Interaction between dispatching and next station selection rules in a dedicated flexible manufacturing system

The interaction between nine dispatching and four next station selection rules in a relatively large dedicated FMS is investigated. The FMS contains 16 workstations with local buffers, nine load/unload stations, and produces six different part types. A simulation model is used, and analysed as a steady-state model. Flowtime is taken as the main criterion. It is found that WINQ (select the station whose input buffer contains the smallest amount of work) dominates, performing significantly better than the other next station selection rules considered across all dispatching rules, with few significant differences between dispatching rules when combined with WINQ. SIO/TOT (select the job with the smallest ratio obtained by dividing the processing time of the imminent operation by the total processing time for the part) performs marginally better than the other dispatching rules, particularly SIO (select the job with the shortest imminent processing time). Reasons for when a next station selection rule is more important than a dispatching rule, and vice versa, are discussed.     

Heuristic dispatching rule to maximize TDD and IDD performance

Although mean flow time and tardiness have been used for a long time as indicators in both manufacturing plants and academic research on dispatching rules, according to Theory of Constraints (TOC), neither indicator properly measures deviation from production plans. TOC claims that using throughput dollar-day (TDD) and inventory dollar-day (IDD) can induce the factory to take appropriate actions for the organization as a whole, and that these can be applied to replace various key performance indices used by most factories. However, no one has studied dispatching rules based on TDD and IDD performance indicators. The study addresses two interesting issues. (1) If TDD and IDD are used as performance indicators, do those dispatching rules that yield a better performance in tardiness and mean flow time still yield satisfactory results in terms of TDD and IDD performance? (2) Does a dispatching rule exist to outperform the current dispatching rules in terms of TDD and IDD performance? First, a TDD/IDD-based heuristic dispatching rule is developed to answer these questions. Second, a computational experiment is performed, involving six simulation examples, to compare the proposed TDD/IDD-based heuristic-dispatching rule with the currently used dispatching rules. Five dispatching rules, shortest processing time, earliest due date, total profit, minimum slack and apparent tardiness cost, are adopted herein. The results demonstrate that the developed TDD/IDD-based heuristic dispatching rule is feasible and outperforms the selected dispatching rules in terms of TDD and IDD.     

Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.     

Simulation analysis of dispatching rules for an automated interbay material handling system in wafer fab

Here, the performance evaluation of a double-loop interbay automated material handling system (AMHS) in wafer fab was analysed by considering the effects of the dispatching rules. Discrete event simulation models based on SIMPLE++ were developed to implement the heuristic dispatching rules in such an AMHS system with a zone control scheme to avoid vehicle collision. The layout of an interbay system is a combination configuration in which the hallway contains double loops and the vehicles have double capacity. The results show that the dispatching rule has a significant impact on average transport time, waiting time, throughput and vehicle utilization. The combination of the shortest distance with nearest vehicle and the first encounter first served rule outperformed the other rules. Furthermore, the relationship between vehicle number and material flow rate by experimenting with a simulation model was investigated. The optimum combination of these two factors can be obtained by response surface methodology.     

Scheduling a dynamic flexible flow line with sequence-dependent setup times: a simulation analysis

This paper presents a simulation-based experimental study of scheduling rules for scheduling a dynamic flexible flow line problem considering sequence-dependent setup times. A discrete-event simulation model is presented as well as eight adapted heuristic algorithms, including seven dispatching rules and one constructive heuristic, from the literature. In addition, six new proposed heuristics are implemented in the simulation model. Simulation experiments are conducted under various conditions such as setup time ratio and shop utilisation percentage. One of the proposed rules performs better for the mean flow time measure and another one performs better for the mean tardiness measure. Finally, multiple linear regression based meta-models are developed for the best performing scheduling rules.     

An optimised dynamic bottleneck dispatching policy for semiconductor wafer fabrication

A dynamic bottleneck dispatching (DBD) policy is designed in this paper to detect bottlenecks in a timely way and make adaptive dispatching decisions of semiconductor wafer fabrication systems according to the real-time conditions. Control parameters of the proposed DBD algorithm are optimised by response surface methodology (RSM) and desirability functions. Numerical results show that DBD outperforms common scheduling rules such as CR?+?FIFO, EDD, SRPT, SPT, SPNB and an existing dynamic bottleneck dispatching method.     

