Scalable Heuristics Research Articles

Feature Subset Selection With Multi-Scale Fuzzy Granulation

As a typical multigranularity data analysis model, multi-scale rough sets have attracted considerable attention in recent years. However, classical multi-scale rough sets and most of its extended models can only deal with discrete data, which limits its popularization and application. To overcome this problem, we investigate the fuzzy generalization of multi-scale rough sets as well as their application in feature selection for continuous data. To this end, a new type of decision systems, i.e., multi-scale fuzzy decision systems, is formalized to represent knowledge at different scales. Scaled fuzzy granules in terms of a family of scaled fuzzy relations are introduced, using which the granular structures of fuzzy lower and upper approximations are presented. A heuristic lattice-based optimal scale selection algorithm is then put forward from the viewpoint of maintaining the consistency of decision systems. Decision rules with strong generalization ability can be obtained by selecting appropriate scales. Finally, a forward feature selection algorithm was developed by means of the optimal scale to reduce redundant fuzzy relations. Extensive numerical experiments are further conducted to compare the proposed algorithm with some state-of-the-art algorithms. The experimental results show that our model can improve the generalization ability of fuzzy rough set, so as to be more feasible and effective.

Development, validation, and usability testing of prototype mobile application for oral health promotion during pregnancy in India.

Oral diseases are a silent epidemic. The objectives of the study were to develop, validate, and assess the usability of an oral health prototype mobile application for oral health promotion among pregnant women in India. A cross-sectional study was conducted in Tertiary Care Hospital in Delhi, India, after obtaining Ethical Clearance from the Institutional Ethical Committee Board. The study was conducted in three phases: development of the prototype app, its validation, followed by usability testing of the app. Mobile app was validated by 30 pregnant women and 30 subject experts using Heuristic Analysis Scale and usability testing by 30 pregnant women based on System Usability Scale (SUS). Data were analyzed with IBM SPSS version 21.0. Majority (over 90%) of pregnant women and subject experts strongly acknowledged that the app educated the users using positive motivation strategies, instilling comprehensive knowledge and faced no issues with the appropriate functionality of the app. The prototype app scored 73.75 on SUS, indicating high usability. This study holistically explored various dimensions of oral health care in pregnant women. Its novelty is proven by the fact that the content of the prototype application has been phase wise developed and validated by pregnant women and subject experts. Usability testing of the app indicated its high acceptability and ease of use among pregnant women in India.

Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics

Bayesian networks represent relations between variables using a directed acyclic graph (DAG). Learning the DAG is an NP-hard problem and exact learning algorithms are feasible only for small sets of variables. We propose two scalable heuristics for learning DAGs in the linear structural equation case. Our methods learn the DAG by alternating between unconstrained gradient descent-based step to optimize an objective function and solving a maximum acyclic subgraph problem to enforce acyclicity. Thanks to this decoupling, our methods scale up beyond thousands of variables.

Evolutionary neural network for learning of scalable heuristics for pickup and delivery problems with time windows

In this paper, we address the pickup and delivery problem with time windows (PDP-TW) and heterogenous vehicles for minimisation of total tardiness by learning heuristics from a given set of solutions. In order to extract scalable heuristics from optimal or best feasible solutions, we propose a machine-learning (ML)-based approach called ENSIGHT (Evolutionary Neural network with Scalable Information for Generation of Heuristics for Transportation). ENSIGHT consists of three phases: solution generation, interpretation of solutions, and improvement of heuristics by an evolutionary neural network (ENN). First, a set of optimal or best feasible solutions for the training set of problem instances is acquired by using the proposed mathematical model. Second, as for the process interpreting those solutions, an approach for transforming them into training data by way of scalable input attributes as well as output discretisation is followed. Third, the ENN improves the learned heuristics by an evolutionary parameter optimisation process for minimization of total tardiness. To verify the performance of the proposed ENSIGHT, we conducted experiments and the results of which showed that it outperforms other ML techniques and the current dispatching rules (DRs). Moreover, the approach was demonstrated to be effective in learning scalable heuristics based on combined scalable inputs and discretisation as well as an evolutionary improvement process.

