Distributed Manufacturing Environment Research Articles

A self-adaptive Gaussian mutation-based arithmetic optimiser algorithm for integrated production scheduling and vehicle routing problem in the distributed manufacturing environment

ABSTRACT In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method.

Trusted Data Acquisition in Microelectronics Manufacturing as a Basis for ML-Optimized Processing

In the digitization of manufacturing processes, one of the major goals is the connection of production facilities and the use of data to digitize business processes. In order to optimize manufacturing processes and maximize the quality of the resulting product, further process information and data directly from the work piece and from the manufacturing environment are required to achieve a holistic system view in addition to the selected data that the manufacturing systems already provide. Within the SiEvEI 4.0 project, a research consortium from industry and academia works on process digitization for a manufacturing scenario where high value electronic goods are built in a distributed manufacturing environment. The key research topics addressed are the implementation of a Chain of Trust [CoT] for trustworthy distributed manufacturing and the application of artificial intelligence/machine learning to analyze and eventually optimize manufacturing processes. The basic concepts of this approach have been presented at IMAPS 2021. As an update, this paper reports on the actual experimental evaluation of these concepts in two different assembly lines, including data acquisition, data handling and AI processing with the goal to optimize processing targeting higher production yield and product quality. Specific for this work is the focus on high mix/low volume SMD assembly using fully automated equipment and Solder Ball Application. As a result, the paper presents the experimental validation of the manufacturing process digitization and the use this digital description of a process combination to make a distributed manufacturing flow safe and increase product/process quality.

An exact algorithm for unpaired pickup and delivery vehicle routing problem with multiple commodities and multiple visits

This paper addresses a new multi-commodity unpaired pickup and delivery vehicle routing problem in which each vehicle is allowed to visit each customer more than once for both pickup and delivery of each commodity. It is a complex variant of the classical capacitated vehicle routing problem and comes from resource transfer among different plants in a distributed manufacturing environment. Notably, the pairing decisions of pickup and delivery for each commodity and the routing decisions among customers are interdependent, and they need to be made simultaneously and collaboratively. Firstly, a mixed integer programming model is developed, and then new valid inequalities are derived to improve the linear programming relaxation. Afterwards, a branch-and-cut algorithm is proposed by exploring different branching strategies and tailored separation algorithms. Computational results based on real-case instances from an automobile manufacturing company demonstrate that the costs are considerably reduced by considering the multiple visits compared to the single visit of each customer. In addition, our proposed branch-and-cut algorithm outperforms a manual solution method adopted by the company in practice and two solution methods in the literature. Furthermore, two adaptations of our branch-and-cut algorithm also perform better than two state-of-the-art algorithms in solving two closely-related problems in terms of solution quality and computation time. Sensitivity analyses with respect to vehicle capacity, vehicle duration, and vehicle speed are conducted to derive several valuable managerial insights.

Modeling and Scheduling a Constrained Flowshop in Distributed Manufacturing Environments

This paper addresses a constrained distributed flowshop scheduling problem (CDFSP) that widely exists in modern manufacturing industry but has not been investigated before. Different from the distributed flowshop scheduling problem (DFSP) in the literature, CDFSP considers that all jobs are grouped for processing efficiently, and part of the groups subject to production requirements have a common deadline while the others do not have such a constraint. This constraint not only increases the complexity of the problem but also makes many solutions generated by the existing algorithms infeasible. Therefore, in this paper, a mathematical model is first established for the CDFSP with makespan criterion. After that, we explore the problem-specific knowledge to exclude infeasible solutions, and provide an acceleration method to evaluate feasible solutions in the insertion neighborhood. By automatically selecting parameters to match different search stages, a self-adaptive cooperative iterated greedy algorithm is designed where re-insertion and re-destruction mechanisms are integrated to address the infeasible solutions caused by a paradox and greedy mechanism. A cooperative mechanism is proposed to strengthen the coordination between family scheduling and job scheduling. Finally, the comprehensive experiments verify the effectiveness of the proposed algorithmic components and self-adaptive mechanism, and demonstrate the significant superiority of the proposed algorithm over five state-of-the-art metaheuristics.

