Manufacturing Planning Systems Research Articles

A modified bi-objective NSGA-II approach to sustainability in reconfiguration planning of dynamic cellular manufacturing systems

Manufacturing plant layouts are developed to facilitate optimal process flow. Modern manufacturing systems must meet present production demands and be adaptable to changes in process flow in the future. Dynamic Cellular Manufacturing Systems (DCMS) increase the flexibility of layouts by reconfiguring cell structure and equipment distribution, to effectively adjust part routings for optimal process flow. Frequent reconfiguring of plant layout may not always be feasible or economical, however, when new product releases are planned, reconfiguring the plant layout to optimise the work-flow may be extremely beneficial. This paper presents a Non-dominated Sorting Genetic Algorithm (NSGA-II) approach to solving a DCMS problem in a sustainable, and responsible manner. A bi-objective integer programming model was developed over multiple planning horizons with fluctuating product demands. This model aims to achieve sustainability by reducing the cost of production, mitigating the environmental impact of production, and minimise negative social impacts on labourers that work in such environments. A penalty function approach was used to enforce the model constraints during optimisation. This study details trade-offs between the economic factors of a DCMS, the environmental implications of reconfiguring such a system, and the social impacts of reconfigurations on the workforce.

Intuitive toolpath planning in computer-aided manufacturing systems using artificial intelligence in a virtual reality environment

CAM systems are widely used in many key industries and are essential for many machining processes such as 5-axis milling. For toolpath planning, state-of-the-art CAM systems require the user to enter many abstract parameters, making these systems difficult to use. This paper presents a more intuitive approach to toolpath planning. The approach uses convolutional neural networks to interpret user gestures in a virtual reality environment and predicts the values of the aforementioned parameters. The results show that many of these parameters can be extracted from user gestures with high accuracy, which can be used to greatly simplify toolpath planning.

Optimising production, maintenance, and quality control for imperfect manufacturing systems considering timely replenishment

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy.

Application of simulated annealing algorithm for multi criteria operation planning in flexible manufacturing systems

Application of simulated annealing algorithm for multi criteria operation planning in flexible manufacturing systems

A reinforcement learning/ad-hoc planning and scheduling mechanism for flexible and sustainable manufacturing systems

The process scheduling is still considered a crucial subject for manufacturing industry, due to the ever-changing circumstances dictated by the nowadays product demand and customer trends. These conditions are often associated with increasing costs and energy consumption, considerably affecting the long-term sustainability of manufacturing plants. To mitigate that effect, one should create an effective strategy tailoring integrated operations and processes to the customer demand and trends faced by the nowadays industry. A well-known approach to this matter is the technologies introduced by manufacturing paradigms, e.g., Industry 4.0 and smart manufacturing. As suggested in literature, these technologies are capable of helping decision-makers by continuously gathering significant information about the state of machinery and manufactured goods. This information is thereafter utilized to identify weaknesses and strengths demonstrated within manufacturing plants. To this end, the present paper presents a process optimization framework implemented in a three-stage production line prone to systematic degradation faults. Aiming at strengthening profitability, the framework engages reinforcement learning with ad-hoc manufacturing/maintenance control in decision-making carried out in implemented machines. Simulation experiments showed improved process planning and inventory management enabling cost-effective green and sustainable manufacturing in manufacturing plants.

Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning

Companies with Make-to-Order (MTO) manufacturing have always faced the conflict of meeting a large volume of individual customer orders on time while remaining as flexible as possible. Unlike Make-to-Stock (MTS) manufacturing, planning the production requirements for MTO is a challenging task since incoming orders may vary in time and quantity, while also being subject to a number of variables. In some cases, the delivery time allocated by the customer can be less than the required Order Lead Time to fulfil the order. Manufacturers can respond to this with either with approached from production management or from data science. This paper presents a framework to leverage the benefit of both domains. We conduct a literature review and present the results of an expert workshop. We propose criteria for a suitable data enrichment and the application of Machine Learning (ML) methods in sales and demand forecasting. In conclusion, the proposed framework helps to equip manufacturing companies with a structured strategy for data management and to utilize the benefit of ML for sales and demand forecasting.

Special Issue: “The Planning and Scheduling of Manufacturing Systems”

The “Fourth Industrial Revolution” (alternatively known as “Industry 4 [...]

