Showing papers on "Flow shop scheduling published in 2009"

A survey of dynamic scheduling in manufacturing systems

Abstract: In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling. The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.

Scheduling for Parallel Processing

Abstract: This book presents scheduling models for parallel processing, problems defined on the grounds of certain scheduling models, and algorithms solving the scheduling problems. The book also provides helpful generalizations about scheduling models. Features: Introduces the fundamental scheduling concepts; Discusses the technological aspects of scheduling for parallel processing; Presents the notions, concepts, and algorithms that are most immediately applicable in parallel processing; Examines the parallel task model; Outlines the methodology of computational complexity theory and introduces the basic metrics of parallel application performance; Explores scheduling with communication delays; Examines scheduling divisible loads in systems with limited memory, various interconnection types, and cost of usage; Includes detailed illustrations, a bibliography, and a notation section. This text will be valuable for researchers in parallel computing, operating systems, management science, and applied mathematics.

Multistage-Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem

Abstract: Flexible job shop scheduling problem (fJSP) is an extension of the traditional job shop scheduling problem (JSP), which provides a closer approximation to real scheduling problems. In this paper, a multistage-based genetic algorithm with bottleneck shifting is developed for the fJSP problem. The genetic algorithm uses two vectors to represent each solution candidate of the fJSP problem. Phenotype-based crossover and mutation operators are proposed to adapt to the special chromosome structures and the characteristics of the problem. The bottleneck shifting works over two kinds of effective neighborhood, which use interchange of operation sequences and assignment of new machines for operations on the critical path. In order to strengthen the search ability, the neighborhood structure can be adjusted dynamically in the local search procedure. The performance of the proposed method is validated by numerical experiments on three representative problems.

New high performing heuristics for minimizing makespan in permutation flowshops

Abstract: The well-known NEH heuristic from Nawaz, Enscore and Ham proposed in 1983 has been recognized as the highest performing method for the permutation flowshop scheduling problem under the makespan minimization criterion. This performance lead is maintained even today when compared against contemporary and more complex heuristics as shown in recent studies. In this paper we show five new methods that outperform NEH as supported by careful statistical analyses using the well-known instances of Taillard. The proposed methods try to counter the excessive greediness of NEH by carrying out re-insertions of already inserted jobs at some points in the construction of the solution. The five proposed heuristics range from extensions that are slightly slower than NEH in most tested instances to more comprehensive methods based on local search that yield excellent results at the expense of some added computational time. Additionally, NEH has been profusely used in the flowshop scheduling literature as a seed sequence in high performing metaheuristics. We demonstrate that using some of our proposed heuristics as seeds yields better final results in comparison.

Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research

Abstract: Scheduling is a decision-making process that is concerned with the allocation of limited resources to competing tasks (operations of jobs) over a time period with the goal of optimising one or more objectives. In theory, the objective is usually to optimise some classical system performance measures such as makespan, tardiness/earliness and flowtime under deterministic and static assumptions. In practice, however, scheduling systems operate in dynamic and stochastic environments. Hence, there is a need to incorporate both uncertainty and dynamic elements into the scheduling process. In this paper, the major issues involved in scheduling decisions are discussed and the basic approaches to tackle these problems in manufacturing environments are analysed. Proactive scheduling is then focused on and several robustness and stability measures are presented. Previous research on scheduling robustness and stability is also reviewed and further research directions are suggested.

Memetic algorithms for solving job-shop scheduling problems

Abstract: The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. In this paper, we developed a memetic algorithm (MA) for solving JSSPs. Three priority rules were designed, namely partial re-ordering, gap reduction and restricted swapping, and used as local search techniques in our MA. We have solved 40 benchmark problems and compared the results obtained with a number of established algorithms in the literature. The experimental results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time.

Experience-Based Approach to Scheduling Problems With the Learning Effect

Abstract: The existence of the learning effect in many manufacturing systems is undoubted; thus, it is worthwhile that it be taken into consideration during production planning to increase production efficiency. Generally, it can be done by formulating the specified problem in the scheduling context and optimizing an order of jobs to minimize the given time criteria. To carry out a reliable study of the learning effect in scheduling fields, a comprehensive survey of the related results is presented first. It reveals that most of the learning models in scheduling are based on the learning curve introduced by Wright. However, further study about learning itself pointed out that the curve may be an ldquoSrdquo-shaped function, which has not been considered in the scheduling domain. To fill this gap, we analyze a scheduling problem with a new experience-based learning model, where job processing times are described by ldquoSrdquo-shaped functions that are dependent on the experience of the processor. Moreover, problems with other experience-based learning models are also taken into consideration. We prove that the makespan minimization problem on a single processor is NP-hard or strongly NP-hard with the most of the considered learning models. A number of polynomially solvable cases are also provided.

