Breakout local search for the multi-objective gate allocation problem

The problem of assigning gates to arriving and departing flights is one of the most important problems in airport operations. We take into account the real multi-criteria nature of the problem by optimizing a total of nine gate allocation objectives that are oriented both on convenience for airport/airline services and passenger comfort. As far as we are aware, this is the largest number of objectives jointly optimized in the GAP literature. Given the complexity of the considered problem, we propose a heuristic approach based on the Breakout Local Search (BLS) framework. BLS is a recent variant of the Iterated Local Search (ILS) with a particular focus on the perturbation strategy. Based on some relevant information on search history, it tries to introduce an appropriate degree of diversification by determining adaptively the number and type of moves for the next perturbation phase. Moreover, we use a new memory-based greedy constructive heuristic to generate a starting point for BLS. Benchmark instances used for our experiments and comparisons are based on information provided by Manchester Airport.     

Automated functional testing of online search services

Search services are the main interface through which people discover information on the Internet. A fundamental challenge in testing search services is the lack of oracles. The sheer volume of data on the Internet prohibits testers from verifying the results. Furthermore, it is difficult to objectively assess the ranking quality because different assessors can have very different opinions on the relevance of a Web page to a query. This paper presents a novel method for automatically testing search services without the need of a human oracle. The experimental findings reveal that some commonly used search engines, including Google, Yahoo!, and Live Search, are not as reliable as what most users would expect. For example, they may fail to find pages that exist in their own repositories, or rank pages in a way that is logically inconsistent. Suggestions are made for search service providers to improve their service quality. Copyright © 2010 John Wiley & Sons, Ltd.     

元搜索引擎中的成员选择和结果合并策略研究

近年来,信息检索成为研究热点,搜索引擎成为用户经常使用的服务之一.但是独立搜索引擎的覆盖面狭窄,检索效率低,为了得到比较全面和准确的结果,需要反复调用多个搜索引擎.而元搜索引擎就是一种调用其它独立搜索引擎的引擎,它可以更好的满足用户查询的需求.简述了元搜索引擎的工作原理,分析比较了元搜索引擎的一些技术,并针对元搜索引擎的成员选择和结果合并方面提出了一些实现策略.     

二进制布谷鸟搜索算法

为了寻找求解NP完全问题的新算法,采用二进制编码串表示鸟巢的位置,对布谷鸟寻找新鸟巢的Lévy飞行路径分别按照Kennedy和Eberha公式及刘建华公式进行二进制代码变换,引入二进制编码控制系数对变换得到的二进制编码进行混合更新,保留布谷鸟蛋被淘汰的机制等方法将新型高效的布谷鸟搜索（CS）算法改进为二进制布谷鸟搜索（BCS）算法。将BCS算法用于求解背包问题,结果好于遗传算法和几种混合遗传算法;将BCS算法用于求解旅行商问题,结果好于遗传算法、蚁群算法和微粒群算法,但略差于改进的惯性权重自适应调整微粒群优化算法。二进制布谷鸟搜索算法是求解NP完全问题的新型高效算法。     

A variable neighborhood search algorithm for the surgery tactical planning problem

We address the tactical planning problem of surgeries that consists in building an admission plan of patients over a medium-term horizon planning so as to minimize over and under utilization of several resources such as operating theaters, beds and nursing care, compared with their target level of utilization. The problem is formulated as a mixed integer linear program for which exact solution methods fail to find an optimal solution in a reasonable execution time. We develop a Variable Neighborhood Search algorithm and show its ability to provide high quality solutions in short computational running times compared with CPLEX for numerous real-sized instances based on the surgery planning problem in a Dutch cardiothoracic center. Furthermore, with few parameters' settings and low computational memory requirements, this approach may easily be implemented in a decision support system for hospitals.     