A framework for human-centered production planning and control in smart manufacturing

Smart manufacturing systems have to meet high performance expectations in form of customer individual products, short lead times, high due date reliability and resilience by the integration of new technologies and by intelligent process design. The planning and control of such smart manufacturing systems necessitates several instances of human decision-making. The latest research at the nexus of operations management and psychology reveals that human decision-making in such fast-paced, dynamic and complex environments is often biased, resulting in suboptimal operational performance. Human-centered design of planning and control processes can mitigate biased decisions, thus boosting performance in smart manufacturing systems. Current research is silent about the impact and mitigation of cognitive biases in operations management, though. We intend to close this research gap. We combine systematic reviews of literature on behavioral operations management and cognitive biases in operations management, particularly in production planning and control with an in-depth case study based on 12 expert interviews. We explore two decision-making models and combine them in a PPC framework. We create a framework for human-centered PPC in smart manufacturing systems, which includes the main impact factors, challenges and performance capabilities. Based on a detailed case study of a steel producer, we present a framework of human-centered PPC comprising five hypotheses on the impact of cognitive biases on human decision-making and on manufacturing performance.

A three-step approach for decision support in operational production planning of complex manufacturing systems

In this paper, a practical relevant operational production planning problem in complex manufacturing systems is addressed. In this problem, lots are planned individually to provide a more detailed plan than approaches that only consider production quantities. A three-step approach, which is currently fully integrated and used in a Decision Support System, is then introduced. This work follows the one of Mhiri et al. [2018. “Heuristic Algorithm for a WIP Projection Problem at Finite Capacity in Semiconductor Manufacturing.” IEEE Transactions on Semiconductor Manufacturing 31 (1): 62–75] who addressed this problem. We push the approach a step further by introducing new optimisation possibilities through new smoothing rules, whose performance is studied according to different indicators. Furthermore, we present the production planning process in which the decision support tool is embedded and how it bridges the gap between the upper and lower planning levels.

Design and evaluation of a high− fidelity virtual reality manufacturing planning system

Design and evaluation of a high− fidelity virtual reality manufacturing planning system

Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry

This paper aims to investigate the optimal sorting of orders reflecting on the material changing lead time over the machines in the roofing manufacturing industry. Specifically, a number of jobs were sorted together based on the material used and then consolidated for subsequent processes, i.e., assigned to the corresponding machines. To achieve the optimal sorting for the received orders, a combinatorial dispatch rule was proposed, which were Earliest Due Date (EDD), First In First Out (FIFO), and Shortest Processing Time (SPT). The sequence of orders organized by the scheduling algorithm was able to minimize the changing material lead time and also maximize the number of orders to be scheduled in the production. Consequently, on-time delivery could be achieved. Tests based on real data have been set up to evaluate the performance of the proposed algorithm in sorting the received orders. As a result, the proposed algorithm has successfully reduced the material changing lead time by 47.3% and 40% in the first and second tests, respectively.

A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques

Adequate energy awareness is an essential prerequisite for energy-efficient production planning and operation in manufacturing systems. Nonetheless, a lack of awareness of the impact of managerial and operational energy-influencing factors on energy consumption can be observed in practice due to irrational and inexplicable energy benchmarks. To this end, this paper presents a dual energy benchmarking methodology that specifically promotes energy awareness in terms of managerial and operational factors. The presented methodology uses data mining techniques to excavate energy data as well as managerial and operational data, consisting of three steps: (i) data preparation to provide reliable multi-source heterogeneous data; (ii) dynamic energy benchmarking that uses the decision tree algorithm to classify different energy consumption patterns; (iii) load shape benchmarking that interprets energy use behaviors based on load shape features. To demonstrate the effectiveness and practicality, the methodology was implemented in a die-casting workshop. The results showed that a total of nine dynamic energy benchmarks were created primality under the impact of capacity utilization, two of which generated five representative load curves to characterize the workshop's daily energy use behaviors. It was concluded that the methodology could provide practical recommendations on energy-efficient production planning and operation in practical applications.

Energy-Saving Oriented Manufacturing Workshop Facility Layout: A Solution Approach Using Multi-Objective Particle Swarm Optimization

Low-carbon sustainable development has become the consensus of manufacturing enterprises to fulfill their social responsibilities. Facility layout is an essential part of manufacturing system planning. Current research has demonstrated the advantages of energy saving on the manufacturing system level where operational methods (e.g., energy-efficient production scheduling and path planning) can be utilized and do not require massive investment in the existing legacy system. However, these efforts are mostly based on the existing fixed facility layout. Meanwhile, although facility layout problems have been extensively studied so far, the related work seldom involves the optimization of energy consumption (EC) or other EC-related environmental impact indicators, and does not clearly reveal if EC can be an independent optimization objective in facility layout. Accordingly, whether the energy-saving potential of a manufacturing system can be further tapped through rational facility layout is the gap of the current study. To address this, an investigation into energy-saving oriented manufacturing workshop facility layout is conducted. Correspondingly, an energy-efficient facility layout (EFL) model for the multi-objective optimization problem that minimizes total load transport distance and EC is formulated, and a multi-objective particle swarm optimization-based method is proposed as the solution. Furthermore, experimental studies verify the effectiveness of the presented model and its solution, indicating that EC can be regarded as an independent optimization objective during facility layout, and EFL is a feasible energy-saving approach for a manufacturing system.

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.

