Showing papers on "Job shop published in 2001"
TL;DR: A generation scheme for precedence constraints that achieves a target density which is uniform in the precedence constraint graph and a generation scheme that explicitly considers the correlation of routings in a job shop is presented.
Abstract: The operations research literature provides little guidance about how data should be generated for the computational testing of algorithms or heuristic procedures. We discuss several widely used data generation schemes, and demonstrate that they may introduce biases into computational results. Moreover, such schemes are often not representative of the way data arises in practical situations. We address these deficiencies by describing several principles for data generation and several properties that are desirable in a generation scheme. This enables us to provide specific proposals for the generation of a variety of machine scheduling problems. We present a generation scheme for precedence constraints that achieves a target density which is uniform in the precedence constraint graph. We also present a generation scheme that explicitly considers the correlation of routings in a job shop. We identify several related issues that may influence the design of a data generation scheme. Finally, two case studies illustrate, for specific scheduling problems, how our proposals can be implemented to design a data generation scheme.
212 citations
TL;DR: The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.
Abstract: Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date. A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.
124 citations
18 Jul 2001
TL;DR: This paper shows how the classical job-shop scheduling problem can be modeled as a special class of acyclic timed automata and provides new techniques for solving the optimization problem and allows to model naturally more complex dynamic resource allocation problems which are not captured so easily in traditional models of operation research.
Abstract: In this paper we show how the classical job-shop scheduling problem can be modeled as a special class of acyclic timed automata. Finding an optimal schedule corresponds, then, to finding a shortest (in terms of elapsed time) path in the timed automaton. This representation provides new techniques for solving the optimization problem and, more importantly, it allows to model naturally more complex dynamic resource allocation problems which are not captured so easily in traditional models of operation research. We present several algorithms and heuristics for finding the shortest paths in timed automata and test their implementation in the tool Kronos on numerous benchmark examples.
110 citations
TL;DR: A kind of hybrid heuristic GA is proposed for problem n/m/G/Cmax, where the scheduling rules are integrated into the process of genetic evolution and the neighborhood search technique is adopted as an auxiliary procedure to improve the solution performance.
104 citations
TL;DR: It is conjectured that neighbourhood-based robustness can be expected to improve flexibility on problems with few critical points, and is found to also improve robustness and in some cases flexibility for the same problems, but the improvement is not as substantial as with the neighbourhood- based measures.
98 citations
07 Mar 2001
TL;DR: This paper presents an approach based on hybrid genetic algorithms adapted to the multicriteria case, which allows to increase the population size and the number of generations, and then leads to better results.
Abstract: The resolution of workshop problems such as the Flow Shop or the Job Shop has a great importance in many industrial areas. The criteria to optimize are generally the minimization of the makespan or the tardiness. However, few are the resolution approaches that take into account those different criteria simultaneously. This paper presents an approach based on hybrid genetic algorithms adapted to the multicriteria case. Several strategies of selection and diversity maintaining are presented. Their performances are evaluated and compared using different benchmarks. A parallel model is also proposed and implemented for the hybrid metaheuristic. It allows to increase the population size and the number of generations, and then leads to better results.
96 citations
TL;DR: An embedded simulator is employed to implement the heuristic rules, which greatly enhances the flexibility of the algorithm and shows that the hybrid approach is superior when compared to recently published existing methods for the same problem.
Abstract: The importance of job shop scheduling as a practical problem has attracted the attention of many researchers. However, most research has focused on special cases such as single machine, parallel machine, and flowshop environments due to the “hardness” of general job shop problems. In this paper, a hybrid algorithm based on an integration of a genetic algorithm and heuristic rules is proposed for a general job shop scheduling problem with sequence-dependent setups (Jm|sjk|Cmax ). An embedded simulator is employed to implement the heuristic rules, which greatly enhances the flexibility of the algorithm. Knowledge relevant to the problem is inherent in the heuristic rules making the genetic algorithm more efficient, while the optimization procedure provided by the genetic algorithm makes the heuristic rules more effective. Extensive numerical experiments have been conducted and the results have shown that the hybrid approach is superior when compared to recently published existing methods for the same problem.
