Family based dispatching in manufacturing networks

Intrinsic to family based dispatching is the grouping of similar types of jobs in front of a machine for joint processing. Machine flow times may be improved in this way, as less time is spent on set-ups. Our observations in practice, however, suggest that family based dispatching may result in a bulky arrival pattern for successor manufacturing stages, thereby causing additional delay. So far, literature seems to neglect this effect. To explore this issue we develop queuing theoretical approximations of flow times for a simple two-stage shop. It appears that the optimal batch size for the shop is typically smaller than the optimal batch size for the batch machine. Furthermore, we propose extensions to existing dispatching rules by using information on successor stages. Existing and new extended rules are tested by an extensive simulation study. In line with the queuing theoretical analysis the outcomes indicate that exhaustive rules – assuming batch size to be equal to family queue length – are clearly outperformed by non-exhaustive rules – allowing for smaller batches. Moreover, results show that the inclusion of local information on successor stages in rule decision making improves shop flow times.     

Coordinating batching decisions in manufacturing networks

Family-based dispatching heuristics aim for improving job flow times by reducing time spent on set-ups. They realise set-up efficiencies by batching similar types of jobs. By their intuitiveness and the simplicity of their decision logic, they may contribute to an easy to implement and viable strategy in many practical settings. Similar to common dispatching rules most existing family-based dispatching heuristics are myopic, i.e. their decision scope is restricted to a single manufacturing stage. Hence, they neglect opportunities for improving shop performance by coordinating batching decisions with other manufacturing stages. Case examples from industry underpin the need for exploring these opportunities. We do so by studying a simple two-stage flow shop, entailing a serial and a batch stage. To facilitate shop coordination we propose extensions to existing family-based dispatching heuristics. Extended heuristics seek to further increase set-up efficiencies by allowing for upstream job re-sequencing, and pro-active set-ups, i.e. set-ups that may be initiated prior to the arrival of a job. Outcomes of an extensive simulation study indicate significant performance gains for extended heuristics vs. existing heuristics. Performance gains are largest for moderate and high set-up to run-time ratios.     

The production capacity of job shops with limited storage space

Suppose the number of jobs which can be stored in front of the machines in a job shop is limited. As a result, arriving jobs for which there is no space in the shop will form a shop queue. The production capacity or maximum departure rate of jobs from the shop will depend on the way in which jobs are selected from the shop queue for release to the machine queues. For a job shop with two identical machines and random routing of jobs a number of release rules are compared. It is shown that the production capacity is increased when the number of jobs in the shop is kept less than the available storage space. Among release rules independent'of job processing times and number of operations the optimum release rule is shown, using dynamic programming, to be the idle machine rule, i.e. only release a job to the shop when a machine would otherwise be idle.     

Family-based dispatching with parallel machines

Family-based dispatching heuristics seek to lower set-up frequencies by grouping similar types of jobs for joint processing. Hence, job flow times may be improved as less time is spent on set-ups. So far, family-based dispatching with parallel machines received little attention in literature. We address the perceived gap by proposing extensions to existing family-based dispatching heuristics. Main extensions concern improved rules for family priority settings and for coordinating the number of machines in use by a single family. Extended heuristics are tested by an extensive simulation study. Significant performance gains for extended heuristics vs. existing heuristics are found. Performance gains are largest for high set-up to run-time ratios.     

基于规则和仿真的多机并行作业车间生产调度研究

资源分派和能力分派是作业车间生产调度中的重要问题,路径选择规则和分派规则是解决上述问题的有效途径。采用基于规则的仿真研究多机并行作业车间资源分派和能力分派问题,分析工件加工时间、到达率以及机器加工速率对调度结果的影响,以平均完工时间、平均延迟交货率以及平均资源利用率为评价指标,通过对4种路径选择规则和6种分派规则的仿真试验,确定不同性能指标下最佳的调度规则。仿真研究表明:调度规则的选用取决于车间资源配置和调度目标,应避免仅凭借经验或偏好选择规则的调度方法。     

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 hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.     