Multiobjective Optimization-Aided Decision-Making System for Large-Scale Manufacturing Planning.

This work is geared toward a real-world manufacturing planning (MP) task, whose two objectives are to maximize the order fulfillment rate and minimize the total cost. More important, the requirements and constraints in real manufacturing make the MP task very challenging in several aspects. For example, the MP needs to cover many production components of multiple plants over a 30-day horizon, which means that it involves a large number of decision variables. Furthermore, the MP task's two objectives have extremely different magnitudes, and some constraints are difficult to handle. Facing these uncompromising practical requirements, we introduce an interactive multiobjective optimization-based MP system in this article. It can help the decision maker reach a satisfactory tradeoff between the two objectives without consuming massive calculations. In the MP system, the submitted MP task is modeled as a multiobjective integer programming (MOIP) problem. Then, the MOIP problem is addressed via a two-stage multiobjective optimization algorithm (TSMOA). To alleviate the heavy calculation burden, TSMOA transforms the optimization of the MOIP problem into the optimization of a series of single-objective problems (SOPs). Meanwhile, a new SOP solving strategy is used in the MP system to further reduce the computational cost. It utilizes two sequential easier SOPs as the approximator of the original complex SOP for optimization. As part of the MP system, TSMOA and the SOP solving strategy are demonstrated to be efficient in real-world MP applications. In addition, the effectiveness of TSMOA is also validated on benchmark problems. The results indicate that TSMOA as well as the MP system are promising.

Simulation-based Data Analysis to Support the Planning of Flexible Manufacturing Systems

Simulation-based Data Analysis to Support the Planning of Flexible Manufacturing Systems

Classification of Research and Applications of the Computer Aided Process Planning in Manufacturing Systems

Classification of Research and Applications of the Computer Aided Process Planning in Manufacturing Systems

Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods

The problem of controlling the production rates of failure prone manufacturing systems has stochastic features that make it more complex and challenging. In this study, we consider a network of manufacturing machines based on the hedging point policy where the final goods are perishable, and the demand rate is constant. Our objective in this paper is to control the production rates of multiple machines in failure prone manufacturing systems in the presence of perishable goods in order to minimise the expected cost consisting of holding, shortage, perished goods and repair costs over an infinite horizon. We develop a new framework by way of a simulation-optimisation approach to deal with complexity and uncertainty. To this end, we first formulate the analytical model subject to stochastic failures and corrective repairs. Then, we use a combination of simulated annealing metaheuristic, simulation and Taguchi experimental design to estimate the optimal control policy. In addition, a numerical example is presented to illustrate the applicability and efficacy of the proposed framework.

An Integrated Multi-Period Layout Planning and Scheduling Model for Sustainable Reconfigurable Manufacturing Systems

In a dynamic production environment, not only the customer’s needs change with time, but the economic aspects of that environment, such as energy pricing, also change. Reconfigurable Manufacturing Systems (RMSs) are designed to respond to such changes by reconfiguring system components efficiently. This paper presents a novel mathematical model to maximize energy sustainability of RMS. The novelty aspect of the model is the consideration of energy sustainability concurrently with system configuration and scheduling decisions in each period of the planning horizon. The objective of this mixed integer linear model is to minimize the total cost of energy consumption, system reconfiguration, and part transportation between machines, depending on fluctuations of energy pricing and demand during different periods. Several case studies are solved by GAMS Software to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand to show their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the model in larger scale due to its NP-hardness and compared to GAMS for validation. Results show that the presented GA finds near-optimal solutions in 70% shorter time than GAMS on average.

Integrated yet distributed operations planning approach: A next generation manufacturing planning system

Present paper envisages the need for an innovative operations planning system to handle the challenges and opportunities offered by next industrial revolution called Industry 4.0 or smart manufacturing. In specific, to embrace the increasing level of automation in manufacturing industries, the obligation of joint consideration of multiple operations functions is realized. On the other hand, quick response to dynamic conditions created by machine failures, change in demand, uncertainty in supply, etc., is important in captivating the advantages of the digitization in industries. Easing out the computational complexity, imposed by the integration of multiple functions, therefore, becomes an important aspect of next generation manufacturing planning systems. Consequently, in this paper, an agent-based approach is engineered around the opportunities offered by modern digital factory viz., intelligence at the shop-floor and ubiquity of wireless communications. While intelligence at shop-floor allows distributing the decision-making tasks to various functional agents, the communication among the agents makes it feasible to incite integrated view through the coordination agent. The approach is demonstrated for a representative industrial environment of an automotive plant. Further, comparison over conventional approaches, computational comparison, effect of degree of integration, and performance of the approach under dynamic conditions are investigated. Finally, the approach is comprehensively evaluated to analyze its robustness and implications in various manufacturing settings. This extensive investigation shows that the proposed operations planning system has capability to apprehend the benefits from next generation intelligent factory.