Robust Production Control Research Articles

Self-learning and autonomously adapting manufacturing equipment for the circular factory

Abstract The integration of both linear and circular processes in one production system poses significant challenges. In particular, the reprocessing of end-of-life products is associated with uncertainties at all levels of the production system, from the initial planning and control through to the executing production hardware and intralogistics. To address these challenges, this article presents approaches for self-learning and autonomously adapting production equipment for the Circular Factory. Initially, hardware and software solutions are developed to cover the necessary processes. Reprocessing is covered by modular and reconfigurable manufacturing cells, which also include new process chains such as the combination of additive-subtractive processes. The provided capabilities must be applied to ever new products, for example by transferring human procedures for unknown products to the production equipment. Lastly, an overall robust and dynamic production planning and control system is developed that maintains continuous operation even in unforeseen situations. The resulting highly dynamic overall system is connected by an autonomous intralogistics system.

Robust condition‐based production and maintenance planning for degradation management

We study the robust production and maintenance control for a production system subject to degradation. A periodic maintenance scheme is considered, and the system production rate can be dynamically adjusted before maintenance, serving as a proactive way of degradation management. Optimal control of the degradation rate aims to strike a balance between the risk of failure and the production profit. We first consider the scenario in which the degradation rate increases linearly with the production rate. Different from the existing literature that posits a parametric stochastic degradation process, we suppose that the degradation increment during a period lies in an uncertainty set, and our objective is to minimize the maintenance cost in the worst case. The resulting model is a robust mixed‐integer linear program. We derive its robust counterpart and establish structural properties of the optimal production plan. These properties are then used for real‐time condition‐based control of the production rate through reoptimization. The model is further generalized to the nonlinear production–degradation relation. Based on a real production–degradation dataset from an extruder system, we conduct comprehensive numerical experiments to illustrate the application of the model. Numerical results show that our model significantly outperforms existing methods in terms of the mean and variance of cost rate when degradation model misspecification is presented.

Optimal RFID Deployment in a Multiple-Stage Production System Under Inventory Inaccuracy and Robust Control Policy

Deploying radio frequency identification (RFID) devices or applying robust production control policy can both reduce the negative impact of inventory inaccuracy. In this paper, we investigate the problem of optimally deploying RFID devices in a multistage production system with inventory inaccuracy and robust control policy. We first model the multiple-stage production system as a Markov decision process model, in which the decision of deploying RFID devices is included. After that, an integer programming model is constructed for optimally deploying RFID device problem. Meanwhile, we propose a robust production control strategy by employing the policy iteration and the decomposition/coordination techniques, and a heuristic algorithm is proposed to solve the integer programming model. Finally, numerical experiments are conducted to examine the performance of the proposed optimal RFID deployment approach. We discover that when RFID is deployed at a certain workstation (i.e., production equipment), the benefits of deploying RFID devices can transfer to the upstream and downstream stages (i.e., reduce the production cost of both upstream and downstream stages). Furthermore, numerical experiments reveal that the deploying RFID devices evenly among all the stages of the production system will generate a better performance.

A Robust Production Control Policy for Hedging Against Inventory Inaccuracy in a Multiple-Stage Production System With Time Delay

Inventory inaccuracy has significant negative impact on the performance of production control. In this paper, we investigate the robust production control problem for a multiple-stage production system with inventory inaccuracy and time delay between stages. The objective of this problem is to minimize the average production cost. The problem is solved in two steps. First, an approximate optimal production control policy is developed for a multiple-stage production system with time delay but without inventory inaccuracy. Second, we reveal that the discontinuity of the controls makes the policy sensitive to the error of inventory data, and then propose a robust production control policy to eliminate this sensitivity, in which the control variables change smoothly when state variables cross boundaries between different domains in the state space. We also prove that the robustness of the proposed robust policy is better than that of the approximate optimal policy. Finally, numerical experiments are conducted to examine the performance of the proposed robust policy for hedging against inventory inaccuracy.

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.

A Robust Replenishment and Production Control Policy for a Single-Stage Production/Inventory System with Inventory Inaccuracy

Inaccurate inventory data are often found in production/inventory systems, and have a significant negative impact on the performance of raw materials replenishment and production control. In this paper, robust replenishment and production control problem for a single-stage production/inventory system with inventory inaccuracy is investigated, with the objective of minimizing the average inventory and production cost. In addition to the inventory inaccuracy, the lead-time of raw materials ordering and machine unreliability are also taken into consideration. The problem is solved in two steps. First, for a single-stage production/inventory system with ordering lead-time and uncertain production capacity, but without inventory inaccuracy, an approximate optimal replenishment, and production control policy is constructed. Second, we reveal that it is the discontinuity of the control variables in different domains of the state space that makes the policy sensitive to inventory data error. Consequently, we develop a robust replenishment and production control policy to eliminate this sensitivity. This approach maintains the structural characteristics of the optimal policy, but allows the control variables to change smoothly when the state variables cross boundaries between different domains in the state space. Numerical experiments are conducted to examine the performance of the proposed robust policy for hedging against inventory inaccuracy, and a sensitivity analysis is used to study how the parameters of the production/inventory system and the robust policy impact on the average inventory and production cost.