The joint network vehicle routing game with optional customers

We consider logistic collaborations where multiple carriers collaborate by consolidating demands, combining delivery routes, and serving new customers. Logistic collaborations are known to provide numerous benefits such as an increase in profit as well as a reduction in emissions for all parties involved. One of the challenges associated with collaborations is the allocation of the additional profits. To this end, we model the corresponding profit allocation problem as a collaborative game. Here, the profit obtained by any subset of the collaborating carriers depends on the new customers served by the remaining carriers. Specifically, we try to determine an allocation in the core based on both the best-case profit and the worst-case profit that each subset of carriers can attain. To achieve this, we propose a heuristic row generation algorithm. We verify the performance of this algorithm on instances derived from benchmark instances of the capacitated vehicle routing problem. We show that our heuristic algorithm scales well with respect to the number of carriers considered and provides allocations similar to those obtained by enumerating all best-case and worst-case profits of each coalition.

Optimizing Assemble-to-Order Systems: Decomposition Heuristics and Scalable Algorithms

We consider continuous-review assemble-to-order (ATO) systems with a general bill of materials (BOM) and general leadtimes. ATO systems have the advantage of mass customization and are widely adopted in practice. However, characterizing the optimal inventory policy is notoriously challenging and computational intractable, especially in large-scale systems. We propose effective and computational scalable heuristics through asymptotics, sample-path, linear programming and primal-dual analyses. First, we characterize the asymptotically optimal policy for the M-system. The policy consists of a periodic review priority (PRP) allocation rule and a coordinated base-stock (CBS) replenishment policy. We then construct heuristic policies using insights from the asymptotically optimal policy. In particular, we adopt the PRP allocation rule and develop a decomposition approach for inventory replenishment. This approach decomposes a general system into assembly subsystems and a linear program is constructed to compute policy parameters. However, both the CBS and the assembly decomposition approach are limited to simple systems. We then consider a second approach, which decomposes a system into distribution subsystems and each subsystem has a straightforward solution, which is similar to the newsvendor problem. We use the primal-dual analysis to show that the expected cost under the optimal independent base-stock policy in a general system could be bounded by two newsvendor systems with properly set parameters. Finally, we examine the effectiveness and scalability of these two decomposition approaches in numerical tests. We find that the assembly decomposition is very effective but computationally expensive and thus only good for small-scale systems; the distribution decomposition performs as effective as the optimal independent base-stock (IBS) policy, but is highly scalable for large-scale systems. Numerical tests also provide some interesting insights on the impact of system parameters on the value of past demand information.

The Superstitious Heuristic: Conceptualization and Measurement

The use of superstition as a heuristic in strategic decision-making is a phenomenon that commonly exists in different cultures and influences firms’ strategic behaviors and performance. In spite of its prevalence and importance, the phenomenon has been largely neglected in the strategy literature. As a first step toward filling this research gap, this study introduces the construct of superstitious heuristic and develops its conceptualization and measurement. Synthesizing literature on superstition and heuristics, I explore the definition and dimensionality of the superstitious heuristic, map it into the existing literature, and differentiate it with other traditional heuristics. I develop a Superstitious Heuristic Scale (SHS) that is applicable in different cultures and decision settings and validate it in the strategic decision context. In doing so, this study advances heuristics research in strategic management and offers a new angle to explain firm strategic behavior and performance. In a broader sense, it contributes to behavioral strategy research in response to the call for more endeavors that build the psychological underpinning of strategic management.

Gender-based Game Engagement Model Validation using Low Fidelity Prototype

Lack of female involvement in ICT field calls for new approaches to ICT education. Serious game is an effective tool for learning, so it can be used to motivate and engage female students to study Computing. Engagement elements for serious game were identified from previous researches and a conceptual model for gender-based engagement using serious game was constructed. In this paper, the model validation of a low fidelity prototype using heuristic evaluation technique for educational game is discussed. The heuristic scale was adapted from previous studies, comprise of playability and enjoyment constructs. In addition, educational elements items are also included. The scale was then validated for content validity, and a pilot study with four (5) HCI experts. Finally, five (5) experts from HCI, Game and Computer Networking subject evaluate the model using this scale. Results of the evaluation showed that the model’s constructs have good usability, playability, enjoyment. Future work will be to validate the model using high fidelity working prototype and user testing.

