Heterogeneous dispatching rules in job and flow shops

This paper reports the results of a study of the use of heterogeneous dispatching rules for the scheduling of work in a job shop. The methodology employed included discrete event simulation, using rule combinations determined by prior genetic algorithm searches and generalization using neural networks. Eight dispatching rules were considered, including first in first out (FIFO), earliest due date ( EDD), shortest processing time (SPT), slack/ number of operations (SLK), critical ratio (CR), modified due date (MDD), modified operation due date (MOD), and apparent tardiness cost (ATC). A three-machine job shop was studied, in which three work organizations were employed, pure flow (fixed sequence), pure job shop ( random sequence), and a hybrid shop where flow is random but with unequal probabilities. Three levels of machine loading were used and average tardiness was used as the performance measure. In most cases, modified due date and apparent tardiness cost were the best rules. The application of the best rules effected the results primarily when applied to bottleneck machines or the first machine in a pure flow shop. Nearly any other rule was acceptable on non-botdeneck machines except FIFO and CR, which consistently perform poorly. No major advantage of mixing rules was found.     

An evaluation of the DBR control mechanism in a job shop environment

This study is an evaluation of the drum–buffer–rope (DBR) control mechanism compared to the modified infinite loading (MIL) control mechanism in a job shop environment. Although previous research has shown that the MIL mechanism works well in this environment, this study finds that the DBR control mechanism performs significantly better. The performance of the DBR mechanism improves when the shortest processing time (SPT) dispatching rule is used.     

Application of the Net Present Value Criterion in Random and Flow Shop Scheduling*

While the majority of the literature on shop scheduling has emphasized time-based performance criteria such as mean flow time, lateness, and tardiness, the primary goal of management should be the maximization of shop profitability. In this research the net present value (NPV) criterion is introduced to measure shop profitability. This measure combines aspects of job flow time and inventory holding costs into a single measure. A simulation model of a job shop is used to examine the performance of a variety of time- and value-based scheduling rules. These rules are evaluated with respect to the NPV criterion in both random and flow shop environments. The results suggest that priority rules that utilize monetary information about jobs yield a higher NPV than many time-based rules in most situations, with little sacrifice in job tardiness. A well-researched time-based rule, critical ratio, also provides excellent performance when the shop is heavily loaded.     

Lot Splitting in Stochastic Flow Shop and Job Shop Environments*

In recent years many firms have been implementing small lot size production. Lot splitting breaks large orders into smaller transfer lots and offers the ability to move parts more quickly through the production process. This paper extends the deterministic studies by investigating various lot splitting policies in both stochastic job shop and stochastic flow shop settings using performance measures of mean flow time and the standard deviation of flow time. Using a computer simulation experiment, we found that in stochastic dynamic job shops, the number of lot splits is more important than the exact form of splitting. However, when optimal job sizes are determined for each scenario, we found a few circumstances where the implementation of a small initial split, called a “flag,” can provide measurable improvement in flow time performance. Interestingly, the vast majority of previous research indicates that methods other than equal lot splitting typically improves makespan performance. The earlier research, however, has been set in the static, deterministic flow shop environment. Thus, our results are of practical interest since they show that the specific method of lot splitting is important in only a small set of realistic environments while the choice of an appropriate number of splits is typically more important.     

An Evaluation of Capacity Sensitive Order Review and Release Procedures in Job Shops

We investigate the performance of capacity-sensitive order review and release (ORR) procedures in job shop environments that have not been previously explored. Previous research has ignored the case of job shops which must perform to very tight due-dates because of time-sensitive customers. We propose and test a new capacity sensitive ORR procedure called path based bottleneck (PBB) in such environments, along with the modified infinite loading (MIL) procedure which has been shown to work well in several studies. We compare the performance of these two controlled release rules with that of immediate release rule under different conditions of capacity utilization and customer specified exogenous duedates. Our results indicate that PBB performs well in lowering total costs when due-dates are tight, while MIL is a better procedure with relatively loose to medium due-dates. We also show that in many cases, the shortest processing time (SPT) dispatching rule is a superior performer than a due-date based rule like critical ratio (CR); a conclusion which is contrary to the existing research in this area. In addition, the shop floor control policies recommended are shown to be sensitive to the cost structure of the firm. The managerial implications of this research in providing effective shop floor control in job shops operating under tight due-date conditions are also discussed.     

