Showing papers on "Flow shop scheduling published in 2011"

A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations

Abstract: In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.

An effective genetic algorithm for the flexible job-shop scheduling problem

Abstract: In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.

Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems

Abstract: This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

Scheduling in mapreduce-like systems for fast completion time

Abstract: Large-scale data processing needs of enterprises today are primarily met with distributed and parallel computing in data centers. MapReduce has emerged as an important programming model for these environments. Since today's data centers run many MapReduce jobs in parallel, it is important to find a good scheduling algorithm that can optimize the completion times of these jobs. While several recent papers focused on optimizing the scheduler, there exists very little theoretical understanding of the scheduling problem in the context of MapReduce. In this paper, we seek to address this problem by first presenting a simplified abstraction of the MapReduce scheduling problem, and then formulate the scheduling problem as an optimization problem.We devise various online and offline algorithms to arrive at a good ordering of jobs to minimize the overall job completion times. Since optimal solutions are hard to compute (NP-hard), we propose approximation algorithms that work within a factor of 3 of the optimal. Using simulations, we also compare our online algorithm with standard scheduling strategies such as FIFO, Shortest Job First and show that our algorithm consistently outperforms these across different job distributions.

HyFlex: A Benchmark Framework for Cross-domain Heuristic Search

Abstract: Automating the design of heuristic search methods is an active research field within computer science, artificial intelligence and operational research. In order to make these methods more generally applicable, it is important to eliminate or reduce the role of the human expert in the process of designing an effective methodology to solve a given computational search problem. Researchers developing such methodologies are often constrained on the number of problem domains on which to test their adaptive, self-configuring algorithms; which can be explained by the inherent difficulty of implementing their corresponding domain specific software components. This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems, and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge the problem domains, and thus can concentrate his/her efforts in designing adaptive general-purpose heuristic search algorithms. Four hard combinatorial problems are fully implemented (maximum satisfiability, one dimensional bin packing, permutation flow shop and personnel scheduling), each containing a varied set of instance data (including real-world industrial applications) and an extensive set of problem specific heuristics and search operators. The framework forms the basis for the first International Cross-domain Heuristic Search Challenge (CHeSC), and it is currently in use by the international research community. In summary, HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed, and reliably compared.

A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem.

Abstract: A novel hybrid tabu search algorithm with a fast public critical block neighborhood structure (TSPCB) is proposed in this paper to solve the flexible job shop scheduling problem with the criterion to minimize the maximum completion time (makespan). First, a mix of four machine assignment rules and four operation scheduling rules is developed to improve the quality of initial solutions to empower the hybrid algorithm with good exploration capability. Second, an effective neighborhood structure to conduct local search in the machine assignment module is proposed, which integrates three adaptive approaches. Third, a speedup local search method with three kinds of insert and swap neighborhood structures based on public critical block theory is presented. With the fast neighborhood structure, the TSPCB algorithm can enhance its exploitation capability. Simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed TSPCB algorithm is superior to several recently published algorithms in terms of solution quality, convergence ability, and efficiency.

A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem

Abstract: This paper deals with the two-stage assembly flowshop scheduling problem with minimisation of weighted sum of makespan and mean completion time as the objective. The problem is NP-hard, hence we proposed a meta-heuristic named imperialist competitive algorithm (ICA) to solve it. Since appropriate design of the parameters has a significant impact on the algorithm efficiency, we calibrate the parameters of this algorithm using the Taguchi method. In comparison with the best algorithm proposed previously, the ICA indicates an improvement. The results have been confirmed statistically.