A Test Score-Based Approach to Stochastic Submodular Optimization

We study the canonical problem of maximizing a stochastic submodular function subject to a cardinality constraint, where the goal is to select a subset from a ground set of items with uncertain individual performances to maximize their expected group value. Although near-optimal algorithms have been proposed for this problem, practical concerns regarding scalability, compatibility with distributed implementation, and expensive oracle queries persist in large-scale applications. Motivated by online platforms that rely on individual item scores for content recommendation and team selection, we study a special class of algorithms that select items based solely on individual performance measures known as test scores. The central contribution of this work is a novel and systematic framework for designing test score–based algorithms for a broad class of naturally occurring utility functions. We introduce a new scoring mechanism that we refer to as replication test scores and prove that as long as the objective function satisfies a diminishing-returns condition, one can leverage these scores to compute solutions that are within a constant factor of the optimum. We then extend these scoring mechanisms to the more general stochastic submodular welfare-maximization problem, where the goal is to partition items into groups to maximize the sum of the expected group values. For this more difficult problem, we show that replication test scores can be used to develop an algorithm that approximates the optimal solution up to a logarithmic factor. The techniques presented in this work bridge the gap between the rigorous theoretical work on submodular optimization and simple, scalable heuristics that are useful in certain domains. In particular, our results establish that in many applications involving the selection and assignment of items, one can design algorithms that are intuitive and practically relevant with only a small loss in performance compared with the state-of-the-art approaches. This paper was accepted by Chung Piaw Teo, optimization.

Strategic Decision Process in SME’s Context: A New Perspective Using Indigenous, Institution, Firm, and Environment Characteristics

Purpose: This paper aimed to contribute to fill the gap of the strategic decision-making process framework in the context of an SME. Not only adds a new perspective in the strategic decision-making process framework, but also suggests new perspectives of firms, environment, institutions and indigenous characteristics as the new approach that magnifies strategic decision-making’s models with respect to SMEs’ scale. Design/methodology/approach: The purposive sampling method has been used in this study. First, we used the CEO as the respondent to fulfilll decision involvement criteria. Second, the samples were selected based on the criterion that the last project of this SME has finished in the last 3 years. Then we choose a project bidding decision from construction SMEs to minimize decision bias. Finally, from 4253 SMEs listed in Papua, we finished with 350 respondents. The study had collected 156 SME's project decisions. Findings: The Heuristic decision, that previously neglected because of information bias and short decision process, deemed to be the most profound dimension in strategic decision making and demonstrated significant results toward SMEs’ project performance. All variables, exclude institution, shows good and significant results. The research uses project decision in the construction industry as the main unit analysis. Practical implications: Exploring strategic decision-making theory in the SME context could convince SME’s CEO to evaluate its external factors before taking a project, processing all the information needed toward its project performance. Originality/value: This heuristic measurement scale is the first valid and reliable tool, based on several previous researches (Busenitz and Barney, 1997; Artinger et al., 2015), that could identify and measure the heuristic process in a strategic decision process.

A multimetric investor index for aquaculture: Application to the European Union and Norway

An aquaculture investor (AQI) index was developed in order to provide a broad view of the relative attractiveness of 29 different European countries to aquaculture investors. AQI is based on five complementary categories: Market, Production, Regulatory, Environment, and Social, with each category containing four indicators. The attraction of investment into aquaculture depends on the viability of developing aquaculture for each country, and these five categories account for the connectivity in the aquaculture industry. The index benchmarks and tracks countries' progress by producing a quantitative and scalable tool for stakeholders to assess and monitor aquaculture attractiveness, and is designed to rank aquaculture competitiveness for each country. Index scores calculated for Europe range from moderate to good, on a heuristic five-class scale. Countries in Northern Europe with well-established aquaculture sectors score better than countries in Southern Europe. Countries with developing aquaculture sectors tend to score moderately. While high scores within single categories can be achieved, the index rewards countries with high scores across the five categories, to provide a more useful tool for stakeholders. No countries within Europe rank below the middle of the moderate range. The index identifies several countries with high scores that do not have significant aquaculture industries (e.g. Sweden and Finland), and further research is warranted to identify why aquaculture has not been developed. It is expected that as AQI is expanded to lower income countries spanning other geographic regions, countries with lower quality indicator scores will have lower overall scores. The index provides a broad-scale approach across a wide range of categories, and should be interpreted in that context, since it is designed to provide high-level guidance of the general attractiveness for aquaculture in each country. Appropriate due diligence for specific circumstances is warranted by all stakeholders requiring further knowledge to assist decision-making.To ensure a wide dissemination and maximum visual appeal to both investors and the public, the AQI index was deployed as a smartphone application (AquaInvestor), freely available on the Google Play Store, together with a companion website, and to our knowledge provides the first country-wide comparative assessment of aquaculture potential, available to investors and the general public in eight European languages and Chinese.

