Production Planning System Research Articles

Self-learning and autonomously adapting manufacturing equipment for the circular factory

Abstract The integration of both linear and circular processes in one production system poses significant challenges. In particular, the reprocessing of end-of-life products is associated with uncertainties at all levels of the production system, from the initial planning and control through to the executing production hardware and intralogistics. To address these challenges, this article presents approaches for self-learning and autonomously adapting production equipment for the Circular Factory. Initially, hardware and software solutions are developed to cover the necessary processes. Reprocessing is covered by modular and reconfigurable manufacturing cells, which also include new process chains such as the combination of additive-subtractive processes. The provided capabilities must be applied to ever new products, for example by transferring human procedures for unknown products to the production equipment. Lastly, an overall robust and dynamic production planning and control system is developed that maintains continuous operation even in unforeseen situations. The resulting highly dynamic overall system is connected by an autonomous intralogistics system.

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.

An innovative integrated framework for multi-level production planning and control systems in construction

The construction industry is plagued by chronic inefficiencies, such as low productivity, significant waste, cost overruns, and schedule delays. One of the most critical contributors to these issues is fragmented and ineffective planning and control methods. This research aims to address these challenges by developing an innovative integrated multi-level framework for production planning and control that harmonizes various planning methods and control metrics across different schedule levels. Employing the Design Science Research (DSR) methodology, this study systematically develops and evaluates an innovative framework that integrates internal and external project characteristics, utilizing the concept of "functionality" as a primary key for coherent integration. Furthermore, a multi-objective mathematical model is incorporated to clarify the framework's components and optimize the number of suggested planning methods and control metrics at each scheduling level, aiming to balance the functional requirements of project teams with the practicality of implementation.The application of the framework to a renovation case study demonstrates its practicality and strategic value, effectively addressing real-world project management challenges. Moreover, industry and academic evaluations highlight its potential to significantly improve construction project management practices.

Matheuristic algorithm for dock planning problems considering tandem and parallel methods in the shipbuilding industry

Docks are crucial production resources that significantly impact shipyard productivity. This study addresses a real-world dock planning problem (DPP) focused on ship production scheduling within docks. Unlike traditional scheduling issues, the DPP involves managing multiple concurrent tasks due to the use of tandem and parallel methods common in the shipbuilding industry. This distinctive feature introduces unique considerations and opportunities for optimizing production processes within shipyards. Despite its importance, research on DPPs is limited, with dock planning typically depending on the expertise of planners. This study formally defines a DPP as a mixed-integer programming (MIP) model, incorporating tandem and parallel methods. It also introduces MathLNS, a matheuristic algorithm that combines MIP-based large neighborhood search with heuristic methods, as an efficient solution approach to this problem. The effectiveness of the proposed model and algorithm is evaluated using real-world data from one of the largest shipbuilders globally. The results demonstrate significant improvements over the planner-driven approach. Notably, the developed algorithm has been integrated into the company’s production planning system, resulting in improved dock planning efficiency. Furthermore, the proposed approach offers potential benefits for other shipbuilding companies, enhancing operational efficiency in dock planning.

Development of an Information System for Wood Harvesting Planning

The article presents the results of developing a software system for production planning in the field of wood harvesting. The objects of research are the planning of harvesting operations, forest transport, construction, repair and maintenance of forest roads, as well as solving various related problems. The purpose of the work is to develop a native specialized software system for production planning in the field of wood harvesting, which is integrated with existing accounting and monitoring systems and is based on solving complex optimization problems of large dimensions using original specially developed methods and algorithms that take into account all the features of wood harvesting production in the Russian Federation. As a result of this work, an approach to creating such а system was developed, which includes a set of requirements for the software product, as well as formulations of the main tasks to be solved, listing the necessary initial data and information obtained as an output. The proposed approach was implemented by Russian IT-company Opti-Soft in the form of software product Opti-Wood — a multipurpose Decision Support System (DSS) designed for the use at forestry enterprises as a tool for optimizing production planning, management and control. The Opti-Wood system was developed in close contact with a large wood harvesting enterprise in the Republic of Karelia. The system is based on original algorithms for solving several optimization problems of increased complexity. Using the system allows one to increase the efficiency of logging processes by reducing costs in supply chains, the volume of warehouse stocks, the number of required vehicles; by mutual agreement of plans for harvesting operations, forest transport, road construction, etc., as well as by significantly reducing the time for taking management decisions. Increased profitability occurs due to the identification of additional options for the sale of products.

The Use of Artificial Intelligence and Machine Learning in Forecasting the Financial Growth of Automobile Industries

The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.

