An embedded fuzzy expert system for adaptive WFQ scheduling of IEEE 802.16 networks

WiMAX is a futuristic technology which provides simultaneous support for web, video, and voice applications. WiMAX networks are best suitable to real time traffic however the quantity of non real time and best effort traffic cannot be neglected. Distribution of resources in such heterogeneous applications is therefore a challenging task. There are many schedulers available for WiMAX but adaptive and adequate schedulers are still in growing stage of development. This paper introduces a novel method using which a system is developed based on concepts of fuzzy logic to schedule traffic in WiMAX networks. The proposed fuzzy expert system simplifies fair allocation of resources to real as well as non real time traffic. The implementation is based on changing the weights of the queues serving real and non- real time traffic adaptively. New weights will be calculated for each bandwidth request made to base station and these weights will in turn decide amount of bandwidth allocated to different traffic classes. The weights are calculated based on three parameters that are amount of real time and non real time traffic in queues, change in throughput requirement for non real time flows and latency requirement of real time input data. Results obtained by virtue of simulations justify the significance of the proposed method.     

基于Agent的混合流水车间动态调度系统

针对敏捷制造调度环境的不确定性、动态性以及混合流水车间（HFS）调度问题的特点,设计了一种基于多Agent的混合流水车间动态调度系统,系统由管理Agent、策略Agent、工件Agent和机器Agent构成。首先提出一种针对混合流水车间环境的插值排序（HIS）算法并集成于策略Agent中,该算法适用于静态调度和多种动态事件下的动态调度。然后,设计了各类Agent间的协调机制,在生产过程中所有Agent根据各自的行为逻辑独立工作并互相协调。在发生动态事件时,策略Agent调用HIS算法根据当前车间状态产生工件序列,随后各Agent根据生成的序列继续进行协调直到完成生产。最后进行了发生机器故障、订单插入情况下的重调度以及在线调度等动态调度的实例仿真,结果表明对于这些问题,HIS算法的求解效果均优于调度规则,特别是在故障重调度中,HIS算法重调度前后的Makespan一致度达97.6%,说明系统能够灵活和有效地处理混合流水车间动态调度问题。     

A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints

We study a real-world complex hybrid flow-shop scheduling problem arising from a bio-process industry. There are a variety of constraints to be taken into account, in particular zero intermediate capacity and limited waiting time between processing stages. We propose an exact solution approach for this optimization problem, based on a discrete time representation and a mixed-integer linear programming formulation. The proposed solution algorithm makes use of a new family of valid inequalities exploiting the fact that a limited waiting time is imposed on jobs between two successive production stages. The results of our computational experiments confirm that the proposed method produces good feasible schedules for industrial instances.     

Lagrangian relaxation with cut generation for hybrid flowshop scheduling problems to minimize the total weighted tardiness

In this paper, we address a new Lagrangian relaxation (LR) method for solving the hybrid flowshop scheduling problem to minimize the total weighted tardiness. For the conventional LR, the problem relaxing machine capacity constraints can be decomposed into individual job-level subproblems which can be solved by dynamic programming. The Lagrangian dual problem is solved by the subgradient method. In this paper, a Lagrangian relaxation with cut generation is proposed to improve the Lagrangian bounds for the conventional LR. The lower bound is strengthened by imposing additional constraints for the relaxed problem. The state space reductions for dynamic programming for subproblems are also incorporated. Computational results demonstrate that the proposed method outperforms the conventional LR method without significantly increasing the total computing time.     

A hybrid genetic and linear programming algorithm for two-agent order acceptance and scheduling problem

In this paper, the simultaneous order acceptance and scheduling problem is developed by considering the variety of customers’ requests. To that end, two agents with different scheduling criteria including the total weighted lateness for the first and the weighted number of tardy orders for the second agent are considered. The objective is to maximize the sum of the total profit of the first and the total revenue of the second agents’ orders when the weighted number of tardy orders of the second agent is bounded by an upper bound value. In this study, it is shown that this problem is NP-hard in the strong sense, and then to optimally solve it, an integer linear programming model is proposed based on the properties of optimal solution. This model is capable of solving problem instances up to 60 orders in size. Also, the LP-relaxation of this model was used to propose a hybrid meta-heuristic algorithm which was developed by employing genetic algorithm and linear programming. Computational results reveal that the proposed meta-heuristic can achieve near optimal solutions so efficiently that for the instances up to 60 orders in size, the average deviation of the model from the optimal solution is lower than 0.2% and for the instances up to 150 orders in size, the average deviation from the problem upper bound is lower than 1.5%.     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

Multi-criteria and satisfaction oriented scheduling for hybrid distributed computing infrastructures

