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.     

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.     

大学自动排考算法设计与实现

提出了一种应用于高等院校的自动排考算法.该算法结合了启发式算法的特点,同时建立静态冲突图来降低算法复杂程度,算法充分利用了应用领域经验和规则的优势,提高了自动排考的资源搜索能力.通过在实际工程中应用,表明该算法在解决复杂的高校排考问题时有较好的效果.     

A genetic algorithm for solving economic lot size scheduling problem

The purpose of this research is to determine an optimal batch size for a product and purchasing policy of associated raw materials. Like most other practical situation, this manufacturing firm has a limited storage space and transportation fleet of known capacity. The mathematical formulation of the problem indicates that the model is a constrained nonlinear integer program. Considering the complexity of solving such model, we investigate the use of genetic algorithms (GAs) for solving this model. We develop GA code with three different penalty functions usually used for constraint optimizations. The model is also solved using an existing commercial optimization package to compare the solution. The detailed computational results are presented.     

背包问题混合遗传算法在电力恢复中的应用

文章把电力系统的负荷恢复问题建模为带众多约束条件的0-1背包问题,并设计了一种将贪心算法与改进遗传算法结合起来的改进混合遗传算法来对此问题进行求解.该算法的主要特点是具有群体爬山性和利用了郭涛算子的非凸组合技术使算法具有搜索的遍历性.采用此算法可以得到负荷恢复的某一阶段可恢复的最大的负荷量.求解的过程保证了求得的解是满足系统的约束条件,所以系统的负荷恢复过程是安全的.算例的结果表明了该算法的有效性.     

A hybrid self-adaptive bees algorithm for examination timetabling problems

A hybrid self-adaptive bees algorithm is proposed for the examination timetabling problems. The bees algorithm (BA) is a population-based algorithm inspired by the way that honey bees forage for food. The algorithm presents a type of neighbourhood search that includes a random search that can be used for optimisation problems. In the BA, the bees search randomly for food sites and return back to the hive carrying the information about the food sites (fitness values); then, other bees will select the sites based on their information (more bees are recruited to the best sites) and will start a random search. We propose three techniques (i.e. disruptive, tournament and rank selection strategies) for selecting the sites, rather than using the fitness value, to improve the diversity of the population. Additionally, a self-adaptive strategy for directing the neighbourhood search is added to further enhance the local intensification capability. Finally, a modified bees algorithm is combined with a local search (i.e. simulated annealing, late acceptance hill climbing) to quickly descend to the optimum solution. Experimental results comparing our proposed modifications with each other and with the basic BA show that all of the modifications outperform the basic BA; an overall comparison has been made with the best known results on two examination timetabling benchmark datasets, which shows that our approach is competitive and works well across all of the problem instances.     

Exact formulations and algorithm for the train timetabling problem with dynamic demand

In this paper we study the design and optimization of train timetabling adapted to a dynamic demand environment. This problem arises in rapid train services which are common in most important cities. We present three formulations for the problem, with the aim of minimizing passenger average waiting time. The most intuitive model would consider binary variables representing train departure times but it yields to non-linear objective function. Instead, we introduce flow variables, which allow a linear representation of the objective function. We provide incremental improvements on these formulations, which allows us to evaluate and compare the benefits and disadvantages of each modification. We present a branch-and-cut algorithm applicable to all formulations. Through extensive computational experiments on several instances derived from real data provided by the Madrid Metropolitan Railway, we show the advantages of designing a timetable adapted to the demand pattern, as opposed to a regular timetable. We also perform an extensive computational comparison of all linear formulations in terms of size, solution quality and running time.     

