Production scheduling and rescheduling with genetic algorithms

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.     

Multiprocessor scheduling with communication delays

This paper adresses certain types of scheduling problems that arise when a parallel computation is to be executed on a multiprocessor. We define a model that allows for communication delays between precedence-related tasks, and propose a classification of various submodels. We also review complexity results and optimization and approximation algorithms that have been presented in the literature.     

Two-dimensional patterns and images reconstruction with use of cellular automata

An approach based on an application of cellular automata (CA) to the problem of two-dimensional (2D) patterns or images reconstruction from ones with only partial information available is presented in the paper. 2D CA are used to process patterns/images, and genetic algorithm (GA) is applied to discover CA rules, which will be able to reconstruct original patterns/images from, e.g. destroyed or modified ones. A number of experiments have been conducted to reconstruct patterns and human face images with use of the proposed approach. Results of experiments show that CA rules discovered by GA in the learning process allow to reconstruct images with large number of damaged pixels.     

Multiprocessor task scheduling with resource requirements

One of the most important problems arising in multiprocessor systems is scheduling of tasks on a set of parallel processors. Recently, new models of task processing have been formulated in which certain tasks can require more than one processor at a time. This model is especially justified in some applications of multi-microprocessor systems. In this paper, we extend the above model to cover the case of scheduling in the presence of additional scarce resources. First a subcase of the problem is considered in which preemptable tasks need simultaneously one or two processors and one additional resource in the amount of one unit. For this case a low order polynomial-time algorithm is presented. Then the general case is solved via a linear programming approach.     

Search space reduction of asynchrony immune cellular automata

Natural Computing - We continue the study of asynchrony immunity in cellular automata (CA), which can be considered as a generalization of correlation immunity in the case of vectorial Boolean...     

基于遗传算法的多处理器系统任务调度

用一种遗传算法的调度策略,以大维度矩阵求逆为实验对象,探索在多核中如何完成任务的均衡分配问题,以达到加速效果.算法利用系统资源的弹性,自动搜寻可以并行的子任务并将其合理地分配到相应计算节点中,提高了多核系统资源调度性能,实现了对用户提交的任务的优化调度,达到了均衡系统各处理器计算负载和提高多核系统的总体性能的目标.     

An artificial immune system for solving production scheduling problems: a review

This article reviews the production scheduling problems focusing on those related to flexible job-shop scheduling. Job-shop and flexible job-shop scheduling problems are one of the most frequently encountered and hardest to optimize. This article begins with a review of the job-shop and flexible job-shop scheduling problem, and follow by the literature on artificial immune systems (AIS) and suggests ways them in solving job-shop and flexible job-shop scheduling problems. For the purposes of this study, AIS is defined as a computational system based on metaphors borrowed from the biological immune system. This article also, summarizes the direction of current research and suggests areas that might most profitably be given further scholarly attention.     

A decision support system for the single-depot vehicle rescheduling problem

Disruptions in trips can prevent vehicles from executing their schedules as planned. Mechanical failures, accidents, and traffic congestion often hinder a vehicle schedule. When a vehicle on a scheduled trip breaks down, one or more vehicles need to be rescheduled to serve the passengers/cargo (if there are any) on that trip. The main objective of the vehicle rescheduling problem (VRSP) is to minimize operation and delay costs, while serving the passengers/cargo on the disrupted trip and completing all remaining trips that include the disrupted one. We report on a prototype decision support system (DSS) that recommends solutions for the single-depot rescheduling as well as vehicle scheduling (VSP) problems, since VRSP is closely related to VSP. The system was designed for human schedulers to obtain optimal vehicle assignments and reassignments. An experimental study, using randomly generated data, shows the efficiency of the developed algorithm. A real world problem, which involves the solid waste collection operational planning for a Brazilian city, is selected as the case study to illustrate the effectiveness of the developed DSS.     

Multiprocessor online scheduling of hard-real-time tasks

The problems of hard-real-time task scheduling in a multiprocessor environment are discussed in terms of a scheduling game representation of the problem. It is shown that optimal scheduling without a priori knowledge is impossible in the multiprocessor case even if there is no restriction on preemption owing to precedence or mutual exclusion constraints. Sufficient conditions that permit a set of tasks to be optimally scheduled at run time are derived     

Multiprocessor task scheduling in multistage hybrid flowshops: A hybrid artificial bee colony algorithm with bi-directional planning

In this paper, the problem of scheduling multistage hybrid flowshops with multiprocessor tasks is contemplated. This is a strongly NP-hard problem for which a hybrid artificial bee colony (HABC) algorithm with bi-directional planning is developed to minimize makespan. To validate the effectiveness of the proposed algorithm, computational experiments were tested on two well-known benchmark problem sets. The computational evaluations manifestly support the high performance of the proposed HABC against the best-so-far algorithms applied in the literature for the same benchmark problem sets.     

A cellular automata model of an anticipatory system

An anticipatory system has been modeled using the dynamic characteristics of cellular automata. Rules governing the steps in an enzymatic conversion of substrates to products are operative in the system. A concentration of an intermediate product influences the creation of a supplemental enzyme that enhances the competence of an enzyme down stream. This anticipation of the future event creates a condition in which the concentration of a later substrate is suppressed, a property characteristic of the system. The model presents a useful opportunity to study a variety of aspects of this fascinating phenomena.     

