Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm

In this contribution we present the application of a hybrid cat swarm optimization (CSO) based algorithm for solving the school timetabling problem. This easy to use, efficient and fast algorithm is a hybrid variation of the classic CSO algorithm. Its efficiency and performance is demonstrated by conducting experiments with real-world input data. This data, collected from various high schools in Greece, has also been used as test instances by many other researchers in their publications. Results reveal that this hybrid CSO based algorithm, applied to the same school timetabling test instances using the same evaluation criteria, exhibits better performance in less computational time compared to the majority of other existing approaches, such as Genetic Algorithms (GAs), Evolutionary Algorithms (EAs), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Artificial Fish Swarm (AFS). The algorithm's main process constitutes a variation of the classic CSO algorithm, properly altered so as to be applied for solving the school timetabling problem. This process contains the main algorithmic differences of the proposed approach compared to other algorithms presented in the respective literature.     

Solving fuzzy assembly-line balancing problem with genetic algorithms

Assembly-line balancing problem is known as one of difficult combinatorial optimization problems. This problem has been solved with linear programming, dynamic programming approaches, but unfortunately these approaches do not lead to efficient algorithms. Recently, genetic algorithm has been recognized as an efficient and usefull procedure for solving large and hard combinatorial optimization problems, such as scheduling problems, travelling salesman problems, transportation problems, and so on. Fuzzy sets theory is frequently used to represent uncertainty of information. In this paper, to treat the data of real-world problems we use a fuzzy number to represent the processing time and show that we can get a good performance in solving this problem using genetic algorithms.     

Integrating employee timetabling with scheduling of machines and transporters in a job-shop environment: A mathematical formulation and an Anarchic Society Optimization algorithm

This paper addresses a ternary-integration scheduling problem that incorporates employee timetabling into the scheduling of machines and transporters in a job-shop environment with a finite number of heterogeneous transporters where the objective is to minimize the completion time of all jobs. The problem is first formulated as a mixed-integer linear programming model. Then, an Anarchic Society Optimization (ASO) algorithm is developed to solve large-sized instances of the problem. The formulation is used to solve small-sized instances and to evaluate the quality of the solutions obtained for instances with larger sizes. A comprehensive numerical study is carried out to assess the performance of the proposed ASO algorithm. The algorithm is compared with three alternative metaheuristic algorithms. It is also compared with several algorithms developed in the literature for the integrated scheduling of machines and transporters. Moreover, the algorithms are tested on a set of adapted benchmark instances for an integrated problem of machine scheduling and employee timetabling. The numerical analysis suggests that the ASO algorithm is both effective and efficient in solving large-sized instances of the proposed integrated job-shop scheduling problem.     

Using parallelization and hardware concurrency to improve the performance of a genetic algorithm

Genetic algorithms (GAs) are powerful tools for solving many problems requiring the search of a solution space having both local and global optima. The main drawback for GAs is the long execution time normally required for convergence to a solution. This paper discusses three different techniques that can be applied to GAs to improve overall execution time. A serial software implementation of a GA designed to solve a task scheduling problem is used as the basis for this research. The execution time of this implementation is then improved by exploiting the natural parallelism present in the algorithm using a multiprocessor. Additional performance improvements are provided by implementing the original serial software GA in dedicated reconfigurable hardware using a pipelined architecture. Finally, an advanced hardware implementation is presented in which both pipelining and duplicated hardware modules are used to provide additional concurrency leading to further performance improvements. Copyright © 2006 John Wiley & Sons, Ltd.     

A genetic algorithm-based approach to flexible flow-line schedulingwith variable lot sizes

Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.     