A meta-heuristic based goal-selection strategy for mobile robot search in an unknown environment

The single-robot search problem in an unknown environment is defined as the problem of finding a stationary object in the environment whose map is not known a priori. Compared to exploration, the only difference lies in goal selection as the objectives of search and exploration are dissimilar, i.e. a trajectory that is optimal in exploration does not necessarily minimize the expected value of the time to find an object along it. For this reason, in this paper we extend the preliminary ideas presented in Kulich et al. [1] to a general framework that accounts for the particular characteristics of the search problem. Within this framework, an important decision involved in the determination of the trajectory can be formulated as an instance of the Graph Search Problem (GSP), a generalization of the well-known Traveling Deliveryman Problem (TDP) which has not received much attention in the literature. We developed a tailored Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for the GSP, which generates good quality solutions in very short computing times and is incorporated in the overall framework. The proposed approach produces very good results in a simulation environment, showing that it is feasible from a computational standpoint and the proposed strategy outperforms the standard approaches.     

A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem

Home health care, i.e. visiting and nursing patients in their homes, is a growing sector in the medical service business. From a staff rostering point of view, the problem is to find a feasible working plan for all nurses that has to respect a variety of hard and soft constraints, and preferences. Additionally, home health care problems contain a routing component: a nurse must be able to visit her patients in a given roster using a car or public transport. It is desired to design rosters that consider both, the staff rostering and vehicle routing components while minimizing transportation costs and maximizing satisfaction of patients and nurses.     

A hybrid evolutionary local search with depth first search split procedure for the heterogeneous vehicle routing problems

Routing Problems have been deeply studied over the last decades. Split procedures have proved their efficiency for those problems, especially within global optimization frameworks. The purpose is to build a feasible routing solution by splitting a giant tour into trips. This is done by computing a shortest path on an auxiliary graph built from the giant tour. One of the latest advances consists in handling extra resource constraints through the generation of labels on the nodes of the auxiliary graph. Lately, the development of a new generic split family based on a Depth First Search (DFS) approach during label generation has highlighted the efficiency of this new method for the routing problems, through extensive numerical evaluations on the location-routing problem.In this paper, we present a hybrid Evolutionary Local Search (hybrid ELS) for non-homogeneous fleet Vehicle Routing Problems (VRP) based on the application of split strategies. Experiments show our method is able to handle all known benchmarks, from Vehicle Fleet Mix Problems to Heterogeneous Fleet VRP (HVRP). We also propose a set of new realistic HVRP instances from 50 to more than 250 nodes coming from French counties. It relies on real distances in kilometers between towns. Since many classical HVRP instance sets are solved to optimality, this new set of instances could allow a fair comparative study of methods. The DFS split strategy shows its efficiency and attests the fact that it can be a promising line of research for routing problems including numerous additional constraints.     

一种新的多目标改进和声搜索优化算法

针对标准和声搜索算法存在收敛不稳定及不能用于多目标优化问题的缺陷,通过引入交叉算子、自适应记忆内搜索概率和调节概率,改进了传统的和声搜索算法;根据Pareto支配关系,结合算法和声记忆库内信息完全共享的特性,提出了基于动态Pareto最优前沿的能够求解多目标优化问题的多目标改进和声搜索算法。通过几个典型函数的仿真测试表明,提出的算法能够高效稳定地收敛于Pareto最优前沿,获得分布均匀的Pareto解集。     

A multi-start tabu search method for a single-machine scheduling problem with periodic maintenance and sequence-dependent set-up times

In this paper we study a problem of sequencing jobs in a machine with programmed preventive maintenance and sequence-dependent set-up times. The problem combines two NP-hard problems, so we propose a heuristic method for solving it, which hybridizes multi-start strategies with Tabu Search. We compare our method with the only published metaheuristic algorithm for this problem on a set of 420 instances. The comparison favors the method developed in this work, showing that is able to find high quality solutions in very short computational times.     

An iterated local search for the multi-commodity multi-trip vehicle routing problem with time windows

The Multi-Commodity Multi-Trip Vehicle Routing Problem with Time Windows calls for the determination of a routing planning to serve a set of customers that require products belonging to incompatible commodities. Two commodities are incompatible if they cannot be transported together into the same vehicle. Vehicles are allowed to perform several trips during the working day. The objective is to minimize the number of used vehicles.We propose an Iterated Local Search that outperforms the previous algorithm designed for the problem. Moreover, we conduct an analysis on the benefit that can be obtained introducing the multi-trip aspect at the fleet dimensioning level. Results on classical VRPTW instances show that, in some cases, the fleet can be halved.     

