Showing papers on "Flow shop scheduling published in 2010"
TL;DR: A literature review on exact, heuristic and metaheuristic methods that have been proposed for the solution of the hybrid flow shop problem is presented.
647 citations
TL;DR: In this paper, a survey of deterministic scheduling problems with availability constraints motivated by preventive maintenance is presented, where complexity results, exact algorithms and approximation algorithms in single machine, parallel machine, flow shop, open shop, job shop scheduling environment with different criteria are surveyed briefly.
376 citations
TL;DR: An extensive review of recently published papers on hybrid flow shop (HFS) scheduling problems is presented and the papers are classified first according to the HFS characteristics and production limitations considered in the respective papers and then according toThe solution approach proposed.
370 citations
TL;DR: The paper reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristic and meta-heuristic approaches for the design of efficient Grid schedulers.
364 citations
TL;DR: The DPFSP is characterized and six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance are proposed.
353 citations
01 Jun 2010
TL;DR: A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP) and results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.
Abstract: A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.
285 citations
TL;DR: An Improved Genetic Algorithm to solve the Distributed and Flexible Job-shop Scheduling problem is proposed and has been compared with other algorithms for distributed scheduling and evaluated with satisfactory results on a large set of distributed-and-flexible scheduling problems derived from classical job-shop scheduling benchmarks.
252 citations
TL;DR: In this paper, a new discrete firefly meta-heuristic was proposed to minimize the makespan for the permutation flow shop scheduling problem, and the results of implementation of the proposed method are compared with other existing ant colony optimization technique.
Abstract: Article history: Received 23 January 2010 Received in revised form 23 April 2010 Accepted 26 April 2010 Available online 26 April 2010 During the past two decades, there have been increasing interests on permutation flow shop with different types of objective functions such as minimizing the makespan, the weighted mean flow-time etc. The permutation flow shop is formulated as a mixed integer programming and it is classified as NP-Hard problem. Therefore, a direct solution is not available and metaheuristic approaches need to be used to find the near-optimal solutions. In this paper, we present a new discrete firefly meta-heuristic to minimize the makespan for the permutation flow shop scheduling problem. The results of implementation of the proposed method are compared with other existing ant colony optimization technique. The preliminary results indicate that the new proposed method performs better than the ant colony for some well known benchmark problems. © 2010 Growing Science Ltd. All rights reserved.
248 citations
TL;DR: An artificial immune algorithm (AIA) based on integrated approach is proposed to solve the flexible job-shop scheduling problem (FJSP) to minimize makespan and the computational results validate the quality of the proposed approach.
241 citations
TL;DR: A novel hybrid discrete differential evolution (HDDE) algorithm for solving blocking flow shop scheduling problems to minimize the maximum completion time (i.e. makespan) and a local search algorithm based on insert neighborhood structure is embedded in the algorithm to balance the exploration and exploitation by enhancing the local searching ability.
236 citations
TL;DR: A parallel variable neighborhood search (PVNS) algorithm that solves the FJSP to minimize makespan time and uses various neighborhood structures which carry the responsibility of making changes in assignment and sequencing of operations for generating neighboring solutions.
Abstract: Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. This paper proposes a parallel variable neighborhood search (PVNS) algorithm that solves the FJSP to minimize makespan time. Parallelization in this algorithm is based on the application of multiple independent searches increasing the exploration in the search space. The proposed PVNS uses various neighborhood structures which carry the responsibility of making changes in assignment and sequencing of operations for generating neighboring solutions. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.
TL;DR: This survey reviews the current complexity status of basic cyclic scheduling models, paying special attention to recent results on the unsolvability (NP-hardness) of various cyclic problems arising from the scheduling of robotic cells.
TL;DR: In this article, a multi-objective genetic algorithm (MOGA) based on immune and entropy principle was proposed to solve the flexible job-shop scheduling problem (FJSP).
Abstract: Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm.
TL;DR: The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem and shows the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.
Abstract: Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.
TL;DR: In this article, three genetic algorithms are presented for the permutation flow shop scheduling problem with total tardiness minimisation criterion, which includes advanced techniques like path relinking, local search and a procedure to control the diversity of the population.
