Shop Floor Control Research Articles

A modular factory testbed for the rapid reconfiguration of manufacturing systems

The recent manufacturing trend toward mass customization and further personalization of products requires factories to be smarter than ever before in order to: (1) quickly respond to customer requirements, (2) resiliently retool machinery and adjust operational parameters for unforeseen system failures and product quality problems, and (3) retrofit old systems with upcoming new technologies. Furthermore, product lifecycles are becoming shorter due to unbounded and unpredictable customer requirements, thereby requiring reconfigurable and versatile manufacturing systems that underpin the basic building blocks of smart factories. This study introduces a modular factory testbed, emphasizing transformability and modularity under a distributed shop-floor control architecture. The main technologies and methods, being developed and verified through the testbed, are presented from the four aspects of rapid factory transformation: self-layout recognition, rapid workstation and robot reprogramming, inter-layer information sharing, and configurable software for shop-floor monitoring.

Agent-based Control System: A Review and Platform for Reconfigurable Bending Press Machine

Abstract Agent paradigm is a technology recognized as potent tool for the 21st century manufacturing system. Researches are ongoing in the applications of agent technology to manufacturing enterprise, production process planning and scheduling, shop floor control, and to reconfigurable manufacturing system as an implementation case study. The article presents the results of a review of published articles on agents’ usage at manufacturing enterprise level for reconfigurable manufacturing system between year 2008 and 2018 to provide up to date pictures of the technological function of agents and multi agents within the reconfigurable conceptualized manufacturing circles. The paper is view from the angle of wide coverage, broad scope of multi-agent techniques and a focus on real world applications. The outcomes include behavioral pattern and trends of agents and multi-agent’s usage in manufacturing enterprise resource planning and supply chain managements, the gaps yet to be fill for consolidating future on industry 4.0 in reconfigurable manufacturing system, and development of mobile apps interacting platform for IoT real time database communication and coordination for RBPM controller agents.

An Integrated Solution to Minimize the Energy Consumption of a Resource-Constrained Machining System

Going “energy efficient” has been one of the missions for manufacturers to stay globally competitive. Considering machining as a major manufacturing activity, how to effectively model and control its energy consumption becomes critical. Although researchers have analyzed the energy consumption of machining from the machine, process planning, or shop-floor perspective individually, very litter work has comprehensively studied the concurrent interactions among energy-aware decisions for machine parameter settings, process planning, and shop-floor control. Hence, the work presented in this article undertakes this challenge in the context of a resource-constrained machining system. In particular, the energy characteristics of machining are first analyzed with the consideration of various machine tools, cutting tools, cutting parameters, operation sequences, as well as machine availability. A multiobjective optimization model is then developed to minimize both energy consumption and makespan. The solution is provided through honey bee mating optimization algorithm (HBMOA) combined with shop-floor simulation. In addition, the significance of the proposed approach is exemplified and elucidated by a case study. Note to Practitioners —A well-informed decision made in cutting parameter optimization or process planning relies heavily on the accurate data delivered from the shop floor. In other words, the decision made without the consideration of the shop-floor situation might not achieve the original goal or even fail to materialize. As these factors exist in a real manufacturing cycle, how to integrate shop-floor scheduling with process planning and machining parameter optimization becomes essential for energy-efficient manufacturing. This article undertakes this challenge and develops a multiobjective optimization model, where energy consumption of machining, for the first time to the best of our knowledge, is comprehensively analyzed at both the machine level and shop-floor level. Such broader integration makes this approach more practical and applicable to real industry settings, particularly when the shop-floor resource is limited.

