M-machine Flow Shop Scheduling Problem Research Articles

Robust and Stable Flow Shop Scheduling Problem under Uncertain Processing Times and Machines’ Disruption

This paper presents a predictive robust and stable approach for a two-machine flow shop scheduling problem with machine disruption and uncertain job processing time. Indeed, a general approach is proposed that can be used for robustness and stability optimization in an m-machine flow shop or job shop scheduling problem. The robustness measure is the total expected realized completion time. The expected sum of squared aberration between each jobs’ completion time in the realized and initial schedules is the stability measure. We proposed and compared two methods to deal with such an NP-hard problem; a method based on decomposing the problem into sub-problem and solving each sub-problem, and a theorem-based method. The extensive computational results indicated that the second method has a better performance in terms of robustness and stability, especially in large-sized problems. In other words, the second method is preferable because of the better manufacturer responsiveness to the customer and the production staff satisfaction enhancement.

A better heuristic approach for n-job m-machine flow shop scheduling problem

Scheduling problem has its origin in manufacturing industry.In this paper we describe a simple approach for solving the flow shop scheduling problem. The result we obtained has been compared with Palmers Heuristic and CDS algorithms along with NEH. It was found that our method will reach near optimum solution within few steps compared to CDS and NEHalgorithm and yield better result compared to Palmers Heuristic with objective of minimizing the Makespan for the horizontal rectangular matrix problems.

Special Case Algorithm for N-jobs M-machines Flow Shop Scheduling Problems

In this paper we have developed an algorithm for n-jobs with m-machine flow shop scheduling problems in order to handle variety of data type than Geleta [3]. In other hands, as in Geleta [3] it main objective is how to obtain optimal sequence of jobs with aim of minimum squared value of lateness.

New Algorithm for N-jobs on M-machine Flow Shop Scheduling Problems

Because of flow shop scheduling is one of the most important problems in the area of production management, In this paper, what I have did is that, I have developed a new algorithm for n-jobs m-machine flow shop scheduling problem for special case of n-jobs m-machine flow shop scheduling problem. And also, there is a good self explanatory example to explain the algorithm very well.

A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

In this paper, we consider m-machine flow shop scheduling problems (FSSPs). The objective is to schedule the available n jobs to minimise the total flow time. The flow shop scheduling problems have been proved to be strongly non-deterministic polynomial-time hard (NP-hard). Hence, exact methods cannot be used to solve even small size problems. Researchers addressed many heuristics and meta-heuristics to solve the problems. Firefly algorithm (FA) is one of the recently developed meta-heuristic algorithms. We propose a hybrid discrete firefly algorithm (HDFA) to solve the FSSPs to minimise the total flow time. To validate the performances of the HDFA, computational experiments are conducted on a number of randomly generated test problems with different parameters and the results proved the effectiveness of the proposed algorithm.

A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

In this paper, we consider m-machine flow shop scheduling problems (FSSPs). The objective is to schedule the available n jobs to minimise the total flow time. The flow shop scheduling problems have been proved to be strongly non-deterministic polynomial-time hard (NP-hard). Hence, exact methods cannot be used to solve even small size problems. Researchers addressed many heuristics and meta-heuristics to solve the problems. Firefly algorithm (FA) is one of the recently developed meta-heuristic algorithms. We propose a hybrid discrete firefly algorithm (HDFA) to solve the FSSPs to minimise the total flow time. To validate the performances of the HDFA, computational experiments are conducted on a number of randomly generated test problems with different parameters and the results proved the effectiveness of the proposed algorithm.

Min-Max Regret Version of an m-Machine Ordered Flow Shop with Uncertain Processing Times

We consider an m-machine flow shop scheduling problem to minimize the latest completion time, where processing times are uncertain. Processing time uncertainty is described through a finite set of processing time vectors. The objective is to minimize maximum deviation from optimality for all scenarios. Since this problem is known to be NPhard, we consider it with an ordered property. We discuss optimality properties and develop a pseudo-polynomial time approach for the problem with a fixed number of machines and scenarios. Furthermore, we find two special structures for processing time uncertainty that keep the problem NP-hard, even for two machines and two scenarios. Finally, we investigate a special structure for uncertain processing times that makes the problem polynomially solvable.

