Suitable Heuristics Research Articles

Providing tailored heuristic advice to Systems Engineers

AbstractAn INCOSE‐wide initiative has exposed at least 600 heuristics. Previous work indicates that rationalizing and simplifying this set to make it useful and memorable is difficult, if not intractable. Difficulty Assessment Tools (DATs) have been used for years to characterize the difficulty of a problem and provide tailored advice. This paper explores using a DAT to characterize the problem, and using the outputs to provide heuristic and other forms of advice. To test this approach, 50 heuristics and 10 principles were scored and embedded into an online DAT. An experiment was conducted to determine whether the DAT discussion, recommended approach, and heuristic/principles advice were useful. All teams considered the discussion very useful. As might be expected, the results indicated that the heuristic usefulness was a function of the teams' experience and familiarity with the task. The tool prioritization of suitable heuristics met developers' expectations, but was undetected by the users. This maybe because the heuristics were a hand‐picked set of 50 Heuristics from a set of 600+, meaning all were highly useful. Further work is proposed to check this assessment. The DAT usefulness results indicate that Systems Engineers should use the DAT to inform their approach throughout the lifecycle.

LinSets.zip: Compressing Linear Set Diagrams.

Linear diagrams are used to visualize set systems by depicting set memberships as horizontal line segments in a matrix, where each set is represented as a row and each element as a column. Each such line segment of a set is shown in a contiguous horizontal range of cells of the matrix indicating that the corresponding elements in the columns belong to the set. As each set occupies its own row in the matrix, the total height of the resulting visualization is as large as the number of sets in the instance. Such a linear diagram can be visually sparse and intersecting sets containing the same element might be represented by distant rows. To alleviate such undesirable effects, we present LinSets.zip, a new approach that achieves a more space-efficient representation of linear diagrams. First, we minimize the total number of gaps in the horizontal segments by reordering columns, a criterion that has been shown to increase readability in linear diagrams. The main difference of LinSets.zip to linear diagrams is that multiple non-intersecting sets can be positioned in the same row of the matrix. Furthermore, we present several different rendering variations for a matrix-based representation that utilize the proposed row compression. We implemented the different steps of our approach in a visualization pipeline using integer-linear programming, and suitable heuristics aiming at sufficiently fast computations in practice. We conducted both a quantitative evaluation and a small-scale user experiment to compare the effects of compressing linear diagrams.

Production planning under RTP, TOU and PPA considering a redox flow battery storage system

Due to climate change and the increasing scarcity of resources, the sustainability performance of companies is increasingly becoming the focus of science and practice. Consequently, bicriteria energy-efficient production planning under price-dynamic electricity tariffs—e.g., real-time-pricing (RTP) or time-of-use (TOU)—is meanwhile well established, often fathoming the tradeoffs between electricity costs of production and another criterion such as makespan. However, tradeoffs between electricity costs and electricity consumption in general are rarely the focus of such analyses. So-called green power purchase agreements (PPAs), which are becoming increasingly popular in the European business community as a means of improving corporate sustainability performance, are also largely ignored. Thus, for the first time in the scientific literature, we put this type of electricity tariff to the test by analyzing the tradeoffs between electricity costs and electricity consumption in a lot-sizing and scheduling context. Here, we additionally consider a real-world redox flow battery storage system that may be the system of the future, which is also new to the literature on lot-sizing and scheduling. Even more: due to the complex nature of our bicriteria mixed-integer problem, we develop and present suitable heuristics. These include an energy-efficient allocation heuristic in the case of PPA and, among others, a fix-relax-and-optimize heuristic combined with a decomposition approach in the case of RTP and TOU. Ultimately, a scenario analysis demonstrates the performance of these heuristics.

