Scatter search with path relinking for the job shop with time lags and setup times

This paper addresses the job shop scheduling problem with time lags and sequence-dependent setup times. This is an extension of the job shop scheduling problem with many applications in real production environments. We propose a scatter search algorithm which uses path relinking and tabu search in its core. We consider both feasible and unfeasible schedules in the execution, and we propose effective neighborhood structures with the objectives of reducing the makespan and regain feasibility. We also define new procedures for estimating the quality of the neighbors. We conducted an experimental study to compare the proposed algorithm with the state-of-the-art, in benchmarks both with and without setups. In this study, our algorithm has obtained very competitive results in a reduced run time.     

Exponential neighborhood search for a parallel machine scheduling problem

We consider the parallel machine scheduling problem where jobs have different earliness-tardiness penalties and a restrictive common due date. This problem is NP-hard in the strong sense. In this paper we define an exponential size neighborhood for this problem and prove that finding the local minimum in it is an NP-hard problem. The main contribution of this paper is to propose a pseudo-polynomial algorithm that finds the best solution of the exponential neighborhood. Additionally, we present some computational results.     

An efficient hybrid evolutionary algorithm for scheduling with setup times and weighted tardiness minimization

We confront the job shop scheduling problem with sequence-dependent setup times and weighted tardiness minimization. To solve this problem, we propose a hybrid metaheuristic that combines the intensification capability of tabu search with the diversification capability of a genetic algorithm which plays the role of long term memory for tabu search in the combined approach. We define and analyze a new neighborhood structure for this problem which is embedded in the tabu search algorithm. The efficiency of the proposed algorithm relies on some elements such as neighbors filtering and a proper balance between intensification and diversification of the search. We report results from an experimental study across conventional benchmarks, where we analyze our approach and demonstrate that it compares favorably to the state-of-the-art methods.     

A high performing metaheuristic for job shop scheduling with sequence-dependent setup times

This paper investigates scheduling job shop problems with sequence-dependent setup times under minimization of makespan. We develop an effective metaheuristic, simulated annealing with novel operators, to potentially solve the problem. Simulated annealing is a well-recognized algorithm and historically classified as a local-search-based metaheuristic. The performance of simulated annealing critically depends on its operators and parameters, in particular, its neighborhood search structure. In this paper, we propose an effective neighborhood search structure based on insertion neighborhoods as well as analyzing the behavior of simulated annealing with different types of operators and parameters by the means of Taguchi method. An experiment based on Taillard benchmark is conducted to evaluate the proposed algorithm against some effective algorithms existing in the literature. The results show that the proposed algorithm outperforms the other algorithms.     

A variable neighborhood search for minimizing single machine weighted earliness and tardiness with common due date

Although the concept of just-in-time (JIT) production systems has been proposed for over two decades, it is still important in real-world production systems. In this paper, we consider minimizing the total weighted earliness and tardiness with a restrictive common due date in a single machine environment, which has been proved as an NP-hard problem. Due to the complexity of the problem, metaheuristics, including simulated annealing, genetic algorithm, tabu search, among others, have been proposed for searching good solutions in reasonable computation times. In this paper, we propose a hybrid metaheuristic that uses tabu search within variable neighborhood search (VNS/TS). There are several distinctive features in the VNS/TS algorithm, including different ratio of the two neighborhoods, generating five points simultaneously in a neighborhood, implementation of the B/F local search, and combination of TS with VNS. By examining the 280 benchmark problem instances, the algorithm shows an excellent performance in not only the solution quality but also the computation time. The results obtained are better than those reported previously in the literature.     

Variable neighborhood descent heuristic for solving reverse logistics multi-item dynamic lot-sizing problems

The multi-product dynamic lot sizing problem with product returns and recovery is an important problem that appears in reverse logistics and is known to be NP-hard. In this paper we propose an efficient variable neighborhood descent heuristic algorithm for solving this problem. Furthermore, we present a new benchmark set with the largest instances in the literature. The computational results demonstrate that our approach outperforms the state-of-the-art Gurobi optimizer.     

Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

Recently, iterated greedy algorithms have been successfully applied to solve a variety of combinatorial optimization problems. This paper presents iterated greedy algorithms for solving the blocking flowshop scheduling problem (BFSP) with the makespan criterion. Main contributions of this paper can be summed up as follows. We propose a constructive heuristic to generate an initial solution. The constructive heuristic generates better results than those currently in the literature. We employ and adopt well-known speed-up methods from the literature for both insertion and swap neighborhood structures. In addition, an iteration jumping probability is proposed to change the neighborhood structure from insertion neighborhood to swap neighborhood. Generally speaking, the insertion neighborhood is much more effective than the swap neighborhood for the permutation flowshop scheduling problems. Instead of considering the use of these neighborhood structures in a framework of the variable neighborhood search algorithm, two powerful local search algorithms are designed in such a way that the search process is guided by an iteration jumping probability determining which neighborhood structure will be employed. By doing so, it is shown that some additional enhancements can be achieved by employing the swap neighborhood structure with a speed-up method without jeopardizing the effectiveness of the insertion neighborhood. We also show that the performance of the iterated greedy algorithm significantly depends on the speed-up method employed. The parameters of the proposed iterated greedy algorithms are tuned through a design of experiments on randomly generated benchmark instances. Extensive computational results on Taillard’s well-known benchmark suite show that the iterated greedy algorithms with speed-up methods are equivalent or superior to the best performing algorithms from the literature. Ultimately, 85 out of 120 problem instances are further improved with substantial margins.     

Minimizing Total Weighted Tardiness in a Generalized Job Shop

We consider a generalization of the classical job shop scheduling problem with release times, positive end–start time lags, and a general precedence graph. As objective we consider the total weighted tardiness. We use a tabu search algorithm to search for the order in which the operations are processed on each machine. Given a sequence of operations on each machine, we determine optimal starting times by solving a maximum cost flow problem. This solution is used to determine the neighborhood for our tabu search algorithm. All sequences in our neighborhood are obtained by swapping certain pairs of adjacent operations. We show that only swaps that possess a certain property can improve the current solution; if no such swap is available in the neighborhood, then the current solution is globally optimal. In the computational results we compare our method with other procedures proposed in literature. Our tabu search algorithm seems to be effective both with respect to time and solution quality. The research was carried out at the Technische Universiteit Eindhoven and the Universiteit Utrecht with support of Baan and the Future and Emerging Technologies programme of the EU under contract number IST-1999-14186 (ALCOM-FT).     

FASTEST: a practical low-complexity algorithm for compile-timeassignment of parallel programs to multiprocessors

In the area of parallelizing compilers, considerable research has been carried out on data dependency analysis, parallelism extraction, as well as program and data partitioning. However, designing a practical, low complexity scheduling algorithm without sacrificing performance remains a challenging problem. A variety of heuristics have been proposed to generate efficient solutions but they take prohibitively long execution times for moderate size or large problems. In this paper, we propose an algorithm called FASTEST (Fast Assignment and Scheduling of Tasks using an Efficient Search Technique) that has O(e) time complexity, where e is the number of edges in the task graph. The algorithm first generates an initial solution in a short time and then refines it by using a simple but robust random neighborhood search. We have also parallelized the search to further lower the time complexity. We are using the algorithm in a prototype automatic parallelization and scheduling tool which compiles sequential code and generates parallel code optimized with judicious scheduling. The proposed algorithm is evaluated with several application programs and outperforms a number of previous algorithms by generating parallelized code with shorter execution times, while taking dramatically shorter scheduling times. The FASTEST algorithm generates optimal solutions for a majority of the test cases and close-to-optimal solutions for the rest     

基于自适应邻域模拟退火算法的非合作对策求解

对有多个Nash平衡点的非合作n人有限对策问题进行了研究。首先构造了其非合作n人有限对策的数学规划模型,证明了此模型的解与对策问题的解的等价性;然后提出了求解此类问题的一种自适应邻域模拟退火算法,基于此算法,在不减少问题解的条件下,解决了多解的非合作n人对策问题。通过数值实验说明了此算法的收敛性及稳定性;通过与粒子群算法、免疫粒子群算法、传统模拟退火算法的比较,说明了本文算法的优越性。     

Feature Selection for Multi-label Classification Using Neighborhood Preservation

Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.      

