Family based dispatching with batch availability

Family based dispatching rules seek to lower set-up frequencies by grouping (batching) similar types of jobs for joint processing. Hence shop flow times may be improved, as less time is spent on set-ups. Motivated by an industrial project we study the control of machines with batch availability, i.e. all the jobs of the same batch become available for processing and leave the machine together. So far the literature seems to have neglected this type of shop by restricting its focus on machines with item availability, i.e. assuming machine operations concern single jobs. We address this gap by proposing extensions to existing family based dispatching rules. Extended rules are tested by an extensive simulation study. Best performance is found for non-exhaustive rules, which allow for alternative choices of batch size. Performance gains are highest for low set-up to run-time ratios and/or high workloads.     

Satisfying due-dates in a job shop with sequence-dependent family set-ups

This paper addresses job shop scheduling with sequence dependent family set-ups. Based on a simple, single-machine dynamic scheduling problem, state dependent scheduling rules for the single machine problem are developed and tested using Markov Decision Processes. Then, a generalized scheduling policy for the job shop problem is established based on a characterization of the optimal policy. The policy is combined with a 'forecasting' mechanism to utilize global shop floor information for local dispatching decisions. Computational results show that performance is significantly better than that of existing alternative policies.     

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.     

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.     

蚁群系统结合指派规则求解HFS调度问题

以NP-难的最小化时间表长为目标的混合流水车间调度问题为研究对象。把工件在第1阶段开始加工的排序问题转化为旅行商问题,采用蚁群系统求得初始排序;在第1阶段后各阶段采用工件先到先服务规则选择工件、最先空闲机器优先规则选择机器以构建初始工件的机器指派与排序;充分利用已知的机器布局和工件加工时间特点,确定工件加工瓶颈阶段,并以此为基础对工件的机器指派与排序进行改进。用Carlier和Neron设计的Benchmark算例仿真后与著名的NEH算法比较,表明这种算法是有效的。     

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.     

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.     

Queuing network analysis approach for estimating the sizes of the time buffers in Theory of Constraints-controlled production systems

The present paper describes an open queuing network modelling approach to estimate the size of the time buffers in production systems controlled by the Theory of Constraints philosophy. Workstations in the production network are modelled as GI/G/m queues and a queuing network analysis multiproduct open queuing network modelling method is used to estimate the average flow time to the time buffer origin and the standard deviation of flow time. Using these two values together with an assumption of normally distributed flow times and a chosen service level, the final time buffer length is determined. The queuing network analysis method has been modified to enable the modelling of production networks with machine failures, batch service and varying transfer batch sizes. The modelling approach has also been incorporated in a computerized tool that uses product specific information such as bill-of-material and routing data, and production network information such as resource data to estimate the sizes and location of the necessary time buffers for each product. Simulation experiments indicate that the procedure is sufficiently accurate to provide an initial quick estimate of the needed time buffer lengths at the design stage of the line.     

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.     

Setup-oriented dispatching rules – a survey

This paper provides a survey of dispatching rules that explicitly take into account setup times in their decision making. Rules are classified into the categories of purely setup-oriented, composite and family-based rules, and the most promising rules from the three categories are identified from the literature. These rules are then compared empirically on various job shop problems with sequence-dependent setup times for their performance regarding mean setup time, mean flow time, mean tardiness and proportion of tardy jobs. The setup times are modelled using setup time matrices, and five different types of matrices are applied to assess the influence of this factor on the relative performance of a setup-oriented dispatching rule. Experimental results indicate that the choice of the best rule is often dependent on the setup time matrix structure. While good family-based rules exist for reducing the mean setup time and mean flow time, they are clearly outperformed by effective composite rules for due date-related criteria. Moreover, the better rules all seem to rely on queue information rather than only job attributes.     

A comparative study of priority dispatching rules in a hybrid assembly/job shop

This paper reports on research conducted on the use of priority dispatching rules in a hybrid assembly/job shop which manufactures both single-component and multiple-component products. A simulation model was constructed and a large stale experiment performed. Statistical analysis of the simulation results indicated significant impact of both the priority rules tested, and the product-mix considered on shop performance.

Among the 12 priority rules tested, the SPT (shortest processing time) rule and the ASMF-SPT (assembly jobs first with SPT as tie-breaker) rule performed very well with respect to measures like lateness, flow time, tardiness, staging time, and percent of jobs tardy. These findings lead to further investigation of a combined priority rule, MIXED, which implements the ASMF-SPT rule at all machine centres that process components of assembly jobs, and the SPT rule at the remaining machine centres which process non-component jobs. The additional research results yielded evidence that the MIXED rule can reduce the staging time of the SPT rule, and yielded betrer results than the ASMF-SPT rule with regard to other performance measures.

The most interesting finding, however, was the small variation in flow time distribution resulting from use of the MIXED rule when there were more assembly jobs. In an MRP environment, it is especially desirable to have a priority dispatching rule resulting in minimum variation in individual flow times which allows the replenishment lead times to be estimated with greater accuracy.     

