Dynamic job-shop scheduling with sequence-dependent setup times: simulation modeling and analysis

This paper presents the salient aspects of a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence-dependent. A discrete event simulation model of the job shop system is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model. Five new setup-oriented scheduling rules are proposed and implemented. Simulation experiments were conducted under various experimental conditions characterized by factors such as shop load, setup time ratios, and due date tightness. The results indicate that setup-oriented rules provide better performance than ordinary rules. The difference in performance between these two groups of rules increases with the increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures.     

Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria

In textile industries, production facilities are established as multi-stage production flow shop facilities, where a production stage may be made up of parallel machines. This known as a flexible or hybrid flow shop environment. This paper considers the problem of scheduling n independent jobs in such an environment. In addition, we also consider the general case in which parallel machines at each stage may be unrelated. Each job is processed in ordered operations on a machine at each stage. Its release date and due date are given. The preemption of jobs is not permitted. We consider both sequence- and machine-dependent setup times. The problem is to determine a schedule that minimizes a convex combination of makespan and the number of tardy jobs. A 0–1 mixed integer program of the problem is formulated. Since this problem is NP-hard in the strong sense, we develop heuristic algorithms to solve it approximately. Firstly, several basic dispatching rules and well-known constructive heuristics for flow shop makespan scheduling problems are generalized to the problem under consideration. We sketch how, from a job sequence, a complete schedule for the flexible flow shop problem with unrelated parallel machines can be constructed. To improve the solutions, polynomial heuristic improvement methods based on shift moves of jobs are applied. Then, genetic algorithms are suggested. We discuss the components of these algorithms and test their parameters. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.     

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment.     

Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flowtime and tardiness

The problem of scheduling in dynamic shops is an important operational problem in view of its complexity and significance in terms of associated costs of scheduling. While a number of research studies have investigated the problem of scheduling in flow shops and job shops, only some attempts have been done to study the problem of scheduling in assembly job shops that manufacture multi-level jobs. The problem of scheduling in dynamic assembly job shops with jobs having weights for holding and tardiness of jobs deserves due attention. In this study an attempt has been made to propose new priority dispatching rules that minimize the performance measures related to weighted flowtime and weighted tardiness of jobs. The existing unweighted dispatching rules have been modified in view of the consideration of weights for flowtime and tardiness of jobs. The performances of the (modified) existing dispatching rules and the proposed dispatching rules are compared through exhaustive simulation experiments with the consideration of a number of different experimental settings involving due-date setting, utilization levels and types of job structures. The proposed dispatching rules are found to perform better than the existing ones in most experimental settings and with respect to a number of measures of performance.     

Efficient dispatching rules for dynamic job shop scheduling

This study attempts to provide efficient dispatching rules for dynamic job shop scheduling by combining different dispatching rules. A dispatching rule is used to select the next job to be processed from a set of jobs awaiting service. A job shop will be treated as dynamic, when conditions such as continuously arriving new jobs and deviations from current schedule need to be accommodated, and a job shop should be treated as an integrated part of a manufacturing system. The discussion includes a simulation technique which uses ARENA 4.0. software to simulate the dynamic model of a job shop under various rules and performance measures . Results of the simulation show that, for most of the performance measures, combined rules perform well. In this study, the combined rules MWKR_FIFO and TWKR_SPT do well under most conditions.     

A hybrid intelligent algorithm and rescheduling technique for job shop scheduling problems with disruptions

In most real manufacturing environment, schedules are usually inevitable with the presence of various unexpected disruptions. In this study, a new rescheduling technique based on a hybrid intelligent algorithm is developed for solving job shop scheduling problems with random job arrivals and machine breakdowns. According to the dynamic feature of this problem, a new initialization method is proposed to improve the performance of the hybrid intelligent algorithm, which combines the advantage of genetic algorithm and tabu search. In order to solve the difficulty of using the mathematical model to express the unexpected disruptions, a simulator is designed to generate the disruptions. The performance measures investigated respectively are: mean flow time, maximum flow time, mean tardiness, maximum tardiness, and the number of tardy jobs. Moreover, many experiments have been designed to test and evaluate the effect of different initializations in several disruption scenarios. Finally, the performance of the new rescheduling technique is compared with other rescheduling technologies in various shop floor conditions. The experimental results show that the proposed rescheduling technique is superior to other rescheduling techniques with respect to five objectives, different shop load level, and different due date tightness. The results also illustrate that the proposed rescheduling technique has a good robustness in the dynamic manufacturing environment.     

