Problem In Manufacturing System Research Articles

An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions

The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.

Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems

This paper addresses the challenges associated with nesting and production scheduling in additive manufacturing (AM). The problem studied consists of grouping a set of parts into batches, which are then assigned to and sequenced across the available machines, guaranteeing the production of all parts. This work stands out by proposing exact methods for the AM nesting and scheduling problem considering irregular-shaped parts with specific release dates, processing times, and due dates, with the aim of minimizing the cumulative tardiness. The proposed approaches include two logic-based Benders decompositions: one combining Mixed Integer Programming (MIP) and Constraint Programming (CP), and the other relying solely on CP. To deal with the sub-problems, a strategic procedure was developed to reduce the solution space while maintaining low resolution times per iteration. Problem-specific cuts are also generated to improve the efficiency of these approaches. Computational experiments show that both decompositions significantly outperform a prior monolithic CP model, with the decomposition based solely on CP yielding the best results. Moreover, the results show that this approach has the potential to achieve similar computational performance of non-exact approaches that are currently considered state-of-the-art. A set of instances is provided to serve as a benchmark for future studies.

Uniform-machine scheduling problems in green manufacturing system

Uniform-machine scheduling problems in green manufacturing system

Genetic Algorithm for Solving Mobile Robot Scheduling Problem in Flexible Manufacturing Environment

The scheduling genuinely a complex process, aimed at optimizing operational activities in pursuit of one or more objectives by leveraging production data which may include previous schedules. The scheduling problem in Flexible Manufacturing System (FMS) is commonly categorized as Nondeterministic Polynomial (NP)-hard combinatorial optimization problems and it remains as an endure problem to industrial practitioners and researchers. As part of real production scheduling, once one task is finished processing on a machine, transportation equipment such as mobile robot transports the completed task to the next machine. The problem of scheduling mobile robot in FMS pertains to the task allocation process for the robots, considering the transportation costs and the time spent to complete all operations. In recent years, Genetic Algorithm (GA) has been a remarkably effective search algorithm for solving a wide range of scheduling problems in a manner that achieves near-optimal solutions. This paper presents the metaheuristic techniques, specifically genetic algorithm, to address the NP-hard scheduling problem of two identical mobile robots in Job-Shop FMS environment. The algorithm is developed with the aim of finding feasible solutions to the integrated problem by minimizing the amount of time it takes to finish all tasks, commonly referred to as makespan. The performance of GA is evaluated with some numerical experiments which is executed via Matlab software. The scheduling results shows that the developed GA able to obtained the near-optimal solution of minimal makespan and converge within a short period of time.

Modeling and Simulation of a Digital Twin of a Production System for Industry 4.0 with Work-in-Process Synchronization

One of the main problems of modern manufacturing systems is the increasing complexity related to modern Industry 4.0 technologies that are fundamentally changing manufacturing and logistics processes and operations. Industry 4.0 includes, e.g., flexible automation and robotization, which make complex manufacturing systems difficult to analyze. Some modeling and simulation methods are being used to solve industrial problems and can serve as an interface between the production level and management level. The new trend of the Digital Twin, creating simulation models as similar as possible to the real system, and a Digital Twin framework for a manufacturing line from the automotive industry, was considered. Simulation models typically start from the empty state and some warmup time is required to achieve the stable state. The Key Performance Indicators were also analyzed for the stable state. However, there are many stochastic parameters such as machinery failures, human errors, quality issues, etc., that make the real processes differ from simulated processes, and cause the instability of production throughput and changes in the Work in Process. To analyze the Work in Process in the model, initialization of the model with proper production data is required, as the Digital Twin uses data synchronization with the production database. In this paper, the digital model of a human-robot-operated manufacturing system with Work-in-Process data synchronization is analyzed, and the results of the statistical analysis of simulation experiments are presented. The obtained results show high variability of finished production, which is related to system instability due to random failures, especially when the system starts from an empty state. However, an increase in initial Work in Process results in better efficiency and stability for the whole system. The DT simulation of the manufacturing system can be very helpful, as it becomes a repository of knowledge about the real system and enables the analysis of its dynamics. However, for proper functionality, the model should include information about the current WIP state, which enables the start of the simulation with exactly the same number of queues as in the real system. The presented method can also be used in similar enterprises from other industries, especially for those with discrete processes or high WIP variability, and for further synchronization of other DT parameters.

