A new paradigm for rule-based scheduling in the wafer probe centre

This paper addresses the scheduling problem in the wafer probe centre. The proposed approach is based on the dispatching rule, which is popularly used in the semiconductor manufacturing industry. Instead of designing new rules, this paper proposes a new paradigm to utilize these rules. The proposed paradigm formulates the dispatching process as a 2-D assignment problem with the consideration of information from multiple lots and multiple pieces of equipment in an integrated manner. Then, the dispatching decisions are made by maximizing the gains of multiple possible decisions simultaneously. Besides, we develop a genetic algorithm (GA) for generating good dispatching rules through combining multiple rules with linear weighted summation. The benefits of the proposed paradigm and GA are verified with a comprehensive simulation study on three due-date-based performance measures. The experimental results show that under the proposed paradigm, the dispatching rules and GA can perform much better than under the traditional paradigm.     

Modelling and analysis of wafer fabrication scheduling via generalized stochastic Petri net and simulated annealing

Some of the important characteristics of the semiconductor wafer fabrication factories are re-entrant process flows, a dynamic and uncertain environment, stringent production control requirements, etc. that pose a major challenge to the scheduling decisions in integrated circuit wafer fabrication process. Keeping in view the high capital investment and quick response to the market changes, it is essential for the integrated circuit fabrication process to exercise effective control on its production operations so that production resources can be employed in a flexible and efficient manner. The present research has focussed on the development of a generalized stochastic Petri net model that faithfully captures dynamic behaviours such as re-entrant processing, machine failures, loading and unloading, etc., pertaining to wafer fabrication. A simulated annealing-based scheduling strategy using mean cycle time and tardiness as performance measures was also developed to obtain an efficient and robust schedule for a known hard problem. Analysis of variance was applied to examine the interaction effects of various scheduling rules and to identify the main as well as the interaction effects of dispatching rules, dispatching rules and set-up rules, and set-up rules and batching rules. Paired t-tests were applied to assess the performance of rule combinations for lot and batch scheduling. The proposed simulated annealing-based solution methodology was tested on a well-known data set adopted from the literature and its performance reveals that simulated annealing-based scheduling rules work better than existing rules in terms of the two performance measures mean cycle time and tardiness.     

Agent-based project scheduling

Agent technology offers a new way of thinking about many of the classic problems in operations research. Among these are problems such as project scheduling subject to resource constraints. In this paper, we develop and experimentally evaluate eight agent-based algorithms for solving the multimode, resource-constrained project scheduling problem. Our algorithms differ in the priority rules used to control agent access to resources. We apply our approach to a 51-activity project originally published by Maroto and Tormos [1]. We solve the problem using two types of agent-based systems: (i) a system of simple, reactive agents that we call basic agents; and (ii) a system of more complex, deliberative agents that we call enhanced agents. Of the eight priority rules tested, we find that priority based on shortest processing time performs best in terms of schedule quality when applied by basic agents while the priority based on earliest due date performs best when applied by enhanced agents. In comparing agents across priority rules, we find that enhanced agents generate much better schedules (with makespans up to 66% shorter in some cases) and require only slightly more computation time.     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.     

Generating interpretable fuzzy rules for adaptive job dispatching

Adaptive scheduling is an approach that selects and applies the most suitable strategy considering the current state of the system. The performance of an adaptive scheduling system relies on the effectiveness of the mapping knowledge between system states and the best rules in the states. This study proposes a new fuzzy adaptive scheduling method and an automated knowledge acquisition method to acquire and continuously update the required knowledge. In this method, the criteria for scheduling priority are selected to correspond to the performance measures of interest. The decisions are made by rules that reflect those criteria with appropriate weights that are determined according to the system states. A situated rule base for this mapping is built by an automated knowledge acquisition method based on system simulation. Distributed fuzzy sets are used for evaluating the criteria and recognizing the system states. The combined method is distinctive in its similarity to the way human schedulers accumulate and adjust their expertise: qualitatively establishing meaningful criteria and quantitatively optimizing the use of them. As a result, the developed rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop effective and robust rules.     

Critical Ratio Scheduling with Queue Waiting Time Information: An Experimental Analysis

The Critical Ratio and Slack Time priority scheduling rules have been applied by a number of firms in computer-based scheduling systems for manufacturing operations. One question in using these rules is whether queue waiting time estimates for individual machines should be used in making scheduling decisions. Simulation experiments are reported in this paper that measure the effect of including historical queue time data in the Critical Ratio and Slack Time rules. The results suggest that such data can adversely affect shop performance, measured using criteria such as job flow times, job lateness, and inventory system costs.     

Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling

Today's highly competitive semiconductor markets place a great emphasis on responsiveness to customers. In the past, competition has primarily focused on the product design arena. More recently, short lead times and good on-time delivery performance have become equally important to winning customer satisfaction. To meet these criteria, a recent thrust of manufacturing management has focused on the use of effective scheduling techniques to manage wafer movement. Dabbas and Fowler (1999) proposed an approach that combines multiple dispatching criteria into a single rule with the objective of maximizing multiple response measures simultaneously. This is accomplished using a linear combination with relative weights. The weights identify the contribution of the different criteria. This paper details the use of experimental design methodology as well as a desirability function approach in the optimization of the weights' assignment to the different criteria. The basic idea of the desirability function approach is to transform a multi-response problem into a single-response problem by means of a mathematical transformation. The responses of interest are on time delivery, variance of lateness, mean cycle time and variance of cycle time. Results demonstrate that the proposed approach is superior to the use of single-dispatching criteria with an average of 20% improvement for all responses. All data presented in this paper have been normalized to disguise actual performance results as the raw data are considered to be Motorola confidential data.     

A model to examine the effect of loading conditions on scheduling rules in a job shop environment

Although a great deal of research has been carried out in the field of job scheduling this has generally been directed towards examining the benefits of particular rules and presenting improved algorithms. This paper examines how real job shop problems can be modelled and available scheduling rules examined for particular capacity loading conditions. A model of a medium-size production job shop is developed and it is shown that, for their particular shop layout and job mix, the performance and ranking of particular rules with respect to certain criteria, change with shop conditions. The model developed can easily be applied to a wide range of job shop situations and once performance charts have been produced for those scheduling rules available, they can be used to aid the existing scheduling system whether manual or computer based.     

Job order releasing and throughput planning for multi-priority orders in wafer fabs

To meet the production target of multi-level (multiple priority rank) orders in wafer fabs, this paper uses a hierarchical framework based on a mathematical model, and without the assistance of any simulation tool, to build a production scheduling system to plan wafer lot releasing sequence and time. This system first applies capacity loading analysis to set up the batch policy for each level (rank) of orders. Next, the production cycle time of each product level is estimated with considerations of batching and loading factor. The cycle time is then used to derive system control parameters such as the most appropriate level of work in process (WIP) and the number of daily operations on the bottleneck workstation. Lastly, a Constant WIP mechanism is applied to establish a wafer release sequence table and a throughput timetable. The due date designation for each specific order can hence be confirmed. With the comparison with the result of simulation, it shows that under the designed system the performance and planning measures in the master production schedule can be drawn up quickly and accurately, and the system throughput target and due date satisfaction can be achieved. Overall, the proposed production scheduling system is both effective and practicable, and the planning results are supportive for good target planning and production activity control.     

Integrating a decomposition procedure with problem reduction for factory scheduling with disruptions: a simulation study

Dispatching rules are widely used in industry because schedules obtained from optimization procedures can be difficult to implement in the face of executional uncertainties. Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) implement global schedules obtained from an optimization-based heuristic using a dispatching rule, and outperform myopic dispatching rules in the face of disruptions. However, the computation of the global schedules is still time-consuming for realistic instances. Upasani et al. (Upasani, A., Uzsoy, R. and Sourirajan, K., A problem reduction approach for scheduling semiconductor wafer fabrication facilities. IEEE Trans. Semicon. Manuf., 2006, 19, 216–225) develop a problem reduction scheme based on load disparity between work centres, and report significant reduction in CPU times with minimal loss of solution quality in deterministic experiments. In this paper we integrate the problem-reduction scheme to obtain global schedules with the dispatching approach of Barua et al. (Barua, A., Narasimhan, R., Upasani, A. and Uzsoy, R., Implementing global factory schedules in the face of stochastic disruptions. Int. J. Prod. Res., 2005, 43(4), 793–818) in a multi-product environment with stochastic machine breakdowns and job arrivals. A simulation model of a scaled-down wafer fabrication facility is used to evaluate the performance of the proposed procedures. Results show that the integrated procedure outperforms the benchmark dispatching rules while significantly reducing computation times.     