An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems

Abstract: The hybrid flow shop scheduling with multiprocessor task (HFSMT) problem is a substantial production scheduling problem for minimizing the makespan, and there exist many difficulties in solving large scale HFSMT problems which include many jobs, machines and tasks. The HFSMT problems known as NP-hard and the proposal of an efficient genetic algorithm (GA) were taken into consideration in this study. The numerical results prove that the computational performance of a GA depends on the factors of initial solution, reproduction, crossover, and mutation operators and probabilities. The implementation details, including a new mutation operator, were described and a full factorial experimental design was determined with our GA program by using the best values of the control parameters and the operators. After a comparison was made with the studies of Oguz [1], Oguz and Ercan [2] and Kahraman et al. [3] related to the HFSMT problems, the computational results indicated that the proposed genetic algorithm approach is very effective in terms of reduced total completion time or makespan (C"m"a"x) for the attempted problems.

On scheduling in map-reduce and flow-shops

Abstract: The map-reduce paradigm is now standard in industry and academia for processing large-scale data. In this work, we formalize job scheduling in map-reduce as a novel generalization of the two-stage classical flexible flow shop (FFS) problem: instead of a single task at each stage, a job now consists of a set of tasks per stage. For this generalization, we consider the problem of minimizing the total flowtime and give an efficient 12-approximation in the offline setting and an online (1+µ)-speed O(1/µ2)-competitive algorithm.Motivated by map-reduce, we revisit the two-stage flow shop problem, where we give a dynamic program for minimizing the total flowtime when all jobs arrive at the same time. If there are fixed number of job-types the dynamic program yields a PTAS; it is also a QPTAS when the processing times of jobs are polynomially bounded. This gives the first improvement in approximation of flowtime for the two-stage flow shop problem since the trivial 2-approximation algorithm of Gonzalez and Sahni [29] in 1978, and the first known approximation for the FFS problem. We then consider the generalization of the two-stage FFS problem to the unrelated machines case, where we give an offline 6-approximation and an online (1+µ)-speed O(1/µ4)-competitive algorithm.

Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach

Abstract: This paper studies a flexible job shop problem considering dynamic events such as stochastic job arrivals, uncertain processing times, and unexpected machine breakdowns. Also, the considered job shop problem has routing flexibility and process flexibility. A multi-agent scheduling system has been developed for solution with good quality and robustness. A pheromone-based approach is proposed for coordination among agents. The proposed multi-agent approach is compared with five dispatching rules from literature via simulation experiments to statistical analysis. The simulation experiments are performed under various experimental settings such as shop utilization level, due date tightness, breakdown level, and mean time to repair. The results show that the proposed agent-based approach performs well under all problem settings.

A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem

Abstract: Distributed Permutation Flowshop Scheduling Problem (DPFSP) is a newly proposed scheduling problem, which is a generalization of classical permutation flow shop scheduling problem. The DPFSP is NP-hard in general. It is in the early stages of studies on algorithms for solving this problem. In this paper, we propose a GA-based algorithm, denoted by GA_LS, for solving this problem with objective to minimize the maximum completion time. In the proposed GA_LS, crossover and mutation operators are designed to make it suitable for the representation of DPFSP solutions, where the set of partial job sequences is employed. Furthermore, GA_LS utilizes an efficient local search method to explore neighboring solutions. The local search method uses three proposed rules that move jobs within a factory or between two factories. Intensive experiments on the benchmark instances, extended from Taillard instances, are carried out. The results indicate that the proposed hybrid genetic algorithm can obtain better solutions th...

Non-identical parallel machine scheduling using genetic algorithm

Abstract: Most of the scheduling problems are NP-hard. In the literature, several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems and genetic algorithm (GA) ranks among the most preferred ones in view of its characteristics such as high adaptability, near optimization and easy realization. But, even though it is a common problem in the industry, only a small number of studies deal with non-identical parallel machines. In this paper, the authors propose a new "crossover operator" and a new "optimality criterion" in order to adapt the GA to non-identical parallel machine scheduling problem. New algorithm is tested on a numerical example by implementing it in a simulation software and computational results are compared to those obtained with LPT (Longest Processing Time) dispatching rule; results were promising. Findings show that, in addition to its high computational speed for larger scale problem, the GA proposed here fits the non-identical parallel machine scheduling problem of minimizing the maximum completion time (makespan).