Evaluating the advantages of a novel decentralised scheduling approach in the Industry 4.0 and Cloud Manufacturing era

In the last year, the growing globalisation process has led to a radical change in the production concepts for which it is desirable to create value pursuing the customer need, pointing to provide more personalised product and services. At the same time, the rapid development of the Information Technologies (IT) enabled the creation of new manufacturing paradigm in which every machine is interconnected to each other: the Industry 4.0 and the Cloud Manufacturing. In this regard, this paper introduces a novel decentralised scheduling approach, starting to explore the available possibilities from these manufacturing paradigms. To this extent, a low-level shop floor controller of a Flow-Shop CONWIP production line with a predefined well-defined decision-making quote is introduced. This controller, composed of two newly logic elements to be placed in the production line upstream, is able to efficiently allocate the job within a predefined queue dynamically, following a proper dispatching rule. After the introduction of two different dispatching rule, we developed a hybrid simulation tool, validated on a reference model from the scientific literature, to evaluate the advantages of the introduced decentralised scheduling approach compared to a reference one. The results showed that the proposed low-level controller might lead to an important productivity increase without changing the manufacturing production line working parameter.

Scheduling an assembly job shop production system with multiple objectives: a simulation study

This paper presents the salient aspects of the performance analysis of two mechanisms for shop floor control namely, order review/release (ORR) and dispatching rules in an assembly job shop system in a multi-objective environment. A simulation model of an assembly job shop is developed for experimentation with eight dispatching rules and four ORR mechanisms from the literature. An ORR policy based on the concept of backward scheduling and interval release is developed and incorporated in the simulation model. Grey relational analysis is used for ranking the ORR policy-dispatching rule combinations. Multivariate and univariate hypotheses are developed. The results are analysed using ANOVA and MANOVA. The analysis of results reveals that integrating dispatching rules with ORR policies provides better system performance than using dispatching rules alone. The proposed ORR policy namely, backward scheduling with interval release policy provides better results than other ORR polices.

Scheduling an assembly job shop production system with multiple objectives: a simulation study

This paper presents the salient aspects of the performance analysis of two mechanisms for shop floor control namely, order review/release (ORR) and dispatching rules in an assembly job shop system in a multi-objective environment. A simulation model of an assembly job shop is developed for experimentation with eight dispatching rules and four ORR mechanisms from the literature. An ORR policy based on the concept of backward scheduling and interval release is developed and incorporated in the simulation model. Grey relational analysis is used for ranking the ORR policy-dispatching rule combinations. Multivariate and univariate hypotheses are developed. The results are analysed using ANOVA and MANOVA. The analysis of results reveals that integrating dispatching rules with ORR policies provides better system performance than using dispatching rules alone. The proposed ORR policy namely, backward scheduling with interval release policy provides better results than other ORR polices.

Verification of ERP Standard Time Using TOC Technique and Improvement of MES Routing Point

Recently domestic manufacturing companies have been experiencing worsening profitability and stunted growth due to the long-term economic recession and the rapid rise of developing countries such as China and Southeast Asia. These difficulties force many companies to concentrate their core competencies on new value creation and innovation in order to gain momentum for new growth. Enterprise Resource Planning (ERP) has been considered as one of viable solutions. Among the various modules in ERP, shop floor control function in the production management module is rather limited. In order to overcome this problem, Manufacturing Execution System (MES) has been used as a subsystem which has a strong information gathering power and flexibility. Both systems interact closely with each other. In particular, ERP requires fast, accurate shop floor information at MES. This paper describes how to synchronize relevant information between ERP and MES with theory of constraints (TOC). The processing time information transmitted from the MES workplace is received at the ERP workplace. In the process, the received processing time is causing information distortion in ERP, when the information gathering standard of MES is different from the ERP information interpretation standard. The Drum-Buffer-Rope theory of TOC was applied to resolve this problem, therefore, information synchronization between both systems was made. As a precondition, the standard time of the upper ERP system was rearranged according to the capacity constraints resource. As a result, standard time restructuring has affected changes in labor costs. Standard labor costs have come close to actual ones, and information synchronization of MES transmission data has improved the reliability of standard product costs, such that it enabled various company-wide restructuring actions to be much more effective.

