Production Scheduling System Research Articles

Research on flexible weaving planning based on NSGA-II algorithm

Facing the shift in the weaving industry from mass production to a more diversified, small-batch production model, traditional production scheduling systems are no longer capable of meeting the demand for rapid market response. To address this issue, this paper first analyzes the production process of a weaving workshop, identifying key scheduling challenges such as order allocation, equipment selection, and operation sequencing. Based on this analysis, a flexible job shop multi-objective scheduling model tailored for weaving workshops is developed. To handle the multiple constraints and optimization goals inherent in the model, an improved NSGA-II algorithm is proposed. This algorithm combines artificial bee colony (ABC) algorithm for population initialization with simulated annealing (SA) for population filtering. Simulation examples and case studies from actual workshops demonstrate that the improved NSGA-II algorithm outperforms other algorithms in solving the scheduling problem for weaving workshops. The proposed multi-objective scheduling model and its improved algorithm provide accurate and efficient optimization solutions for workshop scheduling.

Single-Machine Rescheduling with Rejection and an Operator No-Availability Period

In this paper, we investigate a rescheduling problem with rejection and an operator non-availability period on a single machine. An optimal original schedule with the objective of minimizing the total weighted completion time has been made in a deterministic production scheduling system without an unavailable interval. However, prior to the start of formal job processing, a time interval becomes unavailable due to the operator. No jobs can start or complete in the interval; nonetheless, a job that begins prior to this interval and finishes afterward is possible (if there is such a job, we call it a crossover job). In order to deal with the operator non-availability period, job rejection is allowed. Each job is either accepted for processing or rejected by paying a rejection cost. The planned original schedule is required to be rescheduled. The objective is to minimize the total weighted completion time of the accepted jobs plus the total penalty of the rejected jobs plus the weighted maximum tardiness penalty between the original schedule and the new reschedule. We present a pseudo-polynomial time dynamic programming exact algorithm and subsequently develop it into a fully polynomial time approximation scheme.

Solving distributed assembly blocking flowshop with order acceptance by knowledge-driven multiobjective algorithm

In the era of Industry 4.0, industrial artificial intelligence technologies make production planning and scheduling systems more flexible. A new distributed assembly blocking flowshop problem with order acceptance and scheduling decisions (DABFSP_OAS) was investigated in this paper. Specifically, three objectives—the makespan, total energy consumption (TEC), and total profit (TP)—were addressed simultaneously. To address this problem, we established a knowledge-driven non-dominated sorting genetic algorithm-II (KDNSGAII). First, three initialization schemes based on the problem-specific property were introduced to generate diverse initial population. Then, to accelerate the convergence process, we developed multiple Pareto-based crossover and mutation operators. In addition, two novel destructive reinsertion strategies based on product and job sequence length were implemented to enhance the development ability of the algorithm. Finally, the designed strategies were evaluated. Comparisons and discussions showed that the KDNSGAII outperformed the other state-of-art multi-objective algorithms in solving DABFSP_OAS.

Research on the Production Scheduling System for Prefabricated Component Factories in Smart Construction

Research on the Production Scheduling System for Prefabricated Component Factories in Smart Construction

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.

A self-learning framework combining association rules and mathematical models to solve production scheduling programs

ABSTRACT Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

A Review of Game Theory Models to Support Production Planning, Scheduling, Cloud Manufacturing and Sustainable Production Systems

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems.

Kombinasi Algoritma Priority Scheduling dan Earliest Due Date untuk Sistem Penjadwalan Slitting Produk Berbasis Web

Production scheduling is one of the most critical components of production management that can impact efficiency and resource utilization. This research focuses on the development of a web-based production scheduling system that integrates two algorithms, Earliest Due Date (EDD) and Priority Scheduling. This research includes the analysis, design, implementation and testing of a those two algorithms in a web-based application for production management. The result of this research will be analyzed through the results of how both algorithms affects lateness and tardiness. The research findings indicate that the application can effectively manage Work Orders. The application can generate an optimal schedule when conditions arise where some tasks have higher priority than others by combining both algorithms. The system can identify potentially late tasks by calculating the tardiness of the given jobs. And from the analysis results, the application can generate an efficient production schedule with an on-time accuracy rate of 74%, and the average delay for each job is 0.95 hours.

