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In recent years, growing quantities of end-of-life electronics have increased the amount of attention devoted to product recovery. Research on end-of-life electronics returns has primarily focused on manual disassembly operations. In this paper, we focus on the scheduling problem for a facility with staging, manual disassembly operations, and bulk recycling. In bulk recycling, shredding or grinding reduces the size of the material fragments while magnetic, eddy current or other density separation techniques separate the material fragments. Unlike production, there are often no due dates in materials recovery processing. Recyclers can sell the recovered materials to material commodity buyers at any time. However, recyclers wait to accumulate a shipment of material to reduce transportation costs and meet minimum sales quantities. Another important difference between production and recycling is that manufacturers purchase raw materials while recyclers may be paid to receive products. When due dates do not apply to scheduling products for materials recycling and product receipts generate revenue for recycling services, we propose two new metrics: the staging space turnover and the shipment fill time. We use our metrics to analyze new scheduling rules for disassembly and bulk recycling and to evaluate their performance. Using discrete-event simulation models, we test our scheduling rules on seven product families, where product families are defined based on material composition and separation operations. Of the rules we test, the disassembly scheduling rule which ranks product families based on the ratio of product size to disassembly time (SDT) most quickly empties the staging space. Shipment fill time is less sensitive to our scheduling rules. Our results illustrate how a recycler can reduce incoming product inventory with a new scheduling rule.

Consider the dispatch problem for a wafer fabrication where production process is divided into hundreds of operations and takes a few months to complete. In the process, wafers have to go through similar operations several times repeatedly for different layers of circuit, called job re-entrances. General family-based scheduling rules are proved to perform better than the individual job scheduling rule in terms of machine utilization for multiserver and multiple job re-entrance manufacturing systems under the condition that with a positive possibility, a queue exists in front of steppers. Five special family-based scheduling rules are constructed, of which FCFS-F, SRPT-F, EDD-F, and LS-F are modified from previous well-known scheduling rules, while SDA-F is a rule-based algorithm, using threshold control and least slack principles. A simulation model is built to evaluate the performances of these five family-based rules by using the information collected from a wafer fab located in Hsin-Chu, Taiwan. As a result, SDA-F is shown to perform best among all five rules, followed by LS-F and FCFS-F.

The objective of this paper is to study a selected set of scheduling criteria commonly used in scheduling research and develop a set of simple rules that can be easily implemented in relatively small dynamic job-shops. Simulation models were developed for two dynamic production shop environments, namely, a job-shop with jumbled flow and a job-shop with limited jumbled flow. Five scheduling criteria were used with each of the three models. They are, (i) Slack per Remaining Operations (SPRO), (ii) Priority Ratio (PR), (iii) Earliest Due Date (EDD), (iv) Total Work Content Remaining (TWKR), and (v) Smallest Processing Time (SPT). In each of these cases, performance statistics relating to work-in-process (WIP) inventory and job lateness were gathered. The results show that in most cases, simple rules such as SPT and EDD outperform the other rules. SPT rule minimizes the performance metrics that measure WIP inventory levels and the EDD rule minimizes the performance metrics that measure deviation of job completion time from the promised due date. (Received in February 2010, accepted in July 2010. This paper was with the authors 2 months for 1 revision.).

This article describes the development of a decision support system, called ‘PHIL’, for a fibre production scheduling problem. An algorithm to obtain production plans for the spinning department of a case study company is presented. This article shows how the algorithm has been implemented in ‘PHIL’ to allow interaction between the decision it proposes and the human planner. This interaction is possible because of the way in which ‘PHIL’ deals with the problem constraints that differ in terms of ‘hardness’. The methodology uses formal rules but keeps some decision room for the planner to use informal decision practices that are almost impossible to encode. ‘PHIL’ is evaluated comparing the plans obtained with plans generated informally by the human planner. The same data sets are also used to compare plans produced using only formal practices with plans produced by the proposed methodology. The results of this evaluation show that the proposed methodology, which allows a decision maker to use his/her pers...

In research on generating a predictive schedule, the scheduling problem is often viewed as a deterministic problem. However, the real-life job shop environment is stochastic in that information on job attributes and shop floor status is not precisely known in advance. In this situation, in order to increase the effectiveness of a predictive schedule in practice, the focus should be on creating a robust schedule. The purpose of this paper is to investigate the robustness of a number of scheduling rules in a dynamic and stochastic environment using the rolling time horizon approach. A cost-based performance measure is used to evaluate the scheduling rules. The simulation results, under various conditions in a balanced and unbalanced shop, are presented and the effects of the rescheduling interval and operational factors including shop load conditions and a bottleneck on the robustness of the schedule are studied. From the results the key factors that influence the robustness of a scheduling system are ident...

