A hybrid scatter search/electromagnetism meta-heuristic for project scheduling

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions for relatively large instances. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and from a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory. We present computational experiments on standard benchmark datasets, compare the results with current state-of-the-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also demonstrate that the algorithm outperforms state-of-the-art existing heuristics.     

Scheduling flexible job-shops with transportation times: Mathematical models and a hybrid imperialist competitive algorithm

After the completion of a job on a machine, it needs to be transported to the next machine, actually taking some time. However, the transportation times are commonly neglected in the literature. This paper incorporates the transportation times between the machines into the flexible job-shop scheduling problem. We mathematically formulate the problem by two mixed integer linear programming models. Since the problem is NP-hard, we propose an adaptation of the imperialist competitive algorithm hybridized by a simulated annealing-based local search to solve the problem. Various operators and parameters of the algorithm are calibrated using the Taguchi method. The presented algorithm is assessed by comparing it against two other competitive algorithms in the literature. The computational results show that this algorithm has an outstanding performance in solving the problem.     

一种基于双链量子编码的动态车辆路径问题解决策略

针对配送调度事件动态变化的动态车辆路径问题(DVRP), 以最小化运输成本、最小化配送时间 与最大化载货率为目标, 建立了问题的数学模型,提出了改进的多相量子粒子群算法. 针对DVRP问题的特点,提出基于车辆链和货物链的双链量子编码方法; 同时设计了基于周期和 重调度因子驱动的动态调度策略. 最后将方法应用于动态仿真算例, 并与其他经典算法比较, 结果验证了所提出方法的有效性.     

A multi-objective hot-rolling scheduling problem in the compact strip production

This paper addresses a hot-rolling scheduling problem from compact strip production processes. At first, a mathematical model that consists of two coupled sub-problems is presented. The first sub-problem is the sheet-strip assignment problem that is about how to assign sheet-strips to rolling-turns with the objective of minimizing virtual sheet-strips. The second is the sheet-strip sequencing problem that is about how to sort the sheet-strips in each rolling-turn with the objective of minimizing the maximal changes in thickness between adjacent sheet-strips and the change times of the thickness so as to ensure high quality sheet-strips to be produced. And then, an improved hot-rolling scheduling heuristic is proposed to solve the sheet-strip assignment problem. A multi-objective evolutionary algorithm is developed to find the Pareto optimal or near-optimal solutions for the sheet-strip sequencing problem. Besides, the problem-specific knowledge is explored. The key operators including crossover operator, mutation operator and repair operator are designed for the multi-objective evolutionary algorithm. At last, extensive experiments based on real-world instances from a compact strip production process are carried out. The results demonstrate the effectiveness of the proposed algorithms for solving the hot-rolling scheduling problem under consideration.     

Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems

This article presents six parallel multiobjective evolutionary algorithms applied to solve the scheduling problem in distributed heterogeneous computing and grid systems. The studied evolutionary algorithms follow an explicit multiobjective approach to tackle the simultaneous optimization of a system-related (i.e. makespan) and a user-related (i.e. flowtime) objectives. Parallel models of the proposed methods are developed in order to efficiently solve the problem. The experimental analysis demonstrates that the proposed evolutionary algorithms are able to efficiently compute accurate results when solving standard and new large problem instances. The best of the proposed methods outperforms both deterministic scheduling heuristics and single-objective evolutionary methods previously applied to the problem.     

Hybrid evolutionary optimization of the operation of pipeless plants

Pipeless plants are a new production concept in chemical engineering in which automated guided vehicles (AGVs) transport the substances in mobile vessels between processing stations. In the operation of such plants, decisions have to be made on the scheduling of the production, the assignment of the equipment and the routing of the AGVs that carry the vessels. The large number of interacting degrees of freedom prohibit the use of exact mathematical algorithms to compute optimal schedules. This paper describes the combination of an evolutionary scheduling algorithm with a simulation based schedule builder. The algorithm is tested on a real-life example and on a benchmark problem from the literature and yields considerably shorter makespans than a heuristic solution.     

An algorithm for the capacitated vehicle routing problem with route balancing

In this paper, we present a multi-objective evolutionary algorithm for the capacitated vehicle routing problem with route balancing. The algorithm is based on a formerly developed multi-objective algorithm using an explicit collective memory method, namely the extended virtual loser (EVL). We adapted and improved the algorithm and the EVL method for this problem. We achieved good results with this simple technique. In case of this problem the quality of the results of the algorithm is similar to that of other evolutionary algorithms.     

