Optimal Production Schedule Research Articles

Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation.

This research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and programming techniques. The methodological proposal is composed of six stages: (i) Case study, (ii) Hypothesis formulation, (iii) Discrete event simulation, (iv) Measurement of entropic complexity by applying Shannon's information theory, (v) Entropy analysis, and (vi) Statistical analysis by ANOVA. The results confirm that factors such as production sequence and product volume significantly influence the structural complexity of the workstations, with station A being less complex (0.4154 to 0.9913 bits) compared to stations B and C, which reached up to 2.2084 bits. This analysis has shown that optimizing production scheduling can reduce bottlenecks and improve system efficiency. Furthermore, the developed methodology, validated in a case study of the metalworking sector, provides a quantitative framework that combines discrete event simulation and robust statistical analysis, offering an effective tool to anticipate and manage complexity in production. In synthesis, this research presents an innovative methodology to measure static and dynamic complexity in manufacturing systems, with practical application to improve efficiency and competitiveness in the industrial sector.

Modern Technology's role in accounting cost calculation of industrial enterprises: Informatization as a key strategy to improve management efficiency.

The study aims to address challenges encountered by modern industrial enterprises, including inefficient accounting cost calculation, delayed information acquisition, and untimely management decisions. By comprehensively applying modern management, information technology, and cost control methods, this study constructs a real-time cost control model to optimize industrial enterprises. Firstly, the model employs mixed integer linear programming (MILP) to optimize production processes through mathematical modeling. Secondly, it utilizes data mining technology to assist enterprises in cost accounting and efficiency analysis. Big data analysis aids in predicting production process changes and trends, providing data support for management decisions. Intelligent production scheduling and resource optimization are facilitated by artificial intelligence (AI) technology, enabling real-time data collection, processing, and analysis. Finally, the activity-based costing method categorizes, allocates, and measures costs to enhance cost accounting accuracy and efficiency. Research results demonstrate that enterprises adopting the theoretical framework of real-time cost control can enhance the accuracy of cost accounting, cost control efficiency, and overall management effectiveness.

Application of LightGBM Algorithm in Production Scheduling Optimization on Non-Identical Parallel Machines

Production scheduling plays a decisive role in supply chain management, directly influencing the operational efficiency and competitiveness of companies. This study explores the effectiveness of the LightGBM algorithm for production scheduling on non-identical parallel machines, comparing it to algorithms such as logistic regression, KNN, decision tree, and XGBoost. LightGBM was chosen for its speed of execution and its ability to handle large amounts of data. The results show that LightGBM outperforms the other models in terms of RMSE, MAE, explained variance score, and R² score for regression tasks, as well as in classification accuracy for certain features. Its superiority is attributed to its ability to efficiently handle data complexity while reducing computational complexity through its leaf tree growth technique. This study highlights LightGBM's potential for improving the efficiency of supply chain management systems and the challenges associated with computational scalability for large datasets. The results suggest that LightGBM is a robust and effective solution to optimize production scheduling, paving the way for future research in this field.

Optimizing Palm Oil Production Scheduling In Nigeria Using Balanced Transportation Problem

This paper deal with optimizing palm oil production of a production firm in Nigeria using balanced Transportation problem in other to reduce or increase/ minimize the production cost and increase or maximize production, profit and efficiency. Here the scheduling challemnge is developed and modeled as a balanced of transportation problem as requires in creation of an optimum production schedule. This resulted in allocating optimal and maximum production level such that the given demand were meet at a minimum cost .

Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty

This study proposes a robust precast concrete (PC) production scheduling model for PC construction projects with schedule uncertainty by adopting a proactive scheduling approach. The proposed model consists of a PC production simulation module that simulates and evaluates the total tardiness of the schedule at a certain confidence level with contractor schedule uncertainty and a GA-based production schedule optimization module that finds an optimal schedule through iterative schedule generation and evaluation using a PC production simulation module. The experimental study shows that the proposed model can find the best schedule with 303.8 h of tardiness at a 100% confidence level, followed by NEHedd 324.7 h, basic GA 328.5 h, and EDD 335.8 h. The results of this study will help PC production schedulers to perform robustly despite contractors’ schedule changes and will thus contribute to the successful completion of PC construction projects.

