Job Sequencing Rules Research Articles

Simulated-annealing-based hyper-heuristic for flexible job-shop scheduling

The flexible job-shop scheduling problem (FJSP) is common in high-mix industries such as semiconductor manufacturing. An FJSP is initiated when an operation can be executed on a machine assigned from a set of alternative machines. Thus, an FJSP consists of the machine assignment and job sequencing sub-problems, which can be resolved using a pair of problem-dependent machine assignment rules (MARs) and job sequencing rules (JSRs). Selecting an MAR–JSR pair that performs efficiently is a challenge. This study proposes a simulated-annealing-based hyper-heuristic (SA-HH) for assembling an heuristic scheme (HS) consisting of MAR–JSR pairs with a set of problem state features. Two variants of SA-HH, i.e. SA-HH based on HS with problem state features (SA-HH) and without problem state features (SA-HH), are investigated. In terms of the best makespan, SA-HH outperforms or is comparable with over 75% of benchmark algorithms on 8 out of 10 instances in the Brandimarte dataset.

Modeling of job sequencing rule on shop-floor by machine learning techniques

Modeling of job sequencing rule on shop-floor by machine learning techniques

Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming

At present, a lot of references use discrete event simulation to evaluate the fitness of evolved rules, but which simulation configuration can achieve better evolutionary rules in a limited time has not been fully studied. This study proposes three types of hyper-heuristic methods for coevolution of the machine assignment rules (MAR) and job sequencing rules (JSR) to solve the DFJSP, including the cooperative coevolution genetic programming with two sub-populations (CCGP), the genetic programming with two sub-trees (TTGP) and the genetic expression programming with two sub-chromosomes (GEP). After careful parameter tuning, a surrogate simulation model is used to evaluate the fitness of evolved scheduling policies (SP). Computational simulations and comparisons demonstrate that the proposed surrogate-assisted CCGP method (CCGP-SM) shows competitive performance with other evolutionary approaches using the same computation time. Furthermore, the learning process of the proposed methods demonstrates that the surrogate-assisted GP methods help accelerating the evolutionary process and improving the quality of the evolved SPs without a significant increase in the length of SP. In addition, the evolved SPs generated by the CCGP-SM show superior performance as compared with existing rules in the literature. These results demonstrate the effectiveness and robustness of the proposed method.

Toward data-driven production simulation modeling: dispatching rule identification by machine learning techniques

Production simulation is useful to predict and optimize future production. However, it requires effort and expertise to create accurate simulation models. For instance, operational control rules, such as job sequencing rules, are modeled based on interviews with shop-floor managers and some assumptions since those rules are tacit in general. In this paper, we consider a data-driven approach to model operational control rules. We develop job sequencing rule identification methods that model rules from production data using machine learning techniques. These methods are evaluated based on accuracy and robustness against uncertainty in human decision making using virtual and real production data.

Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic

This study proposes four multi-objective genetic programming based hyper-heuristic methods(MO-GPHH) for automated heuristic design to solve the multi-objective dynamic flexible job shop scheduling problem(MO-DFJSP). A scheduling policy(SP) used in the MO-DFJSP includes two decision rules: a job sequencing rule(JSR) and a machine assignment rule(MAR). These two rules are simultaneously evolved to solve three scheduling objectives (mean weighted tardiness, maximum tardiness and mean flow time). The results demonstrate that the pareto front of the proposed methods dominate that of 320 human-made SPs which are selected from literatures on training set, and the evolved SPs outperform manual SPs in 58/64 test scenarios.

Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling

Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the non-dominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios.

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.

Application of Syntactic Pattern Recognition Approach in Design and Optimisation of Group Machining Systems

The focus of this paper is on planning applications of group technology (GT) and the design of related layouts for multi-assortment cellular manufacturing (CM) of mechanical parts. A methodical approach is developed to optimally solve cell formation (CF) problems with CM systems design, which consists in the identification of machine cells and corresponding part families. The approach involves the use of syntactic pattern recognition concepts from the field of artificial intelligence (AI). It is based on methods of strings matching and clustering, applied extensively in genetics, molecular chemistry and biological sciences. The CF strategy followed implies clustering character strings that denote machine sequences in process routings. Numerical quantification of dissimilarity between part routings by a specific distance measure and the concept of average linkage clustering algorithm (ALCA) are at the core of the clustering procedure. The use of the approach is studied numerically with regard to a real industrial case and diverse layouts of cellular system are considered, including those with machine sharing. Group process alternatives with given system layouts and workflows prototyped by definite job sequencing rules, are simulated using programmed models. Generated design solutions are subjected to further analysis and quantitative evaluation by assumed measures of their operational performance.

Heuristic solution approaches for combined-job sequencing and machine loading problem in flexible manufacturing systems

Job sequencing and machine loading are two vital and interrelated production planning problems in flexible manufacturing systems (FMSs). In this research, attempts have been made to address the combined job sequencing and machine loading problem using minimization of system unbalance and maximization of throughput as objective functions, while satisfying the constraints related to available machining time and tool slots. This research describes two heuristics to deal with the problems. Heuristic I uses predetermined fixed job sequencing rules as inputs for operation allocation decision on machines, whereas heuristic II uses genetic algorithm based approach for simultaneously addressing job sequences and operation machine allocation issues. Performance of these heuristics has been tested on problems representing three different FMS scenarios. Heuristic II (Genetic algorithm based) has been found more efficient and outperformed heuristic I in terms of solution quality.