A scatter search algorithm for the distributed permutation flowshop scheduling problem

The distributed permutation flowshop problem has been recently proposed as a generalization of the regular flowshop setting where more than one factory is available to process jobs. Distributed manufacturing is a common situation for large enterprises that compete in a globalized market. The problem has two dimensions: assigning jobs to factories and scheduling the jobs assigned to each factory. Despite being recently introduced, this interesting scheduling problem has attracted attention and several heuristic and metaheuristic methods have been proposed in the literature. In this paper we present a scatter search (SS) method for this problem to optimize makespan. SS has seldom been explored for flowshop settings. In the proposed algorithm we employ some advanced techniques like a reference set made up of complete and partial solutions along with other features like restarts and local search. A comprehensive computational campaign including 10 existing algorithms, together with statistical analyses, shows that the proposed scatter search algorithm produces better results than existing algorithms by a significant margin. Moreover all 720 known best solutions for this problem are improved.     

Minimizing flow time variance in a single machine system using genetic algorithms

In this paper, we address an n-job, single machine scheduling problem with an objective to minimize the flow time variance. We propose heuristic procedure based on genetic algorithms with the potential to address more generalized objective function such as weighted flow time variance. The development and implementation of the algorithm is supported with literature review and statistical analysis of the results. Some general guidelines to select the parameter values of the genetic algorithm are also developed using an experimental design approach.     

Parallel sparse linear algebra and application to structural mechanics

The framework of this paper is the parallelization of a plasticity algorithm that uses an implicit method and an incremental approach. More precisely, we will focus on some specific parallel sparse linear algebra algorithms which are the most time-consuming steps to solve efficiently such an engineering application. First, we present a general algorithm which computes an efficient static scheduling of block computations for parallel sparse linear factorization. The associated solver, based on a supernodal fan-in approach, is fully driven by this scheduling. Second, we describe a scalable parallel assembly algorithm based on a distribution of elements induced by the previous distribution for the blocks of the sparse matrix. We give an overview of these algorithms and present performance results on an IBM SP2 for a collection of grid and irregular problems. This revised version was published online in June 2006 with corrections to the Cover Date.     

双目标可重入混合流水车间调度问题的离散灰狼优化算法

可重入混合流水车间调度问题普遍存在于许多高科技制造产业中,如半导体晶圆制造和TFT-LCD面板生产过程等,但目前关于可重入调度问题的相关研究还比较少。本文设计了一种改进多目标灰狼优化算法(IMOGWO)解决最小化最大完工时间和总拖期时间最小的可重入混合流水车间调度问题,针对该问题特点对基本灰狼优化算法进行了一系列改进操作。通过对小规模测试问题基准算例的数值实验,验证了所设计的IMOGWO算法求解该调度问题的有效性。实验结果表明IMOGWO算法在非劣解的收敛性和支配性方面显著优于已有的NSGA-II和MOGWO算法,在解的分布性指标方面IMOGWO稍微优于其他两种算法。     

A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.     

Scheduling real-time and non-real-time traffic under nonstationary conditions

In this paper a statistical multiplexer that processes a mixture of real-time and non-real-time traffic is studied under bursts of traffic. Different scheduling algorithms are compared under conditions when one of the classes of traffic has a sudden increase in its arrival rate during a short period of time. The results show a difference in the way the scheduling disciplines studied behave under short overloads of traffic even though the scheduling algorithms had been set up to give similar performance under steady-state arrivals. The lifetime of real-time packets is shown to have a great effect on the way in which the performance of the scheduling algorithms compare.Robert Lackman is an IBM employee in the IBM Resident Study Program.     

Multi-objective open shop scheduling by considering human error and preventive maintenance

This study presents an open shop scheduling model by considering human error and preventive maintenance. The proposed mathematical model takes into account conflicting objective functions including makespan, human error and machine availability. In order to find the optimum scheduling, human error, maintenance and production factors are considered, simultaneously. Human error is measured by Human Error Assessment and Reduction Technique (HEART). Three metaheuristic methods including non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO) and strength Pareto evolutionary algorithm II (SPEA-II) are developed to find near-optimal solution. The Taguchi method is applied by adjusting parameters of metaheuristic algorithms. Several illustrative examples and a real case study (auto spare parts manufacturer) are applied to show the applicability of the multi-objective mixed integer nonlinear programming model. The proposed approach of this study may be used for similar open shop problems with minor modifications.     

Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm

This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor selection operator. This algorithm was compared with the evolutionary algorithm with the SAW method of Eiben et al., the Tabucol algorithm of Hertz and de Werra, and the hybrid evolutionary algorithm of Galinier and Hao. The performance of these algorithms were tested on a test suite consisting of randomly generated 3-colorable graphs of various structural features, such as graph size, type, edge density, and variability in sizes of color classes. Furthermore, the test graphs were generated including the phase transition where the graphs are hard to color. The purpose of the extensive experimental work was threefold: to investigate the behavior of the tested algorithms in the phase transition, to identify what impact hybridization with the DSatur traditional heuristic has on the evolutionary algorithm, and to show how graph structural features influence the performance of the graph-coloring algorithms. The results indicate that the performance of the hybrid self-adaptive evolutionary algorithm is comparable with, or better than, the performance of the hybrid evolutionary algorithm which is one of the best graph-coloring algorithms today. Moreover, the fact that all the considered algorithms performed poorly on flat graphs confirms that graphs of this type are really the hardest to color.     