Learning iterative dispatching rules for job shop scheduling with genetic programming

This study proposes a new type of dispatching rule for job shop scheduling problems. The novelty of these dispatching rules is that they can iteratively improve the schedules by utilising the information from completed schedules. While the quality of the schedule can be improved, the proposed iterative dispatching rules (IDRs) still maintain the easiness of implementation and low computational effort of the traditional dispatching rules. This feature makes them more attractive for large-scale manufacturing systems. A genetic programming (GP) method is developed in this paper to evolve IDRs for job shop scheduling problems. The results show that the proposed GP method is significantly better than the simple GP method for evolving composite dispatching rules. The evolved IDRs also show their superiority to the benchmark dispatching rules when tested on different problem instances with makespan and total weighted tardiness as the objectives. Different aspects of IDRs are also investigated and the insights from these analyses are used to enhance the performance of IDRs.     

Mathematical modelling of multi-objective job shop scheduling with dependent setups and re-entrant operations

For the last thirty years, in job shop scheduling, the setup times of operations have been either ignored or combined with their corresponding processing times, independent setups, to simplify the work of model construction. However, in practice, the setup times of an operation are usually considered be sequence-dependent. Moreover, the performance of a scheduling system is not evaluated to satisfy a single objective, but to obtain a trade-off solution regarding multiple objectives. Therefore, this paper investigates job shop scheduling problems with re-entrant operations where the setup times, which vary according to the preceding operation which is processed on the same machine, and which can be separated from their corresponding processing times, cannot be omitted. Three practical performance measures – minimum total job flow time, minimum total job tardiness and minimum machine idle time – are considered. An integer programming model is first developed to optimise each single objective and an acceptable trade-off schedule, which makes use of a multiple-decision-making technique, the global criterion method, is obtained by evaluating three objectives simultaneously .     

New dispatching rules to minimize rejection and tardiness costs in a dynamic flexible flow shop

This paper studies a flexible flow shop system considering dynamic arrival of jobs and the ability of acceptance and rejection of new jobs. The problem objective is to determine a schedule that minimizes sum of the tardiness and rejection costs of jobs. A 0–1 mixed integer model of the problem is formulated. Since this problem class is NP-hard, four dispatching rules have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the flexible flow shop system is developed for the purpose of experimentation. Four dispatching rules from the literature and four new dispatching rules proposed in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization level, due date tightness and number of stages in flexible flow shop. The results indicate that proposed dispatching rules provide better performance under problem assumptions.     

Single-machine scheduling with past-sequence-dependent setup times and effects of deterioration and learning

The paper deals with some single-machine scheduling problems with setup time considerations where the processing time of a job is given as a function of its starting times and position in a sequence. The setup times are proportional to the length of the already processed jobs, i.e., the setup times are past-sequence-dependent (p-s-d). We consider the following objective functions: the makespan, the total completion time, the sum of the δth ( $ \delta \geqslant 0 $ ) power of job completion times, the total weighted completion time, the maximum lateness and the number of tardy jobs. We show that the makespan minimization problem, the total completion time minimization problem, and the sum of the δth power of job completion times minimization problem can be solved in polynomial time, respectively. We also show that the total weighted completion time minimization problem, the maximum lateness minimization problem and the number of tardy jobs minimization problem can be solved in polynomial time under certain conditions.     

A due date density-based categorising heuristic for parallel machines scheduling

The effective management of shop floor resources is an important factor in achieving the goals of a manufacturing company. The need for effective scheduling is particularly strong in complex manufacturing environments. This paper presents an efficient due date density-based categorising heuristic to minimise the total weighted tardiness (TWT) of a set of tasks with known processing times, due dates, weights and sequence-dependent setup times for parallel machines scheduling. The proposed heuristic is composed of four phases. In the first phase, jobs are listed by the earliest due date (EDD). The second phase computes the due date gaps between listed jobs and categorises the jobs based on the due date density. In the third phase, the sequence of jobs is improved by a tabu search (TS) method. The fourth phase allocates each job to machines. The comprehensive simulation results show that the proposed heuristic performs better than other existing heuristics at a significantly reduced total weighted tardiness.     