Integrating load-based order release and priority dispatching

Workload control (WLC) is a well-established production control concept for job shops that put primary emphasis on load-based order release. Recent advances in load-based order release research have led to an improved delivery performance at reduced shop floor workloads. But although order release is the primary focus of WLC research, it must be coupled with priority dispatching on the shop floor if order progress is to be regulated. Prior simulation research suggests that load-based order release methods should only be coupled with simple dispatching rules because other, more powerful rules can conflict with the functioning of the release method. Yet, recent empirical research suggests that powerful priority dispatching rules – such as due date-oriented dispatching rules – are in fact needed for a high level of delivery performance to be obtained in practice. This paper focuses on overcoming the conflict between order release and dispatching, so load-based order release can be combined with due date-oriented dispatching. Preliminary analysis reveals that part of the conflict is because existing due date-oriented dispatching rules overcompensate for schedule deviations that occur when orders are either released earlier or later than planned. Two alternative new dispatching rules based on an improved method of determining operation due dates are then developed to better account for schedule deviations and overcome the conflict with load-based order release. Further improvements in delivery performance are obtained, while the large workload reductions achieved by recently developed load-based order release methods are retained.     

Multiproduct Optimal Batch Sizes with In-Process Inventories and Multi Work Centers

In this paper we extend the classical economic batch size model of Camp to situations where a range of products is made to stock and where the products are manufactured in a production shop consisting of a number of work centers of functionally grouped machines. Assuming that the flow times of the batches along the work centers can be modelled as a queueing process, it is shown that the batch sizes influence the batch waiting times in the shop, and the amount of work in process. The cost function to be minimized consists of ordering costs, final inventory holding costs, and work-in-process carrying costs. We show that the cost function is strictly convex and that the optimal batch sizes can be found by means of well-known numerical methods. We present an outline of an iterative procedure in which the batch size optimization procedure is combined with a shop load optimization procedure to achieve a good overall performance. Using a simplified model that assumes that the products are homogeneous in all respects, we also show that there exists an upper limit for the optimal batch size, which is determined by the ordering costs and the work-in-process carrying costs. Finally, we show that not taking into account work-in-process carrying costs may result in substantial errors in both batch size and cost.     

Tooling constraints and shop floor scheduling: evaluating the impact of sequence dependency

In a previous study, we began the process of examining how finite tooling resources affect the operation of a simulated job shop. The paper extends this line of research by focusing on the problem of how to schedule a shop operating with both finite machine and tooling resources, and faced by varying levels of tooling-related sequence dependency in the setups. Four tool assignment rules and two dispatching procedures are evaluated. Findings show the importance of using tool assignment rules which consider not only tool-related information such as tool availability but also the priority of the jobs waiting to be processed. Specifically, as the level of sequence dependence increases and the availability of tooling decreases, the performance of the shop is greatly influenced by the tool assignment rule used to manage the flow of tooling to and from work centres     

Adaptive scheduling of batch servers in flow shops

Batch servicing is a common way of benefiting from economies of scale in manufacturing operations. Good examples of production systems that allow for batch processing are ovens found in the aircraft industry and in semiconductor manufacturing. In this paper we study the issue of dynamic scheduling of such systems within the context of multi-stage flow shops. So far, a great deal of research has concentrated on the development of control strategies, which only address the batch stage. This paper proposes an integral scheduling approach that also includes succeeding stages. In this way, we aim for shop optimization, instead of optimizing performance for a single stage. Our so-called look-ahead strategy adapts its scheduling decision to shop status, which includes information on a limited number of near-future arrivals. In particular, we study a two-stage flow shop, in which the batch stage is succeeded by a serial stage. The serial stage may be realized by a single machine or by parallel machines. Through an extensive simulation study it is demonstrated how shop performance can be improved by the proposed strategy relative to existing strategies.     

Bottleneck-based heuristics to minimize tardy jobs in a flexible flow line with unrelated parallel machines

This paper develops new bottleneck-based heuristics with machine selection rules to solve the flexible flow line problem with unrelated parallel machines in each stage and a bottleneck stage in the flow line. The objective is to minimize the number of tardy jobs in the problem. The heuristics consist of three steps: (1) identifying the bottleneck stage; (2) scheduling jobs at the bottleneck stage and the upstream stages ahead of the bottleneck stage; (3) using dispatching rules to schedule jobs at the downstream stages behind the bottleneck stage. A new approach is developed to find the arrival times of the jobs at the bottleneck stage, and two decision rules are developed to schedule the jobs on the bottleneck stage. This new approach neatly overcomes the difficulty of determining feasible arrival times of jobs at the bottleneck stage. In order to evaluate the performance of the proposed heuristics, six well-known dispatching rules are examined for comparison purposes. Six factors are used to design 729 production scenarios, and ten test problems are generated for each scenario. Computational results show that the proposed heuristics significantly outperform all the well-known dispatching rules. An analysis of the experimental factors is also performed and several interesting insights into the heuristics are discovered.     

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。     

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.     

Family-based dispatching: anticipating future jobs

Group Technology exploits similarities in product and process design to meet the diversity of customer demand in an economic way. In this paper we consider one of the implementations of this concept?–?family-based dispatching. Intrinsic to family-based dispatching is the grouping of similar types of products for joint processing. In this way the number of set-ups may be reduced. Consequently, lead-time performance of the shop can be improved. We extend existing rules for family-based dispatching by including data on upcoming job arrivals. Typically, this type of data resides in the minds of the operators, or is stored in a shop-floor control system. Its availability allows for (1) better estimates of the composition of a process batch for a family, (2) the consideration of families for which no products are available at the decision moment, and (3) the possibility to start set-ups in anticipation of future job arrivals. The potential of including forecast data in decision-making is demonstrated by an extensive simulation study of a single-machine shop. Results indicate the possibility of significant improvements of flow time performance.