Accounting for parameter uncertainty in large-scale stochastic simulations with correlated inputs

This paper considers large-scale stochastic simulations with correlated inputs having normal-to-anything (NORTA) distributions with arbitrary continuous marginal distributions. Examples of correlated inputs include processing times of workpieces across several workcenters in manufacturing facilities and product demands and exchange rates in global supply chains. Our goal is to obtain mean performance measures and confidence intervals for simulations with such correlated inputs by accounting for the uncertainty around the NORTA distribution parameters estimated from finite historical input data. This type of uncertainty is known as the parameter uncertainty in the discrete-event stochastic simulation literature. We demonstrate how to capture parameter uncertainty with a Bayesian model that uses Sklar's marginal-copula representation and Cooke's copula-vine specification for sampling the parameters of the NORTA distribution. The development of such a Bayesian model well suited for handling many correlated inputs is the primary contribution of this paper. We incorporate the Bayesian model into the simulation replication algorithm for the joint representation of stochastic uncertainty and parameter uncertainty in the mean performance estimate and the confidence interval. We show that our model improves both the consistency of the mean line-item fill-rate estimates and the coverage of the confidence intervals in multiproduct inventory simulations with correlated demands. © 2011 INFORMS

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines. © 2019 by the authors

Dynamic Scheduling of Maintenance by a Reinforcement Learning Approach: A Semiconductor Simulation Study

Scheduling in a semiconductor back-end factory is an extremely sophisticated and complex task. In semiconductor industry, more often than not, the scheduling of maintenance is underexposed to production scheduling. This is a missed opportunity as maintenance and production activities are deeply intertwined. This study considers the dynamic scheduling of maintenance activities on an assembly line. A policy is constructed to schedule a cleaning activity on the last machine of an assembly line such that the average production rate is maximized. The policy takes into account the given flexibility and the buffer content of the buffers in-between the machines in the assembly line. A Markov Decision Process is formulated for the problem and solved using Value Iteration and Reinforcement Learning Algorithms. In addition, for a real world case study, a simulation analysis is performed to evaluate the potential practical benefit

A Simulation Model for Cooperative Robotics in Dairy Farms

Clean floors in dairy farms are of vital importance to mitigate risks regarding cow welfare and to avoid ammonia emissions. In modern dairy farms it is common to deploy manure cleaning robots to automate the cleaning task. In the current state-of-the art, robots operate in relative solitude with no coordination or cooperation within the fleet. This paper employs discrete-event simulation to test the effectiveness of different strategies for cooperative, team-based cleaning in dairy farms. Special attention is paid to the impact of various team compositions and robot characteristics on team routing. The results look promising: the minimum cleanliness can be increased by deploying teams while simultaneously reducing the number of cow-robot interactions

Workload Control in High-Mix-Low-Volume Factories Through the Use of a Multi-Agent System

Order release in High-Mix-Low-Volume machine environments is often difficult to control due to the high variety of these shops. This paper, therefore, proposes an extension to a Multi-Agent System to control order release. Intelligence is introduced to the agent that is responsible for order release to autonomously learn which jobs to release into the shop through the use of sequencing rules, depending on the current environment. The objective is to minimize the mean weighted tardiness of all jobs. Computational results show that the proposed sequencing rules outperform other more common dispatching rules in terms of mean weighted tardiness. Further analysis of the results also reveals that a more accurate prediction of the lead time of jobs can be made, which is one of the main interests of practitioners in High-Mix-Low- Volume environments

Closing The Gap: A Digital Twin as a Mechanism to Improve Spare Parts Planning Performance

In order to meet service level agreements at minimal cost, Original Equipment Manufacturers (OEMs) use spare parts planning models to determine the optimal base stock levels at the warehouses in their service network. In practice, however, these optimized base stock levels result in a realized performance that deviates from the expected performance. Therefore, it is beneficial for these companies to evaluate the base stock levels in terms of service performance, inventory value, and costs. In order to measure this planning performance, we developed a digital twin that is able to measure the planning performance and identify root causes for the performance gap. Our digital twin helped ASML, an OEM in the semiconductor industry, to create a feedback loop between the spare parts planning model and its realized performance in practice, providing a mechanism to learn from past results and determine actions to close the gap between the expected and realized performance

Order Release Strategies for a Collaborative Order Picking System

A collaborative order picking system (COPS) enables human-robot collaboration by using order pickers for picking and autonomous mobile robots (AMRs) for transporting load carriers. Owing to the potential performance enhancement compared to a traditional manual order picking system, COPSs are gaining momentum in the retail warehousing sector. This paper proposes order release strategies based on priority and dispatching rules to achieve the best pick rate performance per AMR. A discrete event simulation model is developed to facilitate the evaluation of the proposed strategies. Their effectiveness is demonstrated with the use of real-world data from a case study warehouse. Our computational results show that a COPS using proposed strategies significantly improves the pick rate performance compared to the current practice

Autonomous Scheduling in Semiconductor Back-End Manufacturing

Production scheduling decisions have a large impact on efficiency and output, especially in complex environments such as those with sequence- and machine-dependent setup times. In practice, these scheduling problems are usually solved for a fixed time ahead. In semiconductor back-end manufacturing, given the dynamics of the environment, it is commonly observed that a schedule is no longer optimal soon after it is made. Here, we propose time-based rescheduling heuristics that can mitigate the effect of these deviations from the schedules. We build a simulation model to represent the dynamics of the shop floor as well as its interaction with the upper management level that decides how orders are released. The simulation model, which is built and validated using real-world data, enables us to evaluate the performance of the rescheduling heuristics. By comparing the results to the case without rescheduling, it is shown that rescheduling can significantly improve relevant performance measures