General Instances Research Articles

Stakeholder involvement in drug inventory policies

This paper experimentally investigates the relationships among three major stakeholders that are involved in drug inventory management at Intensive Care Units (ICUs), namely: i) nurses, who in person manage drug orders and carry out storage operations, ii) clinicians, who choose the therapy and shape demand, and iii) the hospital management, who is in charge of the economic sustainability of the hospital. As a case study, we consider the ICU ward of a major Italian public hospital and we focus on antibiotics. We exploit a previously developed Mixed Integer Linear Programming model which decides, for each drug, when and how much to order, and we improve it by adding different sets of constraints to represent each stakeholders’ point of view. By solving three generalized models, each of which ties the satisfaction of a single stakeholder to different thresholds, we explore the mutual effects of taking explicitly into account different perspectives within the inventory policy. We implemented an instance generator, built on the basis of empirical probability distributions extracted from a large set of observed historical data and representing the decision flow ruling drugs prescription. Extensive experiments have been carried out on a set of realistic instances provided by the generator. Results based on our test case not only provide computational evidence to intuitive relations among stakeholders, but also suggest possible levels of compromise. Improved stakeholder satisfaction would also benefit the patient, the passive stakeholder who is the ultimate subject of the caring process.

Real-value negative selection over-sampling for imbalanced data set learning

The learning problem from imbalanced data set poses a major challenge in data mining community. Conventional machine learning algorithms show poor performance in dealing with the classification problems of imbalanced data set since they are originally designed to work with balanced class distribution. In this paper, we propose a new over-sampling technique, which uses the real-value negative selection (RNS) procedure to generate artificial minority data with no requirement of actual minority data available. The generated minority data with rare actual minority data if available are combined with the majority data as input to a bi-class classification approach for learning. In the experiments, we demonstrate the effectiveness of RNS in avoiding the problems often encountered by the existing over-sampling methods such as the generation of noisy instances and almost duplicated instances in the same clusters. Moreover, the extensive experimental results on the different imbalanced datasets from UCI repository and real-world imbalanced datasets show that when dealing with the classification of imbalanced datasets, the proposed hybrid approach can achieve better performance in terms of both G-Mean and F-Measure evaluation metrics as compared to the other existing imbalanced dataset classification techniques.

An evaluation methodology for 3D deep neural networks using visualization in 3D data classification

"Making 3D deep neural networks debuggable". In the study, we develop and propose a 3D deep neural network visualization methodology for performance evaluation of 3D deep neural networks. Our research was conducted using a 3D deep neural network model, which shows the best performance. The visualization method of the research is a method of visualizing part of the 3D object by analyzing the naive Bayesian 3D complement instance generation method and the prediction difference of each feature. The method emphasizes the influence of the network in the process of making decisions. The result of visualization through the algorithm of the study shows a clear difference based on the result class and the instance within the class, and the authors can obtain insight that can evaluate and improve the performance of the DNN (deep neural networks) model by the analyzed results. 3D deep neural networks can be made "indirectly debuggable", and after the completion of the visualization method and the analysis of the result, the method can be used as the evaluation method of "general non-debuggable DNN" and as a debugging method.

Controlling distribution conveyors and multiline palletizers: theoretical foundations and online algorithms

We consider the Distribution problem for multiline palletizing systems, which arises in centralized distribution centres, where boxes have to be stacked up from conveyor belts onto pallets with respect to customer orders. The problem asks how to distribute boxes to k buffer queues such that an optimal subsequent stacking process requires only p stack-up places. The boxes reach the palletizer on the main conveyor of an order-picking system. A distribution conveyor pushes the boxes out to several buffer conveyors. Robotic arms are placed at the end of these buffer conveyors, where each arm picks up the first box of one of the buffer conveyors and moves it onto a pallet located at one of p stack-up places. In this paper, we seek for an assignment of the boxes from the main conveyor to the buffer conveyors such that only p stack-up places are used during the subsequent stacking process. We present online algorithms and analyse their worst-case behaviour. For restricted problems, an optimal online algorithm is given, which also performs very well for general instances. The average-case behaviour is determined by experiments. For benchmarking the general case, we present two binary integer programs. The Distribution problem for stacking systems has not been investigated up to now, although it is a natural problem to consider.

