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Review on use of Swarm Intelligence Meta heuristics in Scheduling of FMS  [PDF]
Hamesh babu Nanvala,,Gajanan. K. Awari
International Journal of Engineering and Technology , 2011,
Abstract: Due to the high complexity of Flexible Manufacturing Systems(FMS) scheduling problem, approaches that guarantee to find the optimal solution are feasible only for small size instance of the problemswith lot of computational effort and time. In contrast, approaches based on meta heuristics are capable of finding good and “near to optimal” solutions to problem instances of realistic size, in a generally smaller computation time. This work provided a review on the use of swarm intelligence meta heuristics to the scheduling of flexible manufacturing problem. The two main areas of swarm intelligence that are prominently appeared in the literature relevant to this problems are ant colony optimization (ACO) and particle swarmoptimization (PSO). By reviewing the literature related to use of swarm intelligence meta heuristics to FMS scheduling problem, and commented on the basis of the review.
Intelligent Search Heuristics for Cost Based Scheduling  [PDF]
Murphy Choy,Michelle Cheong
Mathematics , 2012,
Abstract: Nurse scheduling is a difficult optimization problem with multiple constraints. There is extensive research in the literature solving the problem using meta-heuristics approaches. In this paper, we will investigate an intelligent search heuristics that handles cost based scheduling problem. The heuristics demonstrated superior performances compared to the original algorithms used to solve the problems described in Li et. Al. (2003) and Ozkarahan (1989) in terms of time needed to establish a feasible solution. Both problems can be formulated as a cost problem. The search heuristic consists of several phrases of search and input based on the cost of each assignment and how the assignment will interact with the cost of the resources.
Wahyudin P. Syam,Ibrahim M. Al-Harkan
International Journal of Engineering Science and Technology , 2012,
Abstract: This study improved and comprehensively compared three Meta heuristics to minimize make-span (Cmax) for Flexible Flow-Shop (FFC) Scheduling Problem. This problem is known to be NP-Hard. This study proposed an improvement for three Meta heuristic searches which are Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). SA and TS are known as deterministic improvement heuristic search. Meanwhile, GA is known as stochastic improvement heuristic search. In addition, in this paper, the three Meta heuristic searches were compared to the GA developed by Kahraman et al. by using a computational analysis. The results for the experiments conducted show that the improved Meta heuristics are better and the TS is the most effective and efficient algorithm to solve FFC scheduling problems.
Trust Based Meta-Heuristics Workflow Scheduling in Cloud Service Environment  [PDF]
G. Jeeva Rathanam, A. Rajaram
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.74044
Abstract: Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. As an effective security services aggregation methodology, Trust Work-flow Technology (TWT) has been used to construct composite services. However, in cloud environment, the existing closed network services are maintained and functioned by third-party organizations or enterprises. Therefore service-oriented trust strategies must be considered in workflow scheduling. TWFS related algorithms consist of trust policies and strategies to overcome the threats of the application with heuristic workflow scheduling. As a significance of this work, trust based Meta heuristic workflow scheduling (TMWS) is proposed. The TMWS algorithm will improve the efficiency and reliability of the operation in the cloud system and the results show that the TMWS approach is effective and feasible.
Solving the Flexible Job-Shop Scheduling Problem by a Genetic Algorithm  [PDF]
M. Zandieh,I. Mahdavi,A. Bagheri
Journal of Applied Sciences , 2008,
Abstract: A meta-heuristic approach for solving the flexible job-shop scheduling problem (FJSP) is presented in this study. This problem consists of two sub-problems, the routing problem and the sequencing problem and is among the hardest combinatorial optimization problems. We propose a Genetic Algorithm (GA) for the FJSP. Our algorithm uses several different rules for generating the initial population and several strategies for producing new population for next generation. Proposed GA is tested on benchmark problems and with due attention to the results of other meta-heuristics in this field, the results of GA show that our algorithm is effective and comparable to the other algorithms.