Optimization of Production Scheduling for the Additive Manufacturing of Ship Models Using a Hybrid Method

This paper introduces a hybrid optimization method that leverages either linear programming (LP) or a genetic algorithm (GA) based on the problem size to enhance the parallel additive manufacturing (AM) process for ship models. The LP ensures optimality but can experience exponential increases in the computation time as the problem size grows. To address this limitation, the GA is employed for larger problems, providing optimal solutions within reasonable quality and time constraints. The method optimizes the module allocation to AM machines and determines the build processing sequence for each machine, while also considering the availability of workers preparing for consecutive module production. Applied to a case study, the proposed method achieves a 14% reduction in the completion time compared to a heuristic method from a previous study. Furthermore, the method is validated by benchmarking against the heuristic method across various problem sizes, consistently demonstrating superior performance.

Integrated optimization of production and maintenance scheduling with third-party worker resource constraints in distributed parallel machines environment

Outsourcing machine maintenance to third parties has been a trend due to the increasing complex of machines and the benefits of maintenance outsourcing. However, this new phenomenon is ignored in previous studies pertaining to the integrated optimization of production and maintenance scheduling (IOPMS) problems, resulting the lack of theoretical guidance for production managers to formulate optimal scheduling schemes under this new trend. In this study, we investigate an IOPMS problem considering maintenance outsourcing in a distributed parallel machine environment, referred as IOPMSTW, which requires the use of third-party worker resources to perform preventive maintenance. Makespan and total cost are two optimization objectives. We first formulate the problem by developing a mixed integer linear programming. Then a memetic algorithm incorporating iterated greedy method (IG) is proposed to solve the IOPMSTW, in which an improved decoding method and a problem-dependent local search operator based on IG are designed to respectively ensure the feasibility of new generated individuals and improve the searching efficiency. The validity of proposed mathematical model is verified by CPLEX based on eight small instances. Based on 240 constructed instances, a set of comprehensive experiments are conducted. The results demonstrate that the local search operator improved the searching performance of the proposed algorithm by 100%. Comparison results with other well-known algorithms show that the proposed algorithm achieved the best results on more than 85% of the tested instances.

Optimalisasi Perencanaan dan Penjadwalan Produksi: Kunci Meningkatkan Efisiensi Operasional

Optimizing production planning and scheduling is a crucial aspect in improving operational efficiency in the competitive industrial era. This article discusses the importance of proper production planning and scheduling strategies to forecast demand and efficiently manage resource allocation, as well as effective scheduling to minimize downtime and increase throughput. The company has the possibility of not only providing better services to customers, but also reducing costs, and increasing efficiency, thus achieving a sustainable competitive advantage. This research uses a qualitative approach with a literature study method. The research was conducted by collecting information, then analyzed, and synthesized from various sources, including, journals, articles, and online publications.

Analysis of apple chips business development strategies using business model canvas approach and SOAR-AHP method

The study focuses on SMEs X, a producer of various processed fruit products, primarily apple chips. The business experienced a 90% decline in revenue due to an 8-month market closure during the Covid-19 pandemic, with ongoing challenges such as reduced revenue and production capacity post-pandemic. This study aims to identify business models in SMEs using the Business Model Canvas (BMC) approach, develop alternative business strategies through SOAR analysis of BMC elements, prioritize these strategies using the Analytical Hierarchy Process (AHP) method, and enhance their BMC. Four expert respondents participated in the study. The research results highlight the current business conditions across the 9 BMC elements. The SOAR matrix produced eight alternative strategies, prioritized using the AHP method, including optimal production planning and scheduling (OR2), strengthening apple chips brand awareness (OA2), disseminating freeze-drying production technology innovations (OA1), developing business unit production (SA2), expanding the marketplace (SA1), maximizing the use of social media (OR1), product quality standardization (SR1), and forming partnerships with other fruit chip SMEs to innovate freeze-dried products (SR2). The BMC was further developed by integrating SR2 and OA1 into the key partnerships element, SA2 into the key resources element, OA2, OR1, and OA2 into the key activities element, SR1 into the value proposition element, and SA1 into the channel element.

