Efficiency Of Problem Solving Research Articles

Scheduling with a weight-modifying activity to minimize the total weighted completion time

This paper considers a single-machine scheduling problem to minimize the total weighted completion time with a weight modifying activity, after which the job weights are discounted by a given factor. The problem is known to be ordinary NP-hard. We propose two mixed integer linear programs (MILPs) and a dynamic programming algorithm to optimally solve the problem. Optimality properties are established and then formulated as pruning constraints to improve the problem-solving efficiency of the MILPs. Special cases are discussed and shown to be solvable by polynomial time algorithms. Complexity status of the studied problem with several instance characteristics is shown. Computational experiments indicate that the optimality properties can reduce the computing efforts and that one of the proposed MILPs can solve instances of 200 jobs in a few seconds.

Chance-Constrained Optimization for a Green Multimodal Routing Problem with Soft Time Window under Twofold Uncertainty

This study investigates a green multimodal routing problem with soft time window. The objective of routing is to minimize the total costs of accomplishing the multimodal transportation of a batch of goods. To improve the feasibility of optimization, this study formulates the routing problem in an uncertain environment where the capacities and carbon emission factors of the travel process and the transfer process in the multimodal network are considered fuzzy. Taking triangular fuzzy numbers to describe the uncertainty, this study proposes a fuzzy nonlinear programming model to deal with the specific routing problem. To make the problem solvable, this study adopts the fuzzy chance-constrained programming approach based on the possibility measure to remove the fuzziness of the proposed model. Furthermore, we use linear inequality constraints to reformulate the nonlinear equality constraints represented by the continuous piecewise linear functions and realize the linearization of the nonlinear programming model to improve the computational efficiency of problem solving. After model processing, we can utilize mathematical programming software to run exact solution algorithms to solve the specific routing problem. A numerical experiment is given to show the feasibility of the proposed model. The sensitivity analysis of the numerical experiment further clarifies how improving the confidence level of the chance constraints to enhance the possibility that the multimodal route planned in advance satisfies the real-time capacity constraint in the actual transportation, i.e., the reliability of the routing, increases both the total costs and carbon emissions of the route. The numerical experiment also finds that charging carbon emissions is not absolutely effective in emission reduction. In this condition, bi-objective analysis indicates the conflicting relationship between lowering transportation activity costs and reducing carbon emissions in routing optimization. The sensitivity of the Pareto solutions concerning the confidence level reveals that reliability, economy, and environmental sustainability are in conflict with each other. Based on the findings of this study, the customer and the multimodal transport operator can organize efficient multimodal transportation, balancing the above objectives using the proposed model.

A survey of dynamic programming algorithms

Dynamic programming is an important algorithmic idea with widespread applications in computer science and other disciplines. With the continuous improvement of computing power and the increasing complexity of practical problems, dynamic programming algorithms have also received more and more attention. However, due to the limitations of the dynamic programming algorithm itself, it also brings challenges to the optimization of the algorithm and its application in different fields. This paper uses the methods of literature review and case analysis to systematically summarize and sort out the basic principles, complexity analysis, classic problems, and applications of dynamic programming algorithms. First of all, this paper discusses the basic concepts of splitting and optimal substructure characteristics of related problems in dynamic programming; secondly, it discusses the time complexity and space complexity of dynamic programming algorithms in detail; then, in the classic dynamic programming The case part focuses on the knapsack problem, including the 0-1 knapsack problem and multiple knapsack problems; finally, this paper discusses the wide application and optimization of dynamic programming algorithms in practice, covering natural language processing, bioinformatics and How to optimize the dynamic programming algorithm to improve the efficiency of problem solving and reduce resource consumption. To sum up, this paper fully demonstrates the basic principles and applications of dynamic programming algorithms, as well as optimization methods and development trends, and provides guidance and references for their academic and practical applications.

Optimizing Topology in Satellite–UAV Collaborative IoT: A Graph Partitioning Simulated Annealing Approach

Currently, space-based information networks, represented by satellite Internet, are rapidly developing. UAVs can serve as airborne mobile terminals, representing a novel node in satellite IoT, offering more accurate and robust data streaming for connecting global satellite–UAV collaborative IoT systems. It is characterized by high-speed dynamics, with node distances and visibility constantly changing over time. Therefore, there is a need for faster and higher-quality topology optimization research. A reliable, secure, and adaptable network topology optimization algorithm has been proposed to handle various complex scenarios. Additionally, considering the dynamic and time-varying nature of these types of networks, the concept of time slices has been introduced to accelerate the iterative efficiency of problem-solving. Experimental results demonstrate that the proposed algorithm is expected to exhibit better convergence and performance in subsequent iterations compared with traditional solutions. Besides being a solution for topology optimization, the proposed algorithm offers a new way of thinking, enabling the handling of larger satellite–UAV collaborative IoT systems.