A Discrete Cooperative Control Method for Production Scheduling Problem of Assembly Manufacturing System

With a sharp decrease in resource utilization of the complex production process, integrated scheduling methods are urgently needed in assembly manufacturing industries. To this end, this paper presents an integrated scheduling with jobs processing and assembly sequence (ISJPAS) problem where jobs and assemblies can be processed simultaneously. As the first contribution to this work, we develop a mixed integer nonlinear programming model (MINLP) that aims at minimizing maximum completion time and determining the earliness and tardiness of jobs and resulting inventory time. The structured property of the optimal scheduling solution is analyzed with regard to job sequencing and assembly sequencing. The NP-hard nature of the problem is proved, which provides upper and lower bounds on the optimal solution. Second, an efficient discrete assembly time and arrival time control (DAATC) method is presented based on continuous time variable control models. From a modified set of benchmark problems, the proposed method is tested by comparing four assembly association levels in real applications. The comparisons indicate the potentiality of our method to satisfy the due date. Lastly, relevance of practical applications are discussed, and several future research avenues are emphasized.

A double-adaptive general variable neighborhood search for an unmanned electric vehicle routing and scheduling problem in green manufacturing systems

In recent years, more and more manufacturing factories have employed unmanned electric vehicles (UEVs) to perform the materials delivery, and efficient routing and scheduling approaches are required to improve the delivery efficiency. However, most of existing papers studied the routing problem of fuel vehicles or the drones. This paper investigates the optimization of UEVs delivery routes and charging strategies. Due to the limited payload capacity and battery capacity, the UEV must return to the depot multiple times to load the materials and go to the recharging station during the route to charge, and thus the problem is modeled as a multi-trip UEV routing and scheduling problem, which assigns the recharging station to the UEV’s route and optimizes a set of UEV’s trips. The investigated problem is firstly formulated as a mixed integer programming model. Then, considering the routing and scheduling challenges, we develop an efficient double-adaptive variable neighborhood search (DA-GVNS), which incorporates the general variable neighborhood search with a new adaptive mechanism, for this problem. The experimental results on the test and benchmark instances highlight that the DA-GVNS outperforms other existing algorithms. Furthermore, sensitivity analyses are conducted to study the impact of model’s characteristics on the optimality and complexity of solutions. Finally, a case study is proposed to present how the developed approach works for a manufacturing factory. This paper can offer decision-makers a valuable approach to construct a high-quality routing and scheduling scheme for the UEV delivery while providing potential managerial insights for implementing efficient green manufacturing systems.

Identifying the Saturated Line Based on the Number of Idle Places: Achieving Precise Maximal Permissiveness without Deadlocks Using Control Transitions or Control Places

In the flexible manufacturing system deadlock prevention domain, researchers’ almost final target is to seek the maximally permissive controllers for solving the deadlock problems of flexible manufacturing systems. However, it seems a challenging work. Whatever you adopt, what kinds of methods, policies, and strategies, it seems complicated to obtain optimal controllers for deadlock prevention even if they claim their algorithms are optimal until the deadlock recovery is developed. Therefore, many experts, including us, have decided to design all kinds of actual maximally permissive recovery policies based on control transitions. It is a pity that these policies usually solve some particular flexible manufacturing systems’ deadlock problems. The controllers failed to recover the deadlock situation of flexible manufacturing systems once the idle or resource places were changed. In other words, these policies could never know or identify the real number of maximally permissive controllers in the same structure with different idle places. The precise combination of the number of idle places is called the saturated line, and it divides the reachability states into two region (saturated region and unsaturated region). Accordingly, this paper proposes a novel concept that achieves the precise maximum permissiveness and fits all kinds of flexible manufacturing systems. The experimental results show how our policy does in practice.

Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems

ABSTRACT Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework.

Modelling and solving approaches for scheduling problems in reconfigurable manufacturing systems

Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS may affect several resources at once, and hence require specific modelling and solving approaches to be considered. This paper first formalises the problem at hand through integer linear programming. An iterative search method is then provided to obtain solutions to larger-scale instances. Results obtained on generated instances show that managing even few possible configurations can yield significant improvements in solutions’ quality. Meanwhile, the extended search space implied by the increase in available configurations hinders the convergence to a good solution in a reasonable computation time, which suggests further investigations.