Decentralised Persistent Identification - an Emerging Technology for Sustainability Maintenance and Knowledge-driven Processes

In the realm of industrial automation and control, the advent of Decentralized Persistent Identifiers (dPID) presents a novel paradigm for enhancing data identification and management. Traditional centralized and federated identification systems often struggle with scalability, resilience, and interoperability challenges, particularly in complex, distributed manufacturing environments. This paper explores the potential of dPID technology to address these issues by aligning with the FAIR principles—findability, accessibility, interoperability, and reusability. We introduce dPID as a transformative solution for the persistent identification, versioning, storage, and management of assets within modern industrial settings. Through dPID, assets such as machines and equipment gain unique, immutable identifiers, facilitating improved tracking, maintenance, and operational efficiency. We present scenarios demonstrating dPID’s application in asset management, from real-time tracking and preventive maintenance to enhancing supply chain transparency and enabling seamless interoperability across diverse systems and platforms. By leveraging decentralized technologies, dPID offers a promising approach to achieving a more resilient, efficient, and transparent industrial ecosystem.

Leveraging Blockchain to Support Collaborative Distributed Manufacturing Scheduling

The recent trend in collaborative distributed manufacturing scheduling (CDMS) has gained significant importance in extended, networked, and virtual manufacturing environments due to its adaptability and integration potential. In a distributed manufacturing environment, CDMS can occur within a single factory or across multiple companies in a dynamic and variable extended or virtual organization. For effective collaboration, the CDMS system must be secure, transparent, and trustworthy. This paper proposes a blockchain-based model for CDMS and discusses its implementation in the processing of manufacturing functions, specifically joint process planning and scheduling. An illustrative example is used to demonstrate the application of the proposed approach and its potential to enhance the management processes of CDMS enterprises. The results of the analysis indicate that the proposed blockchain approach can effectively facilitate communication and integration among CDMS enterprises. Additionally, the approach can be expanded to more complex manufacturing environments under different conditions.

Unrelated parallel machine scheduling with eligibility constraints and delivery times to minimize total weighted tardiness

This paper addresses an unrelated parallel machine scheduling problem with job-machine-dependent delivery times and eligibility constraints motivated by a distributed manufacturing environment. The objective is total weighted tardiness minimization. We present a mixed integer linear programming (MILP) formulation and conduct an analysis of the scheduling problem to derive precedence properties that can be integrated in local search procedures to increase computational efficiency. We implement a variable neighborhood search (VNS) algorithm for the problem at hand and examine the effect of integrating the properties on the performance. The tests show that the theoretical findings can reduce computational effort significantly. Furthermore, we propose another heuristic approach based on the Apparent Tardiness Cost rule and a memetic biased random-key genetic algorithm. In experiments, we compare the MILP and (meta-)heuristics on a large set of randomly generated problem instances. The VNS procedure outperforms the other algorithms.

Advances in Adaptive Scheduling in Industry 4.0

The shift of traditional mass-producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. Moreover, manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands, which increases resource flexibility, lies in the digitization of the manufacturing systems. Furthermore, the distributed manufacturing environment and the ever-increasing product variety and complexity result in reduced time-to market, ubiquitous data access and sharing and adaptability and responsiveness to changes. These requirements can be achieved through smart manufacturing tools and especially Wireless Sensor Networks (WSN). Thus, the aim of this position paper is to summarize the design and development of solutions based on cutting-edge technologies such as Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), Simulation, 5G, and so on. Concretely, the first part discusses the development of a Cloud-based production planning and control system for discrete manufacturing environments. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a “bucket-less” manufacturing environment. Then, an open and interoperable Internet of Things platform is discussed, which is enhanced by innovative tools and methods that transform them into Cyber-Physical Systems (CPS), supporting smart customized shopping, through gathering customers’ requirements, adaptive production, and logistics of vending machines replenishment and Internet of Things and Wireless Sensor Networks for Smart Manufacturing. To that end, all the proposed methodologies are validated using data derived from Computer Numerical Control (CNC) machine building industry, from European Metal-cutting and mold-making SMEs, from white goods industry and SMEs that produces solar panels.

Concurrent local search for process planning and scheduling in the industrial Internet-of-Things environment

Process planning and scheduling is one of the key factors to influence manufacturing quality and efficiency. The Industrial Internet-of-Things is built up to enable intelligent sharing of distributed manufacturing equipment, and more processes and resources are available for a production order. Thus, compared to the traditional single workshop manufacturing environment, process planning and scheduling is extended to a more complex problem of finding reasonable quoted suppliers, selecting suitable processes they offered, sequencing the operations of each process, and assigning resources for these operations to minimize production cost and time. This paper re-models process planning and scheduling in the Industrial Internet-of-Things environment to minimize the overall production time, rental cost, and transportation cost of an order that includes several interconnected jobs. A concurrent local search method is proposed to address this problem. It includes two types of local search operators performing concurrently with an evolutionary operator. An adjustment strategy is also proposed to leverage the concurrent operators toward diverse Pareto-front. A case study on the production of three different parts of an automobile engine demonstrates that the proposed method is able to find high-quality solutions in such a distributed manufacturing environment than four typical multi-objective evolutionary algorithms to save 1.3% to 5.0% of production time, 5.6% to 11.8% rental cost, and 3.2% to 8.2% transportation cost in average.