Modeling a Carbon-Efficient Road–Rail Intermodal Routing Problem with Soft Time Windows in a Time-Dependent and Fuzzy Environment by Chance-Constrained Programming

This study explores a road–rail intermodal routing problem. To improve the carbon efficiency of transportation, reducing CO2 emissions is considered by the routing. Soft time windows are incorporated into the routing to optimize the timeliness of the first-mile pickup and last-mile delivery services in intermodal transportation. The routing is further modeled in a time-dependent and fuzzy environment where the average truck speeds of the road depend on the truck departure times and are simultaneously considered fuzzy along with rail capacities. The fuzzy truck speed leads to the fuzziness of three aspects, including speed-dependent CO2 emissions of the road, a timetable-constrained transfer process from road to rail, and delivery time window violation. This study formulates the routing problem under the above considerations and carbon tax regulation as a combination of transportation path planning problem and truck departure time and speed matching problem. A fuzzy nonlinear optimization model is then established for the proposed routing problem. Furthermore, chance-constrained programming with general fuzzy measure is used to conduct the defuzzification of the model to make the problem solvable, and linearization techniques are adopted to linearize the model to enhance the efficiency of problem-solving. Finally, this study presents an empirical case to demonstrate the effectiveness of the designed approach. This case study evaluates the performance of carbon tax regulation by comparing it with multi-objective optimization. It also focuses on sensitivity analysis to discuss the influence of the optimistic–pessimistic parameter and confidence level on the optimization results. Several managerial insights are revealed based on the case study.

IMPACT OF FAMILY FUNCTIONING ON STUDENTS' PERSONAL ADJUSTMENT ATTITUDES, PERSONALITIES, AND ACADEMIC ACHIEVEMENTS

Family functioning is characterized by the regularity of ordinary family activities, the efficiency of family communication and problem-solving, the closeness of the family, and how well-adjusted the family members are to one another. The family is a critical context in which adolescents work on the major task of identity formation. This study aimed to find out the impact of family functioning on the personal adjustment, attitudes, personalities, and academic achievement of third- and fourth-year college students. The descriptive survey type of research was used in this study to gather the necessary data. There is a significant relationship between family functioning and personal adjustment attitudes towards self, school, and others. Family functioning has a great impact on the personality in terms of dominance-submission, emotional stability, emotional maturity, and academic achievement. Therefore, the school will have to initiate a program for the promotion of intellectual, emotional, and social maturity through varied activities. Parents should also keep close track of their students’ progress in school.

Developments and applications of the CFD-DEM method in particle–fluid numerical simulation in petroleum engineering: A review

Particle and fluid flow is a general problem existing in a series of engineering activities during oil and gas development. Many efforts have been made to investigate the migration, deposition, and plugging characteristics of particles carried by the fluid medium. The discrete element method (DEM) is used for the particle phase, the computational fluid dynamic (CFD) is applied for the fluid phase and many coupling schemes are introduced for interactions between phases. The CFD-DEM studies have been conducted to help mechanism explanation and flow prediction under various work conditions. This work reviews the progress in CFD-DEM coupling simulations about oil and gas development by summarizing the models and relevant applications. Different coupling schemes and their applicable petroleum engineering problems are reviewed. The needs for future research are also discussed. This will be helpful for researchers who are new to particle flow numerical simulation in the CFD-DEM method to shorten the learning cycle and improve the efficiency of problem solving.

Design of Cloud-Based Adaptive Learning System to Promote Creative Problem Solving for the 21st Century Learners

This research developed a cloud-based adaptive learning system to enhance the creative problem-solving efficiency of learners based on their ability levels. The difficulty level of learning activities in the system would be adjusted to fit the creative problem-solving ability of each learner, while there would also be knowledge sharing activities to create new ideas. This cloud-based adaptive learning system has six components, which are 1) adaptive learning system, 2) cloud tools, 3) topic of problem, 4) teaching and learning approach, 5) assessment, and 6) knowledge sharing. The system consists of four steps, which are 1) search (S), 2) solve (S), 3) create (C), and 4) share (S). The system was designed based on the survey results of 55 undergraduate lecturers and 478 undergraduates, then an experiment was conducted. The results showed that creative problem-solving scores after using the system were higher than the before using it, at a significance level of .05. The results were analyzed using F test and found a significance difference of the creative problem-solving scores, which after using the system were higher than before using the system at the significant level of .05

