Showing papers on "Flow shop scheduling published in 2004"

Scheduling Problems with Two Competing Agents

Abstract: We consider the scheduling problems arising when two agents, each with a set of nonpreemptive jobs, compete to perform their respective jobs on a common processing resource. Each agent wants to minimize a certain objective function, which depends on the completion times of its jobs only. The objective functions we consider in this paper are maximum of regular functions (associated with each job), number of late jobs, and total weighted completion times. We obtain different scenarios, depending on the objective function of each agent, and on the structure of the processing system (single machine or shop). For each scenario, we address the complexity of various problems, namely, finding the optimal solution for one agent with a constraint on the other agent's cost function, finding single nondominated schedules (i.e., such that a better schedule for one of the two agents necessarily results in a worse schedule for the other agent), and generating all nondominated schedules.

Robust and reactive project scheduling: a review and classification of procedures

Abstract: The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a framework that should allow project management to identify the proper scheduling methodol...

Improving scheduling of tasks in a heterogeneous environment

Abstract: Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. We introduce a task duplication-based scheduling algorithm for network of heterogeneous systems (TANH), with complexity O(V/sup 2/), which provides optimal results for applications represented by directed acyclic graphs (DAGs), provided a simple set of conditions on task computation and network communication time could be satisfied. The performance of the algorithm is illustrated by comparing the scheduling time with an existing "best imaginary level scheduling (BIL)" scheme for heterogeneous systems. The scalability for a higher or lower number of processors, as per their availability is also discussed. We have shown to provide substantial improvement over existing work on the task duplication-based scheduling algorithm (TDS).

An incremental genetic algorithm approach to multiprocessor scheduling

Abstract: We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.

A review and classification of heuristics for permutation flow-shop scheduling with makespan objective

Abstract: Makespan minimization in permutation flow-shop scheduling is an operations research topic that has been intensively addressed during the last 40 years. Since the problem is known to be NP-hard for ...

An Ant Colony Optimization Algorithm for Shop Scheduling Problems

Abstract: We deal with the application of ant colony optimization to group shop scheduling, which is a general shop scheduling problem that includes, among others, the open shop scheduling problem and the job shop scheduling problem as special cases. The contributions of this paper are twofold. First, we propose a neighborhood structure for this problem by extending the well-known neighborhood structure derived by Nowicki and Smutnicki for the job shop scheduling problem. Then, we develop an ant colony optimization approach, which uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions. We compare this algorithm to an adaptation of the tabu search by Nowicki and Smutnicki to group shop scheduling. Despite its general nature, our algorithm works particularly well when applied to open shop scheduling instances, where it improves the best known solutions for 15 of the 28 tested instances. Moreover, our algorithm is the first competitive ant colony optimization approach for job shop scheduling instances.

Scheduling Space–Ground Communications for the Air Force Satellite Control Network

Abstract: We present the first coupled formal and empirical analysis of the Satellite Range Scheduling application. We structure our study as a progression; we start by studying a simplified version of the problem in which only one resource is present. We show that the simplified version of the problem is equivalent to a well-known machine scheduling problem and use this result to prove that Satellite Range Scheduling is NP-complete. We also show that for the one-resource version of the problem, algorithms from the machine scheduling domain outperform a genetic algorithm previously identified as one of the best algorithms for Satellite Range Scheduling. Next, we investigate if these performance results generalize for the problem with multiple resources. We exploit two sources of data: actual request data from the U.S. Air Force Satellite Control Network (AFSCN) circa 1992 and data created by our problem generator, which is designed to produce problems similar to the ones currently solved by AFSCN. Three main results emerge from our empirical study of algorithm performance for multiple-resource problems. First, the performance results obtained for the single-resource version of the problem do not generalize: the algorithms from the machine scheduling domain perform poorly for the multiple-resource problems. Second, a simple heuristic is shown to perform well on the old problems from 1992; however it fails to scale to larger, more complex generated problems. Finally, a genetic algorithm is found to yield the best overall performance on the larger, more difficult problems produced by our generator.