Developing an artificial retina by evolutionary cellular automata and self-organizing neural networks

Retinas are very important for human beings to get information about their environment. In this paper, we propose a new method to build artificial retinas which have many features similar to real ones. We use evolutionary cellular automata to extract some basic characteristics of objects, and use self-organizing neural networks to distinguish different objects. The results indicate a way to get computer vision by artificial life. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, Janaury 19–21, 1998     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.     

Symbiotic artificial immune system

Artificial Immune System algorithms use antibodies that fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm cannot make use of schemata or classes of partial solutions, while sub solutions can help a lot in faster emergence of a totally good solution in many problems. To exploit schemata in artificial immune systems, this paper presents a novel algorithm that combines traditional artificial immune systems and symbiotic combination operator. The algorithm starts searching with partially specified antibodies and gradually builds more and more specified solutions till it finds complete answers. The algorithm is compared with CLONALG algorithm on several multimodal function optimization and combinatorial optimization problems and it is shown that it is faster than CLONALG on most problems and can find solutions in problems that CLONALG totally fails.     

On real-time cellular automata and trellis automata

Summary It is shown that f(n)-time one-way cellular automata are equivalent to f(n)-time trellis automata, the real-time one-way cellular automata languages are closed under reversal, the 2n-time one-way cellular automata are equivalent to real-time cellular automata and the latter are strictly more powerful than the real-time one-way cellular automata.This work has been done during the second author's visit at the University of Paris and during both authors' visit at the Institute für Informationsverarbeitung Graz, Austria     

Service scheduling and rescheduling in an applications integration framework

Grid technologies are evolving towards a service oriented architecture (SOA) and the traditional client/server architecture of heterogeneous computing (HC) can be transformed into a grid service oriented architecture. In this architecture, when more than one service fulfills the user request, a service which can make scheduling decisions is essential. A scheduling service has been proposed in a framework which achieves the dynamic deployment and scheduling of scientific and engineering applications. The framework treats all components (resource service and scheduler service) as WSRF-compliant services which support the applications integration with underlying native platform facilities and facilitate the construction of the hierarchical scheduling system. In order to enhance the system performance, we replace the MWL scheduling algorithm with an MCT algorithm and integrate a rescheduling mechanism in the framework. The experiments show that the MCT algorithm can achieve a smaller makespan and the rescheduling mechanism ensures the task execution even if an application is removed from the Resource Service.     

Image security system using recursive cellular automata substitution

This paper presents a novel image security system based on the replacement of the pixel values using recursive cellular automata (CA) substitution. This proposed image encryption method exhibits the properties of confusion and diffusion because of the characteristics of CA substitution are flexible. The salient features of the proposed image encryption method are its losslessness, symmetric private key encryption, very large number of secret keys, and key-dependent pixel value replacement. Simulation results obtained using some color and gray-level images clearly demonstrate the strong performance of the proposed image security system.     

Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification

The problem of classifying traffic flows in networks has become more and more important in recent times, and much research has been dedicated to it. In recent years, there has been a lot of interest in classifying traffic flows by application, based on the statistical features of each flow. Information about the applications that are being used on a network is very useful in network design, accounting, management, and security. In our previous work we proposed a classification algorithm for Internet traffic flow classification based on Artificial Immune Systems (AIS). We also applied the algorithm on an available data set, and found that the algorithm performed as well as other algorithms, and was insensitive to input parameters, which makes it valuable for embedded systems. It is also very simple to implement, and generalizes well from small training data sets. In this research, we expanded on the previous research by introducing several optimizations in the training and classification phases of the algorithm. We improved the design of the original algorithm in order to make it more predictable. We also give the asymptotic complexity of the optimized algorithm as well as draw a bound on the generalization error of the algorithm. Lastly, we also experimented with several different distance formulas to improve the classification performance. In this paper we have shown how the changes and optimizations applied to the original algorithm do not functionally change the original algorithm, while making its execution 50–60% faster. We also show that the classification accuracy of the Euclidian distance is superseded by the Manhattan distance for this application, giving 1–2% higher accuracy, making the accuracy of the algorithm comparable to that of a Naïve Bayes classifier in previous research that uses the same data set.     

A flexible nurse scheduling support system

Salaries paid to nursing personnel constitute the largest chunk of a hospital's budget. Therefore, this human resource must be utilized efficiently. Hospitals provide continuous service without the exception of holidays and personal preferences. This causes the nurses' discontent in shift scheduling. And the consequence of this discontent is the nurse shortage. This and the pressures on hospitals to limit costs increase the importance of the nurse scheduling problem. Scheduling nursing personnel in hospitals is very complex due to the variety of conflicting interests or objectives between hospitals and nurses. Also, the demand, which varies widely 24-h a day 7-day a week is skill specific and hard to forecast. In the face of this complexity, the present nurse scheduling models have met with little success. In this paper, we propose a more flexible decision support system that will satisfy the interests of both hospitals and nurses through alternative models that attempt to accommodate flexible work patterns as it integrates time of the day (TOD) and day of the week (DOW) scheduling problems.     

Distributed artificial intelligence for FMS scheduling, control and design support

The aim of this paper is to point out some of the abilities of Distributed Artificial Intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on Distributed Problem Solving, sub-field of Distributed Artificial Intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a Distributed Decision Support System for integrated scheduling, control and design support of production systems.