Assembling and simultaneously using different types of distributed computing infrastructures (DCI) like Grids and Clouds is an increasingly common situation. Because infrastructures are characterized by different attributes such as price, performance, trust, and greenness, the task scheduling problem becomes more complex and challenging. In this paper we present the design for a fault-tolerant and trust-aware scheduler, which allows to execute Bag-of-Tasks applications on elastic and hybrid DCI, following user-defined scheduling strategies. Our approach, named Promethee scheduler, combines a pull-based scheduler with multi-criteria Promethee decision making algorithm. Because multi-criteria scheduling leads to the multiplication of the possible scheduling strategies, we propose SOFT, a methodology that allows to find the optimal scheduling strategies given a set of application requirements. The validation of this method is performed with a simulator that fully implements the Promethee scheduler and recreates an hybrid DCI environment including Internet Desktop Grid, Cloud and Best Effort Grid based on real failure traces. A set of experiments shows that the Promethee scheduler is able to maximize user satisfaction expressed accordingly to three distinct criteria: price, expected completion time and trust, while maximizing the infrastructure useful employment from the resources owner point of view. Finally, we present an optimization which bounds the computation time of the Promethee algorithm, making realistic the possible integration of the scheduler to a wide range of resource management software.     

An algorithm for shift scheduling which considers circadian principles

The principles of circadian rhythms in human beings have been applied to devise a methodology for scheduling shift work in a hospital unit which is operating under several important constraints. Considered here are time off between shifts, number of consecutive work days, number of distinct shifts in any two calendar weeks, number of shifts per week and special staffing for informational and training meetings. It is shown how the method of Czeisler et al. (1982) can be adapted for the more constrained work environment. A set of evaluation scales for circadian theory violations is devised and applied to an example from Togus VA Hospital. Results include the actual schedules together with evaluations on three different aspects of circadian theory: length of cycle on a single shift, rotation involving night shifts and rotation not involving night shifts. Improvements of each of these evaluations with respect to previously published results are obtained.     

Development of hybrid evolutionary algorithms for production scheduling of hot strip mill

A hot strip mill (HSM) produces hot rolled products from steel slabs, and is one of the most important production lines in a steel plant. The aim of HSM scheduling is to construct a rolling sequence that optimizes a set of given criteria under constraints. Due to the complexity in modeling the production process and optimizing the rolling sequence, the HSM scheduling is a challenging task for hot rolling production schedulers. This paper first introduces the HSM production process and requirements, and then reviews previous research on the modeling and optimization of the HSM scheduling problem. According to the practical requirements of hot rolling production, a mathematical model is formulated to describe two important scheduling sub-tasks: (1) selecting a subset of manufacturing orders and (2) generating an optimal rolling sequence from the selected manufacturing orders. Further, hybrid evolutionary algorithms with integration of genetic algorithm (GA) and extremal optimization (EO) are proposed to solve the HSM scheduling problem. Computational results on industrial data show that the proposed HSM scheduling solution can be applied in practice to provide satisfactory performance.     

地质分析专家系统GAES的设计

地质分析是一项复杂的人类劳动,采用人工智能技术模拟人类专家活动,可以大大减轻人类的负担.因此,开发了一个地质分析专家系统GAES.该系统以黑板模型为基础,采用分布式系统结构,在中心节点的协调控制下,各普通节点相互合作,共同进行问题求解.在系统技术特点方面,解决了知识源分配、冲突消解和知识源筛选等问题,该系统具有求解效率高、可靠性强、通讯机制好等特点.     

A new Lagrangian relaxation algorithm for hybrid flowshop scheduling to minimize total weighted completion time

We investigate the problem of scheduling n jobs in s-stage hybrid flowshops with parallel identical machines at each stage. The objective is to find a schedule that minimizes the sum of weighted completion times of the jobs. This problem has been proven to be NP-hard. In this paper, an integer programming formulation is constructed for the problem. A new Lagrangian relaxation algorithm is presented in which precedence constraints are relaxed to the objective function by introducing Lagrangian multipliers, unlike the commonly used method of relaxing capacity constraints. In this way the relaxed problem can be decomposed into machine type subproblems, each of which corresponds to a specific stage. A dynamic programming algorithm is designed for solving parallel identical machine subproblems where jobs may have negative weights. The multipliers are then iteratively updated along a subgradient direction. The new algorithm is computationally compared with the commonly used Lagrangian relaxation algorithms which, after capacity constraints are relaxed, decompose the relaxed problem into job level subproblems and solve the subproblems by using the regular and speed-up dynamic programming algorithms, respectively. Numerical results show that the new Lagrangian relaxation method produces better schedules in much shorter computation time, especially for large-scale problems.     

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Effective task scheduling, which is essential for achieving high performance in a heterogeneous multiprocessor system, remains a challenging problem despite extensive studies. In this article, a heuristic-based hybrid genetic-variable neighborhood search algorithm is proposed for the minimization of makespan in the heterogeneous multiprocessor scheduling problem. The proposed algorithm distinguishes itself from many existing genetic algorithm (GA) approaches in three aspects. First, it incorporates GA with the variable neighborhood search (VNS) algorithm, a local search metaheuristic, to exploit the intrinsic structure of the solutions for guiding the exploration process of GA. Second, two novel neighborhood structures are proposed, in which problem-specific knowledge concerned with load balancing and communication reduction is utilized respectively, to improve both the search quality and efficiency of VNS. Third, the proposed algorithm restricts the use of GA to evolve the task-processor mapping solutions, while taking advantage of an upward-ranking heuristic mostly used by traditional list scheduling approaches to determine the task sequence assignment in each processor. Empirical results on benchmark task graphs of several well-known parallel applications, which have been validated by the use of non-parametric statistical tests, show that the proposed algorithm significantly outperforms several related algorithms in terms of the schedule quality. Further experiments are carried out to reveal that the proposed algorithm is able to maintain high performance within a wide range of parameter settings.     

