Auction-based distributed scheduling in a dynamic job shop environment

Developments in computing and communication technology coupled with the inability to address real-time issues in scheduling algorithms based on central control, has led to an interest in solving the problem of a distributed decision-making environment. This paper presents a new job shop formulation that schedules jobs using auctions for distributing control. A theoretical basis is presented for problem decomposition, bid construction, and bid evaluation for the auction using standard mathematical programming tools. Numerical results show that the auction-based approach outperforms the distributed dispatching approaches and can be used to create schedules for a wide range of scheduling objectives.     

Cyclic scheduling heuristics for a re-entrant job shop manufacturing environment

Two efficient cyclic scheduling heuristics for re-entrant job shop environments were developed. Each heuristic generated an efficient and feasible cyclic production schedule for a job shop in which a single product was produced repetitively on a set of machines was to determine an efficient and feasible cyclic schedule which simultaneously minimized flow time and cycle time. The first heuristic considered a repetitive production re-entrant job shop with a predetermined sequence of operations on a single product with known processing times, set-up and material handling times. The second heuristic was a specialization of the first heuristic where the set-up for an operation could commence even while the preceding operation was in progress. These heuristics have been extensively tested and computational results are provided. Also, extensive analysis of worst-case and trade-offs between cycle time and flow time are provided. The results indicate that the proposed heuristics are robust and yield efficient and superior cyclic schedules with modest computational effort.     

A batch splitting method for a job shop scheduling problem in an MRP environment

The job shop scheduling problem has been a major target for many researchers. Unfortunately though, most of the previous research was based on assumptions that are different from the real manufacturing environment. Among those distorted assumptions, two assumptions about set-up time and job composition can greatly influence the performance of a schedule. First, most of the past studies ignored the impact of the before-arrival set-up time. If we know the sequence of operations in advance, we can obtain an improved schedule by preparing the setup before a job arrives. Secondly, most of the past studies assumed that a job consists of only a single part, that is a batch of size one. However, if we assume that a job consists of a batch size greater than one, as in many real manufacturing environments, then we can obtain an improved schedule because we can fill up the idle times of machines with jobs which have smaller processing times by splitting the original batches. However, the number of job orders may then increase due to the split, and the size of the scheduling problem would become too large to be solved in a practical time limit. Consequently, there may be an optimum batch size considering trade-off between better solution and tractability. The current study is the result of an attempt to find an acceptable solution when the production requirement from a MRP system for a planning period exceeds the capacity of a production system. We try to get an improved schedule by splitting the original batch into smaller batches, and consider setting up a machine before the actual arrival of jobs to that machine. Thereby we can meet the due date requirement without resorting to rescheduling of the master production schedule. For the given batch, we disaggregate it according to the algorithm we are proposing. A so-called 'modified shifting bottleneck procedure' is then applied to solve the job shop scheduling problem with a before-arrival family set-up time considering release date, transportation time and due date. The study also shows that we can adapt to unexpected dynamic events more elegantly by allowing the splitting of batches.     

Learning dispatching rules using random forest in flexible job shop scheduling problems

In this paper, we address the flexible job-shop scheduling problem (FJSP) with release times for minimising the total weighted tardiness by learning dispatching rules from schedules. We propose a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) in order to extract dispatching rules from the best schedules. RANFORS consists of three phases: schedule generation, rule learning with data transformation, and rule improvement with discretisation. In the schedule generation phase, we present three solution approaches that are widely used to solve FJSPs. Based on the best schedules among them, the rule learning with data transformation phase converts them into training data with constructed attributes and generates a dispatching rule with inductive learning. Finally, the rule improvement with discretisation improves dispatching rules with a genetic algorithm by discretising continuous attributes and changing parameters for random forest with the aim of minimising the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach and the results showed that it outperforms the existing dispatching rules. Moreover, compared with the other decision-tree-based algorithms, the proposed algorithm is effective in terms of extracting scheduling insights from a set of rules.     

Using the Kanban in a job shop environment

Japanese industrial management techniques have been applied in a large number of large Western enterprises. In particular, the Kanban method has been used to control materials, production rate and volume, and to adjust production to requirements. The authors show how the Kanban method may be adapted to the job shop. This adaptation was extensively tested through simulation and then implemented. Actual performance is consistent with the simulation results, and shows marked improvement over previous practice.     