Lateness minimization with Tabu search for job shop scheduling problem with sequence dependent setup times

We tackle the job shop scheduling problem with sequence dependent setup times and maximum lateness minimization by means of a tabu search algorithm. We start by defining a disjunctive model for this problem, which allows us to study some properties of the problem. Using these properties we define a new local search neighborhood structure, which is then incorporated into the proposed tabu search algorithm. To assess the performance of this algorithm, we present the results of an extensive experimental study, including an analysis of the tabu search algorithm under different running conditions and a comparison with the state-of-the-art algorithms. The experiments are performed across two sets of conventional benchmarks with 960 and 17 instances respectively. The results demonstrate that the proposed tabu search algorithm is superior to the state-of-the-art methods both in quality and stability. In particular, our algorithm establishes new best solutions for 817 of the 960 instances of the first set and reaches the best known solutions in 16 of the 17 instances of the second set.     

Self-organizing maps with asymmetric neighborhood function

The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N(3)) to O(N(2)) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.     

改进人工蜂群求解多目标柔性作业车间调度问题

针对多个目标约束的柔性作业车间问题,本文采用基于Pareto解集的改进离散人工蜂群算法来求解.由于经典人工蜂群算法的选择概率不适用于多目标问题,本文对选择概率进行了重定义,将排序引入选择概率中;同时采用基于变异操作的邻域搜索方法进行局部搜索,并使用混合列交叉算子提高种群的多样性;采用Harmonic平均距离对Pareto解集进行裁剪,完成对Pareto解集的更新.最后通过实例测试及仿真实验,验证了本文算法在求解多目标柔性作业车间调度时的有效性.     

Branch-and-price and adaptive large neighborhood search for the truck and trailer routing problem with time windows

Motivated by a situation faced by infrastructure service providers operating in urban areas with accessibility restrictions, we study the truck and trailer routing problem with time windows (TTRPTW). In this problem the vehicle fleet consists of trucks and trailers which may be decoupled. A set of customers has to be served and some of the customers can only be accessed by the truck without the trailer. This gives rise to the planning of truck-and-trailer routes containing truck-only subroutes, in addition to truck-only routes and truck-and-trailer routes without subroutes. We propose a branch-and-price algorithm for the TTRPTW, using problem specific enhancements in the pricing scheme and alternative lower bound computations. We also tailor an adaptive large neighborhood search algorithm to the TTRPTW in order to obtain good initial columns. When compared to existing metaheuristic algorithms we obtain highly competitive results. Some instances with up to 100 customers are solved to optimality with the proposed branch-and-price algorithm.     

Variable neighborhood search for the linear ordering problem

Given a matrix of weights, the linear ordering problem (LOP) consists of finding a permutation of the columns and rows in order to maximize the sum of the weights in the upper triangle. This NP-complete problem can also be formulated in terms of graphs, as finding an acyclic tournament with a maximal sum of arc weights in a complete weighted graph. In this paper, we first review the previous methods for the LOP and then propose a heuristic algorithm based on the variable neighborhood search (VNS) methodology. The method combines different neighborhoods for an efficient exploration of the search space. We explore different search strategies and propose a hybrid method in which the VNS is coupled with a short-term tabu search for improved outcomes. Our extensive experimentation with both real and random instances shows that the proposed procedure competes with the best-known algorithms in terms of solution quality, and has reasonable computing-time requirements.Variable neighborhood search (VNS) is a metaheuristic method that has recently been shown to yield promising outcomes for solving combinatorial optimization problems. Based on a systematic change of neighborhood in a local search procedure, VNS uses both deterministic and random strategies in search for the global optimum.In this paper, we present a VNS implementation designed to find high quality solutions for the NP-hard LOP, which has a significant number of applications in practice. The LOP, for example, is equivalent to the so-called triangulation problem for input–output tables in economics. Our implementation incorporates innovative mechanisms to include memory structures within the VNS methodology. Moreover we study the hybridization with other methodologies such as tabu search.     