Abstract: In this work three genetic algorithms are presented for the permutation flowshop scheduling problem with total tardiness minimisation criterion. The algorithms include advanced techniques like path relinking, local search and a procedure to control the diversity of the population. We also include a speed up procedure in order to reduce the computational effort needed for the local search technique, which results in large CPU time savings. A complete calibration of the different parameters and operators of the proposed algorithms by means of a design of experiments approach is also given. We carry out a comparative evaluation with the best methods that can be found in the literature for the total tardiness objective, and with adaptations of other state-of-the-art methods originally proposed for other objectives, mainly makespan. All the methods have been implemented with and without the speed up procedure in order to test its effect. The results show that the proposed algorithms are very effective, outperforming the remaining methods of the comparison by a considerable margin.
TL;DR: The authors modified the particle position representation, particle movement, and particle velocity in this study to construct a particle swarm optimization (PSO) for an elaborate multi-objective job-shop scheduling problem.
Abstract: Most previous research into the job-shop scheduling problem has concentrated on finding a single optimal solution (e.g., makespan), even though the actual requirement of most production systems requires multi-objective optimization. The aim of this paper is to construct a particle swarm optimization (PSO) for an elaborate multi-objective job-shop scheduling problem. The original PSO was used to solve continuous optimization problems. Due to the discrete solution spaces of scheduling optimization problems, the authors modified the particle position representation, particle movement, and particle velocity in this study. The modified PSO was used to solve various benchmark problems. Test results demonstrated that the modified PSO performed better in search quality and efficiency than traditional evolutionary heuristics.
TL;DR: The computational tests show that L-NSGA provides better solutions than NSGA2 and SPEA2; moreover, its solutions are closer to the optimal front.
TL;DR: A multi-objective ant colony system algorithm (MOACSA), which combines ant colony optimization approach and a local search strategy in order to solve this scheduling problem with respect to the both objectives of makespan and total flowtime is presented.
Abstract: In this paper, we consider the flow shop scheduling problem with respect to the both objectives of makespan and total flowtime. This problem is known to be NP-hard type in literature. Several algorithms have been proposed to solve this problem. We present a multi-objective ant colony system algorithm (MOACSA), which combines ant colony optimization approach and a local search strategy in order to solve this scheduling problem. The proposed algorithm is tested with well-known problems in literature. Its solution performance was compared with the existing multi-objective heuristics. The computational results show that proposed algorithm is more efficient and better than other methods compared.
TL;DR: Three hybrid harmony search algorithms are developed for solving the flow shop scheduling with blocking to minimize the total flow time and some new pitch adjustment rules are developed to well inherit good structures from the globalbest harmony vector.
Abstract: In this paper, three hybrid harmony search (HS) algorithms, namely, hybrid harmony search (hHS) algorithm, hybrid globalbest harmony search (hgHS) algorithm and hybrid modified globalbest harmony search (hmgHS) algorithm, are developed for solving the flow shop scheduling with blocking to minimize the total flow time. Firstly, a largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Secondly, an initialization scheme based on a variant of the NEH heuristic is presented to construct the initial harmony memory with certain quality and diversity. Thirdly, HS is employed to evolve harmony vectors in the harmony memory to perform exploration, whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. In addition, some new pitch adjustment rules are developed to well inherit good structures from the globalbest harmony vector. Based on a set of well-known benchmark instances, extensive computational experiments are carried out. Computational results show the effectiveness of the hybrid harmony search algorithms, especially the (hmgHS) algorithm, in solving the blocking flow shop scheduling with total flow time criterion.
07 Jul 2010
TL;DR: The priority rules generated by GP are evaluated on dynamic job shop scenarios from literature and compared with manually developed rules yielding very promising results also interesting for Simulation Optimization in general.
Abstract: Developing dispatching rules for manufacturing systems is a process, which is time- and cost-consuming. Since there is no good general rule for different scenarios and objectives automatic rule search mechanism are investigated. In this paper an approach using Genetic Programming (GP) is presented. The priority rules generated by GP are evaluated on dynamic job shop scenarios from literature and compared with manually developed rules yielding very promising results also interesting for Simulation Optimization in general.
03 Dec 2010
TL;DR: An optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation, which efficiently completes tasks scheduling in the cloud computing environment computing.
Abstract: Scheduling is a very important part of the cloud computing system. This paper introduces an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation. Algorithm considers the QOS requirements of different type tasks, the QOS parameters are dealt with dimensionless. The algorithm efficiently completes tasks scheduling in the cloud computing environment computing.