On the beat of the drum: improving the flow shop performance of the Drum–Buffer–Rope scheduling mechanism

One of the main elements of the theory of constraints is its Drum–Buffer–Rope (DBR) scheduling (or release) mechanism that controls the release of jobs to the system. Jobs are not released directly to the shop floor – they are withheld in a backlog and released in accordance with the output rate of the bottleneck (i.e. the drum). The sequence in which jobs are considered for release from the backlog is determined by the schedule of the drum, which also determines in which order jobs are processed or dispatched on the shop floor. In the DBR literature, the focus is on the urgency of jobs and the same procedure is used both for backlog sequencing and dispatching. In this study, we explore the potential of using different combinations of rules for sequencing and dispatching to improve DBR performance. Based on controlled simulation experiments in a pure and general flow shop we demonstrate that, although the original procedure works well in a pure flow shop, it becomes dysfunctional in a general flow shop where job routings vary. Performance can be significantly enhanced by switching from a focus on urgency to a focus on the shortest bottleneck processing time during periods of high load.     

A batch splitting method for a job shop scheduling problem in an MRP environment

The job shop scheduling problem has been a major target for many researchers. Unfortunately though, most of the previous research was based on assumptions that are different from the real manufacturing environment. Among those distorted assumptions, two assumptions about set-up time and job composition can greatly influence the performance of a schedule. First, most of the past studies ignored the impact of the before-arrival set-up time. If we know the sequence of operations in advance, we can obtain an improved schedule by preparing the setup before a job arrives. Secondly, most of the past studies assumed that a job consists of only a single part, that is a batch of size one. However, if we assume that a job consists of a batch size greater than one, as in many real manufacturing environments, then we can obtain an improved schedule because we can fill up the idle times of machines with jobs which have smaller processing times by splitting the original batches. However, the number of job orders may then increase due to the split, and the size of the scheduling problem would become too large to be solved in a practical time limit. Consequently, there may be an optimum batch size considering trade-off between better solution and tractability. The current study is the result of an attempt to find an acceptable solution when the production requirement from a MRP system for a planning period exceeds the capacity of a production system. We try to get an improved schedule by splitting the original batch into smaller batches, and consider setting up a machine before the actual arrival of jobs to that machine. Thereby we can meet the due date requirement without resorting to rescheduling of the master production schedule. For the given batch, we disaggregate it according to the algorithm we are proposing. A so-called 'modified shifting bottleneck procedure' is then applied to solve the job shop scheduling problem with a before-arrival family set-up time considering release date, transportation time and due date. The study also shows that we can adapt to unexpected dynamic events more elegantly by allowing the splitting of batches.     

Family-based scheduling of shops with functional layouts

Simulation studies of job shop scheduling have typically assumed that either setup times are zero (subsumed within the processing time), or that every part has such a unique setup that no setup advantages can be gained by better scheduling policies. These studies also assume that the shop has exactly one copy of every machine. Some researchers have proposed heuristics that explicitly consider setup times and parallel machines in the context of a one stage shop with static arrivals. In contrast, family-based scheduling centred around setup time reduction has been credited with achieving economic savings in batch production industries where GT is employed. We motivate this study by the case of an existing realworld semi-conductor testing facility that has family setups, parallel machines and dynamic job arrival. Using this setting, we investigate whether benefits can still be obtained by using a family-based scheduling philosophy in those environments which do not permit the physical creation of cellular layouts due to the presence of process related or other constraints. We propose and evaluate two new dispatching procedures in a functional job shop that is modelled after the semiconductor testing facility. Results show that a family-based scheduling philosophy centred around coordinating machine setups is advantageous at relatively high setup to processing time ratios, while classic job shop rules suffice otherwise. Based on these results, we present recommendations for managing such environments. We also suggest future research directions in this area.     