Digital Twin-based Demand-Driven Production Planning and Scheduling System: A New Conceptual Framework

In the ever-evolving manufacturing landscape, companies are increasingly confronted with both external and internal uncertainties. These challenges necessitate a responsive and agile production planning and scheduling system. While adopting digital twin (DT) technology has enhanced dynamic scheduling at the operational level, there remains a notable gap in the literature regarding tactical-level production planning. This crucial aspect involves determining what and how much to produce and directly addressing customer demands while efficiently managing inventory. This paper introduces a novel conceptual framework that synergistically integrates Demand-Driven Material Requirements Planning (DDMRP) with DT-based scheduling and optimization. Our framework is developed to execute tactical production planning as well as operational-level scheduling seamlessly. It employs a genetic algorithm to refine the scheduling process, aiming to significantly improve production performance. Moreover, the framework is adept at managing rescheduling, effectively addressing both external and internal disturbances. By bridging the gap between high-level planning and operational execution, this framework presents a holistic approach to modern manufacturing challenges, ensuring effective inventory management, demand fulfillment, and enhanced production performance.

A production scheduling and control system for CAR T cell manufacturing

This paper addresses some of the new challenges posed by scheduling and controlling the production of chimeric antigen receptor (CAR) T cells for personalized therapies. In this therapy, the patient’s T cells are genetically modified by adding a gene for a receptor to help T cells attach to the cancer cell antigens. Then, the modified cells are grown and multiplied (called cell expansion) and finally returned to the patient. The production takes place in a manufacturing system that comprises several devices, which are capable of processing cells from multiple patients at the same time. The efficient operation of such a complex system requires a sophisticated control system accompanied by a scheduler to optimize the production of CAR T cells for several patients simultaneously. We propose a scheduling algorithm, which can handle the uncertain duration of cell expansion, and the maximum time lags between consecutive processes simultaneously, a combination hardly studied in the literature. We also describe the main features of an adaptive control system including service-oriented device connections as well as centralized control and monitoring. Finally, we describe how the overall system works, including a JSON based communication between scheduler and control system.

An agent-based heuristics optimisation model for production scheduling of make-to-stock connector plates manufacturing systems

The manufacturing systems’ success directly relates to their accurate, reliable and flexible schedules, including how production is planned and scheduled and which constraints are considered in generating the schedules. The study's objective arises from the need to generate an optimal production scheduling system in a connecting plates manufacturing company that works on a Make-To-Stock basis. This research investigates the impact of demand and operational constraints on production schedules, including the facility capacity, operators and machines availability, raw materials availability, inventory level and warehouse capacity. A multi-agent-based optimisation model is developed to face the complexity of considering demand and operational constraints and reflects their impact on generating a reliable production schedule. This model involves a proposed heuristic algorithm that considers demand and operations constraints in such a manufacturing environment and optimises the production schedule based on these restrictions/requirements. A real-life case study based on a connecting plates manufacturer company is used as a test bench of the proposed agent-based heuristic optimisation model. The proposed algorithm is compared with other related approaches to check its superiority based on key criteria, including inventory levels, missed/unsatisfied orders and total production time. Results show that the proposed heuristics algorithm reduced the number of missed orders by 34% compared with similar approaches.

Identifying the promising production planning and scheduling method for manufacturing in Industry 4.0: a literature review

ABSTRACT Industry 4.0 technologies create a connected ecosystem that allows for real-time monitoring, control, and optimization. This enables the manufacturing industry to move the ultimate goal of their businesses from lean to leagile. Then, to meet the needs of leagile manufacturing, an integrated production planning and scheduling system is needed. The academic world responds to the need by the growth of papers. Many methodologies, models, frameworks, and methods have been proposed. Literature abounds on classifying them, yet it remains inconclusive mainly on their applicability to Industry 4.0. In this regard, this paper conducts a literature review aiming to show the state of the art and discover the most promising production planning and scheduling method for Industry 4.0 among the many. Lastly, on the basis of this study, recommendations on possible future research directions are provided for researchers, and the design of the next-generation production planning and scheduling system for Industry 4.0 is also suggested.

