Reasonable Time Limit Research Articles

An optimal and near-optimal strategy to selecting individuals for transfer in captive breeding programs

As species extinction rates continue to rise, zoos have adopted a more active role in the conservation of endangered species. A central concern is to preserve genetic diversity of zoological populations. Accordingly, when selecting individuals to transfer to new or existing populations, zoo managers must consider the genetic effects on all populations involved. We propose a quadratic integer programming (IP) model to identify a group of individuals to transfer that maximizes genetic diversity within two subpopulations. We then reduce this model to a linear IP formulation and apply it to the California condor ( Gymnogyps californianus) studbook. After simplifying the linear IP model, optimality is achieved within a reasonable time limit when a limited number of individuals are relocated. We also develop a local improvement algorithm (LIA) to efficiently provide near-optimal solutions when we increase the number of transferred individuals. The LIA quickly obtains optimal solutions when few individuals are transferred and in most cases, the LIA outperforms MetaMK, an existing program used to select animals for transfer.

Temperature as an Accelerating Factor for Long-Term Durability Testing of FRPs: Should There Be Any Limitations?

Fiber-reinforced polymer (FRP) composites are increasingly being used in civil engineering applications due to their numerous advantages. Moreover, some environmental conditions can potentially enhance their long-term durability. Therefore, the study of their long-term behavior is crucial to ensure their durability. To perform durability study in a reasonable time limit, accelerating factor, such as high temperature, is generally used. However, the use of very high temperature of conditioning could amplify the reduction of the properties leading to conservative prediction of long-term properties. The present paper attempts to clarify the effects of high temperatures on the mechanical and barrier properties (moisture absorption) of GFRP’s internal reinforcement, by presenting some experimental results and conclusions of laboratory accelerated studies.

Community Detection in Large-scale Bipartite Networks

Community detection in networks receives much attention recently. Most of the previous works are for unipartite networks composed of only one type of nodes. In real world situations, however, there are many bipartite networks composed of two types of nodes. In this paper, we propose a fast algorithm called LP&BRIM for community detection in large-scale bipartite networks. It is based on a joint strategy of two developed algorithms -- label propagation (LP), a very fast community detection algorithm, and BRIM, an algorithm for generating better community structure by recursively inducing divisions between the two types of nodes in bipartite networks. Through experiments, we demonstrate that this new algorithm successfully finds meaningful community structures in large-scale bipartite networks in reasonable time limit.

Optimal multicast route packing

This paper considers the hop-constrained multicast route packing problem with a bandwidth reservation to build QoS-guaranteed multicast routing trees with a minimum installation cost. Given a set of multicast sessions, each of which has a hop limit constraint and a bandwidth requirement, the problem is to determine the set of multicast routing trees in an arc-capacitated network with the objective of minimizing the cost. For the problem, we propose a branch-and-cut-and-price algorithm, which can be viewed as a branch-and-bound method incorporating both the strong cutting plane algorithm and the column generation method. We implemented and tested the proposed algorithm on randomly generated problem instances with sizes up to 30 nodes, 570 arcs, and 10 multicast sessions. The test results show that the algorithm can obtain the optimal solution to practically sized problem instances within a reasonable time limit in most cases.

Tracking multiple sperm whales with widely spaced bottom-mounted hydrophones.

One dataset from the 2nd Int. Workshop on Detection and Localization of Marine Mammals Using Passive Acoustics Data featured multiple clicking sperm whales recorded for 20 min. Data were recorded on five widely spaced bottom-mounted hydrophones at the Atlantic Undersea Test and Evaluation Center. This dataset is used to develop and apply a model-based time difference of arrival (TDOA) method that is capable of simultaneously tracking multiple animals. Clicks on all hydrophones are detected. For every receiver pair, all possible time-differences of arrival (within reasonable time limits) are formed from the detected clicks. A scatterplot of TDOAs versus time, although noisy due to many false TDOAs formed from incorrectly associated clicks, reveals clear “tracks” of slowly varying TDOAs. These tracks are extracted and input to a TDOA tracking algorithm that gives 3-D likelihood surfaces of whale position. Local maxima in the likelihood surfaces are extracted and clustered to give estimated whale tracks.