Evaluation of simple dispatching rules for dynamic single-machine earliness/tardiness problem

Abstract. This paper investigates the effects of four simple dispatching rules on just-in-time production related performance measures of mean and maximum absolute lateness. The rules used are modified due date (MDD), shortest processing time (SPT), earliest due date (EDD), and first in first out (FIFO). A single machine is used under three utilization levels. Due-dates are set according to total work content rule. The results indicate that each rule performs well under certain conditions. The MDD rule is the best one to minimize mean absolute lateness. The EDD and FIFO rules do well in minimizing the maximum absolute lateness. Economic interpretation of these performance measures are also discussed.     

THE IMPACT OF SIMPLE PRIORITY RULE COMBINATIONS ON DELIVERY SPEED AND DELIVERY RELIABILITY IN A HYBRID SHOP

This study examines the effects of using different priority rules at different stages of a multistage, flow-dominant shop. A simulation model is constructed of a manufacturing system comprised of three stages: gateway, intcrmcdiatc, and finishing. As is typical of a flow-dominant shop, the overall flow of the simulated system (gateway to intermediate to finishing) is consistent with a flow shop, but processing in the intermediate stage involves multiple work centers and resembles a job shop. Shop performance is observed when four well-known priority heuristics are applied in different combinations in the gateway, intermediate, and finishing stages of the process. Multiple performance measures addressing the strategic objectives of delivery speed and delivery reliability are recorded under two different shop load conditions. Results show that the measures of both delivery speed and delivery reliability are affected by the priority rule combinations, and that a tradeoff exists between average performance and consistency of performance. Certain priority rule combinations affect performance in predictable ways, allowing the user to assess tradeoffs between delivery speed and delivery reliability.     

Fundamental Insights into Part Family Scheduling: The Single Machine Case

A simulation study was conducted to investigate the behavior of family scheduling procedures in a dynamic dispatching environment. Two scheduling rules that incorporate setup avoidance mechanisms (FCFS-F and SPT-F) and two that do not (FCFS and SPT) were applied to a single machine. The scheduling environment was varied by controlling several important factors: the machine utilization, the number of setup configurations (families), the size of the family setup times relative to the job run times, the frequency by which members of the part families were released for processing, and the distribution of job interarrival and job run times. The major results from the study are the following: (1) The degree of stability in the system is the most influential factor with respect to mean flow time and flow time variance. Under low variance service and interarrival time distributions, the impact of scheduling rule selection is minor. (2) Conversely, under unstable scheduling situations, family scheduling procedures can have a substantial impact. (3) Clear interaction effects are noticed between all factors. The environment most conducive to family scheduling is characterized by high resource utilization, low setup-to-run time ratio, few part families, and erratic job arrivals. (4) Under conditions favorable to family scheduling, setup avoiding procedures can be used to increase output while at the same time reduce the mean and variance of flow time. (5) The shortest processing time rule (SPT) performs well with respect to mean flow time when relative setup times are small. Overall, however, SPT-F generates the lowest mean flow time while FCFS-F produces the lowest flow time variance. This study shows that scheduling procedures that consider setups in their structure can outperform rules that do not under many different operating conditions. However, the magnitude of this advantage very much depends on the scheduling environment. The results also highlight the fact that it may be better to try to reshape the manufacturing environment than worry about selecting the correct scheduling rule. If the environment cannot be stabilized, then the choice of a setup avoiding procedure, allocation of families to machines, and setup reduction become important issues.     

An Evaluation of the NERJIT Priority Rule in a Kanban‐Controlled Flowshop

Significant progress in production and information technologies and innovations in management of operations during the last couple of decades have made the production of small lots and deployment of Just‐In‐Time (JIT) concepts in flowshops possible. As a result, some researchers and practitioners have been seeking to improve the performance of non‐repetitive systems using JIT concepts. In this process, the JIT concepts that were originally designed for mass production have been modified to adapt JIT to non‐repetitive systems. This article uses a priority rule that is based on real‐time demand and production information for sequencing jobs in a kanban‐controlled flowshop. The analysis of the effect of this priority rule; the number of kanbans; the length of the withdrawal cycle; First‐Come, First‐Served (FCFS); and Shortest Processing Time (SPT) on four performance measures—customer wait time, total inventory, input stock‐point inventory, and output stock‐point inventory, shows that the use of this priority rule results in a significant reduction of customer wait time and a slight decrease in inventory.     

A comparative analysis of due date based job sequencing rules in a flow shop with multiple processors

The literature on job scheduling recognizes the importance of due date performance criteria such as mean tardiness and maximum tardiness. A number of studies test a large number of sequencing rules for these criteria in job shop and flow shop settings. The object of this present research is to examine the performance of some well-known priority rules in a flow shop with multiple processors. This study investigates the performance of ten priority rules in terms of mean and maximum tardiness. It examines the effects of problem characteristics, such as number of jobs, number of machines stages and number of parallel processors at each stage, and the performance of priority rules using regression analysis. The findings of the study suggest that the primary determinants of tardiness-based criteria are problem characteristics. In addition, both the regression analysis and the analysis of variance provide strong evidence of the strategy-effect. Finally, a detailed performance review of examined priority rules for various problem characteristics is presented.     

