Variable Search Research Articles

Direct-access retrieval during sentence comprehension: Evidence from Sluicing

Language comprehension requires recovering meaning from linguistic form, even when the mapping between the two is indirect. A canonical example is ellipsis, the omission of information that is subsequently understood without being overtly pronounced. Comprehension of ellipsis requires retrieval of an antecedent from memory, without prior prediction, a property which enables the study of retrieval in situ ( Martin & McElree, 2008, 2009). Sluicing, or inflectional-phrase ellipsis, in the presence of a conjunction, presents a test case where a competing antecedent position is syntactically licensed, in contrast with most cases of nonadjacent dependency, including verb–phrase ellipsis. We present speed–accuracy tradeoff and eye-movement data inconsistent with the hypothesis that retrieval is accomplished via a syntactically guided search, a particular variant of search not examined in past research. The observed timecourse profiles are consistent with the hypothesis that antecedents are retrieved via a cue-dependent direct-access mechanism susceptible to general memory variables.

Modernization of the regional heat and power engineering: problems of fund-raising

The consideration in the article is given to the state of objects of heat power engineering in Sverdlovsk region. Particular attention is paid to social importance of delivering of heat and power energy under climatic conditions of Sverdlovsk region. The necessity of modernization of equipment participating at delivering of heat and power energy is admitted. The main problem is a search of financing source of measures related to overhaul and current repair as well as to replacement of old equipment. Tariffs on heat and power energy are set by government and their increase has big social resonance. Therefore three possible variants of search of financing sources are considered: liberalization of tariffs on heat power energy, governmental support of heat power engineering branch, attracting of private investments with applying of mechanisms of governmental support. The conclusion is made about favourite developing of the third source of financing.

Solving two-stage hybrid flow shop using climbing depth-bounded discrepancy search

This paper investigates how to adapt a discrepancy-based search method to solve two-stage hybrid flowshop scheduling problems in which each stage consists of several identical machines operating in parallel. The objective is to determine a schedule that minimizes the makespan. We present an adaptation of the Climbing Depth-bounded Discrepancy Search (CDDS) method based on Johnson’s rule and on dedicated lower bounds for the two-stage hybrid flow shop problem. We report the results of extensive computational experiments, which show that the proposed adaptation of the CDDS method solves instances in restrained CPU time and with high quality of makespan.

Tabu search with path relinking for an integrated production–distribution problem

This paper deals with the problem of integrating production and distribution planning over periods of a finite horizon. We consider a capacity-constrained plant that produces a number of items distributed by a fleet of homogenous vehicles to customers with known demand for each item in each period. The production planning defines the amount of each item produced in every period, while the distribution planning defines when customers should be visited, the amount of each item that should be delivered to customers, and the vehicle routes. The objective is to minimize production and inventory costs at the plant, inventory costs at the customers and distribution costs. We propose two tabu search variants for this problem, one that involves construction and a short-term memory, and one that incorporates a longer term memory used to integrate a path relinking procedure to the first variant. The proposed tabu search variants are tested on generated instances with up to ten items and on instances from the literature involving a single item.

A Fast Algorithm for the First-Passage Times of Gauss-Markov Processes with Hölder Continuous Boundaries

Even for simple diffusion processes, treating first-passage problems analytically proves intractable for generic barriers and existing numerical methods are inaccurate and computationally costly. Here, we present a novel numerical method that is faster and has more tightly controlled accuracy. Our algorithm is a probabilistic variant of dichotomic search for the computation of first passage times through non-negative homogeneously Holder continuous boundaries by Gauss-Markov processes. These include the Ornstein-Uhlenbeck process underlying the ubiquitous “leaky integrate-and-fire” model of neuronal excitation. Our method evaluates discrete points in a sample path exactly, and refines this representation recursively only in regions where a passage is rigorously estimated to be probable (e.g. when close to the boundary).

Column Generation based Primal Heuristics

In the past decade, significant progress has been achieved in developing generic primal heuristics that made their way into commercial mixed integer programming (MIP) solver. Extensions to the context of a column generation solution approach are not straightforward. The Dantzig-Wolfe decomposition principle can indeed be exploited in greedy, local search, rounding or truncated exact methods. The price coordination mechanism can bring a global view that may be lacking in some “myopic” approaches based on a compact formulation. However, the dynamic generation of variables requires specific adaptation of heuristic paradigms. The column generation literature reports many application specific studies where primal heuristics are a key to success. There remains to extract generic methods that could be seen as black-box primal heuristics for use across applications. In this paper we review generic classes of column generation based primal heuristics. We then focus on a so-called “diving” method in which we introduce diversification based on Limited Discrepancy Search. While being a general purpose approach, the implementation of our heuristic illustrates the technicalities specific to column generation. The method is numerically tested on variants of the cutting stock and vehicle routing problems.

Climbing discrepancy search for flowshop and jobshop scheduling with time lags

This paper addresses the jobshop and the flowshop scheduling problems with minimum and maximum time lags. To solve this kind of problems, we propose adaptations of Climbing Discrepancy Search (CDS). We study various parameter settings. Computational experiments are provided to evaluate the propositions.