Solving exclusionary side constrained transportation problem by using a hybrid spanning tree-based genetic algorithm

In real life applications we often have the following problem: How to find the reasonable assignment strategy to satisfy the source and destination requirement without shipping goods from any pairs of prohibited sources simultaneously to the same destination so that the total cost can be minimized. This kind of problem is known as the transportation problem with exclusionary side constraint (escTP). Since this problem is one of nonlinear programming models, it is impossible to solve this problem using a traditional linear programming software package (i.e., LINDO). In this paper, an evolutionary algorithm based on a genetic algorithm approach is proposed to solve it. We adopt a Prüfer number to represent the candidate solution to the problem and design the feasibility of the chromosome. Moreover, to handle the infeasible chromosome, here we also propose the repairing procedure. In order to improve the performance of the genetic algorithm, the fuzzy logic controller (FLC) is used to dynamically control the genetic operators. Comparisons with other conventional methods and the spanning tree-based genetic algorithm (st-GA) are presented and the results show the proposed approach to be better as a whole.     

造船监理员调度模型和混合遗传算法求解

有效快速地调度不同专业的造船监理员至不同厂区进行监理工作可以提高船舶建造效率,确保船只建造质量。针对我国造船监理公司监理员调度方面缺乏通用模型和调度手段落后的问题,建立起带有一系列硬性约束和软性约束的数学模型。随后针对该数学模型采用了基于模拟退火遗传算法的混合遗传算法进行求解。模拟仿真实验表明该模型与算法取得了理想效果。     

解非线性约束规划问题的新型多目标遗传算法

给出非线性约束规划问题的一种新解法。把带约束的非线性规划问题转化成为两个目标的多目标优化问题,并为转化后的多目标优化模型设计了一种新型多目标遗传算法,数据实验表明该算法对带约束的非线性规划问题求解是非常有效的。     

A hybrid grouping genetic algorithm for the cell formation problem

The machine-part cell formation problem consists of constructing a set of machine cells and their corresponding product families with the objective of minimizing the inter-cell movement of the products while maximizing machine utilization. This paper presents a hybrid grouping genetic algorithm for the cell formation problem that combines a local search with a standard grouping genetic algorithm to form machine-part cells. Computational results using the grouping efficacy measure for a set of cell formation problems from the literature are presented. The hybrid grouping genetic algorithm is shown to outperform the standard grouping genetic algorithm by exceeding the solution quality on all test problems and by reducing the variability among the solutions found. The algorithm developed performs well on all test problems, exceeding or matching the solution quality of the results presented in previous literature for most problems.     

A genetic algorithm-based approach for solving the resource-sharing and scheduling problem

We introduce a heuristic that is based on a unique genetic algorithm (GA) to solve the resource-sharing and scheduling problem (RSSP). This problem was previously formulated as a continuous-time mixed integer linear programming model and was solved optimally using a branch-and-bound (B&B) algorithm. The RSSP considers the use of a set of resources for the production of several products. Producing each product requires a set of operations with precedence relationships among them. Each operation can be performed using alternative modes which define the subset of the resources needed, and an operation may share different resources simultaneously. The problem is to select a single mode for each operation and accordingly to schedule the resources, while minimizing the makespan time. The GA we propose is based on a new encoding schema that adopts the structure of a DNA in nature. In our experiments we compared the effectiveness and runtime of our GA versus a B&B algorithm and two truncated B&B algorithms that we developed on a set of 118 problem instances. The results demonstrate that the GA solved all the problems (10 runs each), and reaches optimality in 75% of the runs, had an average deviation of less than 1% from the optimal makespan, and a runtime that was much less sensitive to the size of the problem instance.     

基于混合遗传算法的订单生产调度在机车车辆行业中的应用

机车车辆行业作为典型的面向订单的机械制造企业,优化的生产调度方法能提高订单的准时交货,缩短产品的生产周期,提高企业的市场竞争力。订单生产调度问题是典型的NP-hard问题。遗传算法(Genetic Algorithms)为求具有多个约束的复杂问题提供了有效的方法。但是遗传算法的局部搜索能力比较差,在解决订单生产调度问题中存在着明显的不足。本文引入了局部搜索能力很强的禁忌搜索算法,用遗传算法和禁忌搜索算法相结合的混合遗传算法来解决机车车辆行业中面向订单生产调度问题。     

Regularization of a lexicographic vector problem of integer programming

A stability criterion for a vector integer linear problem of lexicographic optimization is obtained. A regularization method is proposed that allows us to reduce a possible unstable output problem to a sequence of perturbed stable equivalent problems. Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 125–130, November–December, 1999.     

