmark algorithms in a wide range set of instances, it further confirms the superiority and stability of the Q-NEH algorithm in statistics. With a total of 2570 independent tests in two phases of experimental evaluations, the superiority of the Q-NEH algorithm for the PFSP suggests that the integration of OR and AI is a promising research direction for solving complex scheduling problems.

Improving deep neural network random initialization through neuronal rewiring

The deep learning literature is continuously updated with new architectures and training techniques. However, weight initialization is overlooked by most recent research, despite some intriguing findings regarding random weights. On the other hand, recent works have been approaching Network Science to understand the structure and dynamics of Artificial Neural Networks (ANNs) after training. Therefore, in this work, we analyze the centrality of neurons in randomly initialized networks. We show that a higher neuronal strength variance may decrease performance, while a lower neuronal strength variance usually improves it. A new method is then proposed to rewire neuronal connections according to a preferential attachment (PA) rule based on their strength, which significantly reduces the strength variance of layers initialized by common methods. In this sense, PA rewiring only reorganizes connections, while preserving the magnitude and distribution of the weights. We show through an extensive statistical analysis on image classification tasks that performance is improved in most cases, both during training and testing, when using both simple and complex architectures and learning schedules. Our results show that, aside from the magnitude, the organization of the weights is also relevant for better initialization of deep ANNs.

A simulated annealing metaheuristic approach to hybrid flow shop scheduling problem

This study investigates a complex hybrid flow shop scheduling problem prevalent in the industrial sector, characterized by dedicated machines, availability dates, and delivery times. The primary objective is to minimize the total completion time (makespan) in a two-stage workshop setting. We conducted a comprehensive literature review, revealing a scarcity of research on this specific configuration, and employed the Simulated Annealing metaheuristic as our main resolution method. Special emphasis was placed on the meticulous parameterization and configuration of this metaheuristic, crucial for navigating the complexity of the problem.Our findings demonstrate the remarkable effectiveness of the Simulated Annealing method, particularly in achieving low deviation from the lower bound in larger problem sizes and specific instance classes. This consistency highlights the method’s robustness and suitability for complex scheduling scenarios. The study also reveals varying degrees of problem solvability across different instance classes, with computation times generally reasonable except in more challenging scenarios.