A comprehensive analysis of group scheduling heuristics in a job shop cell

This paper describes a broad-based simulation study of the performance of two-stage group scheduling heuristics in a job shop cell. The objective of this study was to examine the direct and interactive effects of a variety of shop factors on the performance of the best, previously reported, group scheduling heuristics. A set of traditional single-stage scheduling heuristics were examined as well. Shop factors considered include: setup to runtime ratio, cell load level and variability of inter-arrival times. An assumption common to group scheduling research which provides for an equal division of the part family into subfamilies is also examined. This is accomplished through the creation of an alternative scenario where the majority of the parts are assigned to one subfamily, i.e. one subfamily dominates the part family population. The effects of set up to runtime ratio and cell load have been examined in previous group scheduling research, but not in conjunction with the inter-arrival time variability factor. Further, no study has examined the impact of subfamily dominance on group scheduling heuristics in a full-scale simulation study. The results indicate that performance comparable to that of the two -stage heuristics can be obtained with the easily implementable single-stage heuristics when factors which lessen the impact of setup times are in place. In particular, the tardiness performance of two-stage scheduling heuristics deteriorates when subfamily dominance is in effect while the single-stage heuristics exhibit dramatic improvements in tardiness performance. Low setup to runtime ratio, shop load, and less variable inter-arrivals all induce dramatic performance gains across all measures among the single-stage heuristics, while yielding only marginal improvement in the performance of the two-stage heuristics. As a result, in many instances when combinations of these factors are in effect, the single-stage heuristics yield similar performance to the two-stage heuristics.     

A generalized permutation approach to job shop scheduling with genetic algorithms

In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique — mathematically known as permutation with repetition is presented. The main advantage of this single chromosome representation is — in analogy to the permutation scheme of the traveling salesman problem (TSP) — that it cannot produce illegal operation sequences. As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually theGOX operator for permutations with repetition arises from aGeneralisation ofOX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of genetic search for scheduling problems strongly.Supported by the Deutsche Forschungsgemeinschaft (Project Parnet)     

A scatter search approach to sequence-dependent setup times job shop scheduling

Scheduling jobs on multiple machines is a difficult problem when real-world constraints such as the sequence setup time, setup times for jobs and multiple criteria are used for solution goodness. It is usually sufficient to obtain a near-optimal solution quickly when an optimal solution would require days or weeks of computation. Common scheduling heuristics such as Shortest Processing Time can be used to obtain a feasible schedule quickly, but are not designed for multiple simultaneous objectives. We use a new meta-heuristic known as a scatter search (SS) to solve these types of job shop scheduling problems. The results are compared with solutions obtained by common heuristics, a tabu search, simulated annealing, and a genetic algorithm. We show that by combining the mechanism of diversification and intensification, SS produces excellent results in a very reasonable computation time. The study presents an efficient alternative for companies with a complicated scheduling and production situation.     

Implementing a loading heuristic in a discrete release job shop

The scheduling literature has developed two almost mutually exclusive problems; a static and a dynamic scheduling problem. The result has been that different tools have been developed to solve each problem. In this research, a model is developed such that it contains elements of both the static and the dynamic problems. This model, called here the discrete release model, considers the routing, loading, and release decisions as well as the customary sequencing decision evident in most job shop research. The objective of the research is to implement a method for examining tentative loading assignments prior to their release to the shop floor.

The research develops a single pass, sequential routing and loading system. A heuristic is then developed that will enable jobs to be reloaded and rerouted prior to their start of processing. The system is tested in various shop environments to ascertain the effectiveness of this loading heuristic.     

Workshop scheduling using practical (inaccurate) data Part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach

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.     

Optimal allocation of resources in a job shop environment

In this paper we study the allocation of production services (e.g., maintenance) to machining centers in a job shop when there is a limited amount of resources for such services. Different classes of jobs go through the shop. A job class is characterized by its route, its processing requirements, and its priority. The problem we address is how to optimally allocate production services (the resources) to machining centers so as to minimize the total Work-In-Process in the entire system. In order to analyze this problem we model the job shop as an open queueing network. Assuming certain relationships between performances of machining centers (i.e., speeds) and the amounts of resources allocated, we present methods for allocating the resources to the individual machining centers optimally.     

Information and scheduling in a dual resource constrained job shop

In today's job shop, computers make information readily available, but there is limited research about which information to use and how to use it controlling a dual resource constrained (DRC) job shop. We develop and test new order review/ release (ORR) job dispatching and labour assignment rules that use different types of information. We compare the performance of a naive ORR rule (i.e., releasing all jobs immediately upon receipt) and other rules in the literature which use little information to a new ORR rule, modified load conversion (MLC), which uses all the available information. Our tests demonstrate that MLC performs better than the other ORR rules we tested. But the new job dispatching and labour assignment rules developed to use different types of information do not significantly improve performance. A sensitivity analysis demonstrates that although the shop performance is sensitive to the cost structure for every policy tested, the new MLC ORR rule performs well over a wide range of cost structures.     

Implementation of a demand-pull system in a job shop environment

This paper describes an implementation of JIT concepts in a medium-sized make-to-order manufacturing company. We develop a hybrid production control system for the company based on separating jobs with high production volumes following a standard routeing from relatively low-volume jobs with more complex routeings. The machines processing the high-volume standard-routeing jobs are treated as a virtual flow shop and controlled by a hybrid push/pull system, while the remainder of the floor is managed as a job shop. Initial tests have shown that this system can significantly improve throughput and simplify the production scheduling task. We believe that the system can be applied to a wide range of industries with similar characteristics. We also describe the testing of the system and the circumstances under which its implementation was discontinued, which indicate some of the difficulties faced by small manufacturers in implementing such systems.     