Robust production control policy for a multiple machines and multiple product-types manufacturing system with inventory inaccuracy

Inventory inaccuracy often exists in manufacturing systems, which has great negative impact on the performance of production control, e.g. very high work-in-process holding cost or backlog penalty. To hedge against inventory inaccuracy, the robust production control problems will be investigated for a multiple machines and multiple product-types manufacturing system with uncertain production capacity. The objective of our problem is to minimise the average production cost. To solve this problem, a robust production control policy is developed, which is insensitive to the inventory record errors, and whose robustness is better than the traditional hedging point policy for optimal production control. Finally, numerical experiments are conducted to examine the performance of the proposed robust production control policy against inventory inaccuracy. Based on the experimental results, the conditions of applying the proposed policy are also obtained.

Robust production control policy for a single machine and single part-type manufacturing system with inaccurate observation of production surplus

The optimal production control policy (i.e., the hedging point policy) for a single machine and single part-type manufacturing system for the case where the production surplus is accurately known has been reported in the literature. However, the production surplus is often observed inaccurately in practice. Ignoring this inaccuracy in using the optimal policy leads to not only non-robust situations but also high production costs. To consider the effect of the inaccurate observation, robustness of the production control policy is required. The robustness can be evaluated in terms of the difference between the production surplus trajectory under the policy with an inaccurate observation and the trajectory under the policy with an accurate production surplus (i.e., the hedging point policy). A difference of zero is the ideal case; however, this is generally impossible to achieve since the extent of the observation error is generally unknown. This article proposes a new robust production control policy that can give a small difference between the two trajectories. Compared with the hedging point policy, the proposed policy is more insensitive to production surplus inaccuracy in the neighborhood of the hedging point but responds more slowly to a failure of the machine. Simulation studies show that the proposed policy has a better robustness than the hedging point policy for cases where, just as in actual industrial practice, the machine failure time is much shorter than the up time of the machine. In these cases, with an increase in the observation error, the robustness of the proposed policy becomes increasingly better than that for the hedging point policy.

A lean pull system design analysed by value stream mapping and multiple criteria decision-making method under demand uncertainty

Lean philosophy is a systematic approach for identifying and eliminating waste through continuous improvement in pursuit of perfection, using a pull-control strategy derived from customers' requirements. However, not all lean implementations have produced such desired results because of not having a clear implementation procedure and execution guide. This article proposes a lean pull system implementation procedure based on combining a supermarket supply with two constant work-in-process (CONWIP) structures that can concurrently consider manufacturing system variability and demand uncertainty in multi-products, multi-stage processes to achieve lean pull system. The study uses a multiple criteria decision-making (MCDM) method, using a hybrid Taguchi technique for order preference by similarity to ideal solution (TOPSIS) method that takes customer demand uncertainty as a noise factor. This allowed identification of the most robust production control strategy to identify an optimal scenario from alternative designs. Value stream mapping (VSM) was applied to visualise what conditions would work when improvements are introduced. Finally, a real-world, thin film transistor-liquid crystal display (TFT-LCD) manufacturing case-study under demand uncertainty is used to demonstrate and test findings. After comparing the current-state map and the future-state map of the case-study, the simulation results indicate that the average cycle time reduced from 15.4 days to 4.82 days without any loss of throughput.

Robust production control against propagation of disruptions

In hierarchical production control systems, planning decides on capacities and operations so as to meet demand, while scheduling should guarantee the execution of production plans even in face of uncertainties. The management practice advocates rolling horizon approaches despite the danger of plan nervousness. We propose a novel framework to handle uncertainties closer to the root of their sources, when scheduling local resources. The method keeps the complexity of planning and scheduling at bay and stops the propagation of local disruptions to other resources. The paper presents the theoretical model, the solution technique, and shows their applicability on a case study taken from the tool industry.

Robust production control policies considering WIP balance and setup time in a semiconductor fabrication line

This paper discusses the problem of determining workload in a semiconductor fabrication line for providing robust production control. A mathematical model is proposed to determine the amount of wafers to be processed on equipment in a photolithography process where the setup time is incurred if the type of wafers is changed on the equipment. The objective of the model is to control the fabrication line by maintaining the target work-in-process (WIP) level as close as possible for the purpose of short cycle time and by minimizing the setup time loss for maximal throughput. The proposed model is formulated using mixed integer programming (MIP) to minimize the weighted sum of two objective functions. A heuristic approach is suggested using linear relaxation and its adjustment. Performances are evaluated for the optimal solution with computational cost and for the heuristics. It is shown that the heuristics give good solutions which are 10% away, on average, from the optimal solution, but which can be obtained in a few seconds.

Preprocessing and exploratory analysis of chromatographic profiles of plant extracts

The characterization of herbal extracts to compare samples from different origin is important for robust production and quality control strategies. This characterization is now mainly performed by analysis of selected marker compounds. Metabolic fingerprinting of full metabolite profiles of plant extracts aims at a more rapid and thorough screening or classification of plant material. We will show that HPLC is an appropriate technique for metabolic fingerprinting of secondary metabolites, given that adequate preprocessing of raw profiles is performed. Additional variation, which results from sample preparation and changing measurement conditions, usually obscures the information of interest in these raw profiles. This paper illustrates the importance of preprocessing of chromatographic fingerprinting data. Different alignment methods are discussed as well as the influence of normalization. Weighted principal component analysis is introduced as a valuable alternative to autoscaling of data. LC–UV data on Willow ( Salix sp.) extracts is used to evaluate these preprocessing methods and their influence on exploratory data analysis.

A robust production control policy for VLSI wafer fabrication

The authors present control policy for shop-level scheduling in a semiconductor wafer fabrication facility. The policy is designed to reduce the work in process in the shop floor and to follow the production plan as closely as possible. It is also robust against random interference such as machine breakdowns. The flow rate control policy is compared with two other approaches using event-driven simulation. Simulation results are presented to show the advantages of the proposed approach. >