Multi-objective genetic algorithms for scheduling of radiotherapy treatments for categorised cancer patients

This paper presents a multi-objective optimisation model and algorithms for scheduling of radiotherapy treatments for categorised cancer patients. The model is developed considering real life radiotherapy treatment processes at Arden Cancer Centre, in the UK. The scheduling model considers various real life constraints, such as doctors’ rota, machine availability, patient’s category, waiting time targets (i.e., the time when a patient should receive the first treatment fraction), and so on. Two objectives are defined: minimisation of the Average patient’s waiting time and minimisation of Average length of breaches of waiting time targets. Three genetic algorithms (GAs) are developed and implemented which treat radiotherapy patient categories, namely emergency, palliative and radical patients in different ways: (1) Standard-GA, which considers all patient categories equally, (2) KB-GA, which has an embedded knowledge on the scheduling of emergency patient category and (3) Weighted-GA, which operates with different weights given to the patient categories. The performance of schedules generated by using the three GAs is compared using the statistical analyses. The results show that KB-GA generated the schedules with best performance considering emergency patients and slightly outperforms the other two GAs when all patient categories are considered simultaneously. KB-GA and Weighted-GA generated better performance schedules for emergency and palliative patients than Standard-GA.     

求解Flow-shop调度问题的HSGATS算法研究

通过研究流程企业典型生产加工方式的Flow-shop加工调度问题,分析了求解这一调度问题的各种算法的性能,特别是混合遗传算法中的混合策略,进而提出了一种以启发式搜索算法为前置算法、遗传算法为主体算法、禁忌搜索算法为后置算法的新的混合遗传算法-HSGATS算法,并针对置换Flow-shop调度问题提出了具体的算法设计和实现方法;通过对大量置换Flow--ShopBenchmark问题进行实验求解和其他算法的比较分析,验证了此算法是十分有效的。     

一种新的FMS优化调度算法

提出一种将遗传算法和启发式算法相结合的新的混合算法,以解决ＦＭＳ中的优化调度问题。该混合算法克服了以往遗传算法在ＦＭＳ中应用的不足之处,并具有搜索效率高且稳定的特点。最后以实例验证了该算法的高效性和稳定性。     

一种快速收敛的混合遗传算法

利用遗传算法早熟的特点 ,构造出一种快速收敛的混合算法来求解优化问题 ,并分析了它的收敛性。它是使用遗传算法来生成搜索方向 ,从而保证了算法的收敛性。该算法利用遗传算法的全局搜索能力 ,并采用 Nelder- Mead单纯形法来加强算法的局部搜索能力 ,加快了算法的收敛速率。模拟实验表明 ,该方法具有高效性和鲁棒性     

Optimal switch location in mobile communication networks using hybrid genetic algorithms

The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so-called capacitated p-median problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem.     

A tutorial survey of job-shop scheduling problems using genetic algorithms-I. representation

Job-shop scheduling problem (abbreviated to JSP) is one of the well-known hardest combinatorial optimization problems. During the last three decades, the problem has captured the interest of a significant number of researchers and a lot of literature has been published, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. This has led to recent interest in using genetic algorithms (GAs) to address it. The purpose of this paper and its companion (Part II: Hybrid Genetic Search Strategies) is to give a tutorial survey of recent works on solving classical JSP using genetic algorithms. In Part I, we devote our attention to the representation schemes proposed for JSP. In Part II, we will discuss various hybrid approaches of genetic algorithms and conventional heuristics. The research works on GA/JSP provide very rich experiences for the constrained combinatorial optimization problems. All of the techniques developed for JSP may be useful for other scheduling problems in modern flexible manufacturing systems and other combinatorial optimization problems.     

A modified genetic algorithm for distributed scheduling problems

Genetic algorithms (GAs) have been widely applied to the scheduling and sequencing problems due to its applicability to different domains and the capability in obtaining near-optimal results. Many investigated GAs are mainly concentrated on the traditional single factory or single job-shop scheduling problems. However, with the increasing popularity of distributed, or globalized production, the previously used GAs are required to be further explored in order to deal with the newly emerged distributed scheduling problems. In this paper, a modified GA is presented, which is capable of solving traditional scheduling problems as well as distributed scheduling problems. Various scheduling objectives can be achieved including minimizing makespan, cost and weighted multiple criteria. The proposed algorithm has been evaluated with satisfactory results through several classical scheduling benchmarks. Furthermore, the capability of the modified GA was also tested for handling the distributed scheduling problems.     