In this research the problem of scheduling n jobs to minimize the Total Weighted Late Work (TWLW) is evaluated within the single machine context. As the problem complexity increases, so does the solution complexity, and often the objective is to identify a heuristic or algorithm that may return a near optimal solution. Various scheduling rules, heuristics and algorithms, including the weighted shortest processing rule, a variation of the modified due date rule, a genetic algorithm, neighborhood job search, and space smoothing with neighborhood job search; are empirically evaluated using different parameters to determine the utility of each approach.

It is important in developing intelligent scheduling systems to build an effective method for acquisition of decision rules. We have proposed a method for acquisition of scheduling rules by using inductive learning. Cases needed for rule acquisition are generated by interchanging two jobs in a sample schedule, and job attributes and location properties of jobs are used as the basic information for the rule acquisition. The proposed method for rule acquisition has been applied to single machine problems and flowshop problems.This paper extends the authors' previous work to jobshop scheduling problems and describes a method to acquire scheduling rules by generating a number of sample schedules and analyzing their properties. The effectiveness of the proposed method for rule acquisition and the applicability of such obtained rules are demonstrated by applying them to the exploration process of local search.

Just-In-Time (JIT) production systems capitalize on simplicity and the ability of workers to make decisions in a decentralized manner. In a multiproduct line operating under kanban control, the production worker at a given station must schedule the different jobs awaiting processing using information available locally. In practice, the first-come-first-served (FCFS) rule is commonly used. Recent results reported in the literature indicated that the shortest-processing-time (SPT) rule performed better than the FCFS rule. In this paper, we provide a simulation evaluation of the performance of a number of scheduling rules operating under different JIT production scenarios. Our hypothesis is that there are differences in the relative performance of the scheduling rules under different production scenarios. We differentiate among the JIT scenarios by the extent of setup time reduction already carried out (as indicated by the ratio of setup to processing times), the amount of slack in the system (as measured by the number of kanbans circulating), the extent to which uncertainty has been eliminated (as determined by the stochasticity of processing times), and the complexity of production requirements (as specified by the product-mix in mixed-model assembly). In this way, this paper provides further insights into the performance of scheduling rules operating under different JIT production environments, thereby adding to the scope and depth of research in this particular aspect of JIT production systems.

This study examines the effects of scheduling rules on the performance of flexible manufacturing systems (FMSs). Several machine and AGV scheduling rules are tested against the mean flowtime criterion. In general, scheduling rules are widely used in practice ranging from direct applications as a stand-alone scheduling scheme to indirect application as a part of complicated scheduling systems. In this paper, we compare the rules under various experimental conditions by using an FMS simulation model. Our objective is to measure sensitivity of the rules to changes in processing time distributions, various levels of breakdown rates, and types of AGV priority schemes. A comprehensive bibliography is also presented in the paper.

The layout and flow of real manufacturing shops are most often neither pure job-shop nor pure flow-shop. This paper presents the results of a simulation-based study in which some of the most common and simple scheduling rules are tested in mixed shops containing jobs with both job-shop and flow-shop routing. Specifically, the rules that are used are first in first out, shortest processing time, modified shortest imminent operation, non-decreasing slack, and modified slack. Two performance measures are considered, a modified mean flow time to assess the amount of time that jobs spend waiting for processing and the cost of earliness and tardiness to evaluate how closely the rules complete jobs relative to their due date. The data is analyzed using both graphical and hypothesis tests so that not only conclusions based on general trends could be drawn, but also assessed with statistical rigor. Using these techniques, the results are somewhat mixed but, in general, indicate that the shortest processing time rule is preferred when the mean flow time is the performance measure and modified slack is preferred when cost is the performance measure. By adding additional insight from the actual simulation runs to these conclusions, we recommend that practitioners can likely get good performance by using either of these rules as the basis for a heuristic that adds logic to avoid extreme conditions.

This paper is a report on a simulation study to investigate the performance of a number of scheduling rules on the basis of a rolling time horizon approach for a dynamic job shop environment. The performance measure considered is an economic objective which includes the main costs involved in a scheduling decision. The first purpose of the study was to find the best scheduling rule and the second to investigate the effects of the rescheduling interval on performance and examine whether there is a policy that can always improve performance. The simulation study, which is part of a larger project on practical workshop scheduling, has been carried out under widely varying conditions in terms of due date tightness, shop load level, and shop load balance level. The results show that a recently developed scheduling rule, SPT-C/R, is the most appropriate scheduling rule in minimizing overall cost and that the relationship between performance and rescheduling interval can be shown.

