Number Of Real-life Applications Research Articles

An effective hybrid evolutionary algorithm for the set orienteering problem

The Set Orienteering Problem (SOP) is a variant of the popular Orienteering Problem (OP) arising from a number of real-life applications. The aim is to find a tour across a subset of customers, while maximizing the collected profit within a given travel time limit. In SOP, vertices (customers) are partitioned into clusters, where a profit is associated to each cluster. The profit of a cluster is collected only if at least one vertex belonging to the cluster is contained in the tour. For this NP-hard problem, we present a highly effective hybrid evolutionary algorithm that integrates a cluster-based crossover operator, a randomized mutation operator to generate multiple distinct promising offspring solutions, and a two-phase local refinement procedure that explores both feasible and infeasible solutions in search of high-quality local optima. Extensive experiments on 192 large benchmark instances show that the proposed algorithm significantly outperforms all the existing approaches from the SOP literature. In particular, it reports improved best-known solutions (new lower bounds) for 54 instances, while matching the existing best-known result for 133 instances. We further investigate the contribution of the key algorithmic elements to the success of the proposed approach.

Solving the clustered traveling salesman problem via traveling salesman problem methods.

The Clustered Traveling Salesman Problem (CTSP) is a variant of the popular Traveling Salesman Problem (TSP) arising from a number of real-life applications. In this work, we explore a transformation approach that solves the CTSP by converting it to the well-studied TSP. For this purpose, we first investigate a technique to convert a CTSP instance to a TSP and then apply powerful TSP solvers (including exact and heuristic solvers) to solve the resulting TSP instance. We want to answer the following questions: How do state-of-the-art TSP solvers perform on clustered instances converted from the CTSP? Do state-of-the-art TSP solvers compete well with the best performing methods specifically designed for the CTSP? For this purpose, we present intensive computational experiments on various benchmark instances to draw conclusions.

Chlorosulfuric acid-assisted production of functional 2D materials

The use of two-dimensional materials in bulk functional applications requires the ability to fabricate defect-free 2D sheets with large aspect ratios. Despite huge research efforts, current bulk exfoliation methods require a compromise between the quality of the final flakes and their lateral size, restricting the effectiveness of the product. In this work, we describe an intercalation-assisted exfoliation route, which allows the production of high-quality graphene, hexagonal boron nitride, and molybdenum disulfide 2D sheets with average aspect ratios 30 times larger than that obtained via conventional liquid-phase exfoliation. The combination of chlorosulfuric acid intercalation with in situ pyrene sulfonate functionalisation produces a suspension of thin large-area flakes, which are stable in various polar solvents. The described method is simple and requires no special laboratory conditions. We demonstrate that these suspensions can be used for fabrication of laminates and coatings with electrical properties suitable for a number of real-life applications.

Fortification Against Cascade Propagation Under Uncertainty

Network cascades represent a number of real-life applications: social influence, electrical grid failures, viral spread, and so on. The commonality between these phenomena is that they begin from a set of seed nodes and spread to other regions of the network. We consider a variant of a critical node detection problem dubbed the robust critical node fortification problem, wherein the decision maker wishes to fortify nodes (within a budget) to limit the spread of cascading behavior under uncertain conditions. In particular, the arc weights—how much influence one node has on another in the cascade process—are uncertain but are known to lie in some range bounded by a worst-case budget uncertainty. This problem is shown to be [Formula: see text]-hard even in the deterministic case. We formulate a mixed-integer program (MIP) to solve the deterministic problem and improve its continuous relaxation via nonlinear constraints and convexification. The robust problem is computationally more difficult, and we present an MIP-based expand-and-cut exact solution algorithm, in which the expansion is enhanced by cutting planes, which are themselves tied to the expansion process. Insights from these exact solutions motivate two novel (interrelated) centrality measures, and a centrality-based heuristic that obtains high-quality solutions within a few seconds. Finally, extensive computational results are given to validate our theoretical developments as well as provide insights into structural properties of the robust problem and its solution.

