Deterministic Job-shop Scheduling Research Articles

A simulation-optimization framework for generating dynamic dispatching rules for stochastic job shop with earliness and tardiness penalties

Most of real-world problems regarding production and manufacturing contain stochastic parameters. In this research, job shop scheduling problem with stochastic process times and weighted earliness-tardiness objective function is considered. When machines are freed in the course of manufacturing, a job has to be selected from the line and passed on the machine. In deterministic job shop scheduling, the selection process is static. In the stochastic variants however, dynamic or real-time dispatching rules may be used. The goal of this paper is to develop a solution method for stochastic job shop scheduling problem that delivers dynamic and global dispatching rules that use information pertaining to the entire shop floor. In order to achieve this, the problem is converted to a near-Markov decision process that comprises an alternate sequence of states (machines becoming free) and actions (selecting a job). Using simulation and ant colony, a database of states is generated and a meaningful pheromone trail for each state is formed gradually. The pheromones act as a memory mechanism and, in combination with a heuristic based on the Central Limit Theorem, comprise the dispatching rule. Efficiency of this approach is investigated through extensive simulation. Results show an average of 92% cost reduction in comparison with the random dispatching rule. Moreover, the approach yields better results in congested scheduling systems.

A neural network meta-model for identification of optimal combination of priority dispatching rules and makespan in a deterministic job shop scheduling problem

Selection of appropriate priority dispatching rules (PDRs) is a major concern in practical scheduling problems. Earlier research implies that using one PDR does not necessarily yield to an optimal schedule. Hence, this paper puts forward a novel approach based on discrete event simulation (DES) and artificial neural networks (ANNs) to decide on the optimal PDR for each machine from a set of rules so as to minimize the makespan in job shop scheduling problems. Non-identical PDRs are considered for each machine. Indeed, for a given number of machines, all permutations of PDRs are taken into account which could lead to nondeterministic polynomial-time hardness of the problem when the number of machines increases. To address this issue, DES and ANNs are employed as a meta-model. First, the problem is modeled and quite a number of feasible solutions are obtained from DES on its own. Afterward, a back-propagation neural network is developed in accordance with the results of DES to calculate the makespan based on all potential permutations of PDRs. The performance of the proposed approach is investigated through a set of test-bed problems.

Hybrid Rollout Approaches for the Job Shop Scheduling Problem

In this paper, we focus on heuristic approaches for solving the deterministic job shop scheduling problem. More specifically, a new priority dispatch rule and hybrid rollout algorithms are developed for approaching the problem under consideration. The proposed solution algorithms are tested on a set of instances taken from the literature and compared with other methods. The computational results validate the effectiveness of the developed solution approaches and show that the proposed rollout algorithms are competitive with respect to several state-of-art heuristics for solving the job shop scheduling problem.

IMMUNE EVOLUTION ALGORITHM FOR DETERMINISTIC JOB SHOP SCHEDULING

An immune evolution algorithm CHIEA(Clonal selection and hyper mutations based immune evolution algorithm) is proposed for solving deterministic job shop scheduling problems. The algorithm is based on clonal selection and hyper mutations. A random permutation operator and a consecutive mutation method of antibodies is introduced to extend the concept of hyper mutations. The preference list based representation and the immune evolution operator improves searching efficiency, accelerates convergence of the algorithm and advances solutions generated. A thorough comparison between CHIEA and COELLO'AIS(Artificial immune system) proves CHIEA has better optimizing performances for deterministic job shop scheduling problems varying in styles and appropriate sizes.

Job shop scheduling techniques in semiconductor manufacturing

This paper presents a brief review on job shop scheduling techniques in semiconductor manufacturing. The manufacturing environment in a semiconductor industry is considered a highly complex job shop, involving multiple types of work centers, large and changing varieties of products, sequence-dependent setup times, reentrant process flow, etc., in a dynamic scheduling environment. Due to the stubborn nature of the deterministic job shop scheduling problem itself, many of the solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem-specific strategies. In this paper, we seek to provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.

Deterministic job-shop scheduling: Past, present and future

Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. © 1999 Elsevier Science B.V. All rights reserved.

