Large Job Shop Research Articles

A new linguistic based algorithm for creating manufacturing cell

This paper presents a new approach for modeling formation of machine groups and the associated parts families using a linguistic model. The method outlines a hierarchical approach for deciding the number of clusters/machine groups. The solution is improved further by utilizing a genetic algorithm with the objective of decreasing actual job-shop inter-cell distance moved. A unique feature of the proposed method is that it recognizes the operation sequence of manufacturing the parts specified in the process sheets. The concept of null machine is introduced in the linguistic model to calculate the dissimilarity among parts. The movement of parts can be either flow type or random job-shop type for the discrete manufacturing shop, required to be partitioned. Back flow/tracking are minimized. The flexibility of changing number of machine groups is also built-in the model. The model has been found useful and flexible enough to solve any realistic discrete manufacturing system and is found to work comfortably in medium to large job shop situations when the machine part incidence matrix shows sparse density.

A Simulated Annealing-Based Heuristic Algorithm for Job Shop Scheduling to Minimize Lateness

A decomposition-based optimization algorithm is proposed for solving large job shop scheduling problems with the objective of minimizing the maximum lateness. First, we use the constraint propagation theory to derive the orientation of a portion of disjunctive arcs. Then we use a simulated annealing algorithm to find a decomposition policy which satisfies the maximum number of oriented disjunctive arcs. Subsequently, each subproblem (corresponding to a subset of operations as determined by the decomposition policy) is successively solved with a simulated annealing algorithm, which leads to a feasible solution to the original job shop scheduling problem. Computational experiments are carried out for adapted benchmark problems, and the results show the proposed algorithm is effective and efficient in terms of solution quality and time performance.

An RFID application in large job shop remanufacturing operations

In this study, we evaluate the use of radio-frequency identification (RFID) technology for improving remanufacturing efficiency. We report the results of discrete-event simulation model that analyzes how RFID creates value within the remanufacturing operation. We find that the simulated gains from using RFID are quite modest, and propose alternative justifications for the major benefits seen in practice. We then provide a framework for deciding on the adoption of active RFID technology such as real-time location system (RTLS) for easy identification of components in the remanufacturing process and the adoption of passive RFID for permanently tagging components of remanufacturable products.

A divide-and-conquer strategy with particle swarm optimization for the job shop scheduling problem

An optimization algorithm based on the ‘divide-and-conquer’ methodology is proposed for solving large job shop scheduling problems with the objective of minimizing total weighted tardiness. The algorithm adopts a non-iterative framework. It first searches for a promising decomposition policy for the operation set by using a simulated annealing procedure in which the solutions are evaluated with reference to the upper bound and the lower bound of the final objective value. Subproblems are then constructed according to the output decomposition policy and each subproblem is related to a subset of operations from the original operation set. Subsequently, all these subproblems are sequentially solved by a particle swarm optimization algorithm, which leads directly to a feasible solution to the original large-scale scheduling problem. Numerical computational experiments are carried out for both randomly generated test problems and the real-world production data from a large speed-reducer factory in China. Results show that the proposed algorithm can achieve satisfactory solution quality within reasonable computational time for large-scale job shop scheduling problems.

A fluid approach to large volume job shop scheduling

We consider large volume job shop scheduling problems, in which there is a fixed number of machines, a bounded number of activities per job, and a large number of jobs. In large volume job shops it makes sense to solve a fluid problem and to schedule the jobs in such a way as to track the fluid solution. There have been several papers which used this idea to propose approximate solutions which are asymptotically optimal as the volume increases. We survey some of these results here. In most of these papers it is assumed that the problem consists of many identical copies of a fixed set of jobs. Our contribution in this paper is to extend the results to the far more general situation in which the many jobs are all different. We propose a very simple heuristic which can schedule such problems. We discuss asymptotic optimality of this heuristic, under a wide range of previously unexplored situations. We provide a software package to explore the performance of our policy, and present extensive computational evidence for its effectiveness.

A two-stage genetic algorithm for large size job shop scheduling problems

The majority of large size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past few decades, genetic algorithms (GAs) have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature available considering the optimal parameters when designing GAs. Unsuitable parameters may generate an inadequate solution for a specific scheduling problem. In this paper, we proposed a two-stage GA which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, and probability of mutation, for a given job shop problem with a fraction of time using the optimal computing budget allocation method, and then the fittest parameters are used in the GA for a further searching operation to find the optimal solution. For large size problems, the two-stage GA can obtain optimal solutions effectively and efficiently. The method was validated based on some hard benchmark problems of job shop scheduling.

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.

The application of Adaptive Genetic Algorithms in FMS dynamic rescheduling

Uncertainties in the production process would inevitably result in deviations from the existing schedule in flexible manufacturing systems (FMSs). This paper tries to study the problem in an environment with realistic interruptions and a requirement of time-restricted response in rescheduling. In our paper, the rescheduling system is based on the records of a dynamic database (DDB). It is able to reform the up-to-date status of a disturbed system via summarizing the remaining resources and works in process (WIPs) precisely. By using it as the new initial state, the new schedule is configured smoothly in conjunction with the existing schedule to improve the efficiency of FMS at this critical instant. Considering both speed and economic benefit, an adaptive genetic algorithm (AGA) is proposed for finding the new sub-optimal schedule of a large and complicated job shop FMS shortly after the interruption occurred. The AGA is designed to prevent the premature convergence and refine the performance of genetic algorithms in re-scheduling. During the AGA evolution process, the probabilities of crossover and mutation are varied, depending on both the fitness value and the normalized fitness distances between solutions. With help from DDB, the FMS scheduling model and AGA, the results obtained in the test examples of rescheduling are quite satisfactory.