Optimal and Heuristic Models for Lot Splitting in a Flow Shop

This paper investigates optimal lot-splitting policies in a multiprocess flow shop environment with the objective of minimizing either mean flow time or makespan. Using a quadratic programming approach to the mean flow time problem, we determine the optimal way of splitting a job into smaller sublots under various setup times to run time ratios, number of machines in the flow shop, and number of allowed sublots. Our results come from a deterministic flow shop environment, but also provide insights into the repetitive lots scheme using equal lot splits for job shop scheduling in a stochastic environment. We indicate those conditions in which managers should implement the repetitive lots scheme and where other lot-splitting schemes should work better.     

A comparative analysis of two different approaches to scheduling in flexible flow shops

This work is an investigation about the relative effectiveness of two approaches to scheduling in flexible flow shops: one approach advocating the possible use of different dispatching rules at different stages of the flow shop, and the other suggesting the use of the same dispatching rule at all the stages of the flow shop. In the latter approach, the dispatching rule contains the information related to both process time and duedate. Both approaches aim at the minimization of measures related to flowtime and tardiness of jobs. This paper essentially is an attempt at exploring the relative effectiveness of these two approaches to scheduling.     

Concerning Workload Control and Order Release: The Pre‐Shop Pool Sequencing Decision

Every production planning concept that incorporates controlled order release will initially withhold jobs from the shop floor and create a pre‐shop pool. Order release is a key component of the Workload Control concept that aims to maintain work‐in‐process within limits while ensuring due dates are met. Order release includes two decisions: (i) a sequencing decision that establishes the order in which jobs are considered for release; and, (ii) a selection decision that determines the criteria for choosing jobs for release. While selection has received much research attention, sequencing has been largely neglected. Using simulation, this study uncovers the potential for performance improvement in the sequencing decision and improves our understanding of how order release methods should be designed. Although most prior studies apply time‐oriented sequencing rules and load‐oriented selection rules, analysis reveals that load balancing considerations should also be incorporated in the sequencing decision. But an exclusive focus on load balancing is shown to increase mean tardiness and, paradoxically, require high workloads. A new sequencing rule is developed that only balances loads when multiple orders become urgent. It avoids high mean tardiness and allows the shop to operate at a low workload level. At the same time, the percentage tardy is reduced by up to 50% compared to a purely time‐oriented rule. The findings have implications not only for Workload Control but for any concept that features order release control, such as ConWIP and Drum‐Buffer‐Rope.     

Replanning the Master Production Schedule for a Capacity-Constrained Job Shop

This research examines the use of both frozen and replanning intervals for planning the master production schedule (MPS) for a capacity-constrained job shop. The results show that forecast error, demand lumpiness, setup time, planned lead time, and order size have a greater impact on the mean total backlog, total inventory, and number of setups than the frozen and replanning intervals. The study also shows that a repetitive lot dispatching rule reduces the importance of lot sizing, and a combination of repetitive lot dispatching rule and single-period order size consistently produces the lowest mean total backlog and total inventory. The results also indicate that rescheduling the open orders every period produces a lower mean total backlog and total inventory when the forecast errors are large relative to the order sizes. This result suggests that the due date of an open order should be updated only when a significant portion of the order is actually needed on the new due date.     

An Entropy Measure of Flow Dominance for Predicting Operations Performance

The flow of jobs within a system is an important operating characteristic that influences system performance. While the majority of previous studies on manufacturing performance consider product flows only as an implicit parameter of the design, we introduce an explicit measure of flow dominance based on entropy and test its efficacy in predicting the performance of manufacturing systems. In computing entropy flow dominance (EFD), we aggregate information embedded in the routings of all products within a system into a single measure. EFD is designed to indicate on a 0–1 scale the level of flow dominance, where 1 represents a pure flow shop and 0 represents a pure job shop. The result is a simple measure that provides managers a way to explain and predict complex phenomena. Our experimental results indicate that EFD is a statistically significant determinant of manufacturing system performance. Furthermore, the model including EFD as an independent variable accurately predicts manufacturing system performance as measured by job flow time, flow time standard deviation, and work in process. We note that the same results can also apply to service systems, such as the “back‐room” low‐contact type systems, that have similar characteristics as manufacturing systems.     

