264 research outputs found
A DECOMPOSITION-BASED HEURISTIC ALGORITHM FOR PARALLEL BATCH PROCESSING PROBLEM WITH TIME WINDOW CONSTRAINT
This study considers a parallel batch processing problem to minimize the makespan under constraints of arbitrary lot sizes, start time window and incompatible families. We first formulate the problem with a mixed-integer programming model. Due to the NP-hardness of the problem, we develop a decomposition-based heuristic to obtain a near-optimal solution for large-scale problems when computational time is a concern. A two-dimensional saving function is introduced to quantify the value of time and capacity space wasted. Computational experiments show that the proposed heuristic performs well and can deal with large-scale problems efficiently within a reasonable computational time. For the small-size problems, the percentage of achieving optimal solutions by the DH is 94.17%, which indicates that the proposed heuristic is very good in solving small-size problems. For large-scale problems, our proposed heuristic outperforms an existing heuristic from the literature in terms of solution quality
Intelligent shop scheduling for semiconductor manufacturing
Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process.
Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant
Hybrid ASP-based multi-objective scheduling of semiconductor manufacturing processes (Extended version)
Modern semiconductor manufacturing involves intricate production processes
consisting of hundreds of operations, which can take several months from lot
release to completion. The high-tech machines used in these processes are
diverse, operate on individual wafers, lots, or batches in multiple stages, and
necessitate product-specific setups and specialized maintenance procedures.
This situation is different from traditional job-shop scheduling scenarios,
which have less complex production processes and machines, and mainly focus on
solving highly combinatorial but abstract scheduling problems. In this work, we
address the scheduling of realistic semiconductor manufacturing processes by
modeling their specific requirements using hybrid Answer Set Programming with
difference logic, incorporating flexible machine processing, setup, batching
and maintenance operations. Unlike existing methods that schedule semiconductor
manufacturing processes locally with greedy heuristics or by independently
optimizing specific machine group allocations, we examine the potentials of
large-scale scheduling subject to multiple optimization objectives.Comment: 17 pages, 1 figure, 4 listings, 1 table; a short version of this
paper is presented at the 18th European Conference on Logics in Artificial
Intelligence (JELIA 2023
A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory
This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the ruleâs effectiveness. The findings in this research point to several directions that can be exploited in the future
Production Scheduling
Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume
A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems
Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job
shop scheduling problems (JSP) is proposed.
Design/methodology/approach: In the algorithm, a number of sub-problems are
constructed by iteratively decomposing the large-scale JSP according to the process route of
each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the
sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality,
a detection method for multi-bottleneck machines based on critical path is proposed. Therewith
the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck
operations. According to the principle of âBottleneck leads the performance of the whole
manufacturing systemâ in TOC (Theory Of Constraints), the bottleneck operations are
scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are
scheduled by dispatching rules for the improvement of the solving efficiency.
Findings: In the process of the subproblems' construction, partial operations in the previous
scheduled sub-problem are divided into the successive sub-problem for re-optimization. This
strategy can improve the solution quality of the algorithm. In the process of solving the sub problems, the strategy that evaluating the chromosome's fitness by predicting the global
scheduling objective value can improve the solution quality.
Research limitations/implications: In this research, there are some assumptions which
reduce the complexity of the large-scale scheduling problem. They are as follows: The
processing route of each job is predetermined, and the processing time of each operation is
fixed. There is no machine breakdown, and no preemption of the operations is allowed. The
assumptions should be considered if the algorithm is used in the actual job shop.
Originality/value: The research provides an efficient scheduling method for the large-scale
job shops, and will be helpful for the discrete manufacturing industry for improving the
production efficiency and effectiveness.Peer Reviewe
A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems
Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job
shop scheduling problems (JSP) is proposed.
Design/methodology/approach: In the algorithm, a number of sub-problems are
constructed by iteratively decomposing the large-scale JSP according to the process route of
each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the
sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality,
a detection method for multi-bottleneck machines based on critical path is proposed. Therewith
the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck
operations. According to the principle of âBottleneck leads the performance of the whole
manufacturing systemâ in TOC (Theory Of Constraints), the bottleneck operations are
scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are
scheduled by dispatching rules for the improvement of the solving efficiency.
Findings: In the process of the subproblems' construction, partial operations in the previous
scheduled sub-problem are divided into the successive sub-problem for re-optimization. This
strategy can improve the solution quality of the algorithm. In the process of solving the sub problems, the strategy that evaluating the chromosome's fitness by predicting the global
scheduling objective value can improve the solution quality.
Research limitations/implications: In this research, there are some assumptions which
reduce the complexity of the large-scale scheduling problem. They are as follows: The
processing route of each job is predetermined, and the processing time of each operation is
fixed. There is no machine breakdown, and no preemption of the operations is allowed. The
assumptions should be considered if the algorithm is used in the actual job shop.
Originality/value: The research provides an efficient scheduling method for the large-scale
job shops, and will be helpful for the discrete manufacturing industry for improving the
production efficiency and effectiveness.Peer Reviewe
Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning
The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques
A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory
A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future
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