Artificial Bee colony for resource constrained project scheduling problem  [PDF]
Reza Akbari,Vahid Zeighami,Koorush Ziarati
International Journal of Industrial Engineering Computations , 2011,
Abstract: Solving resource constrained project scheduling problem (RCPSP) has important role in the context of project scheduling. Considering a single objective RCPSP, the goal is to find a schedule that minimizes the makespan. This is NP-hard problem (Blazewicz et al., 1983) and one may use meta-heuristics to obtain a global optimum solution or at least a near-optimal one. Recently, various meta-heuristics such as ACO, PSO, GA, SA etc have been applied on RCPSP. Bee algorithms are among most recently introduced meta-heuristics. This study aims at adapting artificial bee colony as an alternative and efficient optimization strategy for solving RCPSP and investigating its performance on the RCPSP. To evaluate the artificial bee colony, its performance is investigated against other meta-heuristics for solving case studies in the PSPLIB library. Simulation results show that the artificial bee colony presents an efficient way for solving resource constrained project scheduling problem.
Review of Solving Software Project Scheduling Problem with Ant Colony Optimization  [PDF]
International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , 2013,
Abstract: SPSP is a problem of scheduling the task and employee. SPSP is a NP-hard (Non Polynomial) problem. SPSP is a problem which is related to RCPSP problem. For solving such problem number of model has been developed. Number of Meta heuristic algorithm is also applied to solve such problem (e.g. GA). This paper presents the survey of methods and models that are put into the historical context. SPSP split the task and distribute dedication of employee to task nodes. Author proposes an ACO Meta heuristics approach to solve the SPSP problem. Author use ACO for solving such problem hence he called it as an ACS: SPSP. Result of this paper is compared with GA to solve SPSP. The proposed algorithm is very efficient and promising and obtains more accuracy.
A Linear Programming Driven Genetic Algorithm for Meta-Scheduling on Utility Grids  [PDF]
Saurabh Garg,Pramod Konugurthi,Rajkumar Buyya
Computer Science , 2009,
Abstract: The user-level brokers in grids consider individual application QoS requirements and minimize their cost without considering demands from other users. This results in contention for resources and sub-optimal schedules. Meta-scheduling in grids aims to address this scheduling problem, which is NP hard due to its combinatorial nature. Thus, many heuristic-based solutions using Genetic Algorithm (GA) have been proposed, apart from traditional algorithms such as Greedy and FCFS. We propose a Linear Programming/Integer Programming model (LP/IP) for scheduling these applications to multiple resources. We also propose a novel algorithm LPGA (Linear programming driven Genetic Algorithm) which combines the capabilities of LP and GA. The aim of this algorithm is to obtain the best metaschedule for utility grids which minimize combined cost of all users in a coordinated manner. Simulation results show that our proposed integrated algorithm offers the best schedule having the minimum processing cost with negligible time overhead.
A New Approach to Solve Flowshop Scheduling Problems by Artificial Immune Systems = Ak Tipi izelgeleme Problemlerinin Yapay Ba kl k Sistemleri ile zümünde Yeni Bir Yakla m  [cached]
Orhan ENG?N,Alper D?YEN
Dogus University Journal , 2007,
Abstract: The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing.
A Virtual Deadline Guided Min-Min Meta-task Scheduling Heuristic

YANG Jiang-Hu,GAO Chuan-Shan,HUANG Chang-Lai,LI Ming,

计算机科学 , 2006,
Abstract: In Grid environment which is a Heterogeneous Computing(HC) environment, resource status and user behavior are very complicated, so scheduling heuristics for meta-task are more complicated than traditional parallel scheduling heuristics. How to map a set of tasks on a set of machines is known to be NP-hard. The goal of those scheduling heuristics is minimize the makespan of the meta-tasks. Some heuristics are introduced to solve such scheduling problem, including Min-Min and other heuristics. In this paper, based on traditional Min-Min scheduling heuristic, a virtual deadline guided meta-task scheduling heuristic is proposed. The simulation results show that the proposed heuristic has less makespan than traditional Min-Min scheduling heuristic.
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