An analysis of job shop performance for different scheduling rules is evaluated using computer simulation. Fourteen different scheduling rules and six performance measures were used in the analysis. The interaction of scheduling rules with two other independent factors, service time distributions and shop load levels, were also analysed. The experimental results showed that the shop load level has a significant effect on system performance but does not interact with the other two independent variables, and the choice of a scheduling rule cannot be made independent of the service time distribution. In general, the simple operation-time-based rules produced the best combination of results for the performance measures evaluated.

Scheduling in flexible manufacturing systems (FMSs) may require some complex solutions due to the complexity of the system. In practice, the application of just-in-time (JIT) theory to an FMS has become an attractive research topic, and heuristic scheduling rules are practical methods for scheduling job orders in real-time scheduling. This article presents a model of an FMS, and then applies the JIT theory with some heuristic scheduling rules to the backward scheduling method in particular. We carry out the simulation test for seeking the performance measures obtained by each rule. It is shown that some proposed heuristic scheduling rules give better results than the others. It is also shown that in JIT, the backward scheduling method can be a useful tool in helping the user to select the most suitable rules for the system at hand.

In real manufacturing environments, variations in production factors (i.e. pro- cessing time, demand, due-dates) are inevitable facts. All these dynamic changes, together with random disturbances (e.g. machine breakdowns) can seriously a ect the system performance. In this paper we focus on load, processing time and due date variation and analyse their impacts on a scheduling system. Speci®cally, we investigate the impact of variation on dispatching policies in a job shop environ- ment via simulation. The statistical analysis of the results leads to two major conclusions: ®rst, the relative performance of rules is not threatened much by PV (processing time variation), LV (load variation) or DDV (due date variation) Ð a result that can be a consolation for practitioners in the ®eld. Secondly, the performance of the rules deteriorates, in particular at high levels of PV, LV and DDV Ð a result that can provide new insights into the problem and produces useful information for researchers in their continuous e ort to develop better dispatching rules.

Most studies on scheduling in manufacturing systems using dispatching rules deal with jobshops, while there are only few reports dealing with dynamic flowshops. It is known that the performance of many dispatching rules in dynamic jobshops is different from that in dynamic flowshops. Moreover, many research reports assume that there are no buffer constraints in the shop, and even those reports dealing with buffer-constrained shops present the evaluation of existing dispatching rules for unconstrained shops in the context of buffer constraints with the consideration of a limited number of objectives of scheduling. In this study, we deal with the problem of scheduling in dynamic flowshops with buffer constraints. With respect to different time-based objectives, the best dispatching rules for scheduling in unconstrained shops have been identified from the existing literature. In addition, two new dispatching rules specially designed for flowshops with buffer constraints are proposed. All dispatching rules un...

Much of the existing job shop scheduling literature has focussed on scheduling rules and heuristics that typically do not incorporate customer specific characteristics into their scheduling decisions. This paper specifically considers the situation in which depending upon the identity of the customer, different jobs have different tardiness penalties associated with them. In addition, the tardiness penalties associated with different customers are comparable in magnitude with one another. We propose here and test four different types of scheduling rules which utilize the jobs' tardiness penalty information, and show that the performance of the shop is only mildly affected by the shape and dispersion of the weighting scheme used in determining the tardiness penalties. The performance of scheduling rules is however contingent on due-date tightness. In general, apart from the traditionally well performing shortest processing time rule, the ATC rule works well on the mean flow time measure in loose due-date situations, while COVERT performs well on mean flow time under tight due-date conditions. Lesser differences are seen to exist between mean tardiness and root mean square tardiness (TRMS) performance of different scheduling rules. Weighted mean tardiness deteriorates with increasing due-date tightness and coefficient of variation of job tardiness penalties. Finally, customer service performance worsens considerably with weighted dispatching rules when the performance criteria are not weighted by each job's tardiness penalty. Implications of our results for providing more effective management of job shops, and exploring future research directions in this area are also presented and discussed.

This paper examines the use of stochastic scheduling rules for maximizing the net present value of a project. A comprehensive set of 1440 test problems, representing five different project characteristics, is constructed. These test problems are used to evaluate the performance of nine stochastic scheduling rules. A simulated annealing scheduling procedure is shown to perform best, generating the highest net present values for most of the test problems. In certain environments, two previously examined rules, the Rank Positional Weight and a Discounted Cumulative Cash Flow Weight rule, also perform well with high net present values.