Simple subcontracting rules for make-to-order shops with limited subcontractor capacity: an assessment by simulation

Companies that produce a high variety of customised products often suffer from large fluctuations in demand. Subcontracting can be an important means of overcoming resulting capacity shortages during high demand periods. A set of effective subcontracting rules, for determining which jobs to subcontract and which to produce internally, has recently been presented in the literature for this type of company. But evaluations of these rules have assumed subcontractor capacity is infinite. This study examines the impact of limited subcontractor capacity on the performance of the three best-performing subcontracting rules for make-to-order companies using a simulation model of an assembly job shop. Limiting subcontractor capacity inhibits the ability of a subcontracting rule to protect the internal shop from surges in demand, which negatively affects performance. However, significant performance differences between the rules evaluated are maintained, which underlines the importance of choosing the right subcontracting rule. Further analysis reveals that a limit on the work that can be subcontracted leads to less work being subcontracted more often, which requires adequate response by management. Meanwhile, the assumption of infinite capacity results in sporadically subcontracting a large amount of work. The results have important implications for future research and practice.     

An Experimental Comparison of Time-Based and Economic-Based Scheduling Methods to Maximize Net Present Value*

In the past, performance in dynamic-scheduling environments was primarily measured in terms of time or physical shop characteristics. Objectives such as mean tardiness, flow time, and work-in-process inventory were commonly used. Today, there is increasing interest in the use of more advanced economic performance measures. These measures have the more comprehensive objective of maximizing ownership wealth by economically scheduling jobs and tasks. This study presents a large-scale experiment testing time-based and economic-based scheduling methods in a dynamic job shop. These methods are evaluated on their ability to maximize net present value (NPV). The study considers the just-in-time (JIT) delivery environment. The job shop is hypothetical, but is based on models of real production situations. Results show that the use of very detailed economic information in a sophisticated manner generally improves economic performance. Where due dates are easy to achieve, however, time-based scheduling methods are at least as good as those based on economics. Also, where utilization is high and due dates tight, early cost information in release and dispatch is detrimental to schedule value.     

RFID as an Enabler of Improved Manufacturing Performance*

The use of radio frequency identification (RFID) versus bar coding has been debated with little quantitative research about how to best use RFID's capabilities and when RFID is more advantageous. This article responds to that need by qualitatively and quantitatively analyzing how RFID facilitates increased traceability and control in manufacturing, which in turn enables the use of more lot splitting and smaller lot sizes. We develop insights about operating policies (RFID vs. bar‐code tracking mechanisms, extent of lot splitting, and dispatching rules) and an operating condition (setup to processing time ratio) that affect the mean flow time and proportion of jobs tardy in a job shop. A simulation model is used to control factors in the experimental design and the output is evaluated using analysis of variance. The results show the following: (i) performance worsens when bar coding is used with extensive lot splitting, (ii) process changes such as extensive lot splitting may be required to justify RFID use instead of bar coding, (iii) the earliest operation due date dispatching rule offers an attractive alternative to other rules studied in previous lot splitting research, and (iv) the performance improvements with RFID and increased lot splitting are larger when the setup to processing time ratio is smaller. In a broader context, we fill a research void by quantitatively showing how RFID can be used as an advanced manufacturing technology that enables more factory automation and better performance along several dimensions. The article concludes by summarizing the results and identifying ideas for future research.     

Performance analysis of re-entrant flow shop with single-job and batch machines using mean value analysis

We propose an approximate method based on the mean value analysis for estimating the average performance of re-entrant flow shop with single-job machines and batch machines. The main focus is on the steady-state averages of the cycle time and the throughput of the system. Characteristics of the re-entrant flow and inclusion of the batch machines complicate the exact analysis of the system. Thus, we propose an approximate analytic method for obtaining the mean waiting time at each buffer of the workstation and a heuristic method to improve the result of the analytic method. We compare the results of the proposed approach with a simulation study using some numerical examples.     

Customer Order Scheduling in a General Job Shop Environment*

The primary objective of this study is to examine the performance of order-based dispatching rules in a general job shop, where the environmental factors are shop utilization and due date tightness. An order is defined as a collection of jobs that are shipped as a group—an order—to the customer, only on completion of the last job of the order. We specifically compare dispatching rules from past job-based studies to some rules adapted to encompass order characteristics. Standard flow time and tardiness measures are used, but in addition, we introduce measures that combine average performance with variation in an attempt to capture the performance of a majority of the orders processed in the shop. Of the 16 dispatching rules tested, our results show that four of the simple rules dominate the others. We also found that order-based rules perform better than their job-based counterparts. The study makes use of multivariate statistical analysis, in addition to the usual univariate tests, which can provide additional insight to managers using multiple criteria in their decision process.