Variable neighborhood search for the pharmacy duty scheduling problem

In this paper, we study on the Pharmacy Duty Scheduling (PDS) problem, where a subset of pharmacies should be on duty on national holidays, at weekends and at nights in order to be able to satisfy the emergency drug needs of the society. PDS problem is a multi-period p-median problem with special side constraints and it is an NP-Hard problem. We propose four Variable Neighborhood Search (VNS) heuristics. The first one is the basic version, BVNS. The latter two, Variable Neighborhood Decomposition Search (VNDS) and Variable Neighborhood Restricted Search (VNRS), aim to obtain better results in less computing time by decomposing or restricting the search space. The last one, Reduced VNS (RVNS), is for obtaining good initial solutions rapidly for BVNS, VNDS and VNRS. We test BVNS, VNRS and VNDS heuristics on randomly generated instances and report the computational test results. We also use VNS heuristics on real data for the pharmacies in central İzmir and obtain significant improvements.     

Public-key encryption with bidirectional keyword search and its application to encrypted emails

A traditional Public-key Encryption scheme with Keyword Search (PEKS) allows multiple senders to encrypt keywords under the public key of a receiver such that the receiver can search on these encrypted keywords using his/her searching secret key. In encrypted email systems, an email user not only needs to search on encrypted emails received from other users, but also needs to search on encrypted emails sent to other users. Motivated by this, the paper proposes a cryptographic method to allow these two types of user (i.e., senders and receivers) to search on encrypted keywords, which is called Public-key Encryption with Bidirectional Keyword Search (PEBKS). We give formal definitions of a PEBKS scheme and its indistinguishable security model to capture the scenario that no adversary can efficiently distinguish two ciphertexts of keywords from each other, even if the adversary can adaptively obtain search trapdoors of many keywords. Specifically, we propose a concrete PEBKS scheme, whose security relies on a standard hard problem, i.e., bilinear Diffie–Hellman problem, in the random oracle model. Finally, we simulate the proposed PEBKS scheme to assess its practicability and convinces that its feasibility to be applied to encrypted email systems.     

QoS-driven multicast tree generation using Tabu search

Many multimedia communication applications require a source to transmit messages to multiple destinations subject to Quality-of-Service (QoS) delay constraint. The problem to be solved is to find a minimum cost multicast tree where each source to destination path is constrained by a delay bound. This problem has been proven to be NP-complete. In this paper, we present a Tabu Search (TS) algorithm to construct a minimum cost delay bounded multicast tree. The proposed algorithm is then compared with many existing multicast algorithms. Results show that on almost all test cases, TS algorithm exhibits more intelligent search of the solution subspace and is able to find better solutions than other reported multicast algorithms.     

A two phase variable neighborhood search for the multi-product inventory routing problem

We address a multi-product inventory routing problem and propose a two-phase Variable Neighborhood Search (VNS) metaheuristic to solve it. In the first phase, VNS is used to solve a capacitated vehicle routing problem at each period to find an initial solution without taking into account the inventory. In the second phase, we iteratively improve the initial solution while minimizing both the transportation and inventory costs. For this, we propose two different algorithms, a Variable Neighborhood Descent and a Variable Neighborhood Search. We present an heuristic and a Linear Programming formulation, which are applied after each local search move, to determine the amount of products to collect from each supplier at each period. During the exploration, we use priority rules for suppliers and vehicles, based on the current delivery schedule over the planning horizon. Computational results show the efficiency of the proposed two-phase approach.     

Efficient execution of composite Web services exchanging intensional data

Web service technologies provide a standard means of integrating heterogeneous applications distributed over the Internet. Successive compositions of new Web services using pre-existing ones usually create a hierarchical structure of invocations among a large number of Web services. For the efficient execution of these composite Web services, we propose an approach which exploits intensional XML data, i.e. an XML document that contains special elements representing the calls to Web services, in order to delegate the invocations of the external Web services to some relevant nodes. We formalize an invocation plan for composite Web services in which intensional data is used as their parameters and results, and define a cost-based optimization problem to obtain an efficient invocation plan for them. We provide an A∗ heuristic search algorithm to find an optimal invocation plan for a given set of Web services and also present a greedy method of generating an efficient solution in a short time. The experimental results show that the proposed greedy method can find a close-to-optimal solution efficiently and has good scalability for a complex call hierarchy of Web services.     