Due-date scheduling on parallel machines with job splitting and sequence-dependent major/minor setup times

This paper addresses job scheduling problems with parallel machines. To satisfy customers better in a manufacturing company, meeting due dates has been an important performance metric. Besides the numerous other factors affecting due date satisfaction, the splitting of a job through parallel machines can contribute to the reduction of production lead time, resulting in less job tardiness against their due dates. Thus, this paper presents heuristic algorithms for minimizing total tardiness of jobs to meet their due dates in a manufacturing shop with identically functioning machines. The algorithms take into account job splitting and sequence-dependent major/minor setup times. The performance of the proposed heuristics is compared with that of past three algorithms in the literature.     

Group shop scheduling with uncertain data and a general cost objective

In this paper, a stochastic group shop scheduling problem with a due date-related objective is studied. The group shop scheduling problem provides a general formulation including two other shop scheduling problems, the job shop and the open shop. Both job release dates and processing times are assumed to be random variables with known distributions. Moreover, earliness and tardiness of jobs are penalized at different rates. The objective is to minimize the expected maximum completion cost among all jobs. A lower bound on the objective function is proposed, and then, a hybrid approach following a simulation optimization procedure is developed to deal with the problem. An ant colony optimization algorithm is employed to construct good feasible solutions, while a discrete-event simulation model is used to estimate the performance of each constructed solution that, taking into account its lower bound, may improve the best solution found so far. The proposed approach is then evaluated through computational experiments.     

A study of the flexible job shop scheduling problem with parallel machines and reentrant process

This paper develops a scheduling algorithm for the job shop scheduling problem with parallel machines and reentrant process. This algorithm includes two major modules: the machine selection module (MSM) and the operation scheduling module (OSM). An order has several jobs and each job has several operations in a hierarchical structure. The MSM helps an operation to select one of the parallel machines to process it. The OSM is then used to schedule the sequences and the timing of all operations assigned to each machine. A real-life weapons production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of delays in military orders, the on-time delivery rate is the most important performance measure and then makespan is the next most important measure. Well-known performance measures in the scheduling literature, such as maximum lateness and average tardiness, are also evaluated. The simulation results demonstrate that the MSM and OSM using the combination of earliest due date (EDD), the operations’ lowest level code (LLC) of the bill of materials (BOM), and the longest processing time (LPT) outperforms the other scheduling methods.     

ACO-based multi-objective scheduling of parallel batch processing machines with advanced process control constraints

This research was motivated by a scheduling problem in the dry strip operations of a semiconductor wafer fabrication facility. The machines were modeled as parallel batch processing machines with incompatible job families and dynamic job arrivals, and constraints on the sequence-dependent setup time and the qual-run requirements of advanced process control. The optimization had multiple objectives, the total weighted tardiness (TWT) and makespan, to consider simultaneously. Since the problem is NP-hard, we used an Ant Colony Optimization (ACO) algorithm to achieve a satisfactory solution in a reasonable computation time. A variety of simulation experiments were run to choose ACO parameter values and to demonstrate the performance of the proposed method. The simulation results showed that the proposed ACO algorithm is superior to the common Apparent Tardiness Cost-Batched Apparent Tardiness Cost rule for minimizing the TWT and makespan. The arrival time distribution and the number of jobs strongly affected the ACO algorithm’s performance.     

New dispatching rules for scheduling in a job shop — An experimental study

We present two new dispatching rules for scheduling in a job shop. These rules combine the process-time and work-content in the queue for the next operation on a job, by making use of additive and alternative approaches. An extensive and rigorous simulation study has been carried out to evaluate the performance of the proposed dispatching rules compared with those by the SPT rule, the WINQ rule, a random rule based on the SPT and WINQ rules, and the best existing rule. The important aspects of the results of the experimental investigation are also discussed in detail.     