A highly effective hybrid evolutionary algorithm for the covering salesman problem

Covering salesman problem (CSP) is an extension of the popular traveling salesman problem (TSP) arising from a number of real-life applications. Given a set of vertices and a predetermined coverage radius associated with each vertex, the goal of CSP is to find a minimum cost Hamiltonian cycle across a subset of vertices, such that each unvisited vertex must be within the coverage radius of at least one vertex included in the tour. For this NP-hard problem, we present a highly effective hybrid evolutionary algorithm (HEA) that integrates a crossover operator based on solution reconstruction, a destroy-and-repair mutation operator to generate multiple distinct offspring solutions, and a two-phase tabu search procedure to seek for high-quality local optima. Another distinguishing feature of HEA is the use of the Lin–Kernighan TSP heuristic to find an improved node sequence of a CSP tour during multiple stages of HEA. Extensive experiments on a large set of benchmark instances show that the proposed approach is able to surpass the current best-performing CSP heuristics. In particular, it reports new upper bound (improved best-known solution) for 21 out of the 27 large instances, while matching the best-known result for the remaining small and medium instances. In addition to CSP, the proposed HEA is adapted to solve the generalized covering traveling salesman problem (GCTSP). Extensive experimental results on the GCTSP benchmark disclose that the proposed adaptation of HEA outperforms all the existing GCTSP heuristics from the literature.

Cloud and IoT Applications in Material Handling Automation and Intralogistics

During the last decade, digitalization has borne tremendous changes on the way we live and do business. Industry 4.0, the new industrial revolution, is merging the physical, digital and virtual worlds through emerging technologies that collide with each other and create a distinctive paradigm shift. Even though the topic of Industry 4.0, has attracted significant attention during the past few years, literature in this subject area is still limited. The main objective of this paper is to study the current state of the art and identify major trends and research shortcomings. To that end, the authors conducted a methodological literature review based primarily on the SCOPUS bibliographic database. The review returned 49 relative papers dealing with the paper’s subject area. Through a thorough study of the selected papers, four dominant literature categories were recognized and discussed in detail. According to the literature reviewed, it is evident that massive changes are underway for warehouses and intralogistics facilities. Still, despite the intense discussion and appeal of the subject, one of the most important challenges in the scientific area under study, as the literature highlights, is the absence of a matching, to its significance, number of real-life applications. To that end, this paper provides a detailed description of a Cloud-based IoT application drawn from a Distribution Center (DC) that supplies retail home furnishing and sporting goods products to stores in Greece and the Balkan region, with the objective to showcase the feasibility of such an investment, highlight its potential and provide motivation to practitioners to evaluate and proceed in similar technological investments.

A graph representation for search-based approaches to graph layout problems

A graph consists of a finite set of vertices and edges. Graphs are used to represent a significant number of real life applications. For example, in computer science, graphs are employed for the representation of networks of communication, organisation of data, flow of computation, computational devices, etc. Several data structures have been proposed for representing graphs among which the adjacency matrix, adjacency list and edge list are the most important and widely used ones. The choice of a graph representation is mainly situation-specific and depends on the type of operations required to be performed on a given graph as well as the ease of use. In this research, a specialised graph representation is proposed, specifically designed for use when coping with graph-based optimisation problems (e.g., graph layout problems) through heuristic search methods with the aim of speeding up the search. The results of numerical experiments show that for the purpose of this study, the proposed approach performs extremely well compared to well-known graph representation approaches.

Fuzzy Logical Techniques in Social Science Research - Application and Adoption

Fuzzy logic is extremely useful for many people involved in research and development including engineers (electrical, mechanical, civil, chemical, aerospace, agricultural, biomedical, computer, environmental, geological, industrial, and mechatronics), mathematicians, computer software developers and researchers, natural scientists (biology, chemistry, earth science, and physics), medical researchers, social scientists (economics, management, political science, and psychology), public policy analysts, business analysts, and jurists. The objective of this article is to explore the advances of fuzzy logic in a large number of real-life applications and commercial products in a variety of fields.

A graph representation for search-based approaches to graph layout problems

A graph consists of a finite set of vertices and edges. Graphs are used to represent a significant number of real life applications. For example, in computer science, graphs are employed for the representation of networks of communication, organisation of data, flow of computation, computational devices, etc. Several data structures have been proposed for representing graphs among which the adjacency matrix, adjacency list and edge list are the most important and widely used ones. The choice of a graph representation is mainly situation-specific and depends on the type of operations required to be performed on a given graph as well as the ease of use. In this research, a specialised graph representation is proposed, specifically designed for use when coping with graph-based optimisation problems (e.g., graph layout problems) through heuristic search methods with the aim of speeding up the search. The results of numerical experiments show that for the purpose of this study, the proposed approach performs extremely well compared to well-known graph representation approaches.