Fusion-based methods for result diversification in web search

Search result diversification of text documents is especially necessary when a user issues a faceted or ambiguous query to the search engine. A variety of approaches have been proposed to deal with this issue in recent years. In this article, we propose a group of fusion-based result diversification methods with the aim to improve performance that considers both relevance and diversity. They are linear combinations of scores that are obtained from different component search systems. The weight of each search system is determined by considering three factors: performance, dissimilarity, and complementarity. There are two major contributions. Firstly, we find that all the three factors of performance and complementarity and dissimilarity are useful for effective weighting of linear combination. Secondly, we present the logarithmic function-based model for converting ranking information into scores. Experiments are carried out with four groups of results submitted to the TREC web diversity task. Experimental results show that some of the fusion methods that use the aforementioned techniques perform more effectively than the state-of-the-art fusion methods for result diversification.     

Applying the MOVNS (multi-objective variable neighborhood search) algorithm to solve the path planning problem in mobile robotics

Mobile robots must calculate the appropriate navigation path before starting to move to its destination. This calculation is known as the Path Planning (PP) problem. The PP problem is one of the most researched topics in mobile robotics. Taking into account that the PP problem is an NP-hard problem, Multi-Objective Evolutionary Algorithms (MOEAs) are good candidates to solve this problem. In this work, a new multi-objective evolutionary approach based on the Variable Neighborhood Search (MOVNS) is proposed to solve the PP problem. To the best of our knowledge, this is the first time that MOVNS is proposed to solve the path planning of mobile robots. The proposed MOVNS handles three different objectives in order to obtain accurate and efficient paths. These objectives are: the path safety, the path length, and the path smoothness (related to the energy consumption). Furthermore, in order to test the proposed MOEA, we have used eight realistic scenarios for the paths calculation. On the other hand, we also compared our proposal with other approaches of the state of the art, showing the advantages of MOVNS. In particular, in order to evaluate the obtained results we applied different quality metrics. Moreover, to demonstrate the statistical robustness of the obtained results we also performed a statistical analysis. Finally, the study shows that the proposed MOVNS is a good alternative to solve the PP problem, producing good paths with less length, more safety, and more smooth movements. We think this is an important contribution to the mobile robotics, and therefore, to the field of expert and intelligent systems.     

An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem

We model and solve the Railway Rapid Transit Network Design and Line Planning (RRTNDLP) problem, which integrates the two first stages in the Railway Planning Process. The model incorporates costs relative to the network construction, fleet acquisition, train operation, rolling stock and personnel management. This implies decisions on line frequencies and train capacities since some costs depend on line operation. We assume the existence of an alternative transportation system (e.g. private car, bus, bicycle) competing with the railway system for each origin–destination pair. Passengers choose their transportation mode according to the best travel times. Since the problem is computationally intractable for realistic size instances, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm, which can simultaneously handle the network design and line planning problems considering also rolling stock and personnel planning aspects. The ALNS performance is compared with state-of-the-art commercial solvers on a small-size artificial instance. In a second stream of experiments, the ALNS is used to design a railway rapid transit network in the city of Seville.     

求解作业车间调度问题的差分和声搜索算法

为能够应用和声搜索算法(HSA)高效求解作业车间调度问题(JSSP),提出一种新的差分和声搜索算法(DEHSA)。首先,针对和声函数连续而工序离散现象,设计了排序工序数量转换法,将浮点数的和声转换成工件序列;其次,为提高HSA的收敛速度,改进了HSA的进化模式,不仅是替换一个最差解,还提出了和声变量进化时依赖于当前最优解的“导优”概率;最后,将差分进化算法(DEA)引入到HSA中,克服了HSA方向性差和后期停滞的现象。仿真实验结果表明,DEHSA在求解JSSP上具有可行性和有效性。