Production capacity planning and scheduling in a no-wait environment with controllable processing times: An integrated modeling approach

This paper develops an integrated model between a production capacity planning and an operational scheduling decision making process in which a no-wait job shop (NWJS) scheduling problem is considered incorporating with controllable processing times. The duration of any operations are assumed to be controllable variables based on the amount of capacity allocated to them, whereas in classical NWJS it is assumed that the machine capacity and hence processing times are fixed and known in advance. The suggested problem which is entitled no-wait job shop crashing (NWJSC) problem is decomposed into the crashing, sequencing and timetabling subproblems. To tackle the addressed NWJSC problem, an improved hybrid timetabling procedure is suggested by employing the concept of both non-delay and enhanced algorithms which provides better solution than each one separately. Furthermore, an effective two-phase genetic algorithm approach is devised integrating with hybrid timetabling to deal with the crashing and sequencing components. The results obtained from experimental evaluations support the outstanding performance of the proposed approach.     

An effective hybrid algorithm for university course timetabling

The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an ‘International Timetabling Competition’ to which 24 algorithms were submitted by various research groups active in the field of timetabling. We describe and analyse a hybrid metaheuristic algorithm which was developed under the very same rules and deadlines imposed by the competition and outperformed the official winner. It combines various construction heuristics, tabu search, variable neighbourhood descent and simulated annealing. Due to the complexity of developing hybrid metaheuristics, we strongly relied on an experimental methodology for configuring the algorithms as well as for choosing proper parameter settings. In particular, we used racing procedures that allow an automatic or semi-automatic configuration of algorithms with a good save in time. Our successful example shows that the systematic design of hybrid algorithms through an experimental methodology leads to high performing algorithms for hard combinatorial optimisation problems.     

Genetic algorithms to solve the cover printing problem

Inspired by successful application of evolutionary algorithms to solving difficult optimization problems, we explore in this paper, the applicability of genetic algorithms (GAs) to the cover printing problem, which consists in the grouping of book covers on offset plates in order to minimize the total production cost. We combine GAs with a linear programming solver and we propose some innovative features such as the “unfixed two-point crossover operator” and the “binary stochastic sampling with replacement” for selection. Two approaches are proposed: an adapted genetic algorithm and a multiobjective genetic algorithm using the Pareto fitness genetic algorithm. The resulting solutions are compared. Some computational experiments have also been done to analyze the effects of different genetic operators on both algorithms.     

一种基于遗传算法的分布式系统的任务调度

一般而言,分布式多处理机上的任意任务图的调度问题即使做了简化假设后依然是NP完全的。遗传算法被证明是解决任务调度等组合问题的有效工具。对现有文献中的关于调度问题的遗传算法进行研究和比较后,该文提出了一种基于遗传算法的任务调度方法,在算法中设计了一种与其他算法不同的变异算子。     

A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet

Nowadays genetic algorithms stand as a trend to solve NP-complete and NP-hard problems. In this paper, we present a new hybrid metaheuristic which uses parallel genetic algorithms and scatter search coupled with a decomposition-into-petals procedure for solving a class of vehicle routing and scheduling problems. The parallel genetic algorithm presented is based on the island model and its performance is evaluated for a heterogeneous fleet problem, which is considered a problem much harder to solve than the homogeneous vehicle routing problem.     

A Neurogenetic approach for the resource-constrained project scheduling problem

A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.     

A hybrid genetic algorithm to optimize simple distillation column sequences

Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.     

基于QoS的卫星信息应用链服务调度方法

服务调度问题是卫星信息应用链功能结构设计完成之后必须解决的问题．在卫星信息应用链服务调度问题的相关概念进行描述之后,提出了卫星信息应用链的服务质量参数体系,为链的性能评价和服务选择提供了依据,并在此基础上建立了卫星信息应用链的服务调度问题的数学模型,提出了一种基于GA的服务调度算法．实验结果表明,本文所提出的服务质量参数体系能有效解决应用链性能评估问题,基于QoS的卫星信息应用链服务调度方法可行性较强．