Parameter Coverage for Testing of Autonomous Driving Systems under Uncertainty

Autonomous Driving Systems (ADSs) are promising, but must show they are secure and trustworthy before adoption. Simulation-based testing is a widely adopted approach, where the ADS is run in a simulated environment over specific scenarios. Coverage criteria specify what needs to be covered to consider the ADS sufficiently tested. However, existing criteria do not guarantee to exercise the different decisions that the ADS can make, which is essential to assess its correctness. ADSs usually compute their decisions using parameterised rule-based systems and cost functions, such as cost components or decision thresholds. In this article, we argue that the parameters characterise the decision process, as their values affect the ADS’s final decisions. Therefore, we propose parameter coverage, a criterion requiring to cover the ADS’s parameters. A scenario covers a parameter if changing its value leads to different simulation results, meaning it is relevant for the driving decisions made in the scenario. Since ADS simulators are slightly uncertain, we employ statistical methods to assess multiple simulation runs for execution difference and coverage. Experiments using the Autonomoose ADS show that the criterion discriminates between different scenarios and that the cost of computing coverage can be managed with suitable heuristics.

Increasing safety levels in human-machine interaction by beyond-5G wireless redundancy

Factory automation in the context of Industry 4.0/5.0 requires safety levels to satisfy more stringent and tight limits than those available so far. This goal is further challenged by the extension to the wireless environment of industrial shop floor communications that were traditionally based on cabled networks. Starting with wireless LANs, the trend towards the use of industrial wireless is fostered by the advent of fifth Generation (5G) private connectivity and is bound to increase its pace in the evolution towards 6G. In particular, the interaction of human operators with industrial robots and autonomous vehicles on the shop floor is posing stringent safety requirements that in turn push forward the dependability and reliability limits of wireless connectivity. To help achieve these limits, this paper proposes a dynamic redundancy mechanism based on the real-time activation/deactivation of radio bearers instantiated between mobile devices carried by humans and machines and multiple base stations, to achieve guaranteed upper bounds on packet loss probability in the communication of data related to operational safety control loops. An optimization problem is posed, and suitable heuristics are evaluated by simulation in a 5G and beyond wireless environment, aiming to dynamically maintain the required reliability levels with small computational effort.

A novel approach to solve AI planning problems in graph transformations

The aim of AI planning is to solve the problems with no exact solution available. These problems usually have a big search space, and planning may not find plans with the least actions and in the shortest time. Recent researches show that using suitable heuristics can help to find desired plans. In planning problems specified formally through graph transformation system (GTS), there are dependencies between applied rules (actions) in the search space. This fact motivates us to solve the planning problem for a small goal (instead of the main goal), extract dependencies from the searched space, and use these dependencies to solve the planning problem for the main goal. In GTS based systems, the nodes of a state (really is a graph) can be grouped due to their type. To create a small (refined) goal, we use a refinement technique to remove the predefined percent of nodes from each group of the main goal. Bayesian Optimization Algorithm (BOA) is then used to solve the planning problem for the refined goal. BOA is an Estimation of Distribution Algorithm (EDA) in which Bayesian networks are used to evolve the solution populations. Actually, a Bayesian network is learned from the current population, and then this network is employed to generate the next population. Since the last Bayesian network learned in BOA has the knowledge about dependencies between applied rules, this network can be used to solve the planning problem for the main goal. Experimental results on four well-known planning domains confirm that the proposed approach finds plans with the least actions and in the lower time compared with the state-of-the-art approaches.

EUHSA: Extending Usability Heuristics for Smartphone Application

Emerging development trends in smartphone computing assist the user to enhance skills, health improvement, earning opportunities, sharing ideas, providing entertainment, and so on. Appropriate transfer of these assistances demands a vibrant appearance to a smartphone user. End users emphasize easy-to-use, ergonomics, efficiency, and visual aesthetic, among others. Measuring usability issues allows for improving these features. An inspection-based heuristics set identifies the usability issues for improving the quality of smartphone interfaces. Using suitable heuristics is highly relevant. In this paper, we present an extended and relevant set of 14 Usability Heuristics for Smartphone (EUHSA) application, where 13 heuristics were selected after cross-linking the identified usability flaws with the previously proposed heuristics through a user study with 800 students from a human-computer interaction course. An additional heuristic is proposed against uncaptured usability flaws. Furthermore, the EUHSA was validated using an expert evaluation study. Findings were compared with the previously proposed Joyce and SMASH sets of heuristics. The results explicitly justify the EUHSA and prove its effectiveness in evaluating the usability issues.