Moving toward lean construction through automation of planning and control in last planner system: A systematic literature review

The Last Planner System (LPS) is a production planning and control system based on Lean Construction (LC) principles. Effectively implementing the LPS in the construction industry remains challenging, impeding the realization of LC principles. Automation of planning and control in the LPS has gained attention to address this challenge. However, research exploring the state-of-the-art of such automation and its impacts on realizing LC principles is still lacking. Therefore, this study performed a systematic literature review of 112 relevant studies, identifying 50 functionalities and associated benefits of automation across six LPS stages, with the lookahead planning stage having the maximum functionalities. Analyzing their interactions with LC principles revealed that automated planning and control in the LPS can support achieving ten LC principles. Future research directions are suggested in areas such as constraint management, knowledge reuse, dynamic replanning, and construction workforce training.

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.

Material flow control in make-to-stock production systems: an assessment of order generation, order release and production authorization by simulation

AbstractMaterial flow control (MFC) is a key element of production planning and control. The literature typically categorizes different MFC methods according to how MFC is realized. This distinction overlooks that MFC decisions can be subdivided into three independent tasks that are executed as orders progress through the system: (i) order generation, (ii) order release, and (iii) production authorization. MFC methods are typically designed for only one of these three tasks, which leaves a large part of the order flow uncontrolled. This study therefore not only provides a new categorization of MFC methods, but also argues for the simultaneous application (or the combining) of three different MFC methods for order generation, order release, and production authorization. To support this argument, the performance effects of an integrated MFC approach are evaluated. Findings show that each individual MFC method impacts different performance metrics, which can be explained by the presence of a hierarchy of workloads, where each workload level constrains the succeeding hierarchical level. Each MFC method has a main impact on a different workload. This has important implications for the design of MFC methods and extends recent literature on hierarchical production planning and control systems.

Supporting BIM-driven Factory Design by Engineering Data Management Capabilities

Nearly six thousand factory buildings requiring approval in Germany with a volume of almost 5 billion euros per year. According to recent studies, more than two-thirds of these buildings miss their cost targets by at least 10 percent. Building Information Modelling (BIM) promises considerable potential for optimization in the management of projects (e.g., through the targeted use of digital methods and technologies in project management) and through digital platform-based collaboration between stakeholders in building planning and production system planning in the early design and decision-making phases of engineering. This is where the research project "Holistically plan, build, and operate factories with Building Information Modeling (FaBIM)" comes in. The article presents an IT architecture concept based on a design reference process developed in the research project by experts from construction and factory planning. This supports integrated production system and building planning. The concrete application of an integrated BIM-driven data environment approach along the design reference process is demonstrated using the example of a battery cell factory and platform technology from CONTACT Software.

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.

Towards Logistics 4.0: A Skill-Based OPC UA Communication between WMS and the PLC of an Automated Storage and Retrieval System

In order to bring intralogistics systems to the same level of interoperability as today’s modern production systems, logistics must take the essential steps towards Industry 4.0. This requires an increasing abstraction level of control logic as an enabler for horizontal and vertical integration. The abstraction will lead to the interconnection ofmanufacturing and logistics control with the production planning and warehouse management systems (WMS). A main enabler for these communication paths are service-oriented architectures (SoA). OPC UA has established itself as a widely used and already adopted SoA-based communication standard in industry. The paper describes the realization of an OPC UAbased approach for the communication between a WMS and a PLC of an automated storage and retrieval system (ASRS). The conceptual basis of communication design are skills of the ASRS. The work is supported by an architectural design with a subsequent prototypical implementation.

Re-design of the production planning and control system in the food factory

This scientific article proposes a re-design of the production planning and control system in a food factory with the aim of improving the efficiency and effectiveness of this system. To this end, a thorough analysis of current production planning and control processes is carried out, identifying their limitations and proposing solutions based on best practices and modern production management approaches. The methodology used includes the review of the existing scientific literature, the collection of internal data and the implementation of a prototype of the redesigned system. The results obtained demonstrate a significant improvement in the efficiency and responsiveness of the system, which translates into an increase in productivity and a reduction in operating costs. In conclusion, the re-design of the production planning and control system in the food factory is shown as an effective strategy to improve the competitiveness and profitability of the company.