Performance evaluation of dynamic scheduling approaches in vehicle-based internal transport systems

This paper studies the performance of static and dynamic scheduling approaches in vehicle-based internal transport (VBIT) systems and is one of the first to systematically investigate under which circumstances, which scheduling method helps in improving performance. In practice, usually myopic dispatching heuristics are used, often using look-ahead information. We argue more advanced scheduling methods can help, depending on circumstances. We introduce three basic scheduling approaches (insertion, combined and column generation) for the static problem. We then extend these to a dynamic, real-time setting with rolling horizons. We propose two further real-time scheduling approaches: dynamic assignment with and without look-ahead. The performances of the above five scheduling approaches are compared with two of the best performing look-ahead dispatching rules known from the literature. The performance of the various approaches depends on the facility layout and work distribution. However, column generation, the combined heuristic, and the assignment approach with look-ahead consistently outperform dispatching rules. Column generation can require substantial calculation time but delivers very good performance if sufficient look-ahead information is available. For large scale systems, the combined heuristic and the dynamic assignment approach with look ahead are recommended and have acceptable calculation times.     

Modelling of an automated guided vehicle system (AG VS) in a just-in-time (JIT) environment

Like other production systems, just-in-time (JIT) systems need to address the issue of material transport between workstations such as those served by automated guided vehicles (AGVs). Unlike other production systems, however, the JIT philosophy imposes strict requirements on inventory levels and supply-demand protocols which render conventional AGV delivery strategies ineffective and counter-productive. This paper describes the modelling of an AGV system (AGVS) in a JIT environment in a way that is consistent with JIT principles. The influence of a ‘JIT perspective’ is emphasized throughout the model by introducing threshold values for both input and output queues, performance measures that emphasize lower inventories in addition to transport efficiency, and a new dispatching rule that implements better inventory and transport control. The dispatching rule is shown to perform better in a JIT environment than previously developed AGV dispatching rules in both transport and logistic criteria.     

An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing

The on-line control and scheduling of flexible manufacturing systems has been a major interest in the production research area since these systems first appeared. In this paper, a scheduling algorithm is described which employs discrete simulation in combination with straightforward part dispatching rules in a dynamic fashion. The result is that, instead of scheduling being planned ahead of time and then being applied to a rapidly changing system, a dispatching rule is determined for each short period just before the implementation time occurs. In the long run, the algorithm combines various dispatching rules in response to the dynamic status of the system. The algorithm is described in detail. The efficacy of the algorithm is discussed and demonstrated on a prototype system.     

汽车维修服务站瓶颈工序的实时调度

在结合优化调度理论和约束理论的基础上,从最小化目标、机器环境、加工特征和约束几方面分析了汽车维修服务站瓶颈工序的实时调度问题的特征,建立了对应的数学模型。根据问题特性,设计了包含复合动态分派规则的启发式调度算法。以实例分析验证了算法的可行性,仿真结果展示了所用算法在优化目标函数值上的优越性和计算时间的可行性。     

基于仿真的半导体自动物料搬运系统调度优化

利用Arena仿真软件对半导体制造中的Intrabay系统进行了仿真建模,根据关键因子和等待比例两个变量将系统状态分为三种情况,利用遗传算法与仿真模型结合来确定两个变量的阈值和三种系统状态下各自采用的调度规则,实现系统的动态调度。仿真实验表明,所确定的调度规则比使用静态调度最长等待时间规则获得了更好的性能指标。     

Improving the applicability of workload control (WLC): the influence of sequence-dependent set-up times on workload controlled job shops

Simulation has demonstrated that the workload control (WLC) concept can improve performance in job shops, but positive empirical results are scarce. A key reason for this is that the concept has not been developed to handle a number of practical considerations, including sequence-dependent set-up times. This paper investigates the influence of sequence-dependent set-up times on the performance of a workload-controlled job shop. It introduces new set-up-oriented dispatching rules and assesses the performance impact of controlled order release. Simulation results demonstrate that combining an effective WLC order release rule with an appropriate dispatching rule improves performance over use of a dispatching rule in isolation when set-up times are sequence dependent. The findings improve our understanding of how this key implementation challenge can be overcome. Future research should investigate whether the results hold if set-up time parameters are dynamic and set-up times are not evenly distributed across resources.     

An integrated scheduling and material-handling approach for complex job shops: a computational study

We suggest an extension of the shifting bottleneck heuristic for complex job shops that takes the operations of automated material-handling systems (AMHS) into account. The heuristic is used within a rolling horizon approach. The job-shop environment contains parallel batching machines, machines with sequence-dependent setup times, and re-entrant process flows. Jobs are transported by an AMHS. Semiconductor wafer fabrication facilities (wafer fabs) are typical examples for manufacturing systems with these characteristics. Our primary performance measure is total weighted tardiness (TWT). The shifting bottleneck heuristic (SBH) uses a disjunctive graph to decompose the overall scheduling problem into scheduling problems for single machine groups and for transport operations. The scheduling algorithms for these scheduling problems are called subproblem solution procedures (SSPs). We consider SSPs based on dispatching rules. In this paper, we are also interested in how much we can gain in terms of TWT if we apply more sophisticated SSPs for scheduling the transport operations. We suggest a Variable Neighbourhood Search (VNS) based SSP for this situation. We conduct simulation experiments in a dynamic job-shop environment in order to assess the performance of the suggested algorithms. The integrated SBH outperforms common dispatching rules in many situations. Using near to optimal SSPs leads to improved results compared with dispatching based SSPs for the transport operations.     