Mathematical models and an elephant herding optimization for multiprocessor-task flexible flow shop scheduling problems in the manufacturing resource planning (MRPII) system

Shop floor control (SFC) is one of the main concepts in manufacturing resource planning (MRPII) and production scheduling is a key element in SFC. This paper studies the hybrid flow shop scheduling problem where jobs are multiprocessor. The objective is to minimize total completion time. Although there are several papers considering hybrid flow-shop scheduling problem with multiprocessor tasks, but none propose a mathematical model for this problem. At first, the two problems (fixed and selective cases) are mathematically formulated by mixed integer linear programming models. Using commercial software, the model is used to solve the small instances of the problems. Moreover, an elephant herding optimization is developed to solve large instances of the problems. To numerically evaluate the proposed algorithm, it is compared with two available algorithms in the literature, simulated annealing and shuffled frog-leaping algorithm in the literature.

A unified index for proactive shop floor control

Unpredictability of performance under the influence of various uncertainties makes decision-making a complex task on the shop floor. A measure which can indicate the expected shop floor scenario over a projected timeline can be an effective hands-on tool for a shop floor decision maker. The current work proposes three measures, namely complexity, penalty, and desirability of a shop floor situation and aggregates them into a single unified index (UI). Given an indicative list of uncertainties and the prevailing shop floor situation, a periodic simulation approach is presented to observe the changes in the UI. The extent of change in the UI, both in terms of magnitude and direction, is used to identify those significant environmental variables which need intervention. Developing a proactive control measure from the perspective of a decision maker on the shop floor, demonstration of CRITIC (Criteria Importance through Intercriteria Correlation)-based automated decision-making mechanism in a manufacturing setup and a simulation approach to aid the decision-making process are the highlights of the current work. The results reported are with reference to a high-pressure die casting unit.

A framework for shopfloor material delivery based on real-time manufacturing big data

Although auto-identification devices such as radio frequency identification and smart sensors have been widely used in shopfloor management and control, major challenges still hinder the vision of real-time and multi-source data-driven material delivery in a manufacturing big data environment. For instance, how to collect manufacturing big data in a timely and accurate manner, and how to discover the hidden pattern from the manufacturing big data rapidly to improve the efficiency of material delivery. To address these challenges, in this paper, a framework for shopfloor material delivery based on real-time manufacturing big data is proposed. Key technologies of the proposed framework are investigated. Firstly, a solution of data sensing and acquisition is designed. Secondly, the methods of manufacturing big data preprocessing and storage are developed to integrate and share the manufacturing data in a unified data format, and to ensure the reusability of the data. Thirdly, a graphic model for the manufacturing big data mining is presented. An improved Apriori-based association analysis model is exploited to identify the frequency trajectories of material delivery. In order to demonstrate the implementation of the proposed framework, a proof-of-concept scenario is designed. The key findings and insights from the experimental results are summarized as managerial implications, which can guide manufacturers to make more informed decisions for the shopfloor management.

An Efficient Operations Management Strategy Using Fuzzy Logic Decision Making Based Shop Floor Control Technique

In any organization, shop floor control seems to be a vital and pivotal activity and effective shop floor control paves the way for cent percent production. Effective utilization of resources, proper production planning, and successful maintenance would always pave the way for high productivity which in turn fulfills the commitment to meet out the delivery schedule for customers. Hence it is understood that continuous and consistent shop floor management demands a serious focus that contributes to sound operations management. More developments have taken place in the recent decade in framing new methodologies and adopting new advanced techniques to control the shop floor activities of an organization. The purposes of our paper is to bring out a new model for shop floor control using the Fuzzy Logic Decision Taking technique to help the operations professionals to enable them for successful professional decision making in shop floor control. Multi-Criteria Decision Making (MCDM) Techniques have witnessed tremendous application in operations management. Our paper has successfully attempted to apply Fuzzy Logic Decision-Making Technique which is one among the various MCDM tools. The technique considers many factors like Production, Wastages, Breakdown or Idle Time etc and these factors are treated as criteria and upon using the tool, the Management Information System(MIS) reports generated after mathematical manipulation using Fuzzy Logic seems to be much useful for corporate decision making.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS. The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