Automated system for scheduling pipeline time for small batch production using a symphony spreadsheet

A scheduling pipeline program can be easily developed and customized using Symphony spreadsheet software. Monsanto Research Corporation produces small electro-mechanical component subassemblies in large lot sizes which are subdivided into transfer quantities. Since production takes place in several buildings, the transfer quantities create wait time for parts not being worked. Batch production schedules are constructed and adjusted using the pipeline program. This application uses work centers and/or balanced workstation assignments to determine schedule dates for a given transfer quantity. Production routing information is summarized for each station assignment, and these are considered as schedule points. The program incorporates many data base management, input forms, and graphic features available in Symphony.     

Expert system for selecting expert system shells

A prototype expert system is described that advises end-users in the selection of a suitable development environment for small- to medium-scale expert system applications. The system, running on a microcomputer, assesses the suitability of an application as a whole for expert systems techniques and recommends the five ‘best’ products from its knowledge base of 42 products. The paper takes a case-study approach, using the system itself as a recursive discussion example, applying some of the concepts involved in building an expert system. It describes the operation of the system and suggests improvements; it includes a feature list for classifying expert system shells.     

Two-phase neighborhood search algorithm for two-agent hybrid flow shop scheduling problem

In this paper hybrid flow shop scheduling problem with two agents is studied and its feasibility model is considered. A two-phase neighborhood search (TNS) algorithm is proposed to minimize objectives of two agents simultaneously under the given upper bounds. TNS is constructed through the combination of multiple variable neighborhood mechanisms and a new perturbation strategy for new current solution. A new replacement principle is also applied to decide if the current solution can be updated. TNS is tested on a number of instances and compared with the existing methods. The computational results show the promising advantage of TNS on the considered problem.     

Improved bounds for hybrid flow shop scheduling with multiprocessor tasks

In this paper, we investigate the problem of minimizing makespan in a multistage hybrid flow-shop scheduling with multiprocessor tasks. To generate high-quality approximate solutions to this challenging NP-hard problem, we propose a discrepancy search heuristic that is based on the new concept of adjacent discrepancies. Moreover, we describe a new lower bound based on the concept of dual feasible functions. The proposed lower and upper bounds are assessed through computational experiments conducted on 300 benchmark instances with up to 100 jobs and 8 stages. For these instances, we provide evidence that the proposed bounds consistently outperform the best existing ones. In particular, the proposed heuristic successfully improved the best known solution of 75 benchmark instances.     

An efficient hybrid meta-heuristic for aircraft landing problem

Aircraft landing problem is to assign an airport's runways to the arrival aircrafts as well as to schedule the landing time of these aircrafts. In this paper, first a mixed integer goal programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design a hybrid meta-heuristic applying simulated annealing framework. The computational results show that the proposed algorithm can obtain the optimal solution for instances up to 100 aircrafts, and also it is capable of finding very high quality and comparable solutions for the problems with up to 500 aircrafts and 5 runways in a short time.     

Patterns of interaction in rule-based expert system programming

We study the effect of adding a rule to a rule-based heuristic classification expert system, in particular, a rule that causes an unforeseen interaction with rules already in the rule set. We show that it is possible for such an interaction to occur between sets of rules, even when no interaction is present between any pair of rules contained in these sets. A method is presented that identifies interactions between sets of rules, and an analysis is given which relates these interactions to rule-based programming practices which help to maintain die integrity of the knowledge base. We argue mat the method is practical, given some reasonable assumptions on the knowledge base.     

Development of scheduling strategies in the TITAN operating system

Scheduling strategies are evolved to solve problems of selection in operating systems. Such strategies frequently undergo evolution in time as details of the specification are changed or, more frequently, as inadequacies in them are shown up by use in a heavily loaded system. This paper describes a number of scheduling strategies used in the TITAN system, and their development to meet changing circumstances or to overcome inadequacies.     

A two-stage hybrid flowshop scheduling problem with a function constraint and unrelated alternative machines

This article addresses a two-stage hybrid flowshop scheduling problem with unrelated alternative machines. The problem to be studied has m unrelated alternative machines at the first machine center followed by a second machine center with a common processing machine in the system. The objective is to minimize the makespan of the system. For the processing of any job, it is assumed that the operation can be partially substituted by other machines in the first center, depending on its machining constraints. Such scheduling problems occur in certain practical applications such as semiconductors, electronics manufacturing, airplane engine production, and petrochemical production. We demonstrate that the addressed problem is NP-hard and then provide some heuristic algorithms to solve the problem efficiently. The experimental results show that the combination of the modified Johnson's rule and the First-Fit rule provides the best solutions within all proposed heuristics.Scope and purpose