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"Flexible manufacturing systems"
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Applications of the MOORA method for decision making in manufacturing environment
To meet the challenges of global competitiveness, manufacturing organizations are now facing the problems of selecting appropriate manufacturing strategies, product and process designs, manufacturing processes and technologies, and machinery and equipment. The selection decisions become more complex as the decision makers in the manufacturing environment have to assess a wide range of alternatives based on a set of conflicting criteria. To aid these selection processes, various multi-objective decision-making (MODM) methods are now available. This paper explores the application of an almost new MODM method, i.e., the multi-objective optimization on the basis of ratio analysis (MOORA) method to solve different decision-making problems as frequently encountered in the real-time manufacturing environment. Six decision-making problems which include selection of (a) an industrial robot, (b) a flexible manufacturing system, (c) a computerized numerical control machine, (d) the most suitable non-traditional machining process for a given work material and shape feature combination, (e) a rapid prototyping process, and (f) an automated inspection system are considered in this paper. In all these cases, the results obtained using the MOORA method almost corroborate with those derived by the past researchers which prove the applicability, potentiality, and flexibility of this method while solving various complex decision-making problems in present day manufacturing environment.
Journal Article
A divide-and-conquer-method for the synthesis of liveness enforcing supervisors for flexible manufacturing systems
In this paper a divide-and-conquer-method for the synthesis of liveness enforcing supervisors (LES) for flexible manufacturing systems (FMS) is proposed. Given the Petri net model (PNM) of an FMS prone to deadlocks, it aims to synthesize a live controlled Petri net system. For complex systems, the use of reachability graph (RG) based deadlock prevention methods is a challenging problem, as the RG of a PNM easily becomes unmanageable. To obtain the LESs from a large PNM is usually intractable. In this paper, to ease this problem the PNM of a system is divided into small connected subnets. Each connected subnet prone to deadlocks is then used to compute the LES for the original PNM. Starting from the simplest subnet prone to deadlocks to make the subnet live, monitors (control places) are computed. The RG of each subnet is considered and split into a dead-zone (DZ) and a live-zone. All states in the DZ are prevented from being reached by means of a well-established invariant-based control method. Next, the computation of monitors is followed for bigger subnets. Previously computed monitors are included within the bigger subnets based on a criterion. This process keeps the DZ of the bigger subnets smaller compared with the original uncontrolled subnets. When all subnets are live we obtain a set of monitors that are included within the PNM to obtain a partially controlled PNM (pCPNM). A new set of monitors is also computed for the pCPNM. Finally, a live controlled Petri net system is obtained. The proposed method is generally applicable, easy to use, effective and straightforward although its off-line computation is of exponential complexity in theory. Its use for FMS control guarantees deadlock-free operation and high performance in terms of resource utilization and system throughput. Two FMS deadlock problems from the literature are used to illustrate the applicability and the effectiveness of the proposed method.
Journal Article
Joint production and transportation scheduling in flexible manufacturing systems
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.
Journal Article
A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0
The production management system in Industry 4.0 is emphasizes the improvement of productivity within limited constraints by sustainable production planning models. To accomplish this, several approaches are used which include lean manufacturing, kaizen, smart manufacturing, flexible manufacturing systems, cyber–physical systems, artificial intelligence, and the industrial Internet of Things in the present scenario. These approaches are used for operations management in industries, and specifically productivity maximization with cleaner shop floor environmental management, and issues such as worker safety and product quality. The present research aimed to develop a methodology for cleaner production management using lean and smart manufacturing in industry 4.0. The developed methodology would able to enhance productivity within restricted resources in the production system. The developed methodology was validated by production enhancement achieved in two case study investigations within the automobile manufacturing industry and a mining machinery assembly unit. The results reveal that the developed methodology could provide a sustainable production system and problem-solving that are key to controlling production shop floor management in the context of industry 4.0. It is also capable of enhancing the productivity level within limited constraints. The novelty of the present research lies in the fact that this type of methodology, which has been developed for the first time, helps the industry individual to enhance production in Industry 4.0 within confined assets by the elimination of several problems encountered in shop floor management. Therefore, the authors of the present study strongly believe that the developed methodology would be beneficial for industry individuals to enhance shop floor management within constraints in industry 4.0.