Furniture supply chain tactical planning optimization using a time decomposition approach

We study the supply chain tactical planning problem of an integrated furniture company located in the Province of Québec, Canada. The paper presents a mathematical model for tactical planning of a subset of the supply chain. The decisions concern procurement, inventory, outsourcing and demand allocation policies. The goal is to define manufacturing and logistics policies that will allow the furniture company to have a competitive level of service at minimum cost. We consider planning horizon of 1 year and the time periods are based on weeks. We assume that customer’s demand is known and dynamic over the planning horizon. Supply chain planning is formulated as a large mixed integer programming model. We developed a heuristic using a time decomposition approach in order to obtain good solutions within reasonable time limit for large size problems. Computational results of the heuristic are reported. We also present the quantitative and qualitative results of the application of the mathematical model to a real industrial case.

Modeling of Resin Flow in Reinforced Dielectric Prepregs

Manufacturing of printed circuit boards or chip-packaging substrates involves the use of resin-filled reinforcement materials, known as prepregs, to bond together laminates with patterned copper layers and serve as dielectric material. In the circuit board or substrate lamination process, the prepreg sheet is placed on top of the conductive copper patterns and pressure is applied to squeeze the resin out of the prepreg to flow and fill the gaps between the baseboard and the copper as well as drilled holes and vias. The primary processing requirement is for resin to fill all the gaps within reasonable time and pressure limits, before the resin cures to a hardened thermoset material. As the resin flow path may be nontrivial, it is desirable to model resin flow and filling of the gap as a function of applied pressure and lamination press closing rate so that one can successfully manufacture a variety of circuit board designs with different material systems. In this work, we model the flow during the filling of the gaps and justify noteworthy simplifications to provide a solution in closed form. This allows us to relate the material and process parameters such as prepreg thickness and applied pressure to the circuit board design. It also permits prediction of the transient development of gap filling. We illustrate the factors that influence the flow and fill process and discuss their importance. Finally, we analyze the process with typical material and processing parameters and compare it with laboratory scale and industrial experiments.

OPTIMAL BASIC BLOCK INSTRUCTION SCHEDULING FOR MULTIPLE-ISSUE PROCESSORS USING CONSTRAINT PROGRAMMING

Instruction scheduling is one of the most important steps for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to find a minimum length schedule for a basic block — a straight-line sequence of code with a single entry point and a single exit point — subject to precedence, latency, and resource constraints. Solving the problem exactly is NP-complete, and heuristic approaches are currently used in most compilers. In contrast, we present a scheduler that finds provably optimal schedules for basic blocks using techniques from constraint programming. In developing our optimal scheduler, the key to scaling up to large, real problems was in the development of preprocessing techniques for improving the constraint model. We experimentally evaluated our optimal scheduler on the SPEC 2000 integer and floating point benchmarks. On this benchmark suite, the optimal scheduler was very robust — all but a handful of the hundreds of thousands of basic blocks in our benchmark suite were solved optimally within a reasonable time limit — and scaled to the largest basic blocks, including basic blocks with up to 2600 instructions. This compares favorably to the best previous exact approaches.

Can a Reasonable Time Limit Improve the Effective Usage of a Computerized Decision Aid?

This study examines the impact of a reasonable time limit on the effective usage of a computerized decision aid. Using current decision making models, a theoretical argument about decision aid usage is developed. This argument is then investigated via two lab experiments. The first experiment determines a reasonable time limit for the task used in the study. The second experiment investigates users’ behavior and heart rate variability under this time limit. The results of our study indicate that the reasonable time limit determined in the first study improved effective utilization of the computerized decision aid. The analysis of heart rate variability provides evidence that the given time constraint improved users’ cognitive coherence.

Optimal threshold value of failure-rate for leased products with preventive maintenance actions

This paper investigates the optimal threshold value of failure rates for leased products with a Weibull lifetime distribution. Within a lease period, any product failure is rectified by minimal repairs and the lessor may incur a penalty when the time required to perform a minimal repair exceeds a reasonable time limit. To reduce product failures, additional preventive maintenance actions are carried out when the failure rate reaches a threshold value. Under this maintenance scheme, a mathematical model of the expected total cost is established. Based on the model, the optimal threshold value and the corresponding maintenance degrees are derived such that the expected total cost is minimized. The structural properties of the optimal policy are investigated and an efficient algorithm is provided to search for the optimal policy. Finally, numerical examples are given to illustrate the features of the optimal policy.