Implementing heuristics as an optimization criterion in neural networks for job-shop scheduling

Recently neural network architectures have been developed that are capable of solving deterministic job-shop scheduling problems, part of the large class of NP-complete problems. In these architectures, however, no valid optimization criterion has been implemented. In this paper an enhanced neural network architecture for job-shop scheduling is proposed in which general rules of thumb for job-shop scheduling have been incorporated as a local optimization criterion. Implementation of the rules of thumb, by adaptation of the network architecture, results in a network that actually incorporates the optimization criterion, enabling parallel hardware implementation. Owing to the implemented local optimization criterion the performance of the network architecture is superior to previously presented architectures. Comparison with advanced heuristic sequential schedulers showed equal performance with respect to the quality of the solutions and better performance with respect to calculation speed.

Neural networks for job-shop scheduling

A neural network structure has been developed which is capable of solving deterministic job-shop scheduling problems, part of the large class of np-complete problems. The problem was translated in an integer linear programming format which facilatated translation in an adequate neural network structure. Use of the presented structure eliminated the need for integer adjustments. Elementary precalculation is performed with the objective to reduce the search space allowing more rapid calculation of feasible solutions. In this precalculation the earliest possible starting times of the operations are calculated and set as tresholds in the network. The neural network structure was reliable in simulated operation and its performance was superior to structures which have been presented previously. The network structure always produces feasible solutions, in less time, without the application of integer adjustments.

Neural Nets for Job-shop Scheduling, will they do the Job?

A neural net structure has been developed which is capable of solving deterministic job-shop scheduling problems, part of the large class of np-complete problems. The problem was translated in an integer linear programming format which facilitated translation in an adequate neural structure. Use of the presented structure eliminated the need for integer adjustments. The search space was reduced by the use of precalculation, allowing the rapid calculation of feasible solutions. The neural net structure was reliable in simulated operation and its performance was superior to structures which have been presented previously.

Deterministic job-shop scheduling problems with capacity focus

In this paper a main point of view is the exhausting of capacity in relation to a capacity focus at every node of the production graph. This exhausting process has a local and a global aspect. Optimality means a homogeneous reservation of the production nets and exhaustion of the capacity in the nodes close to the focus. Under weak conditions an optimal production sequence is achievable constructively. A classical field of application of this result is the assembly, but many other applications are possible.

The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling

The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic job-shop scheduling problems (including a two-station version of Conway et al.'s 1967 nine-station symmetric shop) are tested under seven different scenarios, one deterministic and six stochastic, using computer simulation. The deterministic scenario, where the processing times are exponential and observed by the scheduler, has been considered in many simulation studies, including Conway et al.'s. The six stochastic scenarios include the case where the processing times are exponential and only the mean is known by the scheduler, and five different cases where the machines are subject to unpredictable failures. Two policies were tested, the shortest expected processing time (SEPT) rule, and a rule derived from a Brownian analysis of the corresponding queueing network scheduling problem. Although the SEPT rule performed well in the deterministic scenario, it was easily outperformed by the Brownian policies in the six stochastic scenarios for all three problems. Thus, the results from simulation studies of dynamic, deterministic job-shop scheduling problems may not carry over to the more realistic setting where there is unpredictable variability.

Algebraic, mathematical programming, and network models of the deterministic job-shop scheduling problem

In the contemporary literature on deterministic machine scheduling, problems are formulated from three different, but equivalent, perspectives. Algebraic models provide a rigorous problem statement in the language of set theory and are typical of the more abstract development of scheduling theory in mathematics and computer science. Mathematical programming models rely on familiar concepts of nonlinear optimization and are generally the most accessible. Network models (disjunctive graphs) are best suited to the development of solution approaches and figure prominently in discussions of algorithm design and analysis. In this tutorial, it is shown how the minimum-makespan job-shop problem (n/m/G/C/sub max/) is realized in each of these three model forms. A common notation is developed and how the underlying structure and fundamental difficulty of the problem are expressed in each model is demonstrated. >

Job-shop scheduling: limits of the binary disjunctive formulation

SUMMARY The deterministic job-shop scheduling problem exhibits the fundamental computational difficulty implicit in determining an optimal timetable for sharing production resources among competing production activities. While adaptation of the formal model to industrial practice is fraught with difficulties, we show that the underlying binary-disjunctive formulation itself is more robust than might be immediately apparent. Straightforward extensions of the underlying model are sufficient to capture such practical problem features as assembly and disassembly sequences, due-dates and out-processing operations, scheduled maintenance, nonzero release times and dispatch operations, certain sequence-dependent set-ups and materials handling delays, and a great range of operational side-constraints. Technological sequences need not be total orders, job priorities can be assigned explicitly or implicitly, and any regular measure of performance can be represented. The principal structural limitation is that machin...