Image Evolution Using 2D Power Spectra

Procedurally generated images and textures have been widely explored in evolutionary art. One active research direction in the field is the discovery of suitable heuristics for measuring perceived characteristics of evolved images. This is important in order to help influence the nature of evolved images and thereby evolve more meaningful and pleasing art. In this regard, particular challenges exist for quantifying aspects of style and shape. In an attempt to bridge the divide between computer vision and cognitive perception, we propose the use of measures related to image spatial frequencies. Based on existing research that uses power spectral density of spatial frequencies as an effective metric for image classification and retrieval, we posit that Fourier decomposition can be effective for guiding image evolution. We refine fitness measures based on Fourier analysis and spatial frequency and apply them within a genetic programming environment for image synthesis. We implement fitness strategies using 2D Fourier power spectra and phase, with the goal of evolving images that share spectral properties of supplied target images. Adaptations and extensions of the fitness strategies are considered for their utility in art systems. Experiments were conducted using a variety of greyscale and colour target images, spatial fitness criteria, and procedural texture languages. Results were promising, in that some target images were trivially evolved, while others were more challenging to characterize. We also observed that some evolved images which we found discordant and “uncomfortable” show a previously identified spectral phenomenon. Future research should further investigate this result, as it could extend the use of 2D power spectra in fitness evaluations to promote new aesthetic properties.

Scheduling electric vehicles making milk-runs for just-in-time delivery

Battery-operated electric vehicles are frequently used in in-plant logistics systems to feed parts from a central depot to workcells on the shop floor. These vehicles, often called tow trains, make many milk-run trips during a typical day, with the delivery timetable depending on the production schedule. To operate such a milk-run delivery system efficiently, not only do the timetabled trips need to be assigned to vehicles, it is also important to take the limited battery capacity into consideration. Moreover, since most tow trains in use today are still operated by human drivers, fairness aspects with respect to the division of the workload also need to be considered. In this context, we tackle the following problem we encountered at a large manufacturer of engines for trucks and busses in Germany. Given a fixed schedule of milk-runs (round trips) to be performed during a planning horizon, and a fleet of homogeneous electric vehicles stationed at a depot, which vehicle should set out on which milk-run and when should recharging breaks be scheduled, such that all runs can be completed with the minimum number of vehicles and all vehicles are about equally busy? We investigate the computational complexity of this problem and develop suitable heuristics, which are shown to solve instances of realistic size to near-optimality in a matter of a few minutes. We also offer some insight into how battery technology influences vehicle utilization.

Cover-Encodings of Fitness Landscapes

The traditional way of tackling discrete optimization problems is by using local search on suitably defined cost or fitness landscapes. Such approaches are however limited by the slowing down that occurs when the local minima that are a feature of the typically rugged landscapes encountered arrest the progress of the search process. Another way of tackling optimization problems is by the use of heuristic approximations to estimate a global cost minimum. Here, we present a combination of these two approaches by using cover-encoding maps which map processes from a larger search space to subsets of the original search space. The key idea is to construct cover-encoding maps with the help of suitable heuristics that single out near-optimal solutions and result in landscapes on the larger search space that no longer exhibit trapping local minima. We present cover-encoding maps for the problems of the traveling salesman, number partitioning, maximum matching and maximum clique; the practical feasibility of our method is demonstrated by simulations of adaptive walks on the corresponding encoded landscapes which find the global minima for these problems.

Scheduling direct deliveries with time windows to minimise truck fleet size and customer waiting times

This paper tackles the operational problem of scheduling direct deliveries from a single source (e.g. a distribution centre) to multiple customers (e.g. assembly plants). The problem consists of scheduling a set of given round trips such that each trip is processed exactly once within its time window and the employed truck fleet is as small as possible. Moreover, as a secondary objective, customer waiting times should be minimal. Such planning problems arise in many industries like, for instance, the automotive industry, where just-in-time parts are often shipped via direct delivery to OEMs. We propose two different mixed-integer programming models for this problem, discuss similarities to classic routing and scheduling problems from the literature, identify a subproblem that is solvable in polynomial time and propose suitable heuristics. In a computational study, the proposed procedures are shown to perform well both on newly generated instances as well as those from the literature. We also show that minimising waiting times is an adequate measure to make schedules more robust in the face of unforeseen disturbances.

