Query: Job shop scheduling techniques in semiconductor manufacturing

Candidate papers:

abstract: This paper presents a brief review on job shop scheduling techniques in semiconductor manufacturing. The manufacturing environment in a semiconductor industry is considered a highly complex job shop, involving multiple types of work centers, large and changing varieties of products, sequence dependent setup times, reentrant process flow, etc. in a dynamic scheduling environment. Due to the stubborn nature of the deterministic job shop scheduling problem itself, many of the solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. In this paper, we seek to provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.
author_names: Amit Kumar Gupta

Appa Iyer Sivakumar
title: Job shop scheduling techniques in semiconductor manufacturing
year: 2006

abstract: Abstract Simulation Optimization (SO) techniques refer to a set of methods that have been applied to stochastic optimization problems, structured so that the optimizer(s) are integrated with simulation experiments. Although SO techniques provide promising solutions for large and complex stochastic problems, the simulation model execution is potentially expensive in terms of computation time. Thus, the overall purpose of this research is to advance the evolutionary SO methods literature by researching the use of metamodeling within these techniques. Accordingly, we present a new Evolutionary Learning Based Simulation Optimization (ELBSO) method embedded within Ordinal Optimization. In ELBSO a Machine Learning (ML) based simulation metamodel is created using Genetic Programming (GP) to replace simulation experiments aimed at reducing computation. ELBSO is evaluated on a Stochastic Job Shop Scheduling Problem (SJSSP) which is a well known complex production planning problem in most industries such as semiconductor manufacturing. To build the metamodel from SJSSP instances that replace simulation replications, we employ a novel training vector to train GP. This then is integrated into an evolutionary two phased Ordinal Optimization approach to optimize an SJSSP which forms the ELBSO method. Using a variety of experimental SJSSP instances, ELBSO is compared with evolutionary optimization methods from the literature and typical dispatching rules. Our findings include the superiority of ELBSO over all other algorithms in terms of the quality of solutions and computation time. Furthermore, the integrated procedures and results provided within this article establish a basis for future SO applications to large and complex stochastic problems.
author_names: Amirreza Ghasemi

Amir Ashoori

Cathal Heavey
title: Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems
venue: Appl. Soft Comput.
year: 2021

abstract: In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.
author_names: Lars Monch

John W Fowler

Stephane Dauzere-Peres

Scott J Mason

Oliver Rose
title: A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations
venue: J. Sched.
year: 2011

abstract: In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems that can be found in semiconductor manufacturing systems. We describe batch scheduling problems, job shop scheduling problems, scheduling problems with secondary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some challenges in scheduling semiconductor manufacturing operations.
author_names: Lars Monch

John W Fowler

Stephane Dauzere-Peres

Scott J Mason

Oliver Rose
title: Scheduling Semiconductor Manufacturing Operations Problems Solution Techniques and Future Challenges
year: 2009

abstract: Lars MonchJohn W. FowlerStephane Dauzere PeresScott J. MasonOliver Rose Abstract In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems that can be found in semiconductor manufacturing systems. We describe batch scheduling problems, job shop scheduling problems, scheduling problems with secondary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some challenges in scheduling semiconductor manufacturing operations.
author_names: Lars Monch

John W Fowler

Stephane Dauzere-Peres

Scott J Mason

Oliver Rose
title: Scheduling Semiconductor Manufacturing Operations: Problems, Solution Techniques, and Future Challenges
year: 2009

abstract: In semiconductor manufacturing, it requires more than one objective such as cycle time, machine utilization and due date accuracy to be kept in focus simultaneously, while developing an effective scheduling. In this paper, a near optimal solution, which is not inferior to any other feasible solutions in terms of all objectives, is generated with a combination of the analytically optimal and simulation based scheduling approach. First, the job shop scheduling problem is modeled using the discrete event simulation approach and the problem is divided in to simulation clock based lot selection sub problems. Then, at each decision instant in simulated time, a Pareto optimal lot is selected using the various techniques to deal with multiobjective optimization such as weighted aggregation approach, global criterion method, minimum deviation method, and compromise programming. An illustration shows how these techniques work effectively in solving the multiobjective scheduling problem using discrete event simulation.
author_names: Amit Kumar Gupta