자동차 부품 생산라인을 위한 CPS 플랫폼과 디지털트윈 개발 및 적용

Nowadays, many manufacturing companies design, engineer, and produce their products through a globally distributed supply chain. In the automotive industry, globalization of the supply chain is expanding and becoming more complicated under increasing global competition and demand for reduction in production costs. Under such circumstances, automotive parts manufacturers are making efforts to implement smart manufacturing (SM) system that ensures the production of products with reasonable prices and high quality, while meeting the needs of various customers. To implement SM, a production line must be smart based on data from manufacturing sites through the application and convergence of technologies such as cyber-physical system(CPS) and digital twin. With the application of such technologies, it is possible to achieve faster and more accurate decision-making and more efficient utilization of manufacturing resources. In addition, a CPS-based integrated platform is needed to link and integrate data with the distributed manufacturing environment composed of heterogeneous facilities. By utilizing this, it is possible to implement and operate a production system that can flexibly respond to various external changes. In this paper, we propose a methodology for design, implementation and application of CPS platform-based digital twin that meets global standards for SM of global automotive parts manufacturers.

Implementation of Trusted Manufacturing & AI-based process optimization into microelectronic manufacturing research environments

Abstract Digitization is one of the hot topics in all Industry 4.0 efforts that are currently discussed. Often the focus is on digitization of business processes with a financial/organizational perspective on manufacturing, so the tools are adapting to enterprise resource planning [ERP] and manufacturing execution system [MES] rather than on actual manufacturing issues on the shop floor. Within the SiEvEI 4.0 project, a research consortium from the area of electronics manufacturing is working on digitization for a manufacturing scenario where high value electronic goods are built in a distributed manufacturing environment. The key research topics addressed are the implementation of a Chain of Trust [CoT] for such a distributed manufacturing, i.e. and the application of artificial intelligence/machine learning to analyze and eventually optimize manufacturing processes. The paper will introduce the concept of both COT and AI-based process analysis that will later on transferred into a microelectronics production environment. Two reference processes are targeted, SMD assembly using fully automated manufacturing equipment and Solder Ball Application using a high-mix/low volume concept. As a result, the paper presents a concept of how to digitize manufacturing processes and use this digital description of a process combination to make a distributed manufacturing flow safe and increase product/process quality.

Research on Collaborative Order Decision-Making Method for Symmetric Participants in Complex Shipbuilding Projects

More extensive enterprise cooperation is an effective means to increase the competitiveness of shipbuilding companies in the current distributed manufacturing environment. Most cross-enterprise collaborative processes for shipbuilding projects have been widely concerned. However, the symmetry and cooperativity among the order decision-making process is rarely involved. A key issue for decision makers is to balance the interests of each symmetric participant and realize the consistent decision-making for the order. Existing order decision-making methods in the shipbuilding industry are low efficiency. The aim of this paper is to provide an assistant decision-making method to support effective order decision-making and multi-party cooperation for the multi-wining negotiation objectives. To solve this problem, a collaborative order decision-making framework based on decision support system (DSS) and multi-agent system (MAS) theory is presented, simulating the collaborative order decision-making process, and bridging the order decision-making with production scheduling. Then, a multi-stage negotiation method is provided to solve the distributed and symmetric order decision-making problem, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system will be reported as a result.

An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion

The distributed blocking flowshop scheduling problem (DBFSP) plays an essential role in the manufacturing industry and has been proven to be as a strongly NP-hard problem. In this paper, an ensemble discrete differential evolution (EDE) algorithm is proposed to solve the blocking flowshop scheduling problem with the minimization of the makespan in the distributed manufacturing environment. In the EDE algorithm, the candidates are represented as discrete job permutations. Two heuristics method and one random strategy are integrated to provide a set of desirable initial solution for the distributed environment. The front delay, blocking time and idle time are considered in these heuristics methods. The mutation, crossover and selection operators are redesigned to assist the EDE algorithm to execute in the discrete domain. Meanwhile, an elitist retain strategy is introduced into the framework of EDE algorithm to balance the exploitation and exploration ability of the EDE algorithm. The parameters of the EDE algorithm are calibrated by the design of experiments (DOE) method. The computational results and comparisons demonstrated the efficiency and effectiveness of the EDE algorithm for the distributed blocking flowshop scheduling problem.