An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs

The industrial sector is the largest consumer of total energy in the world, with the majority of its consumption in the form of electricity. Recently, to strengthen the capacity building of peak load regulation, many countries have implemented time-of-use (TOU) tariffs to encourage manufacturing enterprises to shift their electricity consumption from on-peak hours to mid-peak and off-peak hours. It brings the urgent requirement for energy-efficient operation and maintenance (O&M) schedules in manufacturing enterprises, among which the maintenance planning and production scheduling (MPPS) are both highly correlated to electricity consumption under this time-varying electricity charging modes. To tackle this key issue, a complex MPPS problem for serial-parallel manufacturing systems under TOU tariffs is studied in this paper. To solve the problem, an energy-efficient two-stage maintenance (ETM) strategy is developed to minimize the sum of the total electricity cost and tardiness cost. In the first stage, the preventive maintenance (PM) planning is presented to obtain the multi-attribute PM intervals for each machine, considering machine availability, maintenance cost, and the potential impact of planned PM actions on average electricity price. Based on the PM intervals, a mixed-integer programming model is proposed for a hybrid flow shop schedule with PM actions in the second stage. Finally, the results confirm the effectiveness of this ETM strategy in achieving electricity cost savings and ensuring system productivity, which can provide instructions for the operation of industrial enterprises.

A Two-Step Approach to Scheduling a Class of Two-Stage Flow Shops in Automotive Glass Manufacturing

Driven from real-life applications, this work aims to cope with the scheduling problem of automotive glass manufacturing systems, that is characterized as a two-stage flow-shop with small batches, inevitable setup time for different product changeover at the first stage, and un-interruption requirement at the second stage. To the best knowledge of the authors, there is no report on this topic from other research groups. Our previous study presents a method to assign all batches to each machine at the first stage only without sequencing the assigned batches, resulting in an incomplete schedule. To cope with this problem, if a mathematical programming method is directly applied to minimize the makespan of the production process, binary variables should be introduced to describe the processing sequence of all the products, not only the batches, resulting in huge number of binary variables for the model. Thus, it is necessary and challenging to search for a method to solve the problem efficiently. Due to the mandatory requirement that the second stage should keep working continuously without interruption, solution feasibility is essential. Therefore, the key to solve the addressed problem is how to guarantee the solution feasibility. To do so, we present a method to determine the minimal size of each batch such that the second stage can continuously work without interruption if the sizes of all batches are same. Then, the conditions under which a feasible schedule exists are derived. Based on the conditions, we are able to develop a two-step solution method. At the first step, an integer linear program (ILP) is formulated for handling the batch allocation problem at the first stage. By the ILP, we need then to distinguish the batches only, greatly reducing the number of variables and constraints. Then, the batches assigned to each machine at the first stage are optimally sequenced at the second step by an algorithm with polynomial complexity. In this way, by the proposed method, the computational complexity is greatly reduced in comparison with the problem formulation without the established feasibility conditions. To validate the proposed approach, we carry out extensive experiments on a real case from an automotive glass manufacturer. We run ILP on CPLEX for testing. For large-size problems, we set 3600 s as the longest time for getting a solution and a gap of 1% for the lower bound of solutions. The results show that CPLEX can solve 96.83% cases. Moreover, we can obtain good solutions with the maximum gap of 4.9416% for the unsolved cases.

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions. Furthermore, a local search heuristic based on three parts encoding is embedded to enhance the searching performance. To enhance the local search abilities, the cooperation of the search operator is designed to obtain better non-dominated solutions. Finally, the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms. The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

Incomparable Single Controller for Solving Deadlock Problems of Flexible Manufacturing Systems

This paper focuses on studying and proposes deadlocks are an obstacle in the flexible manufacturing system (FMS) of Petri nets (PNs) since their occurrence will influence the partial or whole model to stop working. Due to its structural characteristic, the Petri net is an essential model tool to overcome the deadlocks problem. The place invariant (PI) associated with the control algorithm to obtain the maximally permissive by deadlocks forbidding are popular recently; however, the algorithms have reached the maximal permissiveness, but their results are so complex. Our paper applies a single controller and single token to eliminate all the deadlocks in the FMS. of PNs We combine the PI and simplified dead markings to form our single controller deadlock prevention policy (SCDPP) algorithm.

A pythagorean hesitant fuzzy programming approach and its application to multi objective reliability optimization problem

Decision-making problems can often be effectively solved using traditional optimization methods that have a clearly defined configuration. However, in real-world scenarios, decision-makers frequently encounter doubt or hesitation, making it challenging to precisely specify certain parameters. As a result, they often seek input from different experts, leading to conflicting values and varying levels of satisfaction among decision-makers. This uncertainty and lack of crisp values make decision-making problems inherently non-deterministic. In this paper, a novel Pythagorean hesitant fuzzy (PHF) programming method is designed to address the challenges of optimization problems with multiple objectives. Here PHF aggregation operators are used to aggregate the PHF memberships and non-memberships of the objectives. Additionally, to account the uncertainties of the parameters of the optimization problem Parabolic Pythagorean fuzzy number is used and centroid method is applied for defuzzification. We solved an example of multi objective optimization problem of manufacturing system to demonstrate our proposed method and finally, presented a case study on reliability optimization model for Life Support Systems, where the primary objectives are to maximize system reliability and minimize cost. The result is compared with other existing methods by degree of closeness.