Appa Iyer Sivakumar
title: Simulation based multiobjective schedule optimization in semiconductor manufacturing
venue: Proceedings of the Winter Simulation Conference
year: 2002

abstract: In the current literature dealing with job shop scheduling, most of the approaches have developed models based on the assumption that the problem domain does not contain any imprecision. However, this hypothesis is strongly challenged in the implementation phase of such models imprecision is inherent to production systems involving human intervention. The aim of this paper is to demonstrate the advantages of possibilistic production data modeling in a real world application, i.e. semiconductor manufacturing. In this work, a discrete event simulation model (MELISSA) for performance evaluation of a batch manufacturing facility previously developed in our laboratory has been extended to treat uncertainties modeled by fuzzy numbers. Due to the confidential nature of industrial data, an illustrative example, presenting the same typical features as a real problem, is treated and analyzed using fuzzy concepts. Inclusion of fuzzy techniques provides the decision maker with a range of possible values for completion times, average storage times, and operator workload instead of a unique value (which has little significance due to the variety of human operators) In addition, the negative portion of average waiting times yields useful information for the manager to detect deficient resources in the production system.
author_names: Catherine Azzaro-Pantel

Pascal Floquet

Luc Pibouleau

Serge Domenech
title: A fuzzy approach for performance modeling in a batch plant: application to semiconductor manufacturing
venue: IEEE Trans. Fuzzy Syst.
year: 1997

abstract: This paper investigates a difficult scheduling problem on a specialized two stage hybrid flow shop with multiple processors that appears in semiconductor manufacturing industry, where the first and second stages process serial jobs and parallel batches, respectively. The objective is to seek job machine, job batch, and batch machine assignments such that makespan is minimized, while considering parallel batch, release time, and machine eligibility constraints. We first propose a mixed integer programming (MIP) formulation for this problem, then gives a heuristic approach for solving larger problems. In order to handle real world large scale scheduling problems, we propose an efficient dispatching rule called BFIFO that assigns jobs or batches to machines based on first in first out principle, and then give several reoptimization techniques using MIP and local search heuristics involving interchange, translocation and transposition among assigned jobs. Computational experiments indicate our proposed re optimization techniques are efficient. In particular, our approaches can produce good solutions for scheduling up to 160 jobs on 40 machines at both stages within 10 min.
author_names: I-Lin Wang

Taho Yang

Yu-Bang Chang
title: Scheduling two stage hybrid flow shops with parallel batch, release time, and machine eligibility constraints
venue: J. Intell. Manuf.
year: 2012

abstract: The authors present a strategy for shop level scheduling in a semiconductor wafer fabrication facility, which reduces WIP (work in process) and improves on time ship performance. Algorithms for lot release and lot dispatch are defined on the basis of dynamic programming techniques. The strategy is called flow rate control because it treats the production rates according to certain rules. The most important rule is the two boundary rule that consists of a simple inventory control policy and a simple surplus control policy. Development of the strategy was motivated by three features of the wafer manufacturing process: job re entry, the large number of machines and products, and random events such as machine failures and yield fluctuations. Two boundary control works by first forming a virtual job shop corresponding to the manufacturing process flow. Inventory and surplus levels are calculated for each job step and compared with predetermined values. These predetermined values are calculated from the failure rates and yield fluctuations of wafer fabrication. The loading rate at each job step in the wafer fabrication process is determined by the result of the comparison. An event driven simulator designed for job shop production simulation was used to compare the flow rate control strategy with other scheduling strategies. The results showed marked improvement in WIP levels and ship performance over these other approaches.<ETX>
author_names: Patrick Kager

Sheldon X C Lou
title: Wafer fabrication scheduling using flow rate control strategy
venue: IEEE/SEMI International Semiconductor Manufacturing Science Symposium
year: 1989

abstract: Semiconductor manufacturing is a complicated flexible job shop scheduling problem (FJSP) of combinatorial complexity. Because of the adoption of advanced process control and advanced equipment control, the processing time in advanced wafer fabs become uncertain. Existing approaches considering constant processing time may not be appropriate to address the present problem in a real setting. In practice, processing times can be represented as intervals with the most probable completion time somewhere near the middle of the interval. A fuzzy number that is a generalized interval can represent this processing time interval exactly and naturally. This paper developed a hybrid approach integrating a particle swarm optimization algorithm with a Cauchy distribution and genetic operators (HPSO+GA) for solving an FJSP by finding a job sequence that minimizes the makespan with uncertain processing time. In particular, the proposed hybridized HPSO+GA approach employs PSO for creating operation sequences and assigning the time and resources for each operation, and then uses genetic operators to update the particles for improving the solution. To estimate the validity of the proposed approaches, experiments were conducted to compare the proposed approach with conventional approaches. The results show the practical viability of this approach. This paper concludes with discussions of contributions and recommends directions for future research.
author_names: Thitipong Jamrus

Chen-Fu Chien

Mitsuo Gen

Kanchana Sethanan
title: Hybrid Particle Swarm Optimization Combined With Genetic Operators for Flexible Job Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2018