Reliability considerations in the design of cellular manufacturing systems

PurposeThe purpose of this paper is to: develop an effective cellular manufacturing system (CMS) design methodology by simultaneously considering system costs and individual machine reliabilities; and propose a combinatorial search‐based solution procedure to solve large‐sized problems.Design/methodology/approachThis paper presents a multi‐objective mixed integer‐programming model for the design of CMS with the objective of minimizing costs and maximizing system reliability. The approach optimizes inter‐cell material handling costs, the variable cost of machining operations, and the machine under‐utilization costs. It also maximizes the system reliability by selecting process routes for the part types with the highest system reliability for the machines along the routes. To solve the multi‐objective, multiple process plan model, a simulated annealing (SA)‐based algorithm is developed. The algorithm follows the basic steps of SA, but also incorporates the genetic algorithm (GA) operations of crossover and mutations to generate better neighboring solutions from the current good solutions.FindingsThe algorithm in the paper solves the multi‐objective CMS design model and generates near optimal solutions for medium to large‐sized problems within reasonable limits of CPU time.Practical implicationsIn the paper the CMS design approach can be implemented to improve reliability performance of the CMS.Originality/valueA new CMS design methodology in this paper, which minimizes system costs and maximizes machine‐related system reliability, is developed. The proposed algorithm, which combines the basic steps of SA and crossover and mutation operations of GA, will enable CMS designers and users to obtain near optimal solutions for practical‐sized problems within reasonable time limits.

“Any physical change” – New Source Review vindicated

“Any physical change” – New Source Review vindicated

Periodical Preventive-Maintenance Contract for a Leased Facility with Weibull Life-Time

This paper develops a mathematical model to derive the optimal periodical preventive maintenance (PM) policy for a leased facility with Weibull life-time. Within a lease period, any failures of the facility are rectified by minimal repairs and a penalty may occur to the lessor when the time required to perform a minimal repair exceeds a reasonable time limit. To reduce failures of the facility, additional PM actions are carried out periodically during the lease period. When the life-time distribution of a product is Weibull, the optimal number of PM actions and the corresponding maintenance degrees are derived such that the expected total maintenance cost is minimized. The structural properties of the optimal policy are investigated and an efficient algorithm is provided to search for the optimal policy. Finally, numerical examples are provided to illustrate the features of the proposed model.

Automatic path searching for interactive navigation support within virtual medical 3-dimensional objects

This article proposes the use of a disembodied autonomous actor for navigation support within complex virtual medical objects reconstructed from Computed Tomography or Magnetic Resonance Imaging. Such objects are often maze-like, and users risk getting lost within them during Virtual Reality sessions. Therefore, users need paths for guided fly-throughs when performing non-invasive diagnostic tasks. We present a synthetic vision-based actor capable of finding collision-free paths from a given position to a goal point in environments containing loops and impasses. When navigating, the actor voxelizes the virtual environment and searches for collision-free paths in voxel space by using a back tracking search algorithm. Automata and rules control its search behaviour. The resulting paths can be used in dedicated virtual endoscopy applications. Our path search method has been tested within a variety of tubular virtual anatomical structures in 3D such as aortas, colons, or blood vessels of the brain. The actor finds paths within reasonable time limits, even when considering complex anatomical surface models. The method may be used as a valuable tool for assisting virtual endoscopic diagnostic and screening activities in the near future.

A unified modeling and solution framework for combinatorial optimization problems

Combinatorial optimization problems are often too complex to be solved within reasonable time limits by exact methods, in spite of the theoretical guarantee that such methods will ultimately obtain an optimal solution. Instead, heuristic methods, which do not offer a convergence guarantee, but which have greater flexibility to take advantage of special properties of the search space, are commonly a preferred alternative. The standard procedure is to craft a heuristic method to suit the particular characteristics of the problem at hand, exploiting to the extent possible the structure available. Such tailored methods, however, typically have limited usefulness in other problems domains.