Outsourcing in non-developed supplier markets: a lean thinking approach

The benefits organisations can gain from outsourcing include cost savings and higher levels of innovation. On the other hand, outsourcing drawbacks include potential operations and performance issues such as capability and capacity problems. Some large organisations are pursuing the practice of outsourcing, more specifically, targeting global outsourcing from developing and low-wage countries. However, the perception of expected cost saving benefits when outsourcing to low-wage countries does not always reflect reality. A number of companies have failed to achieve the expected cost savings in sourcing from low-wage countries due to operational-level capability. The objective of this study is to develop an extended evaluation method that takes into account operational-level influence factors, in addition to the procurement factors, in the context of non-developed supplier markets. Our extended evaluation method of potential suppliers addressed the paradigm of ‘standard industry recipes’, by encompassing operational-level influence factors and integrating supplier value stream analysis into the evaluation and selection process of outsourcing. A survey was distributed to 369 international manufacturers. Operational levels were categorised into seven latent constructs: Customer Service/Sales, Purchasing, Production Planning and Control, Manufacturing Engineering, Shop Floor and Quality Control, Receiving and Shipping. Structural Equation Modelling (SEM) confirmed the seven constructs, but measuring items were reduced from 39 to 23 significant observed measuring items, compromising the supplier selection framework. Our proposed framework will help practitioners make better informed decisions in outsourcing from non-developed supplier markets.

Integration of machine operators with shop floor control system for Industry 4.0

Integration of machine operators with shop floor control system for Industry 4.0

Real-time scheduling for a smart factory using a reinforcement learning approach

Abstract Previous studies of the real-time scheduling (RTS) problem domain indicate that using a multiple dispatching rules (MDRs) strategy for the various zones in the system can enhance the production performance to a greater extent than using a single dispatching rule (SDR) over a given scheduling interval for all the machines in the shop floor control system. This approach is feasible but the drawback of the previously proposed MDRs method is its inability to respond to changes in the shop floor environment. The RTS knowledge base (KB) is not static, so it would be useful to establish a procedure that maintains the KB incrementally if important changes occur in the manufacturing system. To address this issue, we propose reinforcement learning (RL)-based RTS using the MDRs mechanism by incorporating two main mechanisms: (1) an off-line learning module and (2) a Q-learning-based RL module. According to various performance criteria over a long period, the proposed approach performs better than the previously proposed MDRs method, the machine learning-based RTS using the SDR approach, and heuristic individual dispatching rules.

An Internet of Things-Based Monitoring System for Shop-Floor Control

With the advent of the fourth industrial revolution (Industry 4.0), manufacturing systems are transformed into digital ecosystems. In this transformation, the internet of things (IoT) and other emerging technologies pose a major role. To shift manufacturing companies toward IoT, smart sensor systems are required to connect their resources into the digital world. To address this issue, the proposed work presents a monitoring system for shop-floor control following the IoT paradigm. The proposed monitoring system consists of a data acquisition device (DAQ) capable of capturing quickly and efficiently the data from the machine tools, and transmits these data to a cloud gateway via a wireless sensor topology. The monitored data are transferred to a cloud server for further processing and visualization. The data transmission is performed in two levels, i.e., locally in the shop-floor using a star wireless sensor network (WSN) topology with a microcomputer gateway and from the microcomputer to Cloud using Internet protocols. The developed system follows the loT paradigm in terms of connecting the physical with the cyber world and offering integration capabilities with existing industrial systems. In addition, the open platform communication—unified architecture (OPC-UA) standard is employed to support the connectivity of the proposed monitoring system with other IT tools in an enterprise. The proposed monitoring system is validated in a laboratory as well as in machining and mold-making small and medium-sized enterprises (SMEs).