An empirical analysis of exact algorithms for the unbounded knapsack problem

This work presents an empirical analysis of exact algorithms for the unbounded knapsack problem, which includes seven algorithms from the literature, two commercial solvers, and more than ten thousand instances. The terminating step-off, a dynamic programming algorithm from 1966, presented the lowest mean time to solve the most recent benchmark from the literature. The threshold and collective dominances are properties of the unbounded knapsack problem first discussed in 1998, and exploited by the current state-of-the-art algorithms. The terminating step-off algorithm did not exploit such dominances, but has an alternative mechanism for dealing with dominances which does not explicitly exploits collective and threshold dominances. Also, the pricing subproblems found when solving hard cutting stock problems with column generation can cause branch-and-bound algorithms to display worst-case times. The authors present a new class of instances which favors the branch-and-bound approach over the dynamic programming approach but do not have high amounts of simple, multiple and collective dominated items. This behaviour illustrates how the definition of hard instances changes among algorithm approaches. The codes used for solving the unbounded knapsack problem and for instance generation are all available online.

Modelling and solving the thesis defense timetabling problem

The thesis defense timetabling problem consists in composing the suitable committee for a set of defense sessions and assigning each graduation candidate to one of the sessions.In this work, we define the problem formulation that applies to some Italian universities and we provide three alternative solution methods, based on Integer Programming, Constraint Programming and Local Search, respectively. We also develop a principled instance generator, in order to expand the set of available instances.We perform an experimental analysis and we compare our solvers among themselves, using a testbed composed of both real-world and artificial instances. Even though there is no dominant method, the outcome is that Integer Programming gives the best average results, with Local Search being second, and Constraint Programming last on our testbed. All data is available on the web for verification and future comparisons.

A branch, bound, and remember algorithm for the simple disassembly line balancing problem

In this paper, we deal with a disassembly line balancing problem (DLBP), using an AND/OR graph (AOG) to represent the precedence relations between tasks. The decision maker needs to select a proper processing alternative and assign the corresponding tasks among stations to minimise the number of stations, without violating the cycle time constraint and the precedence relations. The problem was first formulated by Koc et al. (2009) and is denoted as type 1 simple DLBP (SDLBP-1) in this study. We prove that an SDLBP-1 with no parallel tasks is polynomially solvable and develop a branch, bound, and remember (BB&R) algorithm for the general SDLBP-1 with parallel tasks. Moreover, two lower bounding schemes, a strengthened Koc's integer programming (IP) model and a new benchmark instance generation scheme are proposed. Computational results show that the BB&R algorithm is the state-of-the-art exact algorithm for SDLBP-1, and that it can be easily truncated into a state-of-the-art heuristic which optimally solves most instances in very short time. In addition, the lower bounds and the strengthened IP model are also demonstrated to be effective.

Multi-Instance Sketch to Image Synthesis With Progressive Generative Adversarial Networks

Real-world images usually contain multiple objects, as a result, generating an image from a multi-instance sketch is an attractive research topic. However, existing generative networks usually produce a similar texture on different instances for those methods focus on learning the distribution of the whole image. To address this problem, we propose a progressive instance texture reserved generative approach to generate more convincible images by decoupling the generation of the instances and the whole image. Specifically, we create an instance generator to synthesize the primitive color distribution and the detailed texture for each instance. Then, an image generator is designed to combine all of these instances to synthesize an image retaining texture and color. Besides, to generate more significant details, such as eyes, ears, and so on, we propose a novel technique called discriminative sketch augmentation, which can provide structural constraint by obtaining the sketch of the discriminative region. The extensive experiments demonstrate that our model not only generates convincing images but also achieves higher inception score and lower Frechet Inception Distance on the MS-COCO dataset.

Zorizko instantzia uniformeak sortzen al dira optimizazio konbinatorioan?

Konputazio ebolutiboan, algoritmoek optimizazio-problemen gainean duten errendimendua ebaluatzeko, ohikoa izaten da problema horien hainbat instantzia erabiltzea. Batzuetan, problema errealen instantziak eskuragarri daude, eta beraz, esperimentaziorako instantzien multzoa hortik osatzen da. Tamalez, orokorrean, ez da hori gertatzen: instantziak eskuratzeko zailtasunak direla tarteko, ikerlariek instantzia artifizialak sortu behar izaten dituzte. Lan honetan, instantzia artifizialak uniformeki zoriz sortzearen inguruko aspektu batzuk izango ditugu aztergai. Zehazki, bibliografian horrenbestetan onetsi den ideia bati erreparatuko diogu: Instantzien parametroen espazioan zein helburu-funtzioen espazioan uniformeki zoriz lagintzea baliokideak dira. Exekutatu ditugun esperimentuen arabera, baliokidetasuna kasu batzuetan ez dela betetzen frogatuko dugu, eta beraz, sortzen diren instantziek espero diren ezaugarriak ez dituztela erakutsiko dugu.