Linear Scheduling Method-Based Multi-Objective Optimization for Off-Site Construction Manufacturing

Optimal production scheduling in off-site construction is hindered by the lack of comprehensive methods that simultaneously addresses workforce allocation, realistic labor productivity, workstation idle time (WIT), work-in-progress (WIP), and project completion time (PCT). To overcome these limitations, this paper introduces a hybrid planning and scheduling method that integrates the linear scheduling method reflecting workforce allocation, Monte Carlo simulation for estimating realistic labor productivity, and a multi-objective optimization model to minimize WIT, WIP, and PCT concurrently. The proposed method is validated using the Taillard instance benchmark dataset, and a panelized production line located in Edmonton, Canada. As a result, the proposed method is effective in generating realistic and optimal production schedules, achieving reductions of 5.2 % in PCT, 41.45 % in WIT, and 56.42 % in WIP. These findings are crucial for industry professionals aiming to improve efficiency in the production line and provide a foundation for further research in off-site construction scheduling.

Optimasi Penjadwalan Mesin Bor Manual pada Produksi Side Chair Menggunakan Metode Shortest Processing Time (SPT)

CV Mandiri Abadi is a manufacturing industry that operates on a make-to-order system, specifically for side chair products, necessitating a carefully considered scheduling system. Issues with the use of manual drilling machines causing delays in component deliveries to the warehouse prompted an evaluation of optimal production scheduling using the shortest processing time (SPT) method. This study includes detailed calculations to determine average completion time, utilization, average job time, and average job delay. Analysis results show that using manual drilling machines achieved an average completion time of 854.28 minutes, a proposed utilization improvement of 28%, an average job time of 3.46 minutes, and an average delay of 614.14 minutes. This approach is more effective in optimizing processing time and reducing delivery delays. Therefore, the use of the shortest processing time (SPT) method is recommended to improve production performance within the company.

Optimization of machine schedules and vehicle routes in the mobile additive manufacturing mode

Online shopping giant Amazon proposes to use 3D printers mounted on vehicles to print customer purchases on the fly and deliver them immediately. Because of the overlap between production and transportation processes, such a mobile manufacturing mode offers potential savings in lead times and inventory costs. However, the decisions associated with the two processes are intertwined, making it challenging to achieve satisfactory machine schedules and vehicle routes. To address the situation and fully understand the benefits of the mode, we study a related optimisation problem of finding routes for vehicles and production schedules for 3D printers mounted on them with minimum travel costs. First, we construct a mixed-integer programming model to formulate the problem. Then, we develop an exact algorithm to solve it. Specifically, we propose two propositions about the optimal production schedule of machines in the route and develop a labelling algorithm for the pricing problem. And we devise a problem-specific heuristic labelling algorithm to speed up column generation. We use two sizes of instances with five problem parameter settings (including 290 instances) to validate the effectiveness of the algorithm. In addition, we provide some managerial insights for decision-makers by comparing the novel mode with the traditional manufacturing mode.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

Integrated Optimization of Production Scheduling and Haulage Route Planning in Open-Pit Mines

In mining, deposits are divided into blocks, forming the basis for open-pit mine planning, covering production and haulage route planning. Current studies often stage optimization and lack the consideration of road capacity, leading to suboptimal solutions. A novel approach integrates production scheduling and haulage route planning through a bilevel optimization model. The upper-level model integrates ore mining constraints to establish a mixed-integer production scheduling model, minimizing haulage costs. Spatiotemporal correlation constraints for block mining are determined using a two-stage algorithm. The lower-level model incorporates road capacity, forming a haulage route optimization model based on multicommodity network flow. A solution algorithm with a distance penalty strategy facilitates feedback between the upper and lower levels, achieving optimal solutions. Tested on a real open-pit coal mine with over 5 million blocks, this approach reduces haulage costs by 10.06% compared to stage optimization. Additionally, this approach allows for adjusting haulage demand in both temporal and spatial dimensions, effectively preventing road congestion. This study advances rational mining processes and enhances the efficiency of open-pit mining haulage systems.