Community-in-the-loop: Creating Artificial Process Intelligence for Co-production of City Service

Communities have first-hand knowledge about community issues. This study aims to improve the efficiency of social-technical problem-solving by proposing the concept of "artificial process intelligence," based on the theories of socio-technical decision-making. The technical challenges addressed were channeling the communication between the internal-facing and external-facing 311 categorizations. Accordingly, deep learning models were trained on data from Kansas City's 311 system: (1) Bidirectional Encoder Representations from Transformers (BERT) based classification models that can predict the internal-facing 311 service categories and the city departments that handle the issue; (2) the Balanced Latent Dirichlet Allocation (LDA) and BERT clustering (BLBC) model that inductively summarizes residents' complaints and maps the main themes to the internal-facing 311 service categories; (3) a regression time series model that can predict response and completion time. Our case study demonstrated that these models could provide the information needed for reciprocal communication, city service planning, and community envisioning. Future studies should explore interface design like a chatbot and conduct more research on the acceptance and diffusion of AI-assisted 311 systems.

Applying Natural Language Processing and TRIZ Evolutionary Trends to Patent Recommendations for Product Design

Traditional TRIZ theory provides methods and processes for systematic analysis on engineering problems, which can improve the efficiency of solving problems. However, the effect of solving problems is not necessarily guaranteed, and depends on the user’s profession and experience. Therefore, this study proposes a methodology to apply evolutionary benefits in the 37 trend lines developed by TRIZ researchers to assist in intelligently screening relevant patents applicable to the content of the product design. In such a way, the efficiency of problem solving and product design quality may be improved more effectively. First, the patent database is used as the training dataset, words and sentences in the patent documents are analyzed through natural language processing to obtain keywords that may be related to evolutionary benefits. Using word vectors trained by Doc2vec, the semantic similarity can be calculated to obtain the similarity relationship between patent text and evolutionary benefit. Secondly, the goals of the product development project may make be related to the evolutionary benefits, and then applicable patent recommendations can be provided. The proposed methodology may achieve the purpose of intelligent design assistance to enhance the product development process and problem-solving.

An Influence Maximization Algorithm for Dynamic Social Networks Based on Effective Links

The connection between users in social networks can be maintained for a certain period of time, and the static network structure formed provides the basic conditions for various kinds of research, especially for discovering customer groups that can generate great influence, which is important for product promotion, epidemic prevention and control, and public opinion supervision, etc. However, the computational process of influence maximization ignores the timeliness of interaction behaviors among users, the screened target users cannot diffuse information well, and the time complexity of relying on greedy rules to handle the influence maximization problem is high. Therefore, this paper analyzes the influence of the interaction between nodes in dynamic social networks on information dissemination, extends the classical independent cascade model to a dynamic social network dissemination model based on effective links, and proposes a two-stage influence maximization solution algorithm (Outdegree Effective Link—OEL) based on node degree and effective links to enhance the efficiency of problem solving. In order to verify the effectiveness of the algorithm, five typical influence maximization methods are compared and analyzed on four real data sets. The results show that the OEL algorithm has good performance in propagation range and running time.

Students’ Perceptions of Problem-Based Learning in Multidisciplinary Groups When Seeking to Solve an Engineering Grand Challenge

This paper presents findings from a small-scale research study eliciting students’ perceptions of benefits and challenges of working in interdisciplinary groups to solve an engineering challenge using problem-based learning. Penultimate and final year undergraduates and postgraduate MSc students in the School of Engineering and Physical Sciences at a Scottish university, studying Robotics, Mechanical, Chemical, Electrical and Software Engineering worked in interdisciplinary groups of five on a project to provide solutions to the United States National Academy of Engineering Grand Challenges (NAEGC). Students were surveyed twice, using closed and open questions before and towards the end of the project. Data were analysed using a thematic approach. Findings showed that most students saw benefits to problem-based working with students from other disciplines, citing increased awareness of approaches, future ‘real world’ professional preparation and efficiency in problem solving. However, challenges around scheduling meetings and concerns around cross-discipline collaboration indicate that universities should provide training for students before undertaking such problem-based projects, to ensure maximum educational benefits. In addition, greater emphasis needs to be put on students’ awareness of the added benefits of development of the ‘soft skills’ needed for future professional practice.

TRAINING CREATIVE THINKING FOR PRIMARY SCHOOL STUDENTS WITH THE APPROACH OF LEARNING THROUGH PLAYING

In the educational orientation of competence development of Vietnam's General Education Program 2018, creative thinking plays an important role, especially at the primary school level. Creative thinking is a key factor for students to develop problem-solving abilities in real life. The formation and development of creative thinking competence are to help students have the habit of independent thinking, creating new and unique ideas with high efficiency in problem-solving. The article approaches to analyze the characteristics of creative thinking in primary school students and the point of view of teaching according to the learning through playing approach, thereby determining the appropriate relationship when organizing teaching activities to improve the quality of creative thinking training for primary school students to meet the goals and requirements of the General Education Program 2018 of Vietnam.