Grey prediction evolution algorithm for global optimization

This article uses the grey prediction theory to structure a new metaheuristic: grey prediction evolution algorithm based on the even grey model. The proposed algorithm considers the population series of evolutionary algorithms as a time series, and uses the even grey model as a reproduction operator to forecast the next population (without employing any mutation and crossover operators). It is theoretically proven that the reproduction operator based on the even grey model is adaptive. Additionally, the algorithmic search mechanism and its differences with other evolutionary algorithms are analyzed. The performance of the proposed algorithm is validated on CEC2005 benchmark functions and a test suite composed of six engineering constrained design problems. The comparison experiments show the effectiveness and superiority of the proposed algorithm.The proposed algorithm can be regarded as the first case of structuring metaheuristics by using the prediction theory. The novel algorithm is anticipated to influence two future works. The first is to propose more metaheuristics inspired by prediction theories (including some statistical algorithms). Another is that the theoretical results of these prediction systems can be used for this novel type of metaheuristics.     

Measuring the Stability of Results From Supervised Statistical Learning

Stability is a major requirement to draw reliable conclusions when interpreting results from supervised statistical learning. In this article, we present a general framework for assessing and comparing the stability of results, which can be used in real-world statistical learning applications as well as in simulation and benchmark studies. We use the framework to show that stability is a property of both the algorithm and the data-generating process. In particular, we demonstrate that unstable algorithms (such as recursive partitioning) can produce stable results when the functional form of the relationship between the predictors and the response matches the algorithm. Typical uses of the framework in practical data analysis would be to compare the stability of results generated by different candidate algorithms for a dataset at hand or to assess the stability of algorithms in a benchmark study. Code to perform the stability analyses is provided in the form of an R package. Supplementary material for this article is available online.     

Bacterial memetic algorithm for offline path planning of mobile robots

The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. This problem belongs to the group of combinatorial optimization problems which are approached by modern optimization techniques such as evolutionary algorithms. In this paper the bacterial memetic algorithm is proposed for path planning of a mobile robot. The objective is to minimize the path length and the number of turns without colliding with an obstacle. The representation used in the paper fits well to the algorithm. Memetic algorithms combine evolutionary algorithms with local search heuristics in order to speed up the evolutionary process. The bacterial memetic algorithm applies the bacterial operators instead of the genetic algorithm??s crossover and mutation operator. One advantage of these operators is that they easily can handle individuals with different length. The method is able to generate a collision-free path for the robot even in complicated search spaces. The proposed algorithm is tested in real environment.     

Bus driver duty optimization using an integer programming and evolutionary hybrid algorithm

This paper describes the details of a successful application where an integer programming and evolutionary hybrid algorithm was used to solve a bus driver duty optimization problem. The task is NP-hard, therefore theoretically optimal solutions can only be calculated for very small problem instances. Our aim is to obtain solutions of good quality within reasonable time limits. We first applied an integer programming approach to a set partitioning problem. The model was solved with a column generation algorithm in a branch and bound scheme. In order to solve larger real-life problems, we have combined the integer programming method with a greedy 1+1 steady state evolutionary algorithm. The resulting hybrid algorithm was capable of providing near-optimal solutions within reasonable timescales to larger instances of the bus driver scheduling problem. We present the results and running times of our algorithm in detail, as well as possible directions of future improvements.     

Integrated production planning and scheduling for a mixed batch job-shop based on alternant iterative genetic algorithm

An integrated optimization production planning and scheduling based on alternant iterative genetic algorithm is proposed here. The operation constraints to ensure batch production successively are determined in the first place. Then an integrated production planning and scheduling model is formulated based on non-linear mixed integer programming. An alternant iterative method by hybrid genetic algorithm (AIHGA) is employed to solve it, which operates by the following steps: a plan is given to find a schedule by hybrid genetic algorithm; in turn, a schedule is given to find a new plan using another hybrid genetic algorithm. Two hybrid genetic algorithms are alternately run to optimize the plan and schedule simultaneously. Finally a comparison is made between AIHGA and a monolithic optimization method based on hybrid genetic algorithm (MOHGA). Computational results show that AIHGA is of higher convergence speed and better performance than MOHGA. And the objective values of the former are an average of 12.2% less than those of the latter in the same running time.