Matheuristic algorithms for minimizing total tardiness in the m-machine flow-shop scheduling problem

We consider in this paper the m-machine permutation flow-shop problem with total tardiness minimization. We propose several matheuristic algorithms, which are an hybridization of a local search and an exact resolution method. The matheuristics are compared to a genetic algorithm. Computational experiments are performed on benchmark instances and the results show the good performances of the matheuristic algorithms. Finally, some future research directions are given.

한정된 크기의 버퍼가 있는 흐름 공정 일정계획의 스트레치 최소화

In this paper, a hybrid genetic algorithm (HGA) approach is proposed for an n-job, m-machine flow shop scheduling problem with limited capacity buffers with blocking in which the objective is to minimize the total stretch. The stretch of a job is the ratio of the amount of time the job spent before its completion to its processing time. HGA adopts the idea of seed selection and development in order to improve the exploitation and exploration power of genetic algorithms (GAs). Extensive computational experiments have been conducted to compare the performance of HGA with that of GA.

Algoritma Differential Evolution Untuk Penjadwalan Flow Shop Banyak Mesin Dengan Multi Obyektif

This research focuses on the development of Differential Evolution(DE) algorithmto solve m-machine flow shopscheduling problems with respect to both makespan and total flow time. Development of DE algorithm is done by modifyingthe adaptive parameter determination procedure in order to change the value of adaptive parameters in each generation,adding local search strategy to the algorithm in order to improve the quality of the resulting solutions, as ewell as modifyingthe crossover in order to reduce computation time. The result indicates that the proposed DE algorithm has proven to bebetter than the original DE algorithm, Genetic Algorithm (GA), and for certain cases it also out performs Multi-ObjectiveAnt Colony System Algorithm (MOCSA).

Flow shop scheduling to minimize makespan with decreasing time-dependent job processing times

In this study, we consider an n-job, m-machine flow shop scheduling problem with decreasing time-dependent job processing times. By the decreasing time-dependent job processing times, we mean that the processing time is a decreasing function of its execution starting time. When some dominant relationships between m − 1 machines can be satisfied, we show that the makespan minimization problem can be solved in polynomial time.

Note on “Single-machine and flowshop scheduling with a general learning effect model” and “Some single-machine and m-machine flowshop scheduling problems with learning considerations”

In this note, we show that the main results in the two papers [C.C. Wu, W.C. Lee, Single-machine and flowshop scheduling with a general learning effect model, Computers and Industrial Engineering 56 (2009) 1553–1558, W.C. Lee, C.C. Wu, Some single-machine and m-machine flowshop scheduling problems with learning considerations, Information Sciences 179 (2009) 3885–3892] are incorrect.

Flow shop scheduling to minimize the total completion time with a permanently present operator: Models and ant colony optimization metaheuristic

This paper studies the one-operator m-machine flow shop scheduling problem with the objective of minimizing the total completion time. In this problem, the processing of jobs and setup of machines require the continuous presence of a single operator. We compare three different mathematical formulations and propose an ant colony optimization based metaheuristic to solve this flow shop scheduling problem. A series of experiments are carried out to compare the properties of three formulations and to investigate the performance of the proposed ant colony optimization metaheuristic. The computational results show that (1) an assignment-based formulation performs best, and (2) the ant colony optimization based metaheuristic is a computationally efficient algorithm.

Flow shop scheduling with deteriorating jobs under dominating machines to minimize makespan

In this paper, we consider an n-job, m-machine flow shop scheduling problem with deteriorating jobs. By deteriorating jobs, we mean jobs whose processing times are an increasing function of their execution starting time. A simple linear deterioration function is assumed. When some dominant relationships between m − 1 machines can be satisfied, we show that the makespan minimization problem can be solved in polynomial time.