Integration of Health Monitoring of Cutting Tools and Production Scheduling in Smart Factory

Smart factory evolves by adding new functions or upgrading existing functions to meet the needs of manufacturers in the use stage of the life cycle. To reduce complexity of smart factory, these functions must be carefully designed considering interactions with other functions. This study analyzes integration of functions situated in the different layers in the functional hierarchy of smart factory. In this study, a health monitoring system for cutting tools in a shop floor, which offers a function to manage the lifetime of cutting tools, is presented. The system is integrated with a production scheduling system, which offers a function to schedule machining processes considering efficient usage of machines as well as cutting tools, while maintaining the quality machining processes. Primary evaluation of the functional integration shows that activities in the shop floor regarding selection and replacement of cutting tools are considered in defining production schedules. It also shows that such functional integration results in increase in complexity regarding the behavioral model of humans in smart factory.

Scheduling in Industrial environment toward future: insights from Jean-Marie Proth

According to [Dolgui, Alexandre, and Jean Marie Proth. 2010. Supply Chain Engineering: Useful Methods and Techniques. Vol. 539. Springer.], advancing tactical levels in production systems has led to the disappearance of static scheduling in favour of dynamic scheduling. Additionally, the evolving challenges in the supply chain paradigm have significantly impacted the organisation of production systems. This shift has moved scheduling issues from the tactical to the strategic level, resulting in linear organisations encompassing scheduling decisions. [Proth, Jean Marie. 2007. “Scheduling: New Trends in Industrial Environment.” Annual Reviews in Control 31 (1): 157–166. https://doi.org/10.1016/j.arcontrol.2007.03.005.] emphasised that real-time scheduling in production systems has become a pivotal area of research. He presented several open problems for researchers to address in this context, including (1) the development of real-time algorithms capable of handling multiple operations on the same product and unrelated resources, (2) adapting previous schedules with certain modifications, (3) addressing unforeseen actions that arise randomly in real-time planning, and (4) exploring cyclic scheduling problems with size limits as alternative solutions to heuristic approaches. This paper reviews the evolving trends in light of J.M. Proth's predictions and advice within the aforementioned domains.

Analisis Perencanaan Produksi Paving Dengan Menggunakan Metode Campbell Dudek Smith Dan Palmer Di CV. Daya Patra Sentosa

CV. Daya Patra Sentosa is a company that produces paving. Of the several types of products available, T6 paving, T8 paving, and hexagon paving are the types of products with the most demand. CV. Daya Patra Sentosa has problems in production scheduling, namely there are frequent delays in product delivery because they do not yet have an optimal production scheduling system and still use FCFS rules. The purpose of this study is to provide alternative production scheduling suggestions in order to obtain optimal production processing time and prevent delays in product delivery. These problems are solved by the method of Campbell Dudek Smith and Palmer. These two methods are compared to find out the production process time and get the optimal choice of production time. The calculation results show that the application of the method applied by the company, namely FCFS is 12342.9 minutes, the Campbell Dudek Smith method is 11953.9 minutes, and the Palmer method is 12023 minutes. It can be seen that the Campbell-Dudek-Smith method provides a shorter time of 389 minutes (3.15%) to the company's schedule.

Improving the Performance of a SME in the Cutlery Sector Using Lean Thinking and Digital Transformation

The main purpose of this paper is to show that if three specific contextual factors are present in a company, it is possible to achieve great performance improvements with a lean and industry 4.0 implementation. In terms of research methods, a case study was carried out of a project to implement digitalization and Lean practices in a cutlery company, which in fact encompassed a project of master’s degree in engineering and industrial management. Thus, the research question is: “It is possible to achieve major improvements in a lean and industry 4.0 implementation if three specific contextual factors are present in the company, namely (i) commitment of top management, (ii) knowledge on digitalization and lean, and (iii) very low Value-Added Ratio?”. Regarding the company project, action-research was adopted, and the project team began by mapping and diagnosing the production processes of the two product families (knives and spoons/forks). High levels of work in process, long throughput times, poor flow planning and control, and high stocks of finished products, quickly stood out in both families. Improvement proposals were developed and implemented, namely: (i) creation of a production scheduling and control system, (ii) improvement of the warehouse stock management system, and (iii) adoption of new routines, management tools, visual management, and kaizen meetings. The results achieved were excellent (e.g., throughput time reduced by 27.6% and productivity increased by 36.5%) and aligned with Sustainable Development Goals SDG 9 and 12. The findings of this study corroborate that exceptional results in the company performance can be achieved through a lean and industry 4.0 intervention, if the three referred contextual factors occur.