93 citations
TL;DR: In this paper, a Multi Objective Genetic Algorithm (MOGA) is proposed to derive the optimal machine-wise priority dispatching rules (pdrs) to resolve the conflict among the contending jobs in the Giffler and Thompson (GT) procedure applied for job shop problems.
Abstract: In this paper, a Multi Objective Genetic Algorithm (MOGA) is proposed to derive the optimal machine-wise priority dispatching rules ( pdrs ) to resolve the conflict among the contending jobs in the Giffler and Thompson (GT) procedure applied for job shop problems. The performance criterion considered is the weighed sum of the multiple objectives minimization of makespan, minimization of total idle time of machines and minimization of total tardiness. The weights assigned for combining the objectives into a scalar fitness function are not constant. They are specified randomly for each evaluation. This in turn leads to the multidirectional search in the proposed MOGA, which in turn mitigates the solution being entrapped in local minima. The applicability and usefulness of the proposed methodology for the scheduling of job shops is illustrated with 28 benchmark problems available in the open literature.
80 citations
TL;DR: A hybrid approach involving neural networks and genetic algorithm (GA) is presented to solve the expanded job-shop scheduling problem (EJSSP) and it is shown that the hybrid approach is powerful for complex EJSSP.
69 citations
TL;DR: In this article, a multi-attribute dispatching rule for dispatching of an AGV is developed and evaluated, using the additive weighting method, considering three system attributes concurrently: the remaining space in the outgoing buffer of a workstation, the distance between an idle AGV and a job with a job waiting for the vehicle to be serviced, and the remaining spaces in the input buffer of the destination workstation of a job.
Abstract: This paper considers the dispatching problem associated with operations of automated guided vehicles (AGVs). A multi-attribute dispatching rule for dispatching of an AGV is developed and evaluated. The multi-attribute rule, using the additive weighting method, considers three system attributes concurrently: the remaining space in the outgoing buffer of a workstation, the distance between an idle AGV and a workstation with a job waiting for the vehicle to be serviced, and the remaining space in the input buffer of the destination workstation of a job. A neural network approach is used to obtain dynamically adjusting attribute weights based on the current status of the manufacturing system. Simulation analysis of a job shop is used to compare the multi-attribute dispatching rule with dynamically adjusting attribute weights to the same dispatching rule with fixed attribute weights and to several single attribute rules. Results show that the multi-attribute dispatching rule with the ability to adapt attribute...
63 citations
TL;DR: A rolling horizon heuristic is presented for large job shops, in which the total weighted tardiness must be minimized, that decomposes the problems on a time window basis, solving each subproblem using a shifting bottleneck heuristic.
TL;DR: In this article, the situation where workers receive different levels of training across the various departments as well as possessing different level of proficiency at each task is studied, and the results suggest that even at the same level of shop flexibility, the mixture of cross training can have a significant impact on shop performance.
Abstract: The majority of research on labour flexibility in a DRC job-shop has focused on the situation where all workers are equally trained. In practice, it is likely that workers would not be trained equally and would possess different skills. In this paper, the situation where workers receive different levels of training across the various departments as well as possessing different levels of proficiency at each task is studied. Research results suggest that, even at the same level of shop flexibility, the mixture of cross training can have a significant impact on shop performance. Indeed, results suggest that it is better to have a mix of workers with no flexibility and some workers with very high flexibility rather than all workers with equal flexibility.
TL;DR: In this article, the effects of stochastic events such as machine breakdowns and processing time variations on the system performance are investigated. And the effectiveness of the simulation-based approach from the control point of view is evaluated.
TL;DR: This work considers the problem of scheduling independent jobs on two machines in an open shop, a job shop and a flow shop environment that are batching machines, which means that several operations can be combined into a batch and processed simultaneously on a machine.