Planning Machine Maintenance in Two-machine Shop Scheduling

Abstract: We consider the two-machine open shop and two-machine flow shop scheduling problems in which each machine has to be maintained exactly once during the planning period, and the duration of each of these intervals depends on its start time. The objective is to minimize the maximum completion time of all activities to be scheduled. We resolve complexity and approximability issues of these problems. The open shop problem is shown to be polynomially solvable for quite general functions defining the length of the maintenance intervals. By contrast, the flow shop problem is proved binary NP-hard and pseudopolynomially solvable by dynamic programming. We also present a fully polynomial approximation scheme and a fast 3/2-approximation algorithm.

Use of Genetic Algorithms in Resource Scheduling of Construction Projects

Abstract: This paper presents an augmented Lagrangian genetic algorithm model for resource scheduling. The algorithm considers scheduling characteristics that were ignored in prior research. Previous resource scheduling formulations have primarily focused on project duration minimization. Furthermore, resource leveling and resource-constrained scheduling have traditionally been solved independently. The model presented here considers all precedence relationships, multiple crew strategies, total project cost minimization, and time-cost trade-off. In the new formulation, resource leveling and resource-constrained scheduling are performed simultaneously. The model presented uses the quadratic penalty function to transform the resource-scheduling problem to an unconstrained one. The algorithm is general and can be applied to a broad class of optimization problems. An illustrative example is presented to demonstrate the performance of the proposed method.

A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles

Abstract: In this paper, the problem of simultaneous scheduling of machines and identical automated guided vehicles (AGVs) in flexible manufacturing systems is addressed with the objective of minimizing the makespan. This problem is composed of two interrelated decision problems: the scheduling of machines, and the scheduling of AGVs. Both problems are known to be NP-complete, resulting in a more complicated NP-complete problem when they are considered simultaneously. A new hybrid Genetic-algorithm/heuristic coding scheme is developed for the studied problem. The developed coding scheme is combined with a set of genetic algorithm (GA) operators selected from the literature of the applications of GAs to the scheduling problems. The algorithm is applied to a set of 82 test problems, which was constructed by other researchers, and the comparison of the results indicates the superior performance of the developed coding.

Sub optimal scheduling in a grid using genetic algorithms

Abstract: The computing GRID infrastructure could benefit of techniques that can improve the over-all throughput of the system. It is possible that job submission will include different ontology in resource requests due to the generality of the GRID infrastructure. Such flexible resource request could offer the opportunity to optimize several parameters, from network load to job costs in relation to due time, more generally the quality of services. We present the result of the simulation of GRID jobs allocation. The search strategy for this input case does not converge to the optimal case inside the limited number of trial performed, in contrast with previous work on up to 24 jobs [Scheduling in a Grid Computing Environment using Genetic Algorithms, in: Proceedings of the International Parallel and Distributed Processing Symposium: IPDPS 2002 Workshops]. The benefits of the usage of the genetic algorithms to improve the quality of the scheduling is discussed. The simulation has been obtained using an environment GGAS suitable to study the scheduling of jobs in a distributed group of parallel machines. The modular structure of GGAS allows to expand its functionalities to include other first level schedule policy with respect to the FCFS that is considered. The result of this paper suggests the usage of local search strategy to improve the convergence when the number of jobs to be considered is as big as in real world operation.

Fault-tolerant scheduling policy for grid computing systems

Abstract: Summary form only given. With the momentum gaining for the grid computing systems, the issue of deploying support for integrated scheduling and fault-tolerant approaches becomes paramount importance. Unfortunately, fault-tolerance has not been factored into the design of most existing grid scheduling strategies. To this end, we propose a fault-tolerant scheduling policy that loosely couples job scheduling with job replication scheme such that jobs are efficiently and reliably executed. Performance evaluation of the proposed fault-tolerant scheduler against a nonfault-tolerant scheduling policy is presented and shown that the proposed policy performs reasonably in the presence of various types of failures.