Atypical transistor-based chaotic oscillators: Design, realization, and diversity

In this paper, we show that novel autonomous chaotic oscillators based on one or two bipolar junction transistors and a limited number of passive components can be obtained via random search with suitable heuristics. Chaos is a pervasive occurrence in these circuits, particularly after manual adjustment of a variable resistor placed in series with the supply voltage source. Following this approach, 49 unique circuits generating chaotic signals when physically realized were designed, representing the largest collection of circuits of this kind to date. These circuits are atypical as they do not trivially map onto known topologies or variations thereof. They feature diverse spectra and predominantly anti-persistent monofractal dynamics. Notably, we recurrently found a circuit comprising one resistor, one transistor, two inductors, and one capacitor, which generates a range of attractors depending on the parameter values. We also found a circuit yielding an irregular quantized spike-train resembling some aspects of neural discharge and another one generating a double-scroll attractor, which represent the smallest known transistor-based embodiments of these behaviors. Through three representative examples, we additionally show that diffusive coupling of heterogeneous oscillators of this kind may give rise to complex entrainment, such as lag synchronization with directed information transfer and generalized synchronization. The replicability and reproducibility of the experimental findings are good.

A dynamic, compiler guided DVFS mechanism to achieve energy-efficiency in multi-core processors

Continuing progress and integration levels in silicon technologies make possible complete end-user systems on a single chip. This massive level of integration makes modern multi-core chips widely adoptable in multiple domains. In the design of high-performance massive multi-core chips, power and heat are dominant constraints. The increasing power consumption is of growing concern due to several reasons, e.g., cost, performance, reliability, scalability, and environmental impact. Increased power consumption not only raises chip temperature and cooling cost, but also decreases chip reliability and performance. Dynamic voltage and frequency scaling (DVFS) is a popular methodology to optimize the power usage/heat dissipation of electronic systems without significantly compromising overall system performance. The recent trend towards chip-multiprocessors (CMP) executing multi-threaded workloads with heterogeneous behavior motivates the need for per-core DVFS control mechanisms. In this paper, we propose a set of algorithms that use compile-time information to achieve DVFS control at run-time. By using compile-time information, we propose suitable heuristics, which make our power management mechanism more precise at run-time. We demonstrate that the proposed compiler-based DVFS mechanism performs better than the existing history- or profile-based methods that predict the future program behavior depending on the past statistics.

A Framework for Collocation Error Correction in Web Pages and Text Documents

Much of the English in text documents today comes from nonnative speakers. Web searches are also conducted very often by non-native speakers. Though highly qualified in their respective fields, these speakers could potentially make errors in collocation, e.g., "dark money" and "stock agora" (instead of the more appropriate English expressions "black money" and "stock market" respectively). These may arise due to literal translation from the respective speaker's native language or other factors. Such errors could cause problems in contexts such as querying over Web pages, correct understanding of text documents and more. This paper proposes a framework called CollOrder to detect such collocation errors and suggest correctly ordered collocated responses for improving the semantics. This framework integrates machine learning approaches with natural language processing techniques, proposing suitable heuristics to provide responses to collocation errors, ranked in the order of correctness. We discuss the proposed framework with algorithms and experimental evaluation in this paper. We claim that it would be useful in semantically enhancing Web querying e.g., financial news, online shopping etc. It would also help in providing automated error correction in machine translated documents and offering assistance to people using ESL tools.

The impact of combination of decision makers’ heuristics on the supply chain performance

Analytical newsvendor models assume that order decision makers can place optimal orders to maximize profit, but many of their actual decisions are apart from the optimal quantity. Many scholars in operations management have presumed that the problem might occur in the middle of decision-making process and they attempted to verify the cognitive concept, heuristics. Consequently, three typical behaviors by heuristics were discovered: (1) mean anchoring (2) demand chasing, and (3) gambler fallacy. The purpose of this study is to testify how performance is influenced by the combination of diverse or same decision maker’s heuristics in a supply chain, then to verify the finest combination for the highest profit. This study suggests theoretical background of the heuristics, and simulated results from previous empirical evidences reflect which heuristics is hazardous. In addition, it recommends the suitable heuristics relying on various environments around the decision maker.