Multicriteria dynamic scheduling by swapping of dispatching rules

For most shop floors, consideration of more than one criterion would be likely to provide more realistic scheduling of a given set of jobs. The present paper considers this aspect of scheduling and uses an algorithm proposed by the authors in their previous work for implementing several criteria simultaneously in a shop of dynamic nature. The algorithm considers several dispatching rules simultaneously for selecting a job for processing and continuously monitors the attained values of performance measures. The selection of dispatching rules is made by identifying the worst performing criterion. A rule that can improve system performance for the worst-performing criterion is selected to dispatch the part under consideration. In this paper, several case studies have been attempted to evaluate the efficiency of the algorithm. The results of the taken case studies indicate that in a dynamic system the system performance improves by changing the dispatching rules corresponding to the worst-performance criterion at the appropriate deterioration in the performance measures.     

Bottleneck-based heuristic dispatching rule for optimizing mixed TDD/IDD performance in various factories

Most research on scheduling problems focuses on increasing production efficiency. For instance, the shortest processing time (SPT) and earliest due date (EDD) dispatching rules perform well in minimizing mean flow time and reducing maximum tardiness, respectively. However, those indices ignore the financial impact (material cost and order price) on the factory. Previous studies focused mainly on cycle time and due date. However, the theory of constraint (TOC) considers not only the effect of time, but also financial factors. Therefore, TOC addresses the concepts of throughput-dollar-day (TDD) and inventory-dollar-day (IDD). The former index (TDD) represents penalties for tardy deliveries, while the latter index (IDD) refers to the material holding cost. Based on these two indices, this investigation creates a novel mixed TDD/IDD weighted value (Z _value) to replace the other traditional indices for taking measurements in various factories. This study also designs a heuristic dynamic scheduling algorithm (mixed TDD/IDD dispatching rule) for reducing the system Z _value. Some traditional dispatching rules are compared with the proposed rule in terms of TDD, IDD, and Z _value. Analytical results indicate that the mixed TDD/IDD dispatching rule is feasible and generally outperforms other conventional dispatching rules in terms of Z _value under various factories.     

Multicontextual dispatching rules for job shops with dynamic job arrival

The dynamic job shop problem is more challenging than the static job shop problem because dynamic job shops are disrupted by unforeseen events such as job arrivals and machine breakdowns. Each phase of a dynamic job shop problem presents a unique set of circumstances; multicontextual functions can describe the unique characteristics of a dynamic job shop at a specific time. The present work examines 11 basic dispatching rules and 33 composite rules made with multicontextual functions (MCFs) that describe machine idle time (MIT) and job waiting time (JWT). Simple procedures are presented that allow one or both of MIT and JWT to be combined with a single basic dispatching rule. This procedure produced 33 composite dispatching rules; the schedules from all 44 rules for a job shop with dynamic job arrival were compared with regard to make span and mean flow time. One composite rule, most work remaining with MCF₂, produced schedules with the shortest make spans in 21 out of 27 cases; another composite rule, most remaining operations (MRO) with MCF₃, produced schedules with the shortest mean flow times in 27 out of 27 cases. It was possible to combine JWT and MIT usefully only when the relevant dispatching rule did not depend on operation processing time; because MRO did not consider processing time, it benefitted from both JWT and MIT. Clients who demand short mean flow times might benefit from an implementation of MRO with MCF₃.     

An efficient bi-objective heuristic for scheduling of hybrid flow shops

This paper considers the problem of scheduling n independent jobs in hybrid flow shop environment with sequence-dependent setup times to minimize the makespan and total tardiness. For the optimization problem, an algorithm namely; bi-objective heuristic (BOH) is proposed for searching Pareto-optimal frontier. The aim of the proposed algorithm is to generate a good approximation of the set of efficient solutions. The BOH procedure initiates by generating a seed sequence. Since the output results are strongly dependent on the initial solution and in order to increase the quality of output results algorithm, we have considered how the generation of seed sequence with random way and particular sequencing rules. Two methods named Euclidean distance and percent error have been proposed to compare non-dominated solution sets obtain of each seed sequence. It is perceived from these methods that the generation of seed sequence using earliest due date rule is more effective. Then, the performance of the proposed BOH is compared with a simulated annealing proposed in the literature and a VNS heuristic on a set of test problems. The data envelopment analysis is used to evaluate the performance of approximation methods. From the results obtained, it can be seen that the proposed algorithm is efficient and effective.