Revealing causality between heterogeneous data sources with deep restricted Boltzmann machines

In a number of real life applications, scientists do not have access to temporal data, since budget for data acquisition is always limited. Here we challenge the problem of causal inference between groups of heterogeneous non-temporal observations obtained from multiple sources. We consider a family of probabilistic algorithms for causal inference based on an assumption that in case where X causes Y, P(X) and P(Y|X) are statistically independent. For a number of real world applications, deep learning methods were reported to achieve the most accurate empirical performance, what motivates us to use deep Boltzmann machines to approximate the marginal and conditional probabilities of heterogeneous observations as accurate as possible.We introduce a novel algorithm to infer causal relationships between blocks of variables. The proposed method was tested on a benchmark of multivariate cause-effect pairs. We show by our experiments that our method achieves the state-of-the-art empirical accuracy, and sometimes outperforms the state-of-the-art methods. An important part of our contribution is an application of the proposed algorithm to an original medical data set, where we explore relations between alimentary patters, human gut microbiome composition, and health status.

A population algorithm based on randomized tabu thresholding for the multi-commodity pickup-and-delivery traveling salesman problem

The multi-commodity pickup-and-delivery traveling salesman problem (m-PDTSP) is a variant of the classic traveling salesman problem with pickup and delivery. It arises from a number of real-life applications where a capacitated vehicle services a set of customers that provide or require certain amounts of different commodities. In this paper, we present a simple but highly effective population based algorithm for m-PDTSP (denoted as PRTTA) that uses a randomized tabu thresholding algorithm for local improvement. Extensive experiments on a large set of benchmark instances show that the proposed approach competes very favorably with the existing methods from the literature.

Tabu search with feasible and infeasible searches for equitable coloring

The equitable coloring problem is a variant of the classical graph coloring problem that arises from a number of real-life applications where the cardinality of color classes must be balanced. In this paper, we present a highly effective hybrid tabu search method for the problem. Based on three complementary neighborhoods, the algorithm alternates between a feasible local search phase where the search focuses on the most relevant feasible solutions and an infeasible local search phase where a controlled exploration of infeasible solutions is allowed by relaxing the equity constraint. A novel cyclic exchange neighborhood is also proposed in order to enhance the search ability of the hybrid tabu search algorithm. Experiments on a set of 73 benchmark instances in the literature indicate that the proposed algorithm is able to find improved best solutions for 15 instances (new upper bounds) and matches the best-known solutions for 57 instances. Additional analyses show the interest of the cyclic exchange neighborhood and the hybrid scheme combining both feasible and infeasible local searches.

A memetic algorithm for the Orienteering Problem with Mandatory Visits and Exclusionary Constraints

The Orienteering Problem with Mandatory Visits and Exclusionary Constraints (OPMVEC) is to visit a set of mandatory nodes (locations) and some optional nodes, while respecting the compatibility constraint between nodes and the maximum total time budget constraint. It is a variation of the classic orienteering problem that originates from a number of real-life applications. We present a highly effective memetic algorithm (MA) for OPMVEC combining: (i) a dedicated tabu search procedure that considers both feasible and infeasible solutions by constraint relaxation, (ii) a backbone-based crossover, and (iii) a randomized mutation procedure to prevent from premature convergence. Experiments on six classes of 340 benchmark instances from the literature demonstrate highly competitive performance of MA – it reports improved results for 104 instances compared with the existing heuristic approach, while finding matching best-known results for the remaining cases. Additionally, MA can be used to produce a starting point for an exact solver (e.g., CPLEX), leading to an increased number of problem instances that are solved to optimality. We further investigate the contribution of the key algorithmic elements to the success of the proposed approach.

Conformal Kernel Expected Similarity for Anomaly Detection in Time-Series data

The problem of anomaly detection arises in many practical applications. Currently itis highly important to be able to detect outliers in data streams, as recent years have seen a rapidgrowth in the amount of such data. Only a few techniques are applicable to real-time data and evenfewer could provide an interpretable anomaly score. Probabilistic interpretation of the anomalyscore could allow an analyst to choose the anomaly threshold based on the desired false alarm rate,which is highly important in a number of real-life applications. We propose a modification of theEXPoSE algorithm for anomaly detection in time series data, which produces a probabilistic scoreof abnormality. The proposed algorithm is developed within the framework of conformal anomalydetection and utilizes the expected similarity as a measure of non-conformity.