Minimizing edge length to connect sparsely sampled unstructured point sets

Most methods for interpolating unstructured point clouds handle densely sampled point sets quite well but get into trouble when the point set contains regions with much sparser sampling, a situation often encountered in practice. In this paper, we present a new method that provides a better interpolation of sparsely sampled features.We pose the surface construction problem as finding the triangle mesh which minimizes the sum of all triangles’ longest edge. Since searching for matching umbrellas among sparsely sampled points to yield a closed manifold shape is a difficult problem, we introduce suitable heuristics. Our algorithm first connects the points by triangles chosen in order of their longest edge and with the requirement that all edges must have at least two incident triangles. This yields a closed non-manifold shape which we call the Boundary Complex. Then we transform it into a manifold triangulation using topological operations. We show that in practice, runtime is linear to that of the Delaunay triangulation of the points. Source code is available online.

Crystal structure determination by global optimisation in configuration space: a case study for distributed computing on the NRW-grid

The problem of ab-initio crystal structure determination from (incomplete) powder X-ray diffraction data is formulated as a global optimisation problem. The problem is of complexity class P, which suggests that the problem itself is practically infeasible for any but trivial structures. Yet, we show in this report that the inherent symmetry of the problem along with suitable heuristics and with massive computational power, allows the determination of structures with up to 20 atoms in the asymmetric unit from scratch. We give an overview of the methods employed and show numerical examples obtained by solving a model problem on the NRW-grid, which harnesses the computational power of several hundred computers at several universities in the state of Northrhine-Westfalia.

Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms

In this paper, we solve instances of the multiobjective multiconstraint (or multidimensional) knapsack problem (MOMCKP) from the literature, with three objective functions and three constraints. We use exact as well as approximate algorithms. The exact algorithm is a properly modified version of the multicriteria branch and bound (MCBB) algorithm, which is further customized by suitable heuristics. Three branching heuristics and a more general purpose composite branching and construction heuristic are devised. Comparison is made to the published results from another exact algorithm, the adaptive ε-constraint method [Laumanns, M., Thiele, L., Zitzler, E., 2006. An efficient, adaptive parameter variation scheme for Metaheuristics based on the epsilon-constraint method. European Journal of Operational Research 169, 932–942], using the same data sets. Furthermore, the same problems are solved using standard multiobjective evolutionary algorithms (MOEA), namely, the SPEA2 and the NSGAII. The results from the exact case show that the branching heuristics greatly improve the performance of the MCBB algorithm, which becomes faster than the adaptive ε -constraint. Regarding the performance of the MOEA algorithms in the specific problems, SPEA2 outperforms NSGAII in the degree of approximation of the Pareto front, as measured by the coverage metric (especially for the largest instance).

Light-Weight SMT-based Model Checking

Recently, the notion of an array-based system has been introduced as an abstraction of infinite state systems (such as mutual exclusion protocols or sorting programs) which allows for model checking of invariant (safety) and recurrence (liveness) properties by Satisfiability Modulo Theories (SMT) techniques. Unfortunately, the use of quantified first-order formulae to describe sets of states makes fix-point checking extremely expensive. In this paper, we show how invariant properties for a sub-class of array-based systems can be model-checked by a backward reachability algorithm where the length of quantifier prefixes is efficiently controlled by suitable heuristics. We also present various refinements of the reachability algorithm that allows it to be easily implemented in a client-server architecture, where a ''light-weight'' algorithm is the client generating proof obligations for safety and fix-point checks and an SMT solver plays the role of the server discharging the proof obligations. We also report on some encouraging preliminary experiments with a prototype implementation of our approach.

Eine neue Methodik zur Erhöhung der Leistungsfähigkeit Evolutionärer Algorithmen durch die Integration lokaler Suchverfahren

Article Eine neue Methodik zur Erhöhung der Leistungsfähigkeit Evolutionärer Algorithmen durch die Integration lokaler Suchverfahren was published on April 1, 2004 in the journal at - Automatisierungstechnik (volume 52, issue 4).