A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times

In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artificial Bee Colony algorithm, is proposed for a dynamic flexible job-shop scheduling (DFJSP) problem. This problem consists of n jobs that should be processed by m machines and the processing time of jobs deviates from estimated times. The objective is near-optimal scheduling after any change in tasks in order to minimise the maximal completion time (Makespan). In the proposed model, first, scheduling is done according to the estimated processing times and then re-scheduling is performed after determining the exact ones considering machine set-up. In order to evaluate the performance of the proposed model, some numerical experiments are designed in small, medium and large sizes in different levels of changes in processing times and statistical results illustrate the efficiency of the proposed algorithm.     

A job shop distributed scheduling based on Lagrangian relaxation to minimise total completion time

This paper considers a distributed job shop scheduling problem where autonomous sub-production systems share common machines with each other. Each sub-production system is responsible for the scheduling of a set of jobs to minimise the total completion time on shared machines. A sub-production system has ultimate responsibility on maintaining private information such as objective function, processing time and routings on shared machines. Also sub-production systems must cooperate each other in order to achieve a global goal while sharing minimum of private information. In this research, we propose a distributed cooperation method in which sub-production systems and shared machines interact with one another to find a compromised solution between a locally optimised solution and a system-wide solution. We tested the proposed method for small, medium and large size of job shop scheduling problems and compared to a global optimal solutions. The proposed method shows promising results in terms of solution qualities and computational times.     

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.     

A discrete time reactive scheduling model for new order insertion in job shop,make-to-order industries

This research presents a new reactive scheduling methodology for job shop, make-to-order industries. An integer linear programming formulation previously developed by the authors to schedule these types of industries is extended to address the problem of inserting new orders in a predetermined schedule, which is important in order-driven industries. A reactive scheduling algorithm is introduced to iteratively update the schedules. Numerical results on realistic examples of job shops of different sizes illustrate the effectiveness of the approach. In each case, different alternatives for inserting a set of new orders in an initial schedule are optimally generated, enabling the user to choose the most convenient one. Solutions are characterised by measures of scheduling efficiency as well as stability measures that assess the impact of rescheduling operations in a previously defined scheduling solution.     

A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem

The problem that we consider in this article is a flexible job shop scheduling problem issued from the printing and boarding industry. Two criteria have to be minimised, the makespan and the maximum lateness. Two tabu search algorithms are proposed for finding a set of non-dominated solutions: the first is based on the minimisation of one criterion subject to a bound on the second criterion (ε-constraint approach) and the second is based on the minimisation of a linear combination of criteria. These algorithms are tested on benchmark instances from the literature and the results are discussed. The total tardiness is considered as a third criterion for the second tabu search and results are presented and discussed.     

Bad-scenario-set robust scheduling for a job shop to hedge against processing time uncertainty

This paper proposed two robust scheduling formulations in real manufacturing systems based on the concept of bad scenario set to hedge against processing time uncertainty, which is described by discrete scenarios. Two proposed robust scheduling formulations are applied to an uncertain job-shop scheduling problem with the makespan as the performance criterion. The united-scenario neighbourhood (UN) structure is constructed based on bad scenario set for the scenario job-shop scheduling problem. A tabu search (TS) algorithm with the UN structure is developed to solve the proposed robust scheduling problem. An extensive experiment was conducted. The computational results show that the first robust scheduling formulation could be preferred to the second one for the discussed problem. It is also verified that the obtained robust solutions could hedge against the processing time uncertainty through decreasing the number of bad scenarios and the degree of performance degradation on bad scenarios. Moreover, the computational results demonstrate that the developed TS algorithm is competitive for the proposed robust scheduling formulations.     

Impact of sequence-dependent setup time on job shop scheduling performance

In a production system using multi-purpose and flexible machines, reducing setup time is an important task for better shop performance. Numerous cases were reported about successful reduction of setup times by standardization of setup procedures. However, setup times have not been eliminated and remain an important element of real production problems for production systems such as commercial printing, plastics manufacturing, metal processing, etc. It is especially critical when the setup time is sequence dependent. In this situation, shop performance cannot be effectively improved without the aid of an appropriate scheduling procedure. Review of the past studies shows that there has not been a significant amount of research done on the scheduling procedure for a dynamic job shop with sequence dependent setup times. This paper investigates the job shop scheduling problems that are complicated by sequence-dependent setup times. The study classifies and tests scheduling rules by considering whether setup time and/or due date information is employed. These scheduling rules are evaluated in dynamic scheduling environments defined by due date tightness, setup times and cost structure. A simulation model of a nine machine job shop is used for the experiment. A hypothetical, asymmetric, setup time matrix is applied to the nine machines.