Decomposition methods for large job shops

A rolling horizon heuristic is presented for large job shops, in which the total weighted tardiness must be minimized. The method divides a given instance into a number of subproblems, each having to correspond to a time window of the overall schedule, which are solved using a shifting bottleneck heuristic. A number of rules for defining each time window are derived. The method is tested by using instances up to 10 machines and 100 operations per machine, outperforming a shifting bottleneck heuristic that has been shown to generate close to optimal results. Scope and purpose There has been a significant amount of research focused on the scheduling of a job shop, either minimizing the makespan or the tardiness. Although the results for small-size problems are satisfactory, there has been no approach as for yet middle- and large-size problems. This paper presents a heuristic that decomposes the problems on a time window basis, solving each subproblem using a shifting bottleneck heuristic. Its results for a due-date-related objective function are promising.

On satisfying due-dates in large job shops: idle time insertion

We consider the problem of minimizing maximum lateness in a job shop. A conceptually simple simulation based procedure described in a recent paper by Hodgson et al [1] is modified to provide improved schedules. Computational experimentation is provided to identify the conditions under which the approach is most viable, and to report the procedure's performance on known test problems.

On satisfying due-dates in large job shops: idle time insertion

We consider the problem of minimizing maximum lateness in a job shop. A conceptually simple simulation based procedure described in a recent paper by Hodgson et al. [1] is modified to provide improved schedules. Computational experimentation is provided to identify the conditions under which the approach is most viable, and to report the procedure's performance on known test problems.

Note. Satisfying Due Dates in Large Job Shops

For the multi-machine job shop scheduling problem, a conceptually simple simulation-based procedure (first proposed by Lawrence and Morton 1986) is shown to be both effective and efficient in providing optimal, or near optimal, schedules for minimizing the maximum lateness, Lmax. Computational experimentation is used to identify the conditions under which the approach is most viable.

A Temporal Reasoning Expert System for Job Shop Scheduling

The most obvious application of temporal reasoning to intelligent systems is in the development of scheduling and planning systems. In scheduling, a fundamental problem is job shop scheduling, a problem in which the optimization of its solution is classified as NP-complete. Active research is continuously being conducted on this problem, especially for cases when the size of the problem is large. This paper introduces an AI technique based on a temporal reasoning model for solving large job shop scheduling problems. The method, which is essentially an application of duration reasoning in a temporal relations framework, has been successfully developed into a workable frame-based expert system, implemented using the Windows-based Kappa PC hybrid development tool. The system is tested on a rather complex 13/21/G/C problem, and a feasible but acceptable schedule is generated in a very reasonable computation time. Preliminary results are presented to show the viability and potential of this method in real-time control of manufacturing processes.

Curriculum recommendations and guidelines for the community and junior college career program in computer programming

Curriculum recommendations are given for a two year Associate Degree program to prepare computer programmers for jobs. The program is specifically directed toward the education of the computer programmer who works in conjunction with a systems analyst in the programming environment usually found in a medium to large job shop. Graduates should be qualified to do entry-level applications programming to support the general, administrative, and organizational information processing functions of industry, commerce, business, and government service.The history of and need for education in the computer field at the two year college level are discussed. Specific objectives and subject matter for the curriculum are given. Recommendations for facilities and staff resources needed to support such a program are made. Suggestions for implementation and for adaptation for continued relevance are given.

A work progress forecasting algorithm for a job shop manufacturing facility

Abstract The research described in this paper has addressed itself to the development of on algorithm to forecast potential job costs and throughput times in a job shop. The algorithm utilizes historically repetitive relationships between cumulative job costs find time which are defined as cost curves. The algorithm was developed and evaluated using actual historical data from a large job shop production facility. Evaluation tests showed the cost forecast mechanism to be an effective early predictor of actual total job costs. The throughput time forecast mechanism was found to be less effective as an early predictor, but valuable as an early warning indicator of potential scheduling problems and irregularities.

Effects of Shop Size and Labor Flexibility in Labor and Machine Limited Production Systems

This paper describes a simulation study that examines the influence of system size and labor flexibility on the performance effects of dispatching and labor control decision rules in labor and machine limited production systems. Four production systems are used—a small job shop, a medium job shop, a large job shop in which workers may be transferred between major organizational divisions, and a large job shop in which workers cannot be transferred between divisions. For each system the decision rules for dispatching and labor control are varied. Comparisons are made of the relative effects of the decision rules, for the different systems, on mean flow-time, flow-time variance and number of worker transfers. Results indicate that the effects of dispatching and labor control decision rules on flow-time measures are consistent for different size and labor flexibility combinations. With one exception, the effects on labor transfer measures are also consistent. Consistency here means that the directions of change in performance measures are the same for the different systems. The magnitudes of both actual and percentage changes do vary for the different systems.

Simulations to explore alternative sequencing rules

AbstractA flexible simulation system has been designed to permit investigation of sequencing rules in a large job shop, with continual input of new work. The objective is to understand what happens rather than to find optimum rules, with many conflicting measures of effectiveness: work in process, finished goods investment, delays, machine and labor utilization, and effort to implement. A few problem areas have been explored.