Digital twin-based smart production management and control framework for the complex product assembly shop-floor

Digital twin technology is considered as a key technology to realize cyber-physical systems (CPS). However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little progress has been achieved in digital twin application, especially in the complex product assembly shop-floor. In this paper, we propose a framework of digital twin-based smart production management and control approach for complex product assembly shop-floors. Four core techniques embodied in the framework are illustrated in detail as follows: (1) real-time acquisition, organization, and management of the physical assembly shop-floor data, (2) construction of the assembly shop-floor digital twin, (3) digital twin and big data-driven prediction of the assembly shop-floor, and (4) digital twin-based assembly shop-floor production management and control service. To elaborate how to apply the proposed approach to reality, we present detailed implementation process of the proposed digital twin-based smart production management and control approach in a satellite assembly shop-floor scenario. Meanwhile, the future work to completely fulfill digital twin-based smart production management and control concept for complex product assembly shop-floors are discussed.

Shop Floor Control: A Physical Agents Approach for PLC-Controlled Systems

Multiagent technology raised great expectations from the outset, and these expectations are still high today. A technology that allows the creation of systems that are autonomous, reactive, proactive, and have entities with social skills is necessarily very appealing. Many initiatives have emerged to develop the application of this technology in different areas, including manufacturing. Multiagent-based solutions typically use this technology to create distributed systems with decentralized decision making as a way to tackle complex systems or problems by dividing the complexity among agents. Unfortunately, most works do not go further than the definition of agents or architectures, which prevents industry from adopting this technology. This paper presents an approach to implementing the shop floor control of an automated distribution center following a multiagent approach. This implementation divides the system into agents in charge of the management of the system while highlighting the communication channels with shop floor control equipment.

Job Scheduling at Cascading Machines

We consider the serial batching scheduling problem in which a group of machines can process multiple jobs continuously to reduce the processing times of the second and subsequent jobs. The maximum batch size is finite. Since all jobs in the same batch are loaded and unloaded simultaneously, a completed job has to wait for the others. We examine how to schedule all jobs to minimize the total completion time. If the batch size is only one, i.e., a single job per batch, the total processing time will be longer, since no reduction in processing time is possible. As a consequence, the total completion time will also be longer. On the other hand, if the batch size is large, the total completion time can be large. Each job has to wait until all jobs in the same batch are completed. We identify several optimality properties of the optimal batching sequence. These properties are used to develop a dynamic programming algorithm to optimize the batching sequences efficiently. The complexity of the proposed method depends only on the maximum batch size and the number of jobs. The improvement achieved with the proposed method when compared with two other batching rules is illustrated using two practical applications. Note to Practitioners — Machines with job cascading (or cluster tools) are commonly seen in semiconductor manufacturing processes. While meeting production move targets is a common goal for the nondelayed jobs, catching up with the schedule is an important task for the delayed jobs to achieve higher customer service level. From the viewpoint of scheduling, reducing the delay is equivalent to reducing completion time. Hence, we propose scheduling algorithms to reduce the completion time by taking advantage of the special structure of a cascading machine. In contrast to existing scheduling theory, the proposed algorithm can be efficient due to the small maximum batch size of real cascading machines. In addition to the theoretical contributions, this paper aims at solving practical problems through a rigorous approach. The finding and insight from this paper can be used to enhance shop floor control in a semiconductor fab.

A methodology to assess the supply chain performance based on gap-based measures

The motivation of this article lies in the application of two-stage gap-based measurement (GBM) in supply chain management. It adopts GBM model to construct a mixed integer program to measure the various stages of supply chain management in manufacturing practice. This paper proposes a new assessment system that aims to measure the aggregated efficiency of the entire supply chain. In this system, “plan source” and “plan make” are the major elements, they are a widely accepted industry standard by Supply Chain Operations Reference (SCOR). “Plan source” emphasizes the assessment of demand/supply planning strategy and suppliers’ performance; “plan make” emphasizes the shop floor control, profitability and equipment effectiveness. The resulting GBM scores provide complete information on how to project inefficient DMUs on to the frontier. This article continues with a demonstration of GBM in the company cases, for the results of the assessment; this paper finds the worst efficiency of the DMUs and proposes improvement programs.