Optimalisasi Sistem Penjadwalan Produksi Untuk Meminimalisir Keterlambatan Produksi Di PT Fahifa Prima Mandiri

During the production process activities to get the best results, the production process activities need to be well planned. Production scheduling is one way to plan the activities of the production process. This research took place at PT Fahifa Prima Mandiri, at the time of direct research there were Lateness in meeting product requests from consumers. The purpose of this research is to optimize the scheduling of the production scheduling system used by the company by using the SPT, LPT, and EDD methods to minimize Lateness. FCFS is the method used by the company with a total Lateness of 65 days and an average Lateness of 9 days, the SPT method with a total Lateness of 51 days and an average Lateness of 7 days, the LPT method with a total Lateness of 89 days and an average Lateness 13 days, the EDD method with a total Lateness of 53 days and an average Lateness of 8 days. From the results of the calculation of several methods used the SPT method is the optimal method used, with the application of this method so that the production process at the company can be maximized.

Research and implementation of order-oriented textile production scheduling system

ABSTRACT A production planning and scheduling system for small and medium-sized textile enterprises is designed according to the actual needs of textile enterprises in the process of informatization construction. The system considers orders as the primary focus and employs modular design ideas to design functional modules and standardized interfaces based on a three-tier architecture. Furthermore, it uses a database structure design and computer programming to ensure the efficiency and reliability of information services. The main functions of the system include prioritization, automatic production scheduling, order processing, and real-time monitoring of the machine. An analytical hierarchy process optimization method is proposed and applied to a textile enterprise. The running results reveal that this method can effectively reduce the order completion time, improve punctuality delivery rate, save 2–3 days of production planning and machine scheduling for the enterprise, improve the resource allocation ability and production management efficiency, and has guiding significance for small- and medium-sized textile enterprises to realize informatization.

Scheduling Algorithm Using Path Relinking for Production Process with Crane Interference

In manufacturing industries, customers demand a wide variety of products, with high quality and fast delivery. Production scheduling systems have become critical for efficient operation. However, scheduling problems in manufacturing are generally large and complex with many constraints. It is difficult to create an optimal production schedule that satisfies all constraints within a reasonable timeframe. This study targets a factory with multiple working machines and two overhead cranes. Our research aims to obtain a solution algorithm to avoid interference of overhead cranes and machine competition and a production plan that minimizes the total makespan for each job. As the problem must be solved within a reasonable timeframe, we have developed the solution algorithm using metaheuristics and scheduling simulation. In general, metaheuristic algorithms must strike a balance between an intensive search for good solutions and a search for diverse solutions. Accordingly, we propose a new algorithm using path relinking in a scatter search. This method was demonstrated to be effective in obtaining good solutions with little variation in numerical experiments. In this paper, we describe previous research, our target process, and new solution algorithm and discuss algorithm design methods based on computer experiments.

Dynamic shop-floor scheduling using real-time information: A case study from the thermoplastic industry

Practical production planning and scheduling systems must promptly respond to major real-time events and adjust their plans and schedules accordingly. To highlight the importance of concurrency in such systems, this paper addresses the problem of dynamic shop-floor scheduling using real-time information in a case study from the thermoplastic industry. The considered production line is organized as unrelated parallel production cells with a set of identical parallel machines in each cell. Parts are produced in batches using different molds on specific machines. Due to the size and complicated design of the molds, they require extended recovery periods (i.e., maintenance) in case of major failures. Therefore, previously developed plans and schedules need to be revised using real-time information every time a mold’s major failure occurs. The production process is subject to the following constraints: batch processing, safety stocks, dedicated machines, machine-dependent setup times, precedence constraints, mold failures, and real-time updates. The problem is formulated as a mixed-integer programming model to minimize a weighted cost function that includes tardiness and operating costs. To solve the problem, a predictive-reactive scheduling approach is introduced based on a modified simulated annealing (SA) algorithm. The developed approach utilizes an event-driven rescheduling policy. It also embeds a problem-specific neighborhood structure and solution evaluation into the modified SA algorithm. The experimental study indicates that the proposed approach generates better real-life planning and scheduling results than the methods based on dispatching rules. The findings demonstrate that the proposed SA-based predictive-reactive scheduling approach generates the solutions with about a 26.1% less tardiness cost and a 6.99% less total weighted cost (on average). In addition, the results also show the competitiveness of the proposed SA-based predictive-reactive scheduling approach compared to two other approaches based on an iterated greedy (IG) algorithm and a Tabu Search (TS) algorithm from the literature.