Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

Scheduling of Batch Processors in Semiconductor Manufacturing – A Review

In this paper a review on scheduling of batch processors (SBP) in semiconductor manufacturing (SM) is presented. It classifies SBP in SM into 12 groups. The suggested classification scheme organizes the SBP in SM literature, summarizes the current research results for different problem types. The classification results are presented based on various distributions and various methodologies applied for SBP in SM are briefly highlighted. A comprehensive list of references is presented. It is hoped that, this review will provide a source for other researchers/readers interested in SBP in SM research and help simulate further interest.Singapore-MIT Alliance (SMA

A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem

Serial-batch scheduling problems are widespread in several industries (e.g., the metal processing industry or industrial 3D printing) and consist of two subproblems that must be solved simultaneously: the grouping of jobs into batches and the sequencing of the created batches. This problem’s NP-hard nature prevents optimally solving large-scale problems; therefore, heuristic solution methods are a common choice to effectively tackle the problem. One of the best-performing heuristics in the literature is the ATCS–BATCS(β) heuristic which has three control parameters. To achieve a good solution quality, most appropriate parameters must be determined a priori or within a multi-start approach. As multi-start approaches performing (full) grid searches on the parameters lack efficiency, we propose a machine learning enhanced grid search. To that, Artificial Neural Networks are used to predict the performance of the heuristic given a specific problem instance and specific heuristic parameters. Based on these predictions, we perform a grid search on a smaller set of most promising heuristic parameters. The comparison to the ATCS–BATCS(β) heuristics shows that our approach reaches a very competitive mean solution quality that is only 2.5% lower and that it is computationally much more efficient: computation times can be reduced by 89.2% on average

Performance of a Serial-Batch Processor System with Incompatible Job Families under Simple Control Policies

A typical example of a batch processor is the diffusion furnace used in wafer fabrication facilities (otherwise known as wafer fabs). In diffusion, silicon wafers are placed inside the furnace, and dopant is flown through the wafers via nitrogen gas. The higher the temperature, the faster the dopant penetrates the wafer surface. Then, a thin layer of silicon dioxide is grown, to help the dopant diffuse into the silicon. This operation can take 10 hours or more to finish processing, as compared to one or two hours for other wafer fab operations, according to Uzsoy [8]. Diffusion furnaces typically can process six to eight lots concurrently; we call the lots processed concurrently a batch. The quantity of lots loaded into the furnace does not affect the processing time. Only lots that require the same chemical recipe and temperature may be batched together at the diffusion furnace. We wish to control the production of a manufacturing system, comprised of a serial processor feeding the batch processor. The system produces different job types, and each job can only be batched together with jobs of the same type. More specifically, we explore the idea of controlling the production of the serial processor, based on the wip found in front of the batch processor. We evaluate the performance of our manufacturing system under several simple control policies under a range of loading conditions and determine which control policies perform better under which conditions. It is hoped that the results obtained from this small system could be extended to larger systems involving a batch processor, with particular emphasis placed on the applicability of such policies in wafer fabrication.Singapore-MIT Alliance (SMA

Dynamic adjustment of dispatching rule parameters in flow shops with sequence dependent setup times

Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence dependent setup times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs

Control of manufacturing networks which contain a batch processing machine

We consider the control of a batch processing machine which is part of a larger manufacturing network of machines. Systems consisting of a batch processing machine and one or more unit-capacity machines in tandem are considered. The objective is to minimize the average time that jobs spend in the entire system. We present algorithms to determine the optimal policies for certain finite horizon, deterministic problems. We then discuss the structure of the optimal policies for infinite horizon, stochastic problems, and investigate the benefit of utilizing information about upstream and downstream unit-capacity machines in the control of the batch machine. We develop a simple heuristic scheduling policy to control the batch machine which takes into account the state of other machines in the network. Computational results demonstrate the effectiveness of our heuristic over a wide range of problem instances.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45931/1/10756_2004_Article_274353.pd

A new mathematical model for single machine batch scheduling problem for minimizing maximum lateness with deteriorating jobs

This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model

Evolutionary methods for the design of dispatching rules for complex and dynamic scheduling problems

Three methods, based on Evolutionary Algorithms (EAs), to support and automate the design of dispatching rules for complex and dynamic scheduling problems are proposed in this thesis. The first method employs an EA to search for problem instances on which a given dispatching rule performs badly. These instances can then be analysed to reveal weaknesses of the tested rule, thereby providing guidelines for the design of a better rule. The other two methods are hyper-heuristics, which employ an EA directly to generate effective dispatching rules. In particular, one hyper-heuristic is based on a specific type of EA, called Genetic Programming (GP), and generates a single rule from basic job and machine attributes, while the other generates a set of work centre-specific rules by selecting a (potentially) different rule for each work centre from a number of existing rules. Each of the three methods is applied to some complex and dynamic scheduling problem(s), and the resulting dispatching rules are tested against benchmark rules from the literature. In each case, the benchmark rules are shown to be outperformed by a rule (set) that results from the application of the respective method, which demonstrates the effectiveness of the proposed methods

Dynamic Control for Batch Process Systems Using Stochastic Utility Evaluation

Most research studies in the batch process control problem are focused on optimizing system performance. The methods address the problem by minimizing single criterion such as cycle time and tardiness, or bi-criteria such as cycle time and tardiness, and earliness and tardiness. This research demonstrates the use of Stochastic Utility Evaluation (SUE) function approach to optimize system performance using multiple criteria. In long production cycles, the earliness and tardiness weight (utility) of products vary depending on the time. As the time approaches the due-date, it affects contractual penalties, loss of customer goodwill and the storage period for the completed products. It is necessary to reflect the weight of products for earliness and tardiness at decision epochs to decide on the optimal strategy. This research explores how stochastic utility function using stochastic information can be derived and used to strategically improve existing approaches for the batch process control problem. This research first explores how SUE function can be applied to existing model for bi-objective problem such as cycle time and tardiness. Benchmark strategies using SUE function (NACH-SUE, MBS-SUE, No idle and full batch) are compared to each other. The experimental results show that NACH-SUE effectively improves mean cycle time and tardiness performance respectively than other benchmark strategies. Next, SUE function for earliness and tardiness is used in an existing model to develop a tri-objective problem. Typically, this problem is very complex to solve due to its trade-off relationship. However SUE function makes it relatively easy to solve the tri-objective problem since SUE function can be incorporated in an existing model. It is observed that SUE function can be effectively used for solving a tri-objective problem. Performance improvement for averaged value of cycle time, earliness and tardiness is observed under a comprehensive set of experimental conditions