Designing the process of implementing step three of the theory of constraints in a make-to-order environment: Integrating sales and operation planning

Purpose: The theory of constraints (TOC) methodology and its drum-buffer-rope (DBR) production planning and control system are well suited to managing production plants in complex environments. The objective of this study was to design an evolution of the systematic process for implementing the third step of the TOC methodology in make-to-order environments.Design/methodology/approach: Since the research concerned a real context and the phenomenon under investigation is contemporary, a case study was chosen as the research methodology.Findings: The study investigated, through a case study, the phases and steps necessary for the systematic process to be successfully implemented in a make-to-order environment.Originality/value: The three main contributions to the systematic implementation process for the third step of the TOC model are identified as the design of the last version of the systematic process, the integration of sales and operations through the TOC methodology and the introduction of the demand-driven adaptive enterprise model’s capacity buffer.

Considerations on Utilizing the Object-Oriented Paradigm in the Development of an MRP-based Production Planning SystemConsiderations on Utilizing the Object-Oriented Paradigm in the Development of an MRP-based Production Planning System

Manufacturing companies today face an increasingly dynamic environment. Development of production planning systems requires approaches that offer flexibility in development of solutions. Applying the UML (Unified Modeling Language) and MDA (Model Driven Architecture) paradigm can ensure compatibility between applications developed on different platforms. The MDA approach to software development becomes an obvious choice. In this approach, the models drive the process of software development. These models are defined at different levels of abstraction to represent various aspects of the system. The transformation of models from one level of abstraction to another, or the transformation of models to executable code is performed by using automated transformation tools. The strength of MDA lies in the fact that it is based on widely used industry standards for visualizing, storing and exchanging software designs and models. The models in MDA are abstracted at three different levels – the Computation Independent Model (CIM), the Platform Independent Model (PIM) and the Platform Specific Model (PSM). The key to the success of MDA lies in automated or semi-automated model-to-model and model-to-code transformations. In this paper, we examine how a platform-independent model of an MRP-based production planning system can be created using MDA approach, from which several platform-specific systems can be achieved, as required.

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.

Process Re-design and Automation Using Enterprise Resource Planning System for Manufacturing Industry

In the era of Industry 4.0, automation is the backbone for any industry to grow and sustain. Many studies suggest that ERP system helps industries to integrate and automate most of the business processes, enhance production efficiency and financial performance. However, the manufacturing industries with make-to-order business model are struggling with production planning and control systems. The aim of this research is to provide solution for overcoming the problems of late order deliveries, cost overruns and limitation of communications and information flow associated with production planning and control in manufacturing industries. A qualitative case study in a steel parts manufacturing industry is conducted to understand and analyze the prevailing problems and processes. Based on observations, a business model is proposed by re-designing according to standard practices of ERP system, then ERP System is selected and designed according to the case industry requirements. This paper mainly focuses on implementation of the Manufacturing module. To verify and analyze the impact of automation, data is collected from case industry for implementation and execution in ERP System. After implementation, significant reduction of production wastages in terms of paperwork, time and error is observed, saving the costs and time related to these wastages. Hence, it is found that ERP System contribute in improving production planning and control system for case industry by ensuring timely delivery and cost control due to better production planning and monitoring and provide real-time visibility of data and information, enhancing business performance.

Redesigning industrial composite production planning and control for energy awareness

The European Union's requirement for eliminating greenhouse gas emissions from all value chains by 2050 affects most of the manufacturing activities all over the continent. The industrial production of composite parts, favored for new designs throughout the aerospace industry, does not constitute an exemption. For securing global competitiveness, CFRP manufacturers need to adapt to increasing fluctuations in energy supply and prices, induced by a growing share of renewable energy sources. Besides employing more efficient production technologies, decoupling of labor and energy intensive tasks will play a key part. Thereby, energy intensive tasks can be performed while general energy supply is high and demand is low, leading to comparatively lower prices. The perspective of energy supply and price becomes a crucial part in the design of modern production planning and control systems, not only for smokestack businesses, but for small and medium load energy consumers as well. In this contribution, a mathematical representation for modeling industrial scale composite manufacturing is proposed. Beside manufacturing's fundamental production planning and control constraints, CRFP manufacturing's very own peculiarities are outlined and considered as well. It is shown how the virtual mathematical representation of a CFRP manufacturing environment can assist in its energy aware redesign and optimization.

Cloud vs. peer-to-peer manufacturing: a comparative case study

Cloud Manufacturing (CM) applies the concept of manufacturing-as-a-service and offers access to production resources over web-based platforms. Besides centralized CM platforms, the manufacturing-as-a-service principle has been proposed to be integrated in production planning and control systems, creating a decentralized peer-to-peer (P2P) manufacturing paradigma. This paper applies the case study methodology to deduce procurement processes via cloud or P2P manufacturing and to compare them in regard of cost, quality and time. It can be shown that decentralized approaches can outperform centralized CM implementations and that a higher degree of integration into the office floor is mostly beneficial.

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.