Dynamic MPC-Based Scheduling in a Smart Manufacturing System Problem

Dynamic MPC-Based Scheduling in a Smart Manufacturing System Problem

Score-Based Anomaly Detection for Smart Manufacturing Systems

We propose an anomaly detection framework that generates an anomaly score for the current observation of a task. The proposed framework can overcome two major problems in smart manufacturing systems, where a robot learns task execution by human expert demonstrations. First, it can function as a warning system to alert human operators when the robot goes into a significantly different state than those of demonstrations. Second, in order to prevent redundant samples, the proposed framework can be used as a guiding system for human experts throughout the recording of demonstrations for collecting novel states, which are different from already collected samples. Unlike parametric models, the proposed framework is better suited for behavioral cloning-adapted smart manufacturing applications in which the training dataset is continuously extended. We also propose four real-world datasets for different applications, e.g., car plugging and refrigerator production line inspection. To the best of our knowledge, it is the most comprehensive real-world dataset for anomaly detection for smart manufacturing systems with a robotic arm. We evaluate our method quantitatively on the real-world datasets and compare it with several standard baselines from the literature.

A mathematical programming model with present value method for optimum design of flexible cellular manufacturing systems

In this article, an integer mathematical programming model for the design problem of flexible cellular manufacturing systems is proposed. The developed mathematical programming model whose objective function is to minimize total design cost including cost of operating parts on machines, cost of using tools on machines, and cost of assigning employees to cells, also contains the present value method. Thus, it is ensured that the operational costs that occur along a certain time period are also taken into account. LINGO 19.0 optimization program software is used for the optimum solution of the integer mathematical programming model with the present value method whose objective function is to minimize the total design cost. In this article, the application of the model and the analysis related to the model are shown using a developed example problem. In addition, by ensuring the optimum design of flexible cellular manufacturing systems, the results of which alternative routes are used for processing parts, which machines are located in which cells, and which employees are assigned to which cells, are obtained. Then, a sensitivity analysis is presented to show the importance of alternative routes of parts.

Key Technology of Intelligentized Welding Manufacturing and Systems Based on the Internet of Things and Multi-Agent

With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from both academia and industry. This paper investigates sensing technology, multi-information sensor fusion technology, feature recognition technology, the quality prediction method, control method, and intelligent welding production line application in the IWMS. Combining IoT technology and multi-agent systems, a hierarchical structure model welding manufacturing system (IoT-MAS) in the form of “leader-following” was constructed. The multi-agent welding manufacturing system has the advantages of distribution, intelligence, internal coordination and so on. The IoT-MAS consists of several sub-agents, which are divided into five categories according to their functions and internal processing logic. Combined with the functions of the intelligent welding manufacturing system, the agent structure of the whole welding process was proposed, and the matching communication technology and algorithm were designed. The intelligent welding manufacturing system based on IoT-MAS proposed in this paper can effectively solve the integrated design problem of large welding manufacturing systems.

Influence of nozzle structure on the combustion performance of a graphitic hydrogen-chlorine synthesis combustor

The growing semiconductor sector is driving up demand for high-purity gas-phase HCl. Large combustion zones and low HCl concentrations are problems for HCl manufacturing systems. Based on the realizable k-ε model, the eddy breakup combustion model, and the participating media radiation model, the computational fluid dynamics simulation was used as the primary means in this study. The influence of four structural factors, nozzle jet hole diameter, jet angle, and opening angle—another is nozzle mounting height, on the combustion effect of the synthesis combustor was investigated. The extent of the high-temperature zone in the synthesis combustor and the purity of the hydrogen chlorine at the outlet were used as the main evaluation criteria. The results of numerical simulations demonstrate that the better the gas mixing and combustion in the combustor, the lower the nozzle mounting height should be. Small nozzle diameters and large nozzle jet angle increase gas flow velocity in the recirculation zone close to the inner wall of the combustor. Additionally, the recirculation zone will appear in the combustor as a result of the nozzle's presence. The area of the high-temperature zone within the combustor decreases as internal gas velocity increases. On the other hand, the recirculation gas flow is not significantly impacted by the angle at the condition that the jet holes open. The results of the investigation provide a reference for the design of the miniaturization of the combustor in the non-premixed combustion synthesis of gases process.