Semi-supervised soft margin consistency based multi-view maximum entropy discrimination

Multi-view maximum entropy discrimination (MVMED) and alternative MVMED (AMVMED) are proposed as extensions of maximum entropy discrimination (MED). In MVMED and AMVMED, they use hard margin consistency principle that the decision of margin parameter is related to classifier parameter directly. While the decision always be indirectly in practice, thus soft margin consistency based multi-view maximum entropy discrimination (SMVMED) has been proposed. But it is found that SMVMED is only adaptive to supervised problems. In this paper, we extend the model of SMVMED to the semi-supervised problems and develop a semi-supervised SMVMED (SSMVMED). Related experiments on multi-view data sets from different aspects have validated the effectiveness of SSMVMED theoretically and empirically. From the experiments, it is found that (1) compared with SMVMED, the average test accuracy of SSMVMED has a 2% enhancement; (2) SSMVMED costs more training time than SMVMED and the extra time is not more than 10%; (3) in terms of the generation of additional unlabeled instances, ‘mid’ strategy has a better test accuracy than ‘self’ and taking all instances to get the center brings a better test accuracy as well; (4) with SSMVMED, the applications to estimation problem and regression problem will be more feasible.

Large Matching in Large Market with Flexible Supply

The size of a matching is defined as the expected number of agents who are matched to some objects. It is an important design objective in many applications, including refugee resettlement, daycare assignment, and mass vaccination campaigns. This paper studies the matching size achieved by strategy-proof mechanisms in a general model of matching with constraints. Our model generalizes a number of constraint structures considered in the extant literature. We show that naive extensions of classical mechanisms may generate an arbitrarily small matching when we have such generalized constraints. Assuming a large market (in that the variety of objects is fixed but the capacities are large), we develop a technique to reshape a given resource constraint to a more structured one, called a polymatroidal constraint, with which a classical mechanism performs well. We propose a mechanism that (i) satisfies strategy-proofness, (ii) always achieves at least 1-1/e = 63.2% of the maximum size (which is the best approximation ratio a strategy-proof mechanism can achieve), (iii) respects agents' preferences, and (iv) is computationally efficient, for general instances.

New complexity results for \u0141ukasiewicz logic

One aspect that has been poorly studied in multiple-valued logics, and in particular in Łukasiewicz logic, is the generation of instances of varying difficulty for evaluating, comparing and improving satisfiability solvers. With the ultimate goal of finding challenging benchmarks for Łukasiewicz satisfiability solvers, we start by defining a natural and intuitive class of clausal forms (simple Ł-clausal forms) and studying their complexity. Since we prove that the satisfiability problem of simple Ł-clausal forms can be solved in linear time, we then define two new classes of clausal forms (Ł-clausal forms and restricted Ł-clausal forms) that truly exploit the non-lattice operations of Łukasiewicz logic and whose satisfiability problems are NP-complete when clauses have at least three literals, and admit linear-time algorithms when clauses have at most two literals. We also define an efficient satisfiability preserving translation of Łukasiewicz logic formulas into Ł-clausal forms. Finally, we describe a random generator of Ł-clausal forms and report on an empirical investigation in which we identify an easy-hard-easy pattern and a phase transition phenomenon for Ł-clausal forms.

Hybridizing Subgradient Optimization and Very Large Scale Neighborhood Search for Nurse Rostering

SYNOPTIC ABSTRACTThe nurse rostering problem (NRP) requires the production of a roster for a set of nurses subjected to a predefined set of requirements. NRP is computationally intractable for general instances. This article presents a hybrid algorithm that integrates a very large scale neighborhood (VLSN) search metaheuristic within a subgradient optimization framework. The use of a subgradient optimization technique enables better navigation during the local search process. Searching the exponential size neighborhood amounts to solve a very small binary program (BP). The solution method is tested on benchmark instances from NSPLib. Its effectiveness and competitiveness with three recent methods are shown.