A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem

Abstract: In this paper, a local-best harmony search (HS) algorithm with dynamic sub-harmony memories (HM), namely DLHS algorithm, is proposed to minimize the total weighted earliness and tardiness penalties for a lot-streaming flow shop scheduling problem with equal-size sub-lots. First of all, to make the HS algorithm suitable for solving the problem considered, a rank-of-value (ROV) rule is applied to convert the continuous harmony vectors to discrete job sequences, and a net benefit of movement (NBM) heuristic is utilized to yield the optimal sub-lot allocations for the obtained job sequences. Secondly, an efficient initialization scheme based on the NEH variants is presented to construct an initial HM with certain quality and diversity. Thirdly, during the evolution process, the HM is dynamically divided into many small-sized sub-HMs which evolve independently so as to balance the fast convergence and large diversity. Fourthly, a new improvisation scheme is developed to well inherit good structures from the local-best harmony vector in the sub-HM. Meanwhile, a chaotic sequence to produce decision variables for harmony vectors and a mutation scheme are utilized to enhance the diversity of the HM. In addition, a simple but effective local search approach is presented and embedded in the DLHS algorithm to enhance the local searching ability. Computational experiments and comparisons show that the proposed DLHS algorithm generates better or competitive results than the existing hybrid genetic algorithm (HGA) and hybrid discrete particle swarm optimization (HDPSO) for the lot-streaming flow shop scheduling problem with total weighted earliness and tardiness criterion.

Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects

Abstract: Since multi-objective flow shop scheduling problem (MFSP) plays a key role in practical scheduling, there has been an increasing interest in MFSP according to the literature. However, there still have been wide gaps between theories and practical applications, and the review research of multi-objective optimization algorithms in MFSP (objectives > 2) field is relatively scarce. In view of this, this paper provides a comprehensive review of both former and the state-of-the-art approaches on MFSP. Firstly, we introduce a broad description and the complexity of MFSP. Secondly, a taxonomy of multi-objective optimizations and an analysis of the publications on MFSP are presented. It is noteworthy that heuristic and meta-heuristic methods and hybrid procedures are proven much more useful than other methods in large and complex situations. Finally, future research trends and challenges in this field are proposed and analyzed. Our survey shows that algorithms developed for MFSP continues to attract significant research interest from both theoretical and practical perspectives.

An approach for cloud resource scheduling based on Parallel Genetic Algorithm

Abstract: Resource scheduling is a key process for clouds such as Infrastructure as a Service cloud. To make the most efficient use of the resources, we propose an optimized scheduling algorithm to achieve the optimization or sub-optimization for cloud scheduling problems. We investigate the possibility to place the Virtual Machines in a flexible way to improve the speed of finding the best allocation on the premise of permitting the maximum utilization of resources. Mathematically, we consider the scheduling problem come down to an Unbalance Assignment Problem. Our scheduling policy achieved by Parallel Genetic Algorithm which is much faster than traditional Genetic Algorithm. The experiments show that our method improved both the speed of resources allocation and the utilization of system resource.

Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line

Abstract: A reentrant hybrid flow shop, typically found in the electronics industry, is an extended system of the ordinary flow shop in such a way that there exist one or more parallel machines at each serial stage and each job has the reentrant product flow, i.e., a job may visit a stage several times. Among the operational issues in reentrant hybrid flow shops, we focus on the scheduling problem that determines the allocation of jobs to the machines at each stage as well as the sequence of the jobs assigned to each machine. Unlike the theoretical approach on reentrant hybrid flow shop scheduling, we suggest a real-time scheduling mechanism with a decision tree when selecting appropriate dispatching rules. The decision tree, one of the commonly used data mining techniques, is adopted to eliminate the computational burden required to carry out simulation runs to select dispatching rules. To illustrate the mechanism suggested in this study, a case study was performed on a thin film transistor-liquid crystal display (TFT-LCD) manufacturing line and the results are reported for various system performance measures.