Construction of Double-Layer Knowledge Coordination Network for Product Quality Problem Solving

Product quality problems are becoming increasingly complex with the increasing richness of product types and functions, making it challenging to solve quality problems with only personal knowledge effectively. Thus, we propose an approach to extract solution knowledge from quality problem-solving data to improve the efficiency of quality problem-solving. Specifically, by analyzing the characteristics of the data, a method for calculating the membership degree of quality knowledge nodes is first designed. The corresponding quality knowledge triads are constructed based on the relationship between the data structures. Subsequently, we build a double-layer knowledge coordination network (DL-KCN) composed of a problem layer and a solution layer. Finally, using the quality problem-solving data generated during the stamping production process of the body-in-white of an automobile manufacturing enterprise, a DL-KCN with practical application significance is constructed. The DL-KCN is beneficial for problem-solvers to address product quality problems.

Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA.

With their intelligence, flexibility, and other characteristics, automated guided vehicles (AGVs) have been popularized and promoted in traditional industrial markets and service industry markets. Compared with traditional transportation methods, AGVs can effectively reduce costs and improve the efficiency of problem solving in various application developments, but they also lead to serious path-planning problems. Especially in large-scale and complex map environments, it is difficult for a single algorithm to plan high-quality moving paths for AGVs, and the algorithm solution efficiency is constrained. This paper focuses on the indoor AGV path-planning problem in large-scale, complex environments and proposes an efficient path-planning algorithm (IACO-DWA) that incorporates the ant colony algorithm (ACO) and dynamic window approach (DWA) to achieve multiobjective path optimization. First, inspired by the biological population level, an improved ant colony algorithm (IACO) is proposed to plan a global path for AGVs that satisfies a shorter path and fewer turns. Then, local optimization is performed between adjacent key nodes by improving and extending the evaluation function of the traditional dynamic window method (IDWA), which further improves path security and smoothness. The results of simulation experiments with two maps of different scales show that the fusion algorithm shortens the path length by 9.9 and 14.1% and reduces the number of turns by 60.0 and 54.8%, respectively, based on ensuring the smoothness and safety of the global path. The advantages of this algorithm are verified. QBot2e is selected as the experimental platform to verify the practicability of the proposed algorithm in indoor AGV path planning.

Practical Research on Physical Problem Solving with Mathematical Graphical Models

There is the tightest relation between mathematics and physics among all disciplines. Specifically, mathematics can serve as an instrument to describe physical phenomena, while physical theory can be used to interpret mathematical laws. As a basic discipline of all natural disciplines, mathematics can provide operational tools, thinking methods, and process simplification approaches for the proposal and development of physical problems. The mathematical graphical model can effectively improve the efficiency of problem solving. This study is conducted based on the transfer theory. Besides, the methods and practice of physical problem solving with mathematical graphical models are analyzed from the perspective of specific examples in physics.

Non-cooperative target clustering algorithm based on clonal selection

Orderly observation of non-cooperative targets for subsequent identification and control is one of the important prerequisites and key technologies of space on orbit service. Restricted by the limited number of ground observation equipment, the visible time window of each non-cooperative target, and the different types of targets, it is increasingly difficult to observe the transit non-cooperative targets and plan the observation tasks. In this paper, the fuzzy clustering algorithm based on clonal selection is used to cluster the non-cooperative targets which are about to cross the border according to their own characteristics, physical characteristics, geometric characteristics, orbital characteristics and other different attributes. Considering the urgency of the task, the importance and priority of each non-cooperative target relative to the observation task are formed, it can provide effective input for the subsequent task planning of non-cooperative target observation, reduce the problem complexity of orderly observation of non-cooperative target, and improve the efficiency of problem solving.