TL;DR: A list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD), which considers the performance difference in target HCS using variance and significantly surpasses other three well-known algorithms.
TL;DR: A meta-heuristic algorithm based on the genetic algorithm is developed for dynamic scheduling in flexible job shop and it is shown that the proposed algorithm is capable to achieve the optimal solutions for the small size problems and near optimal solution for the medium size problems.
Abstract: Scheduling for the flexible job shop is very important in the fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problems with traditional optimization approaches owing to the high computational complexity. In this paper, dynamic scheduling in flexible job shop is considered. The dynamic status intensifies the complexity of this problem. Nevertheless, there are many industries which have a dynamic status. Two objectives are considered to make a balance between efficiency and stability of the schedules. A multi-objective mathematical model for the considered problem is developed. Since the problem is well known as NP-hard, a meta-heuristic algorithm based on the genetic algorithm is developed. Numerical experiments are used to evaluate the performance and efficiency of the proposed algorithm. The experimental results show that the proposed algorithm is capable to achieve the optimal solutions for the small size problems and near optimal solutions for the medium size problems.
TL;DR: A novel competitive co-evolutionary quantum genetic algorithm that could not only adjust population size dynamically to increase the diversity of genes and avoid premature convergence, but also accelerate the convergence speed with Q-bit representation and quantum rotation gate is proposed.
TL;DR: The computational results show that the proposed EM for scheduling the flow shop problem that minimizes the makespan and total weighted tardiness and considers transportation times between machines and stage skipping outperforms SA and other foregoing heuristics applied to this paper.
Abstract: This paper presents an efficient meta-heuristic algorithm based on electromagnetism-like mechanism (EM), in which has been successfully implemented in a few combinatorial problems. We propose the EM for scheduling the flow shop problem that minimizes the makespan and total weighted tardiness and considers transportation times between machines and stage skipping (i.e., some jobs may not need to be processed on all the machines). To show the efficiency of this proposed algorithm, we also apply simulated annealing (SA) and some other well-recognized constructive heuristics, such as SPT, NEH, (g/2, g/2) Johnson' rule, EWDD, SLACK, and NEH_EWDD for the given problems. To evaluate the performance and robustness of our proposed EM, we experiment a number of test problems. Our computational results show that our proposed EM in almost all cases outperforms SA and other foregoing heuristics applied to this paper.
Patent•
08 Jun 2010TL;DR: Technician control as mentioned in this paper is a control system that is configured to control scheduling and dispatch operations for work orders being handled by technicians, in which a set of scheduling configuration options may be pre-defined and user input weighting at least one of the scheduling configuration option relative to other scheduling option may be received, and a scheduling application used by the control system to perform scheduling operations may be configured based on configuration data that reflects the weighting and scheduling operation may be performed using the configured scheduling application.
Abstract: Technician control, in which a control system is configured to control scheduling and dispatch operations for work orders being handled by technicians. Multiple technician devices are each associated with one or more technicians, are configured to communicate, over a network, with the control system, and also are configured to provide output in response to communications that are received from the control system and that are related to the scheduling and dispatch operations performed by the control system. A set of scheduling configuration options may be pre-defined and user input weighting at least one of the scheduling configuration options relative to other of the scheduling configuration options may be received. A scheduling application used by the control system to perform scheduling operations may be configured based on configuration data that reflects the weighting and scheduling operations may be performed using the configured scheduling application.
TL;DR: The results show that the continuous time scheduling model can be successfully applied for the electrical load tracking scheduling of a steel plant and the plant owner can potentially minimize over- and underconsumption and thereby reduce his costs.
TL;DR: To deal with this variant FJSP problem with maintenance activities, a filtered beam search (FBS) based heuristic algorithm is proposed with a modified branching scheme and the machine availability constraint and maintenance resource constraint can be easily incorporated into the proposed algorithm.
TL;DR: This work considers a two-stage flow shop scheduling problem where the first machine is a batching machine subject to the blocking constraint and the secondmachine is a discrete machine with shared setup times and presents a quadratic mixed integer program and an approximation algorithm.
TL;DR: Two advanced algorithms are proposed that specifically deal with the flexible and setup characteristics of the hybrid flexible flowshop, namely a dynamic dispatching rule heuristic and an iterated local search metaheuristic.