Variable neighborhood search approaches for scheduling jobs on parallel machines with sequence-dependent setup times, precedence constraints, and ready times

In this paper, we discuss a scheduling problem for jobs on identical parallel machines. Ready times of the jobs, precedence constraints, and sequence-dependent setup times are considered. We are interested in minimizing the performance measure total weighted tardiness that is important for achieving good on-time delivery performance. Scheduling problems of this type appear as subproblems in decomposition approaches for large scale job shops with automated transport of the jobs as, for example, in semiconductor manufacturing. We suggest several variants of variable neighborhood search (VNS) schemes for this scheduling problem and compare their performance with the performance of a list based scheduling approach based on the Apparent Tardiness Cost with Setups and Ready Times (ATCSR) dispatching rule. Based on extensive computational experiments with randomly generated test instances we are able to show that the VNS approach clearly outperforms heuristics based on the ATCSR dispatching rule in many situations with respect to solution quality. When using the schedule obtained by ATCSR as an initial solution for VNS, then the entire scheme is also fast and can be used as a subproblem solution procedure for complex job shop decomposition approaches.

Transmission Network Expansion Planning with Simulation Optimization

Within the electric power literature the transmission expansion planning problem (TNEP) refers to the problem of how to upgrade an electric power network to meet future demands. As this problem is a complex, non-linear, and non-convex optimization problem, researchers have traditionally focused on approximate models of power flows. Existing approaches are often tightly coupled to the approximation choice. Until recently, these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (e.g. large amounts of limited control, renewable generation) that necessitates new optimization techniques. In this paper, we propose a local search variation of the powerful Limited Discrepancy Search (LDLS) that encapsulates the complexity of power flows in a black box that may be queried for information about the quality of a proposed expansion. This allows the development of a new optimization algorithm that is independent of the underlying power model.

Fast Local Search Algorithm for Weighted Feedback Arc Set in Tournaments

We present a fast local search algorithm that finds an improved solution (if there is any) in the k-exchange neighborhood of the given solutionto an instance of Weighted Feedback Arc Set in Tournaments. More precisely,given an arc weighted tournament T on n vertices and a feedback arc set F (a set of arcs whose deletion from T turns it into a directed acyclic graph), our algorithm decides in time O(2o(k) n log n) if there is a feedback arc set of smaller weight and that differs from F in at most k arcs. To our knowledge this is the first algorithm searching the k-exchange neighborhood of an NP-complete problem that runs in (parameterized) subexponential time. Using this local search algorithm for Weighted Feedback Arc Set in Tournaments, we obtain subexponential time algorithms for a local search variant of Kemeny Ranking — a problem in social choice theory and of One-Sided Cross Minimization — a problem in graph drawing.

Discrepancy search for the flexible job shop scheduling problem

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop problem in which each operation must be processed on a given machine chosen among a finite subset of candidate machines. The aim is to find an allocation for each operation and to define the sequence of operations on each machine, so that the resulting schedule has a minimal completion time. We propose a variant of the climbing discrepancy search approach for solving this problem. We also present various neighborhood structures related to assignment and sequencing problems. We report the results of extensive computational experiments carried out on well-known benchmarks for flexible job shop scheduling. The results demonstrate that the proposed approach outperforms the best-known algorithms for the FJSP on some types of benchmarks and remains comparable with them on other ones.

Rapid search for gross illumination discrepancies in upright but not inverted images

Rapid search for gross illumination discrepancies in upright but not inverted images

Parallel machine scheduling with precedence constraints and setup times

This paper presents different methods for solving parallel machine scheduling problems with precedence constraints and setup times between the jobs. These problems are strongly NP-hard and it is even conjectured that no list scheduling algorithm can be defined without explicitly considering jointly scheduling and resource allocation. We propose dominance conditions based on the analysis of the problem structure and an extension to setup times of the energetic reasoning constraint propagation algorithm. An exact branch-and-bound procedure and a climbing discrepancy search (CDS) heuristic based on these components are defined. We show how the proposed dominance rules can still be valid in the CDS scheme. The proposed methods are evaluated on a set of randomly generated instances and compared with previous results from the literature and those obtained with an efficient commercial solver. We conclude that our propositions are quite competitive and our results even outperform other approaches in most cases.

A self-adaptive global best harmony search algorithm for continuous optimization problems

This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm, a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate ( HMCR) and pitch adjustment rate ( PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth ( BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants.

A local-best harmony search algorithm with dynamic subpopulations

This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms, the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution, information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore, a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition, an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that, overall, the proposed DLHS algorithm is more effective or at least competitive in finding near-optimal solutions compared with state-of-the-art harmony search variants.