A heuristic for minimizing the expected makespan in two-machine flow shops with consistent coefficients of variation

The paper deals with the classical problem of minimizing the makespan in a two-machine flow shop. When the job processing times are deterministic, the optimal job sequence can be determined by applying Johnson’s rule. When they are independent and exponential random variables, Talwar’s rule yields a job sequence that minimizes the makespan stochastically. Assuming that the job processing times are independently and Weibull distributed random variables, we present a new job sequencing rule that includes both Johnson’s and Talwar’s rules as special cases. The proposed rule is applicable as a heuristic whenever the job processing times are characterized by their means and the same coefficient of variation. Simulation results show that it leads to very encouraging results when the expected makespan is minimized.

Opportunistic retooling of a flexible machine subject to failure

A set of jobs can be processed without interruption by a flexible machine only if the set of tools required by all jobs can be loaded in the tool magazine. However, in practice the total number of tools required by a job set would exceed the tool magazine capacity. In such situations, the job set has to be carefully partitioned at the start of the production run such that each partition can be processed without interruption. During the production run, if there are unscheduled machine downtimes due to machine failure, this provides an additional opportunity to optimally retool the magazine for a smaller job set consisting of just the unprocessed jobs. In this paper, we study job sequencing rules that allow us to minimize the total expected cost of machine down time due to machine failures and magazine retooling, assuming a dynamic re-sequencing of the unprocessed jobs after each machine failure. Using these rules, we develop a branch-and-bound heuristic that allows us to solve problems of reasonable size. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 79–97, 2001

A simulation analysis of sequencing rules in a flexible flowline

The paper concerns a flexible flowline scheduling problem, which arises in the footwear industry. Flexibility of the line allows for manufacturing simultaneously more products in lower quantities, but it also increases the complexity of the task of balancing the line, specially because the mix of products changes everyday. A simulation model to deal with the flexible line is developed and several job sequencing rules and different part input criteria are implemented. The impact of each rule on the quality of the schedules is measured, namely, according to makespan, productivity and average machine utilisation. Computational results concerning a real application are also presented. SIMPLE++ is the simulation language used.

Job sequencing rules for minimizing the expected makespan in flexible machines

We consider scheduling of a deteriorating flexible machine that is capable of processing a number of diverse jobs with negligible setup times between jobs. Specifically, we develop rules for sequencing N jobs on such a machine such that its expected makespan (sum of all job processing times and machine down-time) is minimized. Using the Weibull distribution to characterize machine failures in our model, we permit different jobs to contribute to machine deterioration (and hence its failure) at different failure rates, and do not require these rates to remain constant with machine-use time. We validate the effectiveness of these job sequencing rules for different cases, using extensive simulation tests.

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

Abstract 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...

A simulation analysis of sequencing rules for ASRS in a pull-based assembly facility

Just-in-time (JIT) systems operate on a pull-based production control. The material needed is expected to be at its production site when and only when it is needed. When an automated storage retrieval system (ASRS) is used as a valve warehouse to support JIT manufacturing, the delivery time becomes critical. This paper presents an investigation on the effect of job sequencing rule on delivery performance of an ASRS, which is used as a valve warehouse to support a pull-based Kanban-driven assembly line. The analysis was based on computer simulation. The interaction of the sequencing rules with other control variables was also examined

A simulation study of sequencing and maintenance decisions in a dynamic job shop

This paper presents a simulation study of the performance of a job shop, where the equipment is subject to failure. We evaluate the effectiveness of some maintenance scheduling techniques under conditions involving shop load, job sequencing rule, preventive maintenance policy and maintenance capacity.

A comparative analysis of the COVERT job sequencing rule using various shop performance measures

The COVERT job shop dispatching rule was tested extensively twenty years ago with impressive results, however, since then it has been included in only one comparative analysis with other sequencing rules, and, reported instances of its application have been infrequent. In this paper, the COVERT rule is examined in detail relative to its applicability, its sensitivity to various operating parameters and performance measures, and its performance compared to several other sequencing rules including truncated SPT rules, dynamic slack rules and modified duedate rules. The performance of COVERT is examined for a variety of tardiness measures. The examination is conducted within the context of a simulation model of a machine-constrained job shop with serial jobs and random routings. The results indicate that COVERT performs well as a sequencing rule and in most instances was superior to the other sequencing rules tested both directly and across varying degrees of due-date tightness.

Modifying Job Sequencing Rules for Work-in-Process Inventory Reduction

Abstract In most reported studies of job dispatching rules the primary criterion for evaluating alternative rules has been the mean number of jobs in the shop, or the equivalent. This paper argues that the mean value of jobs in the shop is a more generally useful measure because it is a determinant of work-in-process (WIP) inventory carrying cost. For certain simple single-server systems the shortest processing time rule (SPT) is known to be optimal on the first criterion. A value-weighted version of SPT, termed SWPT, is optimal on the second criterion (mean value). We show by means of a simulation study that introduction of a similar dollar-weighting feature into more complex rules is effective in reducing mean WIP inventory, even in complex multimachine stochastic systems. Since our suggested dispatch rule modifications require value weighting, correct valuation of jobs is essential. The subject of how to determine the value of a job dynamically as it moves through the shop is discussed.