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Background: The idea of computational thinking is underpinned by the belief that anyone can learn and use the underlying concepts of computer science to solve everyday problems. However, most studies on the topic have investigated the development of computational thinking through programming activities, which are cognitively demanding. There is a dearth of evidence on how computational thinking augments everyday problem solving when it is decontextualized from programming. Objectives: In this study, we examined how computational thinking, when untangled from the haze of programming, is demonstrated in everyday problem solving, and investigated the features of such solvable problems. Methods: Using a multiple case study approach, we tracked how seven university students used computational thinking to solve the everyday problem of a route planning task as part of an 8‐week‐long Python programming course. Results and Conclusions: Computational thinking practices are latent in everyday problems, and intentionally structuring everyday problems is valuable for discovering the applicability of computational thinking. Decomposition and abstraction are prominent computational thinking components used to simplify everyday problem solving. Implications: It is important to strike a balance between the correctness of algorithms and simplification of the process of everyday problem solving. Lay Description: What is already known about this topic: Computational thinking is a problem solving skill.Computational thinking is different from programming.There is dearth of evidence on how computational thinking augments everyday problem solving. What this paper adds: Computational thinking practices are latent in everyday problem solving.Intentional restructuring of problems is required to uncover how computational thinking applies in solving them. Implications for practices: Decomposition and abstraction are critical in solving everyday problems.Computational thinking should be taught explicitly through everyday problems.Finding balance between the correctness of algorithm and simplification of the problem solving process is important. [ABSTRACT FROM AUTHOR]

Abstract: Route selection is one of the fundamental problems in emergency logistics management, which plays an important role in real applications. A various paper has been produced to deal with route selection problem, in which the travel time on each arc of the logistics network is a constant. However, the travel speed will change with the extension of the disaster, especially under disasters like hurricane, flood, etc. To address this issue, a novel bio-inspired method is proposed to solve this problem. Furthermore, both the travel time and the path length are taken into consideration. The proposed approach solves how to choose the optimal path from the optional choices. A case study is utilized to evaluate the efficiency of the proposed method. The result shows that the proposed method is effective in dealing with the route selection problem for emergency logistics management. [Copyright &y& Elsevier]

Managers of construction organizations have long been faced with the essential question of whether the knowledge management system (KMS) is truly cost effective. Very few empirical studies have been conducted to measure the effective benefits of implementing knowledge management initiatives. A lack of quantitative measurement models, the difficulty of differentiating knowledge management (KM) from non-KM activities, the difficulty of evaluating the resultant KM activity values, and an unwillingness of organizations to reveal insights (aimed at shielding these insights from their competitors) are critical reasons for their absence from empirical reports. This paper reveals a five-year comprehensive benefit survey on the implementation of a specialized KMS, KM-enabled problem solver, at a major engineering consulting firm. In total, 868 emergency problem-solving scenarios recorded in the KMS were analyzed. Three that were most concerned with benefit indexes (time, cost, and staff hours) selected by the KM managers were measured. Results showed timeliness to be the most remarkable tangible benefit. Both staff hours and cost benefits were negative. Qualitative benefits were identified and addressed through focus group meetings with the KM managers. The KM approach was not suited to solving all types of engineering problems. Strategies should be taken to facilitate engineering problem solving, including development of a more proactive approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents three proposals of multiobjective memetic algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. These three proposals are, respectively, based on evolutionary computation, ant colony optimisation, and greedy randomised search procedure. Different variants of these memetic algorithms have been developed and compared in order to determine the most suitable intensification–diversification trade-off for the memetic search process. Once a preliminary study on nine well-known problem instances is accomplished with a very good performance, the proposed memetic algorithms are applied considering real-world data from a Nissan plant in Barcelona (Spain). Outstanding approximations to the pseudo-optimal non-dominated solution set were achieved for this industrial case study. [Copyright &y& Elsevier]

Abstract: We present a novel transmission expansion planning model that explicitly considers a multi-year planning horizon. The proposed formulation is a mixed-integer linear problem that can be solved using commercially available software. The algorithm determines the best overall transmission expansion plan, reflecting both investment and operational costs. To analyze the results obtained from the model, we use a set of efficiency metrics to appraise the effect of the expansion among generators and demands, as well as congestion metrics to measure changes in nodal prices and line congestions. This paper also provides a case study that considers the expansion plan for a transmission system based on the one of mainland Spain. [Copyright &y& Elsevier]

The paper proposes a methodology for determining management strategies in construction enterprises. For this purpose, the SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis as an instrument for formulating management strategies is recommended. The best practices for this reason are also analysed. The algorithm helps to select the most preferable strategies based on the AHP, expert judgment and permutation method of feasible alternatives. A perspective of construction enterprises management regarding the SWOT is carried out on a basis of selected description of the current state and the feasible future alternatives. Finally, the selected alternatives are ranked according to the permutation method of feasible alternatives. The case study shows the applicability of the proposed model to the real management problems solution. [ABSTRACT FROM AUTHOR]

Abstract: This study employs differential evolution algorithm to solve the optimal chiller loading problem for reducing energy consumption. To testify the performance of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with those of the Lagrangian method, genetic algorithm and particle swarm algorithm. The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions. Moreover, it outperforms the genetic algorithm in finding optimal solution and also overcomes the divergence problem caused by the Lagrangian method occurring at low demands. [ABSTRACT FROM AUTHOR]

Abstract: Although new AI planning algorithms and techniques are being developed and improved rapidly, there is a lack of efficient and easy to use systems able to incorporate and utilize them. Furthermore, while visual representation facilitates design, maintenance and comprehension of planning domains and problems, very few systems incorporate it. This paper presents VLEPPO, an integrated system aiming at visually modeling planning domains and problems through a convenient graphical interface, while maintaining compatibility with the Planning Domain Definition Language (PDDL), with import and export features. Solutions to planning problems can be obtained by invoking different planners employing the web services technology. The demonstration of the system is performed through a case study involving web service composition viewed as a planning problem. [Copyright &y& Elsevier]