Scalable hybrid and ensemble heuristics for economic virtual resource allocation in cloud and fog cyber-physical systems

Scalable hybrid and ensemble heuristics for economic virtual resource allocation in cloud and fog cyber-physical systems

Resilient design of large-scale distribution feeders with networked microgrids

Electrical distribution systems are often vulnerable to severe weather. Upgrades, such as microgrids, system hardening, and line redundancy, can greatly reduce the number of electrical outages during such extreme events. More recently, the networking of microgrids has received attention as a solution to further improve the resilience of distribution feeders. Although these upgrades have the potential to improve resilience, a barrier to their execution is a lack of tools and approaches that support systematic exploration of the underlying parameters of these upgrades and their cost vs. resilience tradeoffs.To address this gap, we develop a method for designing resilient distribution grids, including networked microgrids, by posing the problem as a two-stage stochastic program. When resilience is defined as the ability of a network to supply load immediately following a storm event, we show that a decomposition-based heuristic algorithm scales to a 1200-node distribution system. We also vary the study parameters, i.e., the cost of microgrids relative to system hardening and target resilience metrics. In this study, we find regions in this parametric space that correspond to different resilient distribution system architectures, such as individual microgrids, hardened networks, and a transition region that suggests the benefits of microgrids networked via hardened circuit segments.

GraphCombEx: a software tool for exploration of combinatorial optimisation properties of large graphs

We present a prototype of a software tool for exploration of multiple combinatorial optimisation problems in large real-world and synthetic complex networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial Explorer), provides a unified framework for scalable computation and presentation of high-quality suboptimal solutions and bounds for a number of widely studied combinatorial optimisation problems. Efficient representation and applicability to large-scale graphs and complex networks are particularly considered in its design. The problems currently supported include maximum clique, graph colouring, maximum independent set, minimum vertex clique covering, minimum dominating set, as well as the longest simple cycle problem. Suboptimal solutions and intervals for optimal objective values are estimated using scalable heuristics. The tool is designed with extensibility in mind, with the view of further problems and both new fast and high-performance heuristics to be added in the future. GraphCombEx has already been successfully used as a support tool in a number of recent research studies using combinatorial optimisation to analyse complex networks, indicating its promise as a research software tool.

HBS-CRA: scaling impact of change request towards fault proneness: defining a heuristic and biases scale (HBS) of change request artifacts (CRA)

Accurately calculating the impact of existing change requests is vital for estimating the probability of fault occurrence in future change requests. For a new request like bug fixing, effectiveness in current change requests is required. In a real-time scenario, bug trackers are deployed to save change requests and their associated information. The trackers and associated information are saved in CVS version control systems and these systems assist programmers to carry out multiple analytical functions and generating descriptions. In our earlier works, we devised the set of change request artifacts and also proposed novel statistical bipartite weighted graphical models to evaluate DFP degree of future change requests. With the motivation gained from this model, here we propose a novel strategy that estimates the DFP of the request by assessing the impact of a change request artifact towards fault-proneness that considers the correlation between code blocks as another factor, which is in addition to our earlier strategy. A novel heuristic and biases scale to evaluate the effectiveness of change request for DFP is devised here in this paper that titled as “Defining a Heuristic and Biases Scale (HBS) of Change Request Artifacts (CRA)”, in short HBS-CRA. The devised model makes use of information retrieval methods to identify the change request artifacts of the request. In addition, it also checks for DFP scope through HBS-CRA. The HBS-CRA is empirically assessed by applying on concurrent versioning and Change request logs of the production level maintenance project.

Safety-Level Aware Bin-Packing Heuristic for Automatic Assignment of Power Plants Control Functions

Finding a suitable set of controllers to which a large set of control functions with different safety levels can be assigned, while minimizing cost, is a significant task during the design of the operational control system of a critical process, such as a power plant. This task is currently performed by experts and extremely time-consuming, which explains why its automation is a real concern. This paper shows first that the above-mentioned assignment problem can be identified as a multiple-choice vector bin packing (BP) with conflicts problem, a combination of different variants of the well-known 1-D BP problem. Such a problem is known to be strongly NP-hard and exact techniques to solve it on large-sized examples are too time and/or space-consuming because of the combinatorial explosion. To solve this problem in polynomial time, this paper proposes a fast heuristic based on a first-fit decreasing approach. Two strategies to perform this heuristic and several criteria to rank the functions before assignment are defined. These strategies and criteria are then compared on the basis of numerous experiments. These experiments show that the proposed heuristic scales well and provides results that are very close to optimum; the difference in the worst case is less than 1%.