A two-phase algorithm for the biobjective integer minimum cost flow problem

We present an algorithm to compute a complete set of efficient solutions for the biobjective integer minimum cost flow problem. We use the two phase method, with a parametric network simplex algorithm in phase 1 to compute all non-dominated extreme points. In phase 2, the remaining non-dominated points (non-extreme supported and non-supported) are computed using a k best flow algorithm on single-objective weighted sum problems.     

A fast hybrid tabu search algorithm for the no-wait job shop problem

This paper describes a hybrid tabu search algorithm dedicated to a job shop problem with a no-wait constraint with a makespan criterion. The proposed here algorithm complexity is that the superior algorithm based on the tabu search technique selects parameters controlling the work of a certain constructional algorithm. This approach limits the checked solutions only to a group of solutions being able to be generated by the structural algorithm in question. It bears serious consequences both positive, for example it limits the research scope for a small fraction of relatively extremely well quality of acceptable solutions, and negative that is the lack of possibility of finding the optimal solution. In this paper numerical researches of the proposed algorithm are conducted as well as a comparative analysis with reference to the literature algorithms of the algorithm in question is made.     

Hybrid genetic algorithm approach for precedence-constrained sequencing problem

The objective of precedence-constrained sequencing problem (PCSP) is to locate the optimal sequence with the shortest traveling time among all feasible sequences. Various methods for effectively solving the PCSP have been suggested. This paper proposes a new concept of hybrid genetic algorithm (HGA) with adaptive local search scheme in order that the PCSP should be effectively solved. By the use of the adaptive local search scheme, the local search is automatically adapted into the loop of genetic algorithm. Two types of the PCSP are presented and analyzed to compare the efficiency among the proposed HGA approach and other competing conventional approaches. Finally, it is proved that the proposed HGA approach outperforms the other competing conventional approaches.     

基于云计算的混合并行遗传算法求解最短路径

为提高最短路径求解问题的效率,提出一种基于云计算的细粒度混合并行遗传算法求解最短路径的方法。方法采用云计算中H adoop的Map Reduce并行编程模型,提高编码效率,同时将细粒度并行遗传算法和禁忌搜索算法结合,提高了寻优算法的计算速度和局部寻优能力,进而提高最短路径的求解效率。仿真结果表明,该方法在计算速度和性能上优于经典遗传算法和并行遗传算法,是一种有效的最短路径求解方法。     

Using genetic algorithm and TOPSIS technique for multiobjective transportation problem: a hybrid approach

A hybrid approach to solve the multiobjective transportation problem (TP) is presented. The TP as a special type of the network optimization problems that has the special data structure in solution characterized as transportation graph. In encoding TP, we introduce a new chromosome's structure which is adopted as it is capable of representing all possible feasible solutions. Also, in order to keep the feasibility of the chromosome, the crossover and the mutation were modified. The proposed approach maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ?-dominance. Moreover, to help the decision maker to extract the best compromise solution from a finite set of alternatives, a technique for order performance by similarity to ideal solution (TOPSIS) method is adopted. Numerical simulations show the effectiveness and efficiency of the proposed approach.     

Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm

The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems. The problem becomes even more complicated when dynamic power limit based ramp rate constraint is taken into account. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which genetic algorithm (GA) has been investigated widely since long back. Such proposals have been made for solving only the integer part of the UCP, along with some other approaches for the real part of the problem. Moreover, the ramp rate constraint is usually discussed only in the formulation part, without addressing how it could be implemented in an algorithm. In this paper, the GA is revisited with an attempt to solve both the integer and real parts of the UCP using a single algorithm, as well as to incorporate the ramp rate constraint in the proposed algorithm also. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed GA is found quite satisfactory in comparison with the previously reported results.