Research on Collaborative Planning and Symmetric Scheduling for Parallel Shipbuilding Projects in the Open Distributed Manufacturing Environment

In the current distributed manufacturing environment, more extensive enterprise cooperation is an effective means for shipbuilding companies to increase the competitiveness. However, considering the project scale and the uneven production capacity between the collaborative enterprises, a key issue for shipbuilding companies is to effectively combine the product-oriented project tasks and the specialized production-oriented plants. Due to information privatization, the decision-making process of project planning and scheduling is distributed and symmetric. Existing project scheduling methods and collaboration mechanisms in the shipbuilding industry are somehow inefficient. The aim of the research is to provide an assistant decision-making method to support effective task dispatching and multi-party cooperation for better utilization of the distributed resources and to help project managers control the shipbuilding process. The article initially establishes an agent-based complex shipbuilding project collaborative planning and symmetric scheduling framework, simulating the distributed collaborative decision-making process and bridging the multi-project planning with the individual project scheduling in much detail, which fills the research gap. A negotiation method based on iterative combination auction (ICA) is further proposed to solve the integration problem of project planning and task scheduling, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system on shipbuilding projects collaborative planning and scheduling will be reported as a result.

Development of a Digital Architecture for Distributed CNC Machine Health Monitoring

Abstract With the advancement of the Internet of Things, machines are expected to become more connected by transmitting operational data in ways that can readily be consumed by analytics engines. In recent years, a substantial amount of research has been conducted to create techniques of acquiring and utilizing machine data to inform supply chain decisions and monitor machine health. In modern manufacturing equipment, standard interfaces such as MTConnect and Open Platform Communications–Unified Architecture facilitate the data acquisition process by providing a means of easily capturing machine controller data. With data from these interfaces, monitoring quantities such as machine utilization are possible by monitoring the MTConnect interface and performing analysis on the machine state. In a distributed manufacturing environment, a means of remotely accessing this data must be developed. The infrastructure responsible for this data transmission must be secure, scalable, and standardized. If detailed machine health metrics are to be analyzed, the infrastructure should be able to accommodate data from sources beyond the machine controller itself. Supplementing the controller data with that from additional sensors, such as accelerometers, temperature, humidity, and current sensors, a more complete picture of machine health status can be created. This article will present a digital architecture that meets these criteria and demonstrate the use cases in machine utilization and health monitoring.

Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm

This study attempts to optimize the scheduling decision to save production cost (e.g., energy consumption) in a distributed manufacturing environment that comprises multiple distributed factories and where each factory has one flow shop with blocking constraints. A new scheduling optimization model is developed based on a discrete fruit fly optimization algorithm (DFOA). In this new evolutionary optimization method, three heuristic methods were proposed to initialize the DFOA model with good quality and diversity. In the smell-based search phase of DFOA, four neighborhood structures according to factory reassignment and job sequencing adjustment were designed to help explore a larger solution space. Furthermore, two local search methods were incorporated into the framework of variable neighborhood descent (VND) to enhance exploitation. In the vision-based search phase, an effective update criterion was developed. Hence, the proposed DFOA has a large probability to find an optimal solution to the scheduling optimization problem. Experimental validation was performed to evaluate the effectiveness of the proposed initialization schemes, neighborhood strategy, and local search methods. Additionally, the proposed DFOA was compared with well-known heuristics and metaheuristics on small-scale and large-scale test instances. The analysis results demonstrate that the search and optimization ability of the proposed DFOA is superior to well-known algorithms on precision and convergence.

Integrated process planning and scheduling in networked manufacturing systems for I4.0: a review and framework proposal

Integrated process planning and scheduling in networked manufacturing systems plays a crucial role nowadays and in the forthcoming context of Industry 4.0 to enable effective and efficient decisions, and to improve the business market, based on collaboration, along with computer-based distributed manufacturing and management functions. In this paper some insights regarding a literature review carried out about this main subjects analysed are presented and discussed. Moreover, a framework for integrated process planning and scheduling in networked manufacturing systems is proposed and briefly described, along with some main underlying issues, which are further discussed. Thus, the main purpose of this research consists on presenting a proposed methodology, based on the study conducted, to enable to further assist either academia or industry to develop new tools, techniques and approaches for integrated process planning in networked manufacturing environments. The findings and contributions of this research can help in the implementation and improvement in distributed manufacturing environments, to be linked with small and medium enterprises, to further expand their potentialities through well suited integrated process planning and scheduling decision making processes.