Solving partial constraint satisfaction problems with tree decomposition

AbstractIn this paper, we describe a computational study to solve hard partial constraint satisfaction problems (PCSPs) to optimality. The PCSP is a general class of problems that contains a diversity of problems, such as generalized subgraph problems, MAX‐SAT, Boolean quadratic programs, and assignment problems like coloring and frequency planning. We present a dynamic programming algorithm that solves PCSPs based on the structure (tree decomposition) of the underlying constraint graph. With the use of dominance and bounding techniques, we are able to solve small and medium‐size instances of the problem to optimality and to obtain good lower bounds for large‐size instances within reasonable time and memory limits. © 2002 Wiley Periodicals, Inc.

Comparison of Total Laparoscopic Hysterectomy and Laparoscopically Assisted Vaginal Hysterectomy

We compare the surgical results of 60 women undergoing laparoscopically assisted vaginal hysterectomy (LAVH) and 41 having total laparoscopic hysterectomy (TLH) under the indications of uterine fibroids or adenomyosis. With similar specimen weight, TLH required longer surgery duration (140.4 vs. 115.1 min; p < 0.05) than LAVH. Among women with uteri weighing ≤200 g, TLH resulted in relatively smaller blood loss with comparable operating time (115.6 vs. 116.0 min for LAVH; p > 0.05) although the TLH group had a significantly higher rate of previous abdominal surgery (57.7 vs. 20%; p < 0.05). There were no significant differences between the two groups with respect to the mean cost, length of hospital stay and rate of various complications (p > 0.05). As for sexual symptoms, dyspareunia decreased significantly post-operatively in the LAVH group (p < 0.05), but not in the TLH group. A significant reduction in the frequency of orgasms after surgery was detected in both groups (p < 0.05). In conclusion, LAVH has advantages over TLH with reduced operating time. Although it is a technical challenge, TLH can be effectively performed within reasonable time limits in selected cases. The effects on sexual function, following either LAVH or TLH, are found to be similar.

A Lagrangian Heuristic Based Branch-and-Bound Approach for the Capacitated Network Design Problem

The capacitated network design problem is a multicommodity minimal cost network flow problem with fixed charges on the arcs and is well known to be NP-hard. The problem type is very common in the context of transportation networks, telecommunication networks, etc. In this paper we propose an efficient method for this problem, based on a Lagrangian heuristic within a branch-and-bound framework. The Lagrangian heuristic uses a Lagrangian relaxation to obtain easily solved subproblems and solves the Lagrangian dual by subgradient optimization. It also includes techniques for finding primal feasible solutions. The Lagrangian heuristic is then embedded into a branch-and-bound scheme that yields further primal improvements. Special penalty tests and cutting criteria are developed. The branch-and-bound scheme can either be an exact method that guarantees the optimal solution of the problem or be a quicker heuristic. The method has been tested on networks of various structures and sizes. Computational comparisons between this method and a state-of-the-art mixed-integer code are presented. The method is found to be capable of generating good feasible solutions to large-scale problems within reasonable time and data storage limits.

The Three-Dimensional Bin Packing Problem

The problem addressed in this paper is that of orthogonally packing a given set of rectangular-shaped items into the minimum number of three-dimensional rectangular bins. The problem is strongly NP-hard and extremely difficult to solve in practice. Lower bounds are discussed, and it is proved that the asymptotic worst-case performance ratio of the continuous lower bound is 1/8. An exact algorithm for filling a single bin is developed, leading to the definition of an exact branch-and-bound algorithm for the three-dimensional bin packing problem, which also incorporates original approximation algorithms. Extensive computational results, involving instances with up to 90 items, are presented: It is shown that many instances can be solved to optimality within a reasonable time limit.

Solving Frequency Assignment Problems via Tree-Decomposition

In this paper we describe a computational study to solve hard frequency assignment problems (FAPs) to optimality using a tree decomposition of the graph that models interference constraints. We present a dynamic programming algorithm which solves FAPs based on this tree decomposition. We show that with the use of several dominance and bounding techniques it is possible to solve small and medium-size real-life instances of the frequency assignment problem to optimality. Moreover, with an iterative version of the algorithm we obtain good lower bounds for large-size instances within reasonable time and memory limits