Linguistic-based meta-heuristic optimization model for flexible job shop scheduling

In this paper, a linguistic based meta-heuristic modelling and solution approach for solving the Flexible Job Shop Scheduling Problem (FJSSP) is presented. FJSSP is an extension of the classical job-shop scheduling problem. The present problem definition is to assign each operation to a machine out of a set of capable machines ( the routing problem ) and to order the operations on the machines ( the sequencing problem ), such that a predefined performance measure is optimized. The scope of the problem is widened by taking into account the alternative process plans for each part ( process plan selection problem ) in the present study. Moreover, instead of using operations to represent product processing requirements and machine processing capabilities, machine independent capability units, which are known as Resource Elements (RE), are used. Representation of unique and shared capability boundaries of machine tools and part processing requirements is possible via RE. Using REs in scheduling can also reduce the problem size. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls. Using these controls and the Giffler and Thompson (1960) priority rule-based heuristic, a simulated annealing algorithm is developed to solve FJSSP. This novel approach simplifies the modelling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its solution. The results obtained from the computational study have shown that the proposed algorithm can solve this complex problem effectively within reasonable time. The results have also given some insights on the effect of the selection of dispatching rules and the flexibility level on the job shop performance. It is observed that the effect of dispatching rule selection on the job shop performance diminishes by increasing the job shop flexibility.     

Non-exhaustive family based dispatching heuristics–exploiting variances of processing and set-up times

Group technology exploits similarities in product and process design to effectively meet the diversity of customer demand. In this paper we consider one of the implementations of this concept–heuristics for family based dispatching. Intrinsic to family based dispatching is the grouping of similar types of products for joint processing. Hence, the number of set-ups may be reduced. Consequently, lead time performance of the shop can be improved. Unfortunately, variances of processing and set-up times–as found in practice–have received little attention in heuristics’ construction and testing. To address this issue we propose several new, non-exhaustive heuristics. Whereas existing exhaustive heuristics set batch contents equal to all products available for a family, non-exhaustive heuristics allow for switching families, even when the current family queue is not empty. An extensive simulation study shows how this flexibility in batch composition improves shop performance, especially in case of high variances of processing and set-up times.     

Scheduling a no-wait flow shop containing unbounded batching machines

We study the problem of scheduling n jobs in a no-wait flow shop consisting of m batching machines. Each job has to be processed by all the machines. All jobs visit the machines in the same order. A job completed on an upstream machine should be immediately transferred to the downstream machine. Batching machines can process several jobs simultaneously in a batch so that all jobs of the same batch start and complete together. The processing time of a batch is equal to the maximum processing time of the jobs in this batch. We assume that the capacity of any batch is unbounded. The problem is to find an optimal batch schedule such that the maximum job completion time, that is the makespan, is minimized. For m = 2, we prove that there exists an optimal schedule with at most two batches and construct such a schedule in O(n log n) time. For m = 3, we prove that the number of batches can be limited to nine and give an example where all optimal schedules have seven batches. Furthermore, we prove that the best schedules with at most one, two and three batches are 3-, 2- and 3/2-approximate solutions, respectively. The first two bounds are tight for corresponding schedules. Finally, we suggest an assignment method that solves the problem with m machines and at most r batches in O(n^{m(r-2)+1+[m/r]}) time, if m and r are fixed. The method can be generalized to minimize an arbitrary maximum cost or total cost objective function.     

Heuristics for minimizing total weighted tardiness in complex job shops

Semi-conductor manufacturing is arguably one of the most complex manufacturing processes in existence today. A semi-conductor wafer fabrication facility is comprised of batching machines, parallel machines, machines with sequence-dependent set-ups, and re-circulating product flow. The individual job release times and due dates combine with the other processing environment characteristics to form a ‘complex’ job shop scheduling problem. We first present a mixed-integer program (MIP) to minimize total weighted tardiness in a complex job shop. Since the problem is NP-hard, we compare a heuristic based on the MIP (MIP heuristic) with both a tuned version of a modified shifting bottleneck heuristic (SB heuristic) and three dispatching rules using random problem instances of a representative model from the literature. While the MIP heuristic typically produces superior schedules for problem instances with a small number of jobs, the SB heuristic consistently outperforms the MIP heuristic for larger problem instances. The SB heuristic's superior performance as compared to additional dispatching rules is also demonstrated for a larger, ‘real world’ dataset from the literature.     