Scheduling at an auction logistics centre with physical internet

Auction logistics centre (ALC) performs transshipment operation on auction products from their inbound-from-supplier transporters to their outbound-to-client transporters with goods trading functions. Major third-party trading service providers have solved technological problems of dealing with millions of simultaneous biddings. But logistics that fulfils the massive and lumpy auction demands in the centre is still challengeable. The lack of process visibility and synchronised schedule has made the congestion on material flow, especially for the trolley loading and auction trading stages. Space resource is wasted and auction products deteriorate as holding time increases. This paper aims to provide a first demonstration of scheduling for auctions of perishable goods using Physical Internet (PI). PI-enabled scheduling is vital to facilitate the decision-making process while ensuring required throughput time with large trading volumes. A PI-ALC is created to automate the flow of information and enable the flexible implementation of scheduling. Following the hybrid flowshop classification, a timely operation scheduling model is developed. A heuristic-based solution approach is proposed to minimise either makespan or value loss using a set of dispatching rules. Simulation experiments show that the dispatching–picking mechanisms have statistically significant interaction impacts on both performance criteria. Decision-makers should strike a balance between minimising makespan and value loss based upon the growth in the frozen buffer size. Finally, the sensitivity analyses justify that schedulers can flexibly select dispatching rules under various demand patterns and operation time windows, as well as system configurations and trolley sizes.     

Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming

In this paper, two new approaches are proposed for extracting composite priority rules for scheduling problems. The suggested approaches use simulation and gene expression programming and are able to evolve specific priority rules for all dynamic scheduling problems in accordance with their features. The methods are based on the idea that both the proper design of the function and terminal sets and the structure of the gene expression programming approach significantly affect the results. In the first proposed approach, modified and operational features of the scheduling environment are added to the terminal set, and a multigenic system is used, whereas in the second approach, priority rules are used as automatically defined functions, which are combined with the cellular system for gene expression programming. A comparison shows that the second approach generates better results than the first; however, all of the extracted rules yield better results than the rules from the literature, especially for the defined multi-objective function consisting of makespan, mean lateness and mean flow time. The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems.     

A computational study of heuristics for two-stage flexible flowshops

We consider the scheduling of two-stage flexible flowshops. This manufacturing environment involves two machine centres representing two consecutive stages of production. Each machine centre is composed of multiple parallel machines. Each job has to be processed serially through the two machine centres. In each machine centre, a job may be processed on any of the machines. There are n independent jobs to be scheduled without preemption. The jobs can wait in between the two machine centres and the intermediate storage is unlimited. Our objective will be to minimize the maximum completion time of the jobs. We formulate the problem as a mixed integer program. Given this problem class is NP-hard in the strong sense, we present three lower bounds to estimate the optimal solution. We then propose a sequence-first, allocate-second heuristic approach for its solution. We heuristically decompose the problem by first creating a priority list to order the jobs and then assign the jobs to the available machines in each machine centre based on this order. We describe seven rules for the sequencing phase. The assignment phase consists of a heuristic which attempts to minimize each partial schedule length while looking ahead at the future assignment of the currently unscheduled jobs. The computational performance of the heuristic approach was evaluated by comparing the value of each heuristic variant to the best among the three lower bounds. Its effectiveness was tested on scenarios pertinent to flexible flowshop environments, such as cellular manufacturing, by conducting a computational study of over 3400 problems. Our computational results indicate that the most effective approach used Johnson's rule to provide the priority list for job assignment. This provided integrality gaps which on the average were less than 0·73%.     

The infrastructure of the timed EOPNs-based multiple-objective real-time scheduling system for 300 mm wafer fab

Modern semiconductor wafer fabrication systems are changing from 200?mm to 300?mm wafer processing, and with the dual promises of more chips per wafer and economy of scale, leading semiconductor manufacturers are attracted to developing and implementing 300?mm wafer fabs. However, in today's dynamic and competitive global market, a successful semiconductor manufacturer has to excel in multiple performance indices, such as manufacturing cycle time and on-time delivery, and simultaneously optimize these objectives to reach the best-compromised system achievement. To cope with this challenge, in this paper, the infrastructure of a timed EOPNs-based multiple-objective real-time scheduling system (MRSS) is proposed to tackle complex 300?mm wafer fabs. Four specific performance objectives pursued by contemporary semiconductor manufacturers are integrated into a priority-ranking algorithm, which can serve as the initial scheduling guidance, and then all wafer lots will be dynamically dispatched by the real-time state-dependent dispatching system. This dispatching control system is timed EOPN-based and adopts a heterarchical organization that leads to a better real-time performance and adaptability. As the foundation of real-time schedule, the timed EOPNs modelling approach is expounded in detail, and the prototype of the MRSS simulation system is also provided.     

