Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

Cloud manufacturing (CM) is an emerging manufacturing model based on collaboration among manufacturing enterprises in a cloud computing environment. Naturally, collaboration is one of main factors that impacts performance in a variety of ways such as quality, lead time, and cost. Therefore, collaboration levels should be considered when solving operational issues in CM. However, there has been no attempt to estimate these levels between enterprises participating in CM. The collaboration level among enterprises in CM is defined as the ability to produce a manufacturing service that satisfies a customer by means of collaborative production amongst enterprises. We measure it as the conditional probability that collaborative performances are high given collaborative performance factors (e.g., resource sharing, information sharing, etc.). In this paper, we propose a framework for estimating collaboration levels. We adopt a probabilistic graphical model (PGM) to develop the framework, since the framework includes a lot of random variables and complex dependencies among them. The framework yields conditional probabilities that two enterprises will reduce the total cost, improve resource utilization or quality through collaboration between them given each enterprise’s features, collaboration possibility, and collaboration activities. The collaboration levels the proposed framework yields will help to handle diverse operational problems in CM

Markov Network Model with Unreliable Edges

Network topologies representing the relationships among nodes in supply chain network should be dynamic in time, partly because the relationships are unreliable. Existing network analysis methods such as the Markov network, however, do not consider the time-dependency of the unreliable edges, and therefore these methods cannot consider the dynamics of networks precisely. In order to consider the unreliable edges in Markov network analysis, we suggest a Markov Network with Time-Varying Edge algorithm in this paper, where the discrete time Markov chain is employed to express the time-dependency of the edges. The algorithm consists of: finding unreliable edges for maximal cliques; developing a discrete time Markov chain model for each unreliable edge which composes any maximal cliques and composition; and analyzing the Markov network. We explain how to calculate the transient probabilities of an observation and limiting probability with this algorithm and numerical application to the supply chain network is provided

Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient

Clustering and Dispatching Rule Selection Framework for Batch Scheduling

In this study, a batch scheduling with job grouping and batch sequencing is considered. A clustering algorithm and dispatching rule selection model is developed to minimize total tardiness. The model and algorithm are based on the constrained k-means algorithm and neural network. We also develop a method to generate a training dataset from historical data to train the neural network. We use numerical examples to demonstrate that the proposed algorithm and model efficiently and effectively solve batch scheduling problems

The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing

As a service oriented and networked model, cloud manufacturing (CM) has been proposed recently for solving a variety of manufacturing problems, including diverse requirements from customers. In CM, on-demand manufacturing services are provided by a temporary production network composed of several enterprises participating within an enterprise network. In other words, the production network is the main agent of production and a subset of an enterprise network. Therefore, it is essential to compose the enterprise network in a way that can respond to demands properly. A properly-composed enterprise network means the network can handle demands that arrive at the CM, with minimal costs, such as network composition and operation costs, such as participation contract costs, system maintenance costs, and so forth. Due to trade-offs among costs (e.g., contract cost and opportunity cost of production), it is a non-trivial problem to find the optimal network enterprise composition. In addition, this includes probabilistic constraints, such as forecasted demand. In this paper, we propose an algorithm, named the dynamic enterprise network composition algorithm (DENCA), based on a genetic algorithm to solve the enterprise network composition problem. A numerical simulation result is provided to demonstrate the performance of the proposed algorithm

Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

Cloud manufacturing (CM) is an emerging manufacturing model based on collaboration among manufacturing enterprises in a cloud computing environment. Naturally, collaboration is one of main factors that impacts performance in a variety of ways such as quality, lead time, and cost. Therefore, collaboration levels should be considered when solving operational issues in CM. However, there has been no attempt to estimate these levels between enterprises participating in CM. The collaboration level among enterprises in CM is defined as the ability to produce a manufacturing service that satisfies a customer by means of collaborative production amongst enterprises. We measure it as the conditional probability that collaborative performances are high given collaborative performance factors (e.g., resource sharing, information sharing, etc.). In this paper, we propose a framework for estimating collaboration levels. We adopt a probabilistic graphical model (PGM) to develop the framework, since the framework includes a lot of random variables and complex dependencies among them. The framework yields conditional probabilities that two enterprises will reduce the total cost, improve resource utilization or quality through collaboration between them given each enterprise’s features, collaboration possibility, and collaboration activities. The collaboration levels the proposed framework yields will help to handle diverse operational problems in CM

Interactive Q-Learning Approach for Pick-and-Place Optimization of the Die Attach Process in the Semiconductor Industry

In semiconductor back-end production, the die attach process is one of the most critical steps affecting overall productivity. Optimization of this process can be modeled as a pick-and-place problem known to be NP-hard. Typical approaches are rule-based and metaheuristic methods. The two have high or low generalization ability, low or high performance, and short or long search time, respectively. The motivation of this paper is to develop a novel method involving only the strengths of these methods, i.e., high generalization ability and performance and short search time. We develop an interactive Q-learning in which two agents, a pick agent and a place agent, are trained and find a pick-and-place (PAP) path interactively. From experiments, we verified that the proposed approach finds a shorter path than the genetic algorithm given in previous research

A Graphical Model to Diagnose Product Defects with Partially Shuffled Equipment Data

The diagnosis of product defects is an important task in manufacturing, and machine learning-based approaches have attracted interest from both the industry and academia. A high-quality dataset is necessary to develop a machine learning model, but the manufacturing industry faces several data-collection issues including partially shuffled data, which arises when a product ID is not perfectly inferred and yields an unstable machine learning model. This paper introduces latent variables to formulate a supervised learning model that addresses the problem of partially shuffled data. The experimental results show that our graphical model deals with the shuffling of product order and can detect a defective product far more effectively than a model that ignores shuffling

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

RUL (remaining useful life) shapelets were recently developed to overcome the shortcomings of similarity-based RUL prediction methods, such as high sensitivity to parameters. RUL shapelets are informative subsequences whose distances to a run-to-failure time series sample are very useful for predicting the RUL of the sample. However, the prediction performance and interpretability highly depend on the set of RUL shapelets, and it is very difficult to compose an optimized set. In this paper, we mathematically formalize the RUL shapelet composition problem with multiple objective functions. In addition, we analyze the characteristics of good RUL shapelet sets and develop a solution methodology based on a genetic algorithm. From the various experiments, we validate that the proposed method outperforms previous ones and suggest how to use the proposed method. The solution methodology developed in this paper can be applied to solve various RUL prediction problems. In addition, the findings on the RUL shapelets can help researchers develop their RUL shapelet-based solution