IMOPSE: a library for bicriteria optimization in Multi-Skill Resource-Constrained Project Scheduling Problem

This paper presents a software library as a research and educational tool for Multi-Skill Resource-Constrained Scheduling Problem. The following useful tools have been implemented in Java: instance Generator, solution validator, solution visualizer and example solvers: Greedy algorithm and Genetic Algorithm. All tools are supported by iMOPSE dataset which consists of 36 instances and additional ’small’ 6 instances for educational purpose. In the paper, three test studies are described: (1) educational use of 6 ’small’ instances, (2) optimization of cost or duration of a schedule, and (3) simple bicritieria optimization of cost/duration of a final schedule. All described tools/examples are freely published on iMOPSE homepage.

A Repair-based approach for stochastic quadratic multiple knapsack problem

In this paper, a new problem called stochastic quadratic multiple knapsack problem (SQMKP) is studied. SQMKP is the extension of the quadratic multiple knapsack problem (QMKP) with stochastic factors. Different from QMKP, the objective of SQMKP is no longer to find a single global optimal solution, but to find a solution with the best expected quality under all possible cases. We use the recoverable robustness technique to tackle these stochastic factors. The idea of the recoverable robustness is to first find a solution that is feasible for the static QMKP, and then adjust the solution through a simple recovery algorithm according to the emerging stochastic scenarios. Following the idea of the recoverable robustness technique, we propose a repair-based approach (RBOA) which involves three key components – an instance generator that is able to create a set of representative scenarios, a static QMKP algorithm that is able to provide a set of potentially robust solutions and a simple fast repair algorithm that can quickly restore the feasibility of a solution with respect to a given stochastic scenario. Experimental results on a set of stochastic instances extended from 90 well-known QMKP benchmarks demonstrate the efficacy of the proposed RBOA.

Spanning of Meta-Feature Space for Travelling Salesman Problem

In this paper, we consider the problem of creating artificial training set containing instances of a given task with known meta-feature set. We focus on the Traveling salesman problem, however, the approach we present is applicable for any other optimization task. We modify the Smith-Miles approach for spanning of meta-feature space by introducing set diversity function. We use this quality function to reduce instance generation problem to an optimization problem. We can most fully span the whole meta-feature space by maximizing the set diversity quality function. Another method we present to sample an instance set is generating certain instances one by one, making them close to specific meta-feature vectors. In this case, we use Euclidian distance between the given meta-feature vector and obtained instance meta-features as the target function for minimization. We compare these two approaches with the na¨ıve baseline random instance generation method that did not take into account diversity of the obtained instance set. We use genetic algorithm and simulated annealing method as the optimization algorithms used on the meta-level. The value of the set diversity function for the naïve method is equal to 0.123, for the method that directly maximizes diversity by genetic algorithm: 0.468, and for the method that generates single instances by simulated annealing: 0.595.

Еквівалентність двох систем паралельного виконання

The method for properties proof for parallel programs running multiple-instance interleaving with shared memory is investigated. Two systems for parallel execution of programs are considered and the justification of the mutual expressiveness of these two approaches are presented in this paper. The first one is with a fixed yet parametric number of programs executing in parallel. The second one implements a generation model (start) and joining after the stop (join) of parallel programs (also called multithreading). The corresponding two basic functions are provided, and their semantics are given. Also, the semantics of other functions related to parallel execution, resource management and access synchronization are presented in this paper. The theorem on the (functional) equivalence of these two systems and its justification are presented. The program in this case is considered as a function over the data. It is argued that for an arbitrary program in one of the systems of parallelism it is possible to construct the corresponding program in another system, which returns the same result (that is, functionally equivalent). Only productive programs are considered here in the context of mutual expressiveness, because otherwise they "hang" and do not return any result, thus they are out of our scope. The obtained result allows us to move reasoning from the more complex system (by structure) with a dynamic generation of parallel program instances to the simpler system (for proofs) with a parametric number of identical programs executed in parallel. Questions for further research in this direction are also identified. Problems in programming 2018; 2-3: 093-098

Discovering co-location patterns with aggregated spatial transactions and dependency rules