Abstract: We consider the problem of scheduling independent jobs on two machines in an open shop, a job shop and a flow shop environment. Both machines are batching machines, which means that several operations can be combined into a batch and processed simultaneously on a machine. The batch processing time is the maximum processing time of operations in the batch, and all operations in a batch complete at the same time. Such a situation may occur, for instance, during the final testing stage of circuit board manufacturing, where burn-in operations are performed in ovens. We consider cases in which there is no restriction on the size of a batch on a machine, and in which a machine can process only a bounded number of operations in one batch. For most of the possible combinations of restrictions, we establish the complexity status of the problem.
TL;DR: In this paper, the authors studied reactive scheduling policies developed against unexpected machine failures and found that the proper selection of a good reactive policy is based not only on the system characteristics such as utilization, machine down times and frequency of machine failures, but also on the MHS capacity (in terms of speed and number of MH devices).
Abstract: A scheduling and control system can be viewed as a vital component of modern manufacturing systems that determines companies' overall performance in their respective supply chains. This paper studies reactive scheduling policies developed against unexpected machine failures. These reactive policies are based on rerouting the jobs to their alternative machines when their primary machine fails. Depending on the subset of the jobs considered for rerouting, the long-term performance of four policies are tested under various conditions. Expecting that these rerouting policies would bring an extra load for a material-handling system (MHS), a dynamic job shop environment was studied with and without a MHS. It is shown that the proper selection of a good reactive policy is based not only on the system characteristics such as utilization, machine down times and frequency of machine failures, but also on the MHS capacity (in terms of speed and number of MH devices). The extensive experiments show that when the MHS ...
TL;DR: In this article, the first polynomial-time approximation algorithm for job shop scheduling with a good approximation guarantee was presented. But this algorithm was later improved to a polylogarithmic approximation guarantee.
Abstract: Job-shop scheduling is a classical NP-hard problem. Shmoys, Stein, and Wein presented the first polynomial-time approximation algorithm for this problem that has a good (polylogarithmic) approximation guarantee. We improve the approximation guarantee of their work and present further improvements for some important NP-hard special cases of this problem (e.g., in the preemptive case where machines can suspend work on operations and later resume). We also present NC algorithms with improved approximation guarantees for some NP-hard special cases.
07 Oct 2001
TL;DR: An integrated greedy heuristic that simultaneously deals with the assignment and the sequencing subproblems is developed to solve the general case with more than two jobs.
Abstract: The job shop scheduling problem (JSP) deals with the sequencing operations of a set of jobs on a set of machines with minimum cost. The flexible job shop scheduling problem (FJSP) is a generalization of the JSP, which is concerned with both the assignment of machines to operations and the sequencing of the operations on the assigned machines. The paper first presents an extension of the geometric approach for solving a two-job shop problem, when there is one flexible job and the second job is a job shop job. Based on this extension and the notion of the combined job, an integrated greedy heuristic that simultaneously deals with the assignment and the sequencing subproblems is developed to solve the general case with more than two jobs. The results obtained by the greedy heuristic on existing benchmarks from the literature are promising.
TL;DR: A fast and efficient head–tail adjustment algorithm is presented for the job-shop scheduling problem to improve lower and upper bounds, and to prove the optimality of solutions for benchmark problems which have been open for some time.
Abstract: A fast and efficient head–tail adjustment algorithm is presented for the job-shop scheduling problem. The main feature of this algorithm is to do the adjustments on different machines in parallel and simultaneously to propagate the results between the machines. In connection with a branching scheme this algorithm is used to improve lower and upper bounds, and to prove the optimality of solutions for benchmark problems which have been open for some time. Copyright © 2001 John Wiley & Sons, Ltd.
TL;DR: In this paper, the due-date setting and operations scheduling problem is addressed in an unbalanced, multi-machine random job shop, where the focus is to demonstrate the feasibility of setting reliable static due-dates through operation flow time analysis.
TL;DR: In this article, the effect of reliability consideration on the application of quality index and the relative sensitivity of job shop and cellular manufacturing systems to reliability changes are evaluated by simulation modeling, and performance measures such as mean flow time and work-in-process inventories are used in the comparative study.
TL;DR: This study finds that the DBR control mechanism performs significantly better than the modified infinite loading mechanism in a job shop environment when the shortest processing time (SPT) dispatching rule is used.