A hybrid dynamic programming and memetic algorithm to the Traveling Salesman Problem with Hotel Selection

The Traveling Salesman Problem with Hotel Selection (TSPHS) is a variant of the classic Traveling Salesman Problem. It arises from a number of real-life applications where the maximum travel time for each “day trip” is limited. In this paper, we present a highly effective hybrid between dynamic programming and memetic algorithm for TSPHS. The main features of the proposed method include a dynamic programming approach to find an optimal hotel sequence for a given tour, three dedicated crossover operators for solution recombination, an adaptive rule for crossover selection, and a two-phase local refinement procedure that alternates between feasible and infeasible searches. Experiments on four sets of 131 benchmark instances from the literature show a remarkable performance of the proposed approach. In particular, it finds improved best solutions for 22 instances and matches the best known results for 103 instances. Additional analyses highlight the contribution of the dynamic programming approach, the joint use of crossovers and the two local search phases to the performance of the proposed algorithm.

Solving the bi-objective optimisation problem with periodic delivery operations using a lexicographic method

Periodic deliveries are typical in a number of real-life applications. Minimising the number of vehicles required to make deliveries to a set of customers with known delivery frequencies is called the problem of vehicle minimisation for periodic deliveries (VMPD). Catering to the welfare of vehicle drivers has now become very important. Consequently, this work integrates the vehicle load balance factor into the VMPD problem by considering both the number of vehicles required to make periodic deliveries and the load balance between vehicles. This work presents integer programming formulations and applies a lexicographic method to this bi-objective VMPD problem. This work also examines whether decomposition can significantly reduce the size and difficulty of basic integer programming formulation in order to output close-to-optimal schedules for large problems within a reasonable computational time. A greedy balancing algorithm is also proposed to use it along with a decomposed integer programming formulation to yield a satisfactory solution in a relatively short time. Computational experiments demonstrate the better competitiveness of the proposed approaches compared to that of the existing approaches.

Summarizing large text collection using topic modeling and clustering based on MapReduce framework

Document summarization provides an instrument for faster understanding the collection of text documents and has a number of real life applications. Semantic similarity and clustering can be utilized efficiently for generating effective summary of large text collections. Summarizing large volume of text is a challenging and time consuming problem particularly while considering the semantic similarity computation in summarization process. Summarization of text collection involves intensive text processing and computations to generate the summary. MapReduce is proven state of art technology for handling Big Data. In this paper, a novel framework based on MapReduce technology is proposed for summarizing large text collection. The proposed technique is designed using semantic similarity based clustering and topic modeling using Latent Dirichlet Allocation (LDA) for summarizing the large text collection over MapReduce framework. The summarization task is performed in four stages and provides a modular implementation of multiple documents summarization. The presented technique is evaluated in terms of scalability and various text summarization parameters namely, compression ratio, retention ratio, ROUGE and Pyramid score are also measured. The advantages of MapReduce framework are clearly visible from the experiments and it is also demonstrated that MapReduce provides a faster implementation of summarizing large text collections and is a powerful tool in Big Text Data analysis.

Sustainable PLM through Intelligent Products

The basis for Sustainable PLM is enabling Intelligent Products to communicate between themselves and with other systems in order to reduce CO2 emissions, energy usage, and environmental damage. This type of communication between Intelligent Products and other information systems with the objective of reducing environmental impacts is an example of closed-loop product lifecycle management. The objective of the paper is to outline an architectural information system model for implementing Sustainable PLM based on experiences from a number of real-life applications. The technological challenge for developing Sustainable PLM is the high number and variety of information systems that need to communicate over organizational limits and over time. In the paper we describe how Sustainable PLM has been implemented using Intelligent Products in a number of representative real-life applications.

A Projected Gradient Method for Vector Optimization Problems

Vector optimization problems are a significant extension of multiobjective optimization, which has a large number of real life applications. In vector optimization the preference order is related to an arbitrary closed and convex cone, rather than the nonnegative orthant. We consider extensions of the projected gradient gradient method to vector optimization, which work directly with vector-valued functions, without using scalar-valued objectives. We provide a direction which adequately substitutes for the projected gradient, and establish results which mirror those available for the scalar-valued case, namely stationarity of the cluster points (if any) without convexity assumptions, and convergence of the full sequence generated by the algorithm to a weakly efficient optimum in the convex case, under mild assumptions. We also prove that our results still hold when the search direction is only approximately computed.