Co-location pattern mining focuses on finding associations among spatial features. Existing co-location pattern mining techniques mainly rely on frequency based thresholds which discard the rare patterns and find the noisy patterns. This could be avoided by evaluating co-location patterns based on their statistical significance. Recent studies focused on association rule mining have successfully adopted statistical tests to find significant rules. By transforming spatial data to transaction data, the co-location pattern mining problem can be reduced to an association rule mining problem and such methods can be used to find co-location patterns robustly. A transactionization mechanism has been recently proposed to achieve this. However, this method ignores the effect of general instances, with non-overlapping buffer regions, on the reference instances in their proximity. Addressing this, we propose a novel approach, AGT-Fisher, to robustly transform spatial data to transaction data and use statistically significant dependency rule searching methods to find co-location rules from them. Our work is motivated by an application in environmental health to investigate potential associations between air pollution and adverse birth outcomes in Canada. We used AGT-Fisher to find such associations from real datasets. The discovered co-location patterns were evaluated based on their statistical dependency and the empirical evidence, and results showed that our approach is more robust. Furthermore, we evaluated the resulting patterns to find spatial common and contrast sets, which are two special types of co-location patterns, to compare spatial regions and gain more insights.

Separate, Measure and Conquer

We show a method resulting in the improvement of several polynomial-space, exponential-time algorithms. The method capitalizes on the existence of small balanced separators for sparse graphs, which can be exploited for branching to disconnect an instance into independent components. For this algorithm design paradigm, the challenge to date has been to obtain improvements in worst-case analyses of algorithms, compared with algorithms that are analyzed with advanced methods, notably Measure and Conquer. Our contribution is the design of a general method to integrate the advantage from the separator-branching into Measure and Conquer, for a more precise and improved running time analysis. We illustrate the method with improved algorithms for M ax ( r ,2)-C sp and #D ominating S et . An instance of the problem M ax ( r ,2)-C SP , or simply M ax 2-CSP, is parameterized by the domain size r (often 2), the number of variables n (vertices in the constraint graph G ), and the number of constraints m (edges in G ). When G is cubic, and omitting sub-exponential terms here for clarity, we give an algorithm running in time r (1/5) n = r (2/15) m the previous best was r (1/4) n = r (1/6) m . By known results, this improvement for the cubic case results in an algorithm running in time r (9/50) m for general instances; the previous best was r (19/100) m . We show that the analysis of the earlier algorithm was tight: our improvement is in the algorithm, not just the analysis. The same running time improvements hold for M ax C ut , an important special case of M ax 2-CSP, and for Polynomial and Ring CSP, generalizations encompassing graph bisection, the Ising model, and counting. We also give faster algorithms for #D ominating S et , counting the dominating sets of every cardinality 0, … , n for a graph G of order n . For cubic graphs, our algorithm runs in time 3 (1/5) n the previous best was 2 (1/2) n . For general graphs, we give an unrelated algorithm running in time 1.5183 n the previous best was 1.5673 n . The previous best algorithms for these problems all used local transformations and were analyzed by the Measure and Conquer method. Our new algorithms capitalize on the existence of small balanced separators for cubic graphs—a non-local property—and the ability to tailor the local algorithms always to “pivot” on a vertex in the separator. The new algorithms perform much as the old ones until the separator is empty, at which point they gain because the remaining vertices are split into two independent problem instances that can be solved recursively. It is likely that such algorithms can be effective for other problems too, and we present their design and analysis in a general framework.

Improved integrality gap upper bounds for traveling salesperson problems with distances one and two

We prove new integrality gap upper bounds for the traveling salesperson problem with distances one and two ((1,2)-TSP).We obtain these bounds by investigating the structure of the support graph of optimal solutions to the subtour elimination linear programming relaxation, with one additional cutting plane inequality.For undirected (1,2)-TSP, our main results are as follows:•All instances have an integrality gap of at most 5/4.•Instances admitting half-integral solutions have integrality gap at most 7/6.•Instances admitting subcubic solutions of cost at most the order of the instance have integrality gap at most 10/9, even without the cutting plane. This bound is tight, and holds in particular for basic solutions in the fractional 2-matching polytope with cost at most the order of the instance.For directed (1,2)-TSP instances we show an integrality gap upper bound of 3/2 for general instances, and of 5/4 for instances admitting half-integral solutions.We prove these bounds by providing local search algorithms that, in polynomial time, find 2-matchings with few components in the support of the solution. We show that the run times of our algorithms cannot be considerably improved under standard complexity-theoretic assumptions: we show that finding improved TSP solutions via local search is intractable for edge change parameterized by the size of the neighborhoods even for instances with distances one and two; this strengthens a result of Dániel Marx.