Some single-machine and m-machine flowshop scheduling problems with learning considerations

Scheduling with learning effect has drawn many researchers’ attention since Biskup [D. Biskup, Single-machine scheduling with learning considerations, European Journal of Opterational Research 115 (1999) 173–178] introduced the concept of learning into the scheduling field. Biskup [D. Biskup, A state-of-the-art review on scheduling with learning effect, European Journal of Opterational Research 188 (2008) 315–329] classified the learning approaches in the literature into two main streams. He claimed that the position-based learning seems to be a realistic model for machine learning, while the sum-of-processing-time-based learning is a model for human learning. In some realistic situations, both the machine and human learning might exist simultaneously. For example, robots with neural networks are used in computers, motor vehicles, and many assembly lines. The actions of a robot are constantly modified through self-learning in processing the jobs. On the other hand, the operators in the control center learn how to give the commands efficiently through working experience. In this paper, we propose a new learning model that unifies the two main approaches. We show that some single-machine problems and some specified flowshop problems are polynomially solvable.

7102 List-based Squeezing Branch and Bound法におけるノード選択方法の改良(OS7 学生研究課題発表)

The "list-based squeezing branch and bound (LSQ) algorithm" is a kind of local search algorithms, which searches neighborhood of an initial schedule in an enumerative manner by using a branching procedure in a branch and bound algorithm (B & B) in parallel. In the LSQ, some initial schedules are found first by using promising heuristic methods, and then a B & B-based parallel local search is implemented for obtaining an optimal (or a near-optimal) schedule. In this paper, the LSQ is applied to n-job, m-machine flowshop scheduling problems to minimize makespan. A new procedure for selecting promising nodes to be branched is proposed and incorporated into the searching process of the LSQ to improve the efficiency and accuracy. Numerical experiments are implemented through benchmark problems to demonstrate that the proposed method can efficiently obtain a near optimal schedule with high accuracy.

The two- and m-machine flowshop scheduling problems with bicriteria of makespan and mean flowtime

This paper considers the flowshop scheduling problem with the objective of minimizing the weighted sum of makespan and mean flowtime. Even though the two-machine problem has been addressed and several heuristics have been established in the literature, these heuristics have not been compared. In this paper, a comparison of these available heuristics in the literature is conducted. Moreover, three new heuristic algorithms are proposed, which can be utilized for both the two-machine and m-machine problems. Computational experiments indicate that the proposed heuristics are superior to all the existing heuristics in the literature including a genetic algorithm. Two dominance relations are also developed; one for the two-machine and the other for the three-machine problem. Experimental results show that both relations are efficient.

Permutation vs. non-permutation flow shop schedules

In studying the m-machine flow shop scheduling problem, it is common practice to focus attention on permutation schedules. We show that for the problem of minimizing the maximum completion time, this assumption can be a costly one, by exhibiting a family of instances for which the value of the best permutation schedule is worse than that of the true optimal schedule by a factor of more than 1 2 √m .

Decomposition approaches in permutation scheduling problems with application to the M-machine flow shop scheduling problems

In this article, we consider three decomposition techniques for permutation scheduling problems. We introduce a general iterative decomposition algorithm for permutation scheduling problems and apply it to the permutation flow shop scheduling problem. We also develop bounds needed for this iterative decomposition approach and compare its computational requirements to that of the traditional branch and bound algorithms. Two heuristic algorithms based on the iterative decomposition approach are also developed. extensive numerical study indicates that the heuristic algorithms are practical alternatives to very costly exact algorithms for large flow shop scheduling problems.

Flow-shop scheduling by heuristic decomposition

SUMMARY This paper investigates the classical n-job, M-machine flow-shop scheduling problem under the assumption that jobs are processed on all machines in the same order. Baaed on the heuristic job-pairing technique and the decomposition strategy, a heuristic decomposition algorithm is developed which will generate at least a near-optimal schedule for the flow-shop scheduling problem. The proposed algorithm is compared to the existing decomposition approach and is found to be superior to Ashour's decomposition algorithm, both in increased solution quality and decreased computational time required to solve the problem.