A priority scheduling approach for flexible job shops with multiple process plans

This paper considers the job shop scheduling problem with alternative operations and machines, called the flexible job shop scheduling problem. As an extension of previous studies, operation and routing flexibilities are considered at the same time in the form of multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. Since the problem is highly complicated, we suggest a practical priority scheduling approach in which the two decisions are done at the same time using a combination of operation/machine selection and job sequencing rules. The performance measures used are minimising makespan, total flow time, mean tardiness, the number of tardy jobs, and the maximum tardiness. To compare the performances of various rule combinations, simulation experiments were done on the data for hybrid systems with an advanced reconfigurable manufacturing system and a conventional legacy system, and the results are reported.     

Intelligent dispatching for flexible manufacturing

A decision rule for real-time dispatching of parts, each of which may have alternative processing possibilities, has been developed and tested in a simulated flexible manufacturing system. A part, upon completion of an operation, is not routed to a specific machine, but is, in effect, sent to a general queue. Thus, a machine has a global option for choosing parts which in turn may be processed on alternative machines. For effective use of the system's routeing flexibility under these circumstances, the machine needs an intelligent part-selection strategy (rather than shallow heuristics represented by the conventional dispatching rules) that takes into account the current state and trends of the system. The proposed intelligent reasoning procedure has been found to achieve better shop performance than some of the popular dispatching rules, the improved performance being due to the ability to respond to changing circumstances.     

A comparison of focused cellular manufacturing to cellular manufacturing and job shop

The purpose of this study is to compare a focused cellular manufacturing environment with traditional cellular manufacturing, and job shop environments. We define focused cellular manufacturing as a configuration scheme that groups components by end-items and forms cells of machines to fabricate and assemble end-items. In addition, this research includes three levels of batch sizes and two levels of set-up times in its performance criteria which few researchers in this area have done. The results indicate that the focused cellular manufacturing scheme has a batching advantage. This advantage dominated the balanced machine utilization benefit of the job shop configuration scheme and out weighed the set-up time reduction advantage of the cellular manufacturing scheme for average end-item completion times and average work-in-process inventory levels. The cellular manufacturing and job shop schemes overcame the batch size advantage only when batch sizes were small or set-up times were large.     

A due date-based dispatching rule for flexible manufacturing systems

In the paper a new dispatching rule is proposed, which extends the CEXSPT rule for dispatching jobs in a general cell's environment, i.e., a multiple-machine cell, where each machine type is assigned to a priority value. Each job is represented as a finite sequence of operations, ordered according to the type priorities of the cell's machines they are performed by. The algebraic background of the dispatching rule is described. The dispatching rule is implemented in the GOLDEN COMMON LISP programming language for IBM PC AT and compatible computers. A computer study of the performance of the proposed dispatching rule is conducted based on a four-machine cell, and the results are reported and analysed. The conclusions obtained are that the proposed dispatching rule produces cell's makespans, better than the CEXSPT dispatching rule, because it reduces idle time on cell's machines.     

Learning effective dispatching rules for batch processor scheduling

Batch processor scheduling, where machines can process multiple jobs simultaneously, is frequently harder than its unit-capacity counterpart because an effective scheduling procedure must not only decide how to group the individual jobs into batches, but also determine the sequence in which the batches are to be processed. We extend a previously developed genetic learning approach to automatically discover effective dispatching policies for several batch scheduling environments, and show that these rules yield good system performance. Computational results show the competitiveness of the learned rules with existing rules for different performance measures. The autonomous learning approach addresses a growing practical need for rapidly developing effective dispatching rules for these environments by automating the discovery of effective job dispatching procedures.     

Dispatching rules for scheduling in assembly jobshops - Part 1

Research on jobshop scheduling has tended to concentrate on the development of dispatching rules for jobs that are independent, i.e. single-component jobs. However, in real-life situations, many jobs involve assembly operations that require scheduling of multiple components through the jobshop where both serial and parallel operations take place. In this two-part paper, we consider the problem of scheduling in assembly jobshops, i.e. jobshops that manufacture multi-level assembly jobs. The development of new and efficient dispatching rules with a view to address various measures of performance related to flowtime and staging delay of jobs is first undertaken. A new concept, called 'operation synchronization date' is introduced and made use of in the new dispatching rules. The best existing dispatching rules and the proposed dispatching rules are relatively evaluated by an exhaustive simulation study. The results indicate that the proposed rules emerge to be superior to the existing ones for most measures of performance.