Abstract: 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.
TL;DR: In this paper, the authors investigated the ergonomic advantages of one manufacturing system design over another using high-level, 3-D computer graphics simulation and other engineering analysis tools, and concluded that the design of the manufacturing system may inherently reduce or eliminate physiological problems before they developed.
TL;DR: This paper proposes a method for virtual cell formation by adopting the double-sweep algorithm for the k-shortest path problem, and a heuristic is devised to schedule the virtual cells for the multiple job orders.
Abstract: Virtual cellular manufacturing inherits the benefits of traditional cellular manufacturing and maintains the responsiveness to the changing market and routing flexibility of a job shop by integrating machine-grouping, shop layout design and intercellular flow handling. The primary goal of virtual cell formation is to minimize the throughput time of a given job. This paper proposes a method for virtual cell formation by adopting the double-sweep algorithm for the k-shortest path problem, and a heuristic is devised to schedule the virtual cells for the multiple job orders. Results generated from this method include not only the optimal candidates of the virtual cell with the shortest throughput time with sub-optimal alternative route(s) and throughput time(s) as the alternative candidates in case some resources are restricted or are not available. The procedure of virtual cell creation and scheduling is illustrated explicitly with examples. Since most of the scheduling problems are NP-hard and virtual cell scheduling is even more complex due to the bottleneck machines that are demanded by jobs at other cells. For multiplicity of possible virtual cell candidates, in addition to the precedence and resource constraints, heuristic solutions are found to be reasonable.
TL;DR: In this paper, the authors proposed a new and efficient cyclic scheduling solution framework, called the multiple cycle (MC) method, based on the assumption that the cycle time of each product is an integer multiple of a basic period.
TL;DR: In this article, two cyclic scheduling heuristics for reentrant job shop environments were developed to determine an efficient and feasible cyclic schedule which simultaneously minimized flow time and cycle time.
Abstract: 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 ...
28 Aug 2001
TL;DR: A general grouping technique to devise faster and simpler approximation schemes for several scheduling problems, illustrated on two different scheduling problems: scheduling on unrelated parallel machines with costs and the job shop scheduling problem.
Abstract: In this paper we describe a general grouping technique to devise faster and simpler approximation schemes for several scheduling problems. We illustrate the technique on two different scheduling problems: scheduling on unrelated parallel machines with costs and the job shop scheduling problem. The time complexity of the resulting approximation schemes is always linear in the number n of jobs, and the multiplicative constant hidden in the O(n) running time is reasonably small and independent of the error Ɛ.
TL;DR: A neural adaptive scheduling methodology approached machine scheduling as a control regulation problem is evaluated by comparing its performance with conventional schedulers, through simulation studies.
Abstract: In this paper a neural adaptive scheduling methodology approached machine scheduling as a control regulation problem is evaluated by comparing its performance with conventional schedulers, through simulation studies. The case study chosen constitutes an existing manufacturing cell which can be viewed as a deterministic job shop with extremely heterogenous part processing times. The results facilitate a thorough assessment of our algorithm in terms of the backlogging and inventory cost, system stability, and work in process.
TL;DR: In this article, the authors developed a quality-oriented operating curve to visualize the complex interactions between quality, manufacturing costs, and lead times in a job shop production process and inspection.
TL;DR: Preliminary results from implementing a new auction-based distributed scheduling mechanism for a job shop that can be used for a wide range of objectives indicate that the computing benefits of distributed implementation can be realized despite network delays.
Abstract: This paper presents the details of distributed implementation of a new auction-based distributed scheduling mechanism for a job shop that can be used for a wide range of objectives. The problem is modelled similar to a combinatorial auction with math programming tools used for bid construction and evaluation. Each entity in the shop is represented by a process interacting with other processes over the network as and when needed. Preliminary results from implementing this model on a network of four computers for problems of varying size indicate that the computing benefits of distributed implementation can be realized despite network delays.
TL;DR: A Petri net model in the framework of a general method for on-line scheduling in a job shop environment with multi-resource requirements and setup times and takes account of occurring events to suggest the appropriate scheduling decisions in real time.