Local Search Genetic Algorithms for the Job Shop Scheduling Problem

Abstract: In previous work, we developed three deadlock removal strategies for the job shop scheduling problem (JSSP) and proposed a hybridized genetic algorithm for it While the genetic algorithm (GA) gave promising results, its performance depended greatly on the choice of deadlock removal strategies employed This paper introduces a genetic algorithm based scheduling scheme that is deadlock free This is achieved through the choice of chromosome representation and genetic operators We propose an efficient solution representation for the JSSP in which the job task ordering constraints are easily encoded Furthermore, a problem specific crossover operator that ensures solutions generated through genetic evolution are all feasible is also proposed Hence, both checking of the constraints and repair mechanism can be avoided, thus resulting in increased efficiency A mutation-like operator geared towards local search is also proposed which further improves the solution quality Lastly, a hybrid strategy using the genetic algorithm reinforced with a tabu search is developed An empirical study is carried out to test the proposed strategies

Minimizing Makespan in a Two-Machine Flow Shop with Delays and Unit-Time Operations is NP-Hard

Abstract: One of the first problems to be studied in scheduling theory was the problem of minimizing the makespan in a two-machine flow shop. Johnson showed that this problem can be solved in O(n log n) time. A crucial assumption here is that the time needed to move a job from the first to the second machine is negligible. If this is not the case and if this ‘delay’ is not equal for all jobs, then the problem becomes NP-hard in the strong sense. We show that this is even the case if all processing times are equal to one. As a consequence, we show strong NP-hardness of a number of similar problems, including a severely restricted version of the Numerical 3-Dimensional Matching problem.

A genetic algorithm for an industrial multiprocessor flow-shop scheduling problem with recirculation

Abstract: We propose in this paper an effective heuristic algorithm to solve a scheduling problem that comes from industry. The workshop is an hybrid flow shop with recirculation and the problem is to perform jobs between a release date and a due date, in order to minimize the weighted number of tardy jobs. Firstly, an integer linear programming formulation of the problem is proposed, then a lower bound, a greedy algorithm and a genetic algorithm are described as approximate methods. To evaluate these heuristics, experiences on instances like industrial ones are computed, and show the efficiency of the genetic algorithm.

A bi-criterion single-machine scheduling problem with learning considerations

Abstract: Conventionally, job processing times are assumed to be constant from the first job to be processed until the last job to be completed. However, recent empirical studies in several industries have verified that unit costs decline as firms produce more of a product and gain knowledge or experience. This phenomenon is known as the “learning effect.” This paper focuses on a bi-criterion single-machine scheduling problem with a learning effect. The objective is to find a sequence that minimizes a linear combination of the total completion time and the maximum tardiness. A branch-and-bound and a heuristic algorithm are proposed to search for optimal and near-optimal solutions, respectively. Computational results are also provided for the problem.

Finite capacity scheduling using job prioritization and machine selection

Abstract: In a method, device, and computer-readable medium for finite capacity scheduling, heuristic rules are applied in two integrated stages: Job Prioritization and Machine Selection. During Job Prioritization (“JP”), jobs are prioritized based on a set of JP rules which are machine independent. During Machine Selection (“MS”), jobs are scheduled for execution at machines that are deemed to be best suited based on a set of MS rules. The two-stage approach allows scheduling goals to be achieved for performance measures relating to both jobs and machines. For example, machine utilization may be improved while product cycle time objectives are still met. Two user-configurable options, namely scheduling model (job shop or flow shop) and scheduling methodology (forward, backward, or bottleneck), govern the scheduling process. A memory may store a three-dimensional linked list data structure for use in scheduling work orders for execution at machines assigned to work centers.

Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach

Abstract: This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-hfsp.