A new directed graph approach for automated setup planning in CAPP

The task of setup planning is to determine the number and sequence of setups and the machining features or operations in each setup. Now there are three main methods for setup planning, i.e., the knowledge-based approach, the graph-based approach and the intelligence algorithm-based approach. In the knowledge-based and graph-based approaches reported in the literature, the main problem is that there is no guarantee that all precedence cycles between setups can be avoided during setup formation. The methods to break precedence cycles between setups are to split one setup into smaller setups. However, the implementation of this method is difficult and complex. In the intelligence algorithm-based approach, the method to handle the precedence constraints is a penalty strategy, which does not reflect the influence of precedence constraints on setup plans explicitly. To deal with the above deficiencies, a new directed graph approach is proposed to describe precedence constraints explicitly, which consists of three parts: (1) a setup precedence graph (SPG) to describe precedence constraints between setups. During the generation of the SPG, the minimal number of tolerance violations is guaranteed preferentially by the vertex clusters algorithm for serial vertices and the minimal number of setups is achieved by using variants of the breadth-first search. Precedence cycles between setups are avoided by checking whether two serial vertex clusters can generate a cycle; (2) operation sequencing to minimise tool changes in a setup; and (3) setup sequencing to generate optimal setup plans, which could be implemented by a topological sort. The new directed graph approach will generate many optimal or near-optimal setup plans and provide more flexibility required by different job shops. An example is illustrated to demonstrate the effect of the proposed approach.

A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment

An essential feature of a dynamic multiobjective evolutionary algorithm (MOEA) is to converge quickly to the Pareto-optimal Set before it changes. In cases where the behavior of the dynamic problem follows a certain trend, convergence can be accelerated by anticipating the characteristics of future changes in the problem. A prediction model is usually used to exploit past information and estimate the location of the new Pareto-optimal Set. In this work, we propose the novel approach of tracking and predicting the changes in the location of the Pareto Set in order to minimize the effects of a landscape change. The predicted direction and magnitude of the next change, known as the predictive gradient, is estimated based on the history of previously discovered solutions using a weighted average approach. Solutions updated with the predictive gradient will remain in the vicinity of the new Pareto-optimal Set and help the rest of the population to converge. The prediction strategy is simple to implement, making it suitable for fast-changing problems. In addition, a new memory technique is introduced to exploit any periodicity in the dynamic problem. The memory technique selects only the more promising stored solutions for retrieval in order to reduce the number of evaluations used. Both techniques are incorporated into a variant of the multi-objective evolutionary gradient search (MO-EGS) and two other MOEAs for dynamic optimization and results indicate that they are effective at improving performance on several dynamic multiobjective test problems.

Preferred Operators and Deferred Evaluation in Satisficing Planning

Heuristic forward search is the dominant approach to satisficing planning to date. Most successful planning systems, however, go beyond plain heuristic search by employing various search-enhancement techniques. One example is the use of helpful actions or preferred operators, providing information which may complement heuristic values. A second example is deferred heuristic evaluation, a search variant which can reduce the number of costly node evaluations. Despite the wide-spread use of these search-enhancement techniques however, we note that few results have been published examining their usefulness. In particular, while various ways of using, and possibly combining, these techniques are conceivable, no work to date has studied the performance of such variations. In this paper, we address this gap by examining the use of preferred operators and deferred evaluation in a variety of settings within best-first search. In particular, our findings are consistent with and help explain the good performance of the winners of the satisficing tracks at IPC 2004 and 2008.

Energy Efficient and Scalable Search in Dense Wireless Sensor Networks

In this paper, we consider the problem of information discovery in a densely deployed wireless sensor network (WSN), where the initiator of search is unaware of the location of target information. We propose two protocols: increasing ray search (IRS), an energy efficient and scalable search protocol, and k-IRS, an enhanced variant of IRS. The priority of IRS is energy efficiency and sacrifices latency whereas k-IRS is configurable in terms of energy-latency trade-off and this flexibility makes it applicable to varied application scenarios. The basic principle of these protocols is to route the search packet along a set of trajectories called rays that maximizes the likelihood of discovering the target information by consuming least amount of energy. The rays are organized such that if the search packet travels along all these rays, then the entire terrain area will be covered by its transmissions while minimizing the overlap of these transmissions. In this way, only a subset of total sensor nodes transmits the search packet to cover the entire terrain area while others listen. We believe that query resolution based on the principles of area coverage provides a new dimension for conquering the scale of WSN. We compare IRS and k-IRS with existing query resolution techniques for unknown target location such as expanding ring search (ERS), Random walk search, and variants of Gossip search. We show by analysis, simulation, and implementation in testbed that IRS and k-IRS are highly scalable, the cost of search (total number of transmitted bytes) is independent of node density, and it is much lower than that of existing proposals under high node density.

Solving scheduling problems by untimed model checking

In this article, we show how scheduling problems can be modelled in untimed process algebra, by using special tick actions. A minimal-cost trace leading to a particular action, is one that minimises the number of tick steps. As a result, we can use any (timed or untimed) model checking tool to find shortest schedules. Instantiating this scheme to μCRL, we profit from a richer specification language than timed model checkers usually offer. Also, we can profit from efficient distributed state space generators. We propose a variant of breadth-first search that visits all states between consecutive tick steps, before moving to the next time slice. We experimented with a sequential and a distributed implementation of this algorithm. In addition, we experimented with beam search, which visits only parts of the search space, to find near-optimal solutions. Our approach is applied to find optimal schedules for test batches of a realistic clinical chemical analyser, which performs several kinds of tests on patient samples.