Industrial scheduling solution based on flexible heuristics

This paper presents a generic heuristic-based scheduling solution. It highlights the flexibility that a simple heuristic method can offer and shows that using the ISA-95 standard it is possible to express the most relevant problem requirements. In order to illustrate the possible benefits, the paper also compares the solution quality of a smaller scale example scheduling problem to a rigorous mixed-integer linear programming (MILP) approach and shows how a heuristic approach scales towards large-size industrial problems. The paper concludes with a discussion of the advantages and disadvantages of both approaches, showing that for certain types of problems, the heuristic approach is fully sufficient, even if it cannot be expected to result in optimal solutions.

Reconditioning: A Framework for Automatic Power Optimization of QDI Circuits

This paper introduces reconditioning: a novel systematic technique for reducing unnecessary power consumption of asynchronous gate-level netlists, which involves the optimal reordering of conditional communication and logic primitives. Our technique is applicable to asynchronous circuits with handshaking protocols that encode data and control together, in particular, quasi delay insensitive and 1-of- ${N}$ handshaking circuits. Both an optimal integer linear program (ILP) and a fast heuristic algorithm are presented. We show that our ILP is feasible for moderate size circuits and our heuristic algorithm scales to much larger circuits, completing in seconds on circuits with tens of thousands of gates. Our experimental results show power improvement highly depends on the structure of the circuit but can often be above 26% with typically less than 5% area overhead.

Heuristic Scale to Estimate Premature Malaria Parasites: Scope in Microscopic Blood Smear Images

Objective: Malaria is one of the epidemic diseases and early detection of malaria symptoms in the patients using the current manual procedures are skeptical, as the diagnosis patterns depends more on the experience of professionals. To overcome the challenges, in this paper we are proposing computer aided model to support in malaria detection at early stages using Microscopic Blood Smear Images analysis using machine learning. Methods/Statistical Analysis: There are many computer aided models that were proposed and adapted in the process of addressing the diagnosis models. Some models like machine learning, image processing, neural network based solutions etc are adapted, which reflects more insights into the process. However, the issue of gaps in accuracy still persists, and the proposed model of Heuristic Scale to Estimate Premature Malaria Parasites Scope (SEMPS) with multi stage processing of the microscopic images of blood smear is processed. Findings: The proposed model is compared with the benchmark models like SVM and Bayesian, the outcome in terms of efficiency of the model is imperative from the results. The proposed model has resulted in more effective and accurate detection of malaria symptoms in the test cases, and the result accuracy is higher than the other two benchmarking models of SVM and Bayesian techniques chosen for comparative analysis. Improvements: The computational complexity of the SEMPS is evinced as linear, where the majority of benchmarking models are found to be up-hard.

Multi-criteria selection and scaling of ground motion records using Evolutionary Algorithms

Although large ground motion databases are today widely available, the selection of records still hampers the use of nonlinear response history analysis in engineering practice. We propose a method that is based on a novel optimization algorithm in order to create subset of records chosen from a large database. Existing heuristic methods select and/or scale ground motion records so that their mean spectrum is close to a target/design design spectrum. The records obtained following this practice offer good estimates of the mean response but considerably underestimate the inherent response variability, thus providing no insight regarding the dispersion around the mean. The proposed method selects and scales the ground motion records so that their mean spectrum and the (period-depended) dispersion fit best a target spectrum and its dispersion. The problem is formulated as a two-objective optimization problem, where the record selection considers both the mean spectrum and its dispersion at the range of periods of interest. The problem is solved with the aid of the Differential Evolution algorithm which searches for potential record combinations whose mean and variance match the target spectral values (median and percentiles) obtained either from a code-compatible smooth spectrum or from a ground motion prediction equation (GMPE). The proposed procedure is efficient, easy to implement and able to quickly search a large pool of ground motion records, identifying record subsets that provide estimates of the response quantities of interest with a minimum number of ground motions. A three-storey moment frame building is considered as a benchmark problem in order to demonstrate the efficiency of the proposed optimization scheme.