Power distribution network expansion scheduling using dynamic programming genetic algorithm

A genetic algorithm that is dedicated to the expansion planning of electric distribution systems is presented, with incremental expansion scheduling along a time horizon of several years and treated as a dynamic programming problem. Such a genetic algorithm (called dynamic programming genetic algorithm) is endowed with problem-specific crossover and mutation operators, dealing with the problem through a heuristic search in the space of dynamic programming variables. Numerical tests have shown that the proposed algorithm has found good solutions that considerably enhance the solutions found by non-dynamic programming methods. The algorithm has also shown to work for problem sizes that would be computationally infeasible for exact dynamic programming techniques.     

A multiple-pass heuristic rule for job shop scheduling with due dates

This paper presents an efficient multiple-pass heuristic algorithm for job shop scheduling problems with due dates wherein the objective is to minimize total job tardiness. Algorithm operation is carried out in two phases. In phase 1 a dispatching rule is employed to generate an active or non-delay initial schedule. In phase 2, tasks selected from a predetermined set of promising target operations in the initial schedule are tested to ascertain whether by left-shifting their start times and rearranging some subset of the remaining operations one can reduce total tardiness. Performance evaluation is carried out over a range of shop sizes focusing, first of all, on the quality of the initial schedule produced through five commonly used dispatching rules and, secondly, the schedule improvement achieved with the multiple-pass heuristic. Results indicate that the proposed technique is capable of yielding notable reductions in total tardiness (over initial schedules) for practical size problems and would suggest that the approach presents an efficient scheduling option for this class of complex optimization problems.     

The study of real time scheduling by an intelligent multi-controller approach

Earlier studies indicated that using multiple dispatching rules (MDRs) for the various zones in the system can enhance the production performance to a greater extent than using a single dispatching rule (SDR) over a given scheduling interval for all the machines in the system, since MDRs employ the multi-pass simulation approach for real-time scheduling (RTS). However, if a classical machine learning approach is used, an RTS knowledge base (KB) can be developed by using the appropriate MDRs strategy (this method is called an intelligent multi-controller in this paper) as obtained from training examples. The main disadvantage of using MDRs is that the classes (scheduling decision variables) to which training examples are assigned must be provided. Hence, developing an RTS KB using the intelligent multi-controller approach becomes an intolerably time-consuming task because MDRs for the next scheduling period must be determined. To address this issue, we proposed an intelligent multi-controller incorporating three main mechanisms: (1) simulation-based training example generation mechanism, (2) data pre-processing mechanism and (3) SOM-based real time MDRs selection mechanism. Under various performance criteria over a long period, the proposed approach yields better system performance than the machine learning-based RTS using the SDR approach and heuristic individual dispatching rules.     

Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times

Decentralised 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 the 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 set-up times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.     

A multi-attribute dispatching rule for automated guided vehicle systems

This paper considers the dispatching problem associated with operations of automated guided vehicles (AGVs). A multi-attribute dispatching rule for dispatching of an AGV is developed and evaluated. The multi-attribute rule, using the additive weighting method, considers three system attributes concurrently: the remaining space in the outgoing buffer of a workstation, the distance between an idle AGV and a workstation with a job waiting for the vehicle to be serviced, and the remaining space in the input buffer of the destination workstation of a job. A neural network approach is used to obtain dynamically adjusting attribute weights based on the current status of the manufacturing system. Simulation analysis of a job shop is used to compare the multi-attribute dispatching rule with dynamically adjusting attribute weights to the same dispatching rule with fixed attribute weights and to several single attribute rules. Results show that the multi-attribute dispatching rule with the ability to adapt attribute weights to job shop operational conditions provides a better balance among the performance measures used in the study.     

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.     

On-line dispatching rules for vehicle-based internal transport systems

On-line vehicle dispatching rules are widely used in many facilities such as warehouses and manufacturing facilities to control vehicles’ movements. Single-attribute dispatching rules, which dispatch vehicles based on only one parameter, are usually used. However, multi-attribute dispatching rules prove to be better in general. In this paper, we study the impact of reassigning moving vehicles on some good dispatching rules, both single- and multi-attribute, in the literature. Results suggest that reassigning moving-to-park vehicles has a significant positive effect on reducing the average load waiting time. We evaluate the dispatching rules’ performance using the experimental design of a real-life case study. The performance criteria are: minimizing the average load waiting time, keeping the maximum load waiting time as short as possible and utilizing better vehicles. The results show that the combined dispatching rules which integrates multi-attribute dispatching and vehicle reassignment yields the best performance overall.