Neighborhood preserving regression for image retrieval

In content-based image retrieval (CBIR), relevance feedback has been proven to be a powerful tool for bridging the gap between low level visual features and high level semantic concepts. Traditionally, relevance feedback driven CBIR is often considered as a supervised learning problem where the user provided feedbacks are used to learn a distance metric or classification function. However, CBIR is intrinsically a semi-supervised learning problem in which the testing samples (images in the database) are present during the learning process. Moreover, when there are no sufficient feedbacks, these methods may suffer from the overfitting problem. In this paper, we propose a novel neighborhood preserving regression algorithm which makes efficient use of both labeled and unlabeled images. By using the unlabeled images, the geometrical structure of the image space can be incorporated into the learning system through a regularizer. Specifically, from all the functions which minimize the empirical loss on the labeled images, we select the one which best preserves the local neighborhood structure of the image space. In this way, our method can obtain a regression function which respects both semantic and geometrical structures of the image database. We present experimental evidence suggesting that our algorithm is able to use unlabeled data effectively for image retrieval.     

Static Frequency Assignment in Cellular Networks

A cellular network is generally modeled as a subgraph of the triangular lattice. In the static frequency assignment problem, each vertex of the graph is a base station in the network, and has associated with it an integer weight that represents the number of calls that must be served at the vertex by assigning distinct frequencies per call. The edges of the graph model interference constraints for frequencies assigned to neighboring stations. The static frequency assignment problem can be abstracted as a graph multicoloring problem. We describe an efficient algorithm to multicolor optimally any weighted even or odd length cycle representing a cellular network. This result is further extended to any outerplanar graph. For the problem of multicoloring an arbitrary connected subgraph of the triangular lattice, we demonstrate an approximation algorithm which guarantees that no more than 4/3 times the minimum number of required colors are used. Further, we show that this algorithm can be implemented in a distributed manner, where each station needs to have knowledge only of the weights at a small neighborhood. Received May 13, 1997; revised August 24, 1998.     

Minimizing total tardiness on a single machine with controllable processing times

The concept of time-cost trade-off is commonly considered in PERT/CPM, but it is seldom considered in the scheduling area. Such concept implies that the processing times of jobs are controllable by increasing or decreasing the available resources, such as manpower and equipment. In this paper, we focus on the single machine total tardiness problem with controllable processing times. First, a mixed-integer programming (MIP) model is formulated to find the optimal solution. Then, we propose both a linear programming model and a net benefit of compression (NBC) algorithm to obtain a set of optimal amounts of compression for a given sequence. To solve medium- to large-size problem instances, we develop a heuristic based on the NBC algorithm. To verify the proposed heuristic, the MIP model is used as a comparison for small-size problem instances, whereas for medium- to large-size instances the variable neighborhood search, a useful local search method, is employed. Computational results show that the proposed heuristic has a good performance.     

MTU configuration for real-time switched Ethernet networks

In this paper, we show that in real-time switched Ethernet networks reducing the Maximum Transmission Unit (MTU) size may cause an increase or decrease in the response time of messages. This contradicting behavior arises an optimization problem for configuring the MTU size. We formulate the optimization problem in the context of the multi-hop HaRTES architecture, which is a hard real-time Ethernet protocol. As part of the solution, we propose a search-based algorithm to achieve optimum solutions. We modify the algorithm by presenting two techniques to reduce the search space. Then, we propose a heuristic algorithm with a pseudo-polynomial time complexity based on the search-based algorithm. We perform several experiments, and we show that the proposed heuristic results in an improvement regarding messages response times, compared with configuring the MTU to the maximum or minimum values. Moreover, we show in small network configurations that the heuristic performs as good as the search-based algorithm in many cases.