A decision-making approach for nesting scheduling: A textile case

In many industrial cases, the nesting problem and the scheduling problem have to be addressed at the same time. The complexity of the combined problem often prevents to take effectively into account both nesting efficiency and overall production objectives. This paper presents a scheduling approach for the combined problem of production scheduling and nesting. The aim of the proposed approach is to provide a good solution both for the nesting and scheduling problem. The approach involves the generation of scheduling alternatives, their transformation through a rule base mechanism into nesting solutions and finally their evaluation using different criteria that reflect the overall production objectives such as meeting due dates, minimizing of the cost and maximizing the machines and stock sheet utilisation. The proposed approach has been implemented in a software system for the purpose of solving a problem in the textile industry. Specifically, the scheduling of the carpet weaving processa problem of nesting rectangular patterns under complex production constraints-has been examined. A set of experiments has been conducted for producing realistic nesting schedules in order to evaluate the proposed system's performance. The results show that the proposed approach may be applied in real-life manufacturing processes under complex production constraints and multiple objectives.     

考虑公有与私有资源约束的多项目调度规则对比研究

为了识别出适用于具有公有资源与私有资源约束的多项目调度问题的优先规则,基于标准测试集MPSPLIB进行计算实验,并基于相对偏差指标对比分析25种经典优先规则在最小化最大完工时间、最小化总拖期、最小化加权总拖期3种决策目标下的表现。实验结果表明,优先规则的表现与决策目标、单项目任务数、并行项目数、资源利用系数等因素之间具有显著相关性。在最小化总拖期目标下,尽管大多数规则的表现与现有文献总体一致,但是MAXTWK和MINSLK规则的表现有着显著差异。所得实验结论对于工程实际多项目调度决策具有指导意义。     

Learning dispatching rules using random forest in flexible job shop scheduling problems

In this paper, we address the flexible job-shop scheduling problem (FJSP) with release times for minimising the total weighted tardiness by learning dispatching rules from schedules. We propose a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) in order to extract dispatching rules from the best schedules. RANFORS consists of three phases: schedule generation, rule learning with data transformation, and rule improvement with discretisation. In the schedule generation phase, we present three solution approaches that are widely used to solve FJSPs. Based on the best schedules among them, the rule learning with data transformation phase converts them into training data with constructed attributes and generates a dispatching rule with inductive learning. Finally, the rule improvement with discretisation improves dispatching rules with a genetic algorithm by discretising continuous attributes and changing parameters for random forest with the aim of minimising the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach and the results showed that it outperforms the existing dispatching rules. Moreover, compared with the other decision-tree-based algorithms, the proposed algorithm is effective in terms of extracting scheduling insights from a set of rules.     

A multiple-criteria real-time scheduling approach for multiple-load carriers subject to LIFO loading constraints

This paper studies the problem of scheduling a multiple-load carrier subject to last-in-first-out loading constraints in an automobile assembly line. Two scheduling criteria, the throughput of the assembly line and the material handling distance, are considered in order to maximise the profit of the assembly line. Different from other studies, the product mix and weights of the scheduling criteria are considered to be variable. A scheduling approach is proposed for the problem. At moments when the product mix or weights of the scheduling criteria change, the scheduling approach can select an appropriate rule from a set of given rules. In this study, the proposed approach is compared with other approaches by simulation in order to verify the performance of the proposed approach. The results indicate that, when the product mix and weights of the scheduling criteria are variable, the proposed scheduling approach outperforms other approaches.     

Priority Scheduling and Inventory Control in Job Lot Manufacturing Systems

This paper examines the relationship between the problems of priority scheduling and inventory control and describes several priority scheduling rules that use inventory information in making machine scheduling decisions. These rules include: Minimum Slack Time Per Remaining Operation? Critical Ratio, and a modification of the Shortest Processing Time Rule. Simulation experiments evaluate the gain in shop and inventory performance resulting from the inclusion of inventory data in scheduling rules. The results indicate that an increase in the timeliness of inventory information for scheduling purposes may not lead to improved performance.     

Performance-based dynamic scheduling model for flexible manufacturing systems

A performance-based dynamic scheduling model for random flexible manufacturing systems (FMSs) is presented. The model is built on the mathematical background of supervisory control theory of discrete event systems. The dynamic FMS scheduling is based on the optimization of desired performance measures. A control theory-based system representation is coupled with a goal programming-based multi-criteria dynamic scheduling algorithm. An effectiveness function, representing a performance index, is formulated to enumerate the possible outputs of future schedules. Short-term job scheduling and dispatching decisions are made based on the values obtained by optimizing the effectiveness function. Preventive actions are taken to reduce the difference between actual and desired target values. To analyse the real-time performance of the proposed model, a software environment that included various Visual Basic Application® modules, simulation package Arena®, and Microsoft Access® database was developed. The experimentation was conducted (a) to determine the optimum look-ahead horizons for the proposed model and (b) to compare the model with conventional scheduling decision rules. The results showed that the proposed model outperformed well-known priority rules for most of the common performance measures.