Short-term optimal operation of hydropower station is a high-dimensional, multi-constraint and complex system problem. In this paper, Liyuan hydropower station located in the middle reaches of Jinsha River is cited as an example, and the mathematical model of optimal power generation dispatching has been established. We obtain a large amount of optimal scheduling processes by using the progressive optimality algorithm (POA). Then, the corresponding scheduling rules for single hydropower station which can take advantage of runoff prediction results are derived. A case indicates that the scheduling rules can be effectively used to guide the actual operation run of Liyuan hydropower station.

Appointment scheduling rules are used to determine when a customer is to receive service. Many appointment scheduling rules exist and are being used in practice (e.g., in healthcare and legal services). Which appointment scheduling rule is best, however, is still an open question. In order to answer this question, we develop an analytical model that allows to assess the performance (in terms of customer waiting time, server idle time and server overtime) of appointment scheduling rules in a wide variety of settings. More specifically, the model takes into account: (1) customer unpunctuality, (2) no-shows, (3) service interruptions and (4) delay of the service process. In addition, we allow the use of general distributions to capture system processes. We adopt an efficient algorithm (with respect to computational and memory requirements) to assess the performance of 314 scheduling rules and use data envelopment analysis to compare results.

This paper examines the effects of some independent variables on the performance of an electronic manufacturing Surface Mount Technology (SMT) production line operating in a highly dynamic environment. Variables considered are: scheduling rules and set-up policies. Experimental design, ANOVA and simulation techniques (Awesim, C) are used in order to predict the performance of a SMT line on the work-in-process, the throughput time and set-up time.

This paper considers the design and simulation-based analysis of dispatching rules for scheduling in dynamic job shops. Based upon the combination of well-known rules new scheduling rules are developed. For evaluating the relative performance of the dispatching rules the criteria of mean and maximum flowtime, variance of flowtime, mean and maximum tardiness, percentage of tardy jobs and variance of tardiness have been chosen. The results of the simulation study reveal that the simple structured combination rules are quite efficient.

This paper proposes a method for the acquisition of scheduling rules for job shop problems by using an inductive learning from given cases and investigates its applicability. The cases are generated by interchanging two jobs in a sample schedule. In the computational experiments, the effectiveness and the applicability of the obtained rules are demonstrated by applying them to the exploration process of local search.

Although a significant amount of research has been carried out in the scheduling of flexible manufacturing systems (FMSs), it has generally been focused on developing intelligent scheduling systems. Most of these systems use simple scheduling rules as a part of their decision process. While these scheduling rules have been investigated extensively for a job shop environment, there is little guidance in the literature as to their performance in an FMS environment. This paper attempts to investigate the performances of machine and AGV scheduling rules against the mean flow-time criterion. The scheduling rules are tested under a variety of experimental conditions by using an FMS simulation model.

In many manufacturing settings, an item may require repair at one stage before it is passed on to the next manufacturing operation. This paper examines a single-stage manufacturing system in which all items require a processing operation, they are then inspected, and a fraction (1-p) require rework. Two priority-based scheduling rules, motivated by the goal of minimizing the mean flow time in the repair system, are considered- The LST of the flow time PDF for a random arrival is developed for each priority rule. From the LST, the mean and variance of customer flow times can be obtained. Computational experience with the two rules is reported along with a discussion of the preferability of each rule when both flow time mean and variance are considered as performance measures.

This study aims to determinate which scheduling rule should be applied, for minimizing the makespan value, and maximizing the utilization level of a production system, which includes a set of three parallel processors, each one integrating five machines, by using a simulation approach based on Arena. A first simulation was done under random conditions, not attending to any kind of rule, and after some dispatching rules were applied, namely the shortest queue rule; the shortest processing time rule; the longest processing time rule; and combination of rules through a weighted sum about completion times and the work in process. A comparative analysis about the application of these rules is carried out in this paper. The rules that have been applied to achieve the goals were selected taking into account not only the problem to be studied, but also different conditions present in a real production system environment. The problem consists on a complex and dynamic system, where each job has the same priority and different processing times, without preemption., (undefined)

In order to obtain the better scheduling rules in specific environment, a solving method based on genetic algorithm (GA) is proposed. In this method, each chromosome of GA represents a set of rules which can be used to describe concrete scheduling projects and each is coded by piece wise integer coding. In order to get the adaptive function value of every chromosome, a decision optimization method integrating analytic hierarchy process (AHP) is used. The simulation results indicate that this method can be well used to optimize scheduling rules in loading and unloading scheduling problems of automated storage & retrieval system (namely AS/RS).