Job-shop Scheduling Problem.

Abstract: A sterile intra-circulatory monitoring system utilizing an indwelling catheter with fluid under pressure being forced through a fluid line to the catheter. The system includes a normally stagnant pressure chamber to which a transducer is connected. The contents of the chamber are periodically flushed back into the fluid line by utilizing a flush valve and a venturi unit. The venturi unit may be an integral part of the flush valve or separate component. With the present invention a closed loop system is utilized which does not compromise the sterility of the system or the environment.

Multi-objective production scheduling: a survey

Abstract: The real life scheduling problems often have several conflicting objectives. The solutions of these problems can provide deeper insights to the decision maker than those of single-objective problems. However, the literature of multi-objective scheduling is notably sparser than that of single-objective scheduling. Since the survey paper on multi-objective and bi-objective scheduling was conducted by Nagar et al. in 1995, there has been an increasing interest in multi-objective production scheduling, especially in multi-objective deterministic problem. The goal of this paper was to provide an extensive review of the literature on the scheduling problems with multiple objectives in the past 13 years. This paper also presents some problems receiving less attention than the others.

Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling

Abstract: Flexible job shop schedule is very important in both fields of combinatorial optimization and production management. In this paper, a simulation model is presented to solve the multi-objective flexible job shop scheduling problem. The proposed model has been coded by Matlab which is a special mathematical computation language. After modeling the pending problem, the model is validated by five representative instances based on practical data. The results obtained from the computational study have shown that the proposed approach is a feasible and effective approach for the multi-objective flexible job shop scheduling problem.

RASA-A New Grid Task Scheduling Algorithm.

Abstract: In this paper, a new task scheduling algorithm called RASA, considering the distribution and scalability characteristics of grid resources, is proposed. The algorithm is built through a comprehensive study and analysis of two well known task scheduling algorithms, Min-min and Max-min. RASA takes advantages of the both algorithms and avoids their drawbacks. To achieve this, RASA firstly estimates the completion time of the tasks on each of the available grid resources, and then applies the Maxmin and Min-min algorithms, alternatively. In this respect, RASA uses the Min-min strategy to execute small tasks before the large ones, and applies the Maxmin strategy to avoid delays in the execution of the large tasks and to support concurrency in the execution of the large and small tasks. Our experimental results of applying RASA on scheduling independent tasks within grid environments demonstrate the applicability of RASA in achieving schedules with comparatively lower makespan.

Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Abstract: Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.

An efficient search method for multi-objective flexible job shop scheduling problems

Abstract: Flexible job shop scheduling is very important in both fields of production management and combinatorial optimization. Owing to the high computational complexity, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches. Motivated by some empirical knowledge, we propose an efficient search method for the multi-objective flexible job shop scheduling problems in this paper. Through the work presented in this work, we hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The final experimental results have shown that the proposed algorithm is a feasible and effective approach for the multi-objective flexible job shop scheduling problems.

Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop

Abstract: Much of the research on operations scheduling problems has ignored dynamic events in real-world environments where there are complex constraints and a variety of unexpected disruptions. Besides, while most scheduling problems which have been discussed in the literature assume that machines are incessantly available, in most real life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to a scheduled preventive maintenance where the periods of unavailability are determined in advance (deterministic unavailability). This paper describes how we can integrate simulation into genetic algorithm to the dynamic scheduling of a flexible job shop with machines that suffer stochastic breakdowns. The objectives are the minimization of two criteria, expected makespan and expected mean tardiness. An overview of the flexible job shops and scheduling under the stochastic unavailability of machines are presented. Subsequently, the details of integrating simulation into genetic algorithm are described and implemented. Consequently, problems of various sizes are used to test the performance of the proposed algorithm. The results obtained reveal that the relative performance of the algorithm for both abovementioned objectives can be affected by changing the levels of the breakdown parameters.

Efficient composite heuristics for total flowtime minimization in permutation flow shops

Abstract: In this paper, permutation flow shops with total flowtime minimization are considered. General flowtime computing (GFC) is presented to accelerate flowtime computation. A newly generated schedule is divided into an unchanged subsequence and a changed part. GFC computes total flowtime of a schedule by inheriting temporal parameters from its parent in the unchanged part and computes only those of the changed part. Iterative methods and LR (developed by Liu J, Reeves, CR. Constructive and composite heuristic solutions to the P ∥ Σ C i scheduling problem, European Journal of Operational Research 2001; 132:439–52) are evaluated and compared as solution improvement phase and index development phase. Three composite heuristics are proposed in this paper by integrating forward pair-wise exchange-restart (FPE-R) and FPE with an effective iterative method. Computational results show that the proposed three outperform the best existing three composite heuristics in effectiveness and two of them are much faster than the existing ones.