A novel dynamic assignment rule for the distributed job shop scheduling problem using a hybrid ant-based algorithm

Distributed scheduling problems are among the most investigated research topics in the fields of Operational Research, and represents one of the greatest challenges faced by industrialists and researchers today. The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories and with determining the sequence of operations on each machine in distributed manufacturing environments. The objective is to minimize the global makespan over all the factories. Since the problem is NP-hard to solve, one option to cope with this intractability is to use an approximation algorithm that guarantees near-optimal solutions quickly. Ant based algorithm has proved to be very effective and efficient in numerous scheduling problems, such as permutation flow shop scheduling, flexible job shop scheduling problems and network scheduling, etc. This paper proposes a hybrid ant colony algorithm combined with local search to solve the Distributed Job shop Scheduling Problem. A novel dynamic assignment rule of jobs to factories is also proposed. Furthermore, the Taguchi method for robust design is adopted for finding the optimum combination of parameters of the ant-based algorithm. To validate the performance of the proposed algorithm, intensive experiments are carried out on 480 large instances derived from well-known classical job-shop scheduling benchmarks. Also, we show that our algorithm can process up to 10 factories. The results prove the efficiency of the proposed algorithm in comparison with others.

The FaaS system using additive manufacturing for personalized production

PurposeIn this paper, a three-dimensional (3D) printer-based manufacturing line and supporting system, which supports personalized/customized manufacturing for individual businesses or start-up companies, was studied to evaluate the practicality of using additive manufacturing for personalization/mass customization.Design/methodology/approachFirst, factory-as-a-service (FaaS) system, which provides factory as a service to customers, was proposed and designed to manufacture various products within a distributed manufacturing environment. This system includes 3D printer-based material extrusion processes, vapor machine/computer numerical control machines as post-processes and assembly and inspection processes with an automated material handling robot in the factory. Second, a virtualization module for the FaaS factory was developed using a simulation model interfaced with a cloud-based order and production-planning system and an internet-of-things-based control and monitoring system. This is part of the system for manufacturing operations, which is capable of dynamic scheduling in a distributed manufacturing environment. In addition, simulation-based virtual production was conducted to verify and evaluate the FaaS factory for the target production scenario. Main information of the simulation also has been identified and included in the virtualization module. Finally, the established system was applied in a sample production scenario to evaluate its practicality and efficiency.FindingsAdditive manufacturing is a reliable, feasible and applicable technology, and it can be a core element in smart manufacturing and the realization of personalization/mass customization.Originality/valueVarious studies on additive manufacturing have been conducted with regard to replacing the existing manufacturing methods or integrating with them, but these studies mostly focused on materials or types of additive manufacturing, with few advanced or applied studies on the establishment of a new manufacturing environment for personalization/mass customization.

Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion

This paper deals with a distributed blocking flowshop scheduling problem, which tries to solve the blocking flowshop scheduling in distributed manufacturing environment. The optimization objective is to find a suitable schedule, consisting of assigning jobs to at least two factories and sequencing the jobs assigned to each factory, to make the maximum completion time or makespan minimization. Two different mathematical models are proposed, and in view of the NP-hardness of the problem, a novel hybrid discrete differential evolution (DDE) algorithm is established. First, the problem solution is represented as several job permutations, each of which denotes the partial schedule at a certain factory. Second, four widely applied heuristics are generalized to the distributed environment for providing better initial solutions. Third, both operators of mutation and crossover are redesigned to perform the DDE directly based on the discrete permutations, and a biased section operator is used to increase the diversity of the searching information. Meanwhile, an effective local search based on distributed characteristics and an elitist retain strategy are integrated into the DDE framework to stress both local exploitation and global exploration. Taking into account the time cost, an effective speed-up technique is designed to enhance the algorithmic efficiency. In the experimental section, the parameters of DDE are calibrated by the Taguchi method. Experimental results derived from a wealth of test instances have demonstrated the algorithmic effectiveness, which further concludes that the proposed DDE algorithm is a suitable alternative approach for solving the problem under consideration.