Quality-centered production and maintenance scheduling for multi-machine manufacturing systems under variable operating conditions

Production, maintenance, and quality represent the three primary tasks in manufacturing systems. Previous studies have predominantly focused on optimizing these tasks separately or jointly optimizing two of them. However, it is worth noting that these tasks exhibit mutual interaction, and models failing to consider them simultaneously cannot achieve global optimality. Therefore, this paper aims to develop a quality-centered production and maintenance scheduling methodology (QPMSM) for multi-machine and multi-product manufacturing systems. Specifically, a delivery time-based production scheduling strategy is introduced to emphasize the impact of maintenance activities. A quality loss model is developed to characterize the mapping relationship between quality deterioration and component degradation. Furthermore, a well-designed maintenance strategy determines the optimal timing and frequency of maintenance activities to improve system performance and reduce quality deterioration. Building upon this foundation, an integrated model is proposed to concurrently optimize production scheduling, maintenance planning, and quality control tasks to minimize the overall system cost. The effectiveness of the proposed QPMSM in terms of cost-savings has been verified using a designed hybrid meta-heuristic algorithm, providing a viable approach for simultaneous optimization.

Synchronisation of operations and maintenance in distributed unrelated parallel machine systems: a hierarchical optimisation framework

ABSTRACT Synchronisation of operations and maintenance means that production and maintenance departments work together effectively to achieve integrated optimisation of production scheduling and maintenance activities. This paper addresses such an integrated optimisation problem towards distributed unrelated parallel machine systems, where deteriorating processing time and machine failures are considered. For the trade-off between production and maintenance to obtain a minimal makespan in the worst-case scenario, a hierarchical optimisation framework with reinforcement learning-based encoder and knowledge-based intelligent optimiser (HOFRK) is developed. Experimental results show that HOFRK can be treated as an excellent optimiser for enterprise information systems to solve the proposed problem.

Digital twin-enabled robust production scheduling for equipment in degraded state

Technological advancements are leading to a world where digital twins will become integral to manufacturing operations management. While wide-ranging applications of digital twins are being researched, robust production scheduling remains an enduring challenge, especially considering the numerous sources of uncertainty in a complex manufacturing system that can affect the validity of the obtained solution. Thus, the article proposes a Prognostics and Health Management (PHM)-enabled digital twin framework to perform job scheduling for a flow shop scheduling problem considering real-time equipment health state. The framework combines the adoption of a Genetic Algorithm optimizer with data-driven modelling leveraging algorithms like Principal Component Analysis and models like Discrete Event Simulation with the purpose to solve the engineering task of scheduling at hand. Building on such a technological blend, the framework incorporates the degradation and fault detection and diagnosis of failure modes across multiple components. The effect of degradation and faults on job processing times is learned as a distribution from the field data. The proposed framework is then validated in a laboratory environment where degradation is produced by inducing degradation and faults in the equipment. By means of various experiments, the optimized makespan is compared for the output schedules in different configurations. When equipment is degraded, production scheduling with PHM-enabled field-synchronized digital twin results in better makespan estimation, if compared to the digital twin without PHM. This shows the superiority of the framework in terms of more realistic makespan estimations, which finally corresponds to improved production schedule optimization, sensitive also to degraded states.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

This is manufacturing, efficient production scheduling is crucial for maximizing output and reducing costs. This abstract describes strategies and technologies to achieve this efficiency. It covers aspects like demand forecasting, resource allocation, optimizing task sequencing, and leveraging real-time tracking. By implementing these methods, manufacturers can streamline workflow, minimize idle time, and ensure timely deliveries. This report explores the challenges faced in production scheduling in manufacturing and examines strategies and technologies that can be used to optimize scheduling processes. The report discusses the importance of production scheduling in achieving operational efficiency and meeting customer demands. Various strategies, such as lean manufacturing, just-in- time (JIT) production, and advanced planning and scheduling (APS) systems, are analyzed for their effectiveness in improving production scheduling. Additionally, the report explores technologies such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT) that can enhance production scheduling capabilities. Case studies and examples are used to illustrate how these strategies and technologies have been implemented successfully in real-world manufacturing settings. The report concludes with recommendations for manufacturers looking to optimize their production scheduling processes.

Optimal Scheduling of Off-Site Industrial Production in the Context of Distributed Photovoltaics

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China’s power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.