Journal Article
Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment
The paper presents an algorithm for integrated scheduling, dispatching, and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm. The algorithm generates an integrated schedule and detail routing paths while optimizing makespan, AGV travel time, and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The multi-objective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance improvements of the last two previous generations. Computational experiments was conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. Integrated scheduling of jobs in FMS which are in synchrony with AGV dispatching, scheduling, and routing proved to ensure the feasibility and effectiveness of all the solutions of the integrated constituent elements.
Journal Article
Scheduling optimization of flexible manufacturing system using cuckoo search-based approach
In the present work, a cuckoo search (CS)-based approach has been developed for scheduling optimization of a flexible manufacturing system by minimizing the penalty cost due to delay in manufacturing and maximizing the machine utilization time. To demonstrate the application of cuckoo search (CS)-based scheme to find the optimum job, the proposed scheme has been applied with slight modification in its Levy flight operator because of the discrete nature of the solution on a standard FMS scheduling problem containing 43 jobs and 16 machines taken from literature. The CS scheme has been implemented using Matlab, and results have been compared with other soft computing-based optimization approaches like genetic algorithm (GA) and particle swarm optimization found in the literature. The results shown by CS-based approach have been found to outperform the results of existing heuristic algorithms such as GA for the given problem.
Journal Article
Development of cost-effective IoT module-based pipe classification system for flexible manufacturing system of painting process of high-pressure pipe
This paper researches an IoT module-based pipe classification system for a flexible manufacturing system that recognizes the size and length of pipes used in the painting process of high-pressure pipes. The proposed system is composed of an IoT module, USB camera, and edge TPU for pipe classification. The proposed system recognizes the type of pipe by three processes; object detection of the pipe, line detection algorithm of the three regions of interest, and pipe classification algorithm based on the line detection algorithm. Furthermore, the proposed system enables web-based real-time monitoring, providing convenience to workers and helping them make quick decisions. The IoT module interfaces with the painting robot and the sequence control that paints for each type of pipe is executed in the painting robot, allowing flexible manufacturing of the painting process.
Journal Article
AGV routing and motion planning in a flexible manufacturing system using a fuzzy-based genetic algorithm
Autonomous-guided vehicles (AGVs) are becoming increasingly prevalent and already helping in a variety of activities, ranging from space exploration to domestic housework. Recent advances in the design of sensors, motors and microelectromechanical systems are bringing us closer to the realization of autonomous multi robot systems which perform complex tasks. In this work, we explored the problem of vehicle routing and motion planning for a fleet of AGVs in a flexible manufacturing system (FMS). Considering concurrently fuzzy demands associated with the workstations and fuzzy travel distances while moving between workstations, the problem is addressed in the context of uncertainty both in demands and travel times. The proposed motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. Furthermore, in order to take into account the fuzziness which may arise in the FMS, the proposed planner is integrated with fuzzy theory on fuzzy sets and fuzzy numbers. The efficiency of the solution procedure is demonstrated through numerical examples.
Journal Article
Combining Life Cycle Assessment and Manufacturing System Simulation: Evaluating Dynamic Impacts from Renewable Energy Supply on Product-Specific Environmental Footprints
The eco-efficiency of actual production processes is still one dominating research area in engineering. However, neglecting the environmental impacts of production equipment, technical building services and energy supply might lead to sub-optimization or burden-shifting and thus reduced effectiveness. As an established method used in sustainability management, Life Cycle Assessment aims at calculating the environmental impacts from all life cycle stages of a product or system. In order to cope with shortcomings of the static character of life cycle models and data gaps this approach combines Life Cycle Assessment with manufacturing system simulation. Therefore, the two life cycles of product and production system are merged to assess environmental sustainability on product level. Manufacturing simulation covers the production system and Life Cycle Assessment is needed to relate the results to the final product. This combined approach highlights the influences from dynamic effects in manufacturing systems on resulting life cycle impact from both product and production system. Furthermore, the importance of considering indirect peripheral equipment and its effects on the manufacturing system operation in terms of output and energy demands is underlined. The environmental flows are converted into impacts for the five recommended environmental impact categories. Thus, it can be demonstrate that Life Cycle Assessment can enhance the process simulation and help identify hot-spots along the life cycle. The combined methodology is applied for analysing a case study in fourteen scenarios for the integration of volatile energy sources into energy flexible manufacturing control.
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