Is it possible to use research to learn psychiatry from scratch: a reflective self-study of a pre-clinical year medical student

AimsDespite the abundance of opportunities available for medical students to explore the field of psychiatry, active immersion through experiential learning has proven to be difficult for pre-clinical year students as a result of a busy time table and the need to wait for psychiatry postings during the clinical years. Hence, the question of “how to implement experiential learning of psychiatry in pre-clinical years” arises. This study is aimed to elucidate the attempts that have been made to use research as a proximate approach to learn psychiatry experientially, focusing specifically on the challenges faced and lessons learned by a pre-clinical medical student.MethodThis self-study outlined the informal three-months learning-by-doing journey of a year-one medical student, supervised by a psychiatrist registrar. Employing research as a proximate approach of experiential learning for psychiatry was explored based on reflection from discussion during supervision meetings and messages exchange. The agreed learning method was an active involvement in research projects on psychiatry topics, with the learning outcome of producing publications.ResultThe challenges faced included: 1) the difficulty associated with striking a balance between an ambitious project with high impact versus a feasible smaller project to keep both parties motivated through the means of short-term accomplishment; 2) the ongoing requirement for learning process adjustment to build the foundational knowledge essential for progress. Through active and deliberate effort, every step in the process was found to be an opportunity for active learning. Literature review, for example, was used to build the understanding of psychiatry topics and practise critical appraisal skills, while allowing for the recognition of knowledge gaps, which ultimately encouraged future research idea synthesis. The process of writing and submitting a manuscript was used to learn publication-relevant skills including: journal impact calculation, referencing, indexing and abstracting services, and publication ethics. Certain future proof skills were also developed, including literacy in information and communication technology which improved efficiency of research, problem solving and decision making. This was done using pros and cons whenever difficulties were faced.ConclusionAlthough research is not a comprehensive substitute for clinical posting in the process of learning psychiatry, the lessons learned from psychiatry research can potentially serve as an initial exploration tool for preclinical-year medical students interested in the field. The stimulating process has found to be effective in stimulating further interest in psychiatry but maintaining it will be the next challenge.

Quality Deviation Control for Aircraft Using Digital Twin

Abstract During the manufacturing process of aircraft, quality deviation problems inevitably occur due to the high complexity of aircraft design, manufacturing errors, tooling mistakes, human factors, environmental influences, design defects, and other factors. The current quality deviation control system of civil aircraft suffers from two problems: (1) quality deviation control data are scattered in more than 100 management systems, and it is difficult to extract quality data-related information from the whole life cycle of the aircraft involving the main manufacturer and each supplier and (2) there is a lack of quality data analysis and a closed-loop information-physics fusion system for quality deviation control. Thus, it is difficult to locate the quality deviation problems and it takes a long time to deal with these problems as well. In this paper, a digital twin-based quality deviation control model is proposed. Through the digital twin modeling based on asset management shell technology, the multi-source and heterogeneous quality deviation data can be extracted and integrated. Furthermore, to deal with the second problem, a quality deviation system has been built based on digital twin. In this system, the aircraft quality deviation data can be analyzed by the FP-growth association rule algorithm and the results are provided through the system to guide the assembly site, improving the efficiency and accuracy of quality problem-solving in the physical world. In addition, a case study is stated, where the proposed approach is applied to deal with the aircraft quality deviation problems.

Twice-Daily Huddles Improves Collaborative Problem Solving in the Respiratory Care Department.

Lean Six Sigma processes are used in health care systems to increase safety and efficiency. Daily huddles, one tool of the Lean Six Sigma process, have been used to increase patient safety, facilitate efficiency in problem solving, promote optimal patient outcomes, and reduce poor communication. Huddle utilization by respiratory care departments has not been previously reported. We describe our process of implementing daily huddles and the impact on departmental problem solving. A descriptive study of a quality improvement intervention and a retrospective study of prospectively collected data were performed. The respiratory care department was trained in the utilization of a daily huddle process to resolve issues and identify process improvement opportunities. Huddles were performed at the beginning of each shift. Process improvement opportunities were raised by the respiratory therapy staff using the following categories: Safety/Service, Methods, Equipment, Supplies, and Associates. Opportunities were placed within 3 categories; quick hits (resolution in 1-3 d), complex problems (resolution in 3-7 d), and projects (resolution in > 7 d). All opportunities included a problem statement, an immediate countermeasure, a problem leader, and a due date. Items requiring interdisciplinary support were escalated to the organizational patient care services huddle. We evaluated the number and nature of process improvement opportunities raised in huddles from January 1 through December 31, 2018, to better understand the impact of daily huddles. A total of 366 process improvement opportunities were raised during huddles. Of those, 245 (67%) were quick hits, 77 (21%) were complex, and 44 (12%) were projects. Resolution of 174 (47.5%) opportunities was completed using only the resources of the respiratory care department, and 157 (43%) were resolved with additional interdisciplinary involvement. A small portion 35 (9.5%) of opportunities required escalation to the organizational multidisciplinary huddle for resolution. All process improvement opportunities were resolved at the end of the study period (mean ± SD of 30.5 ± 7.7 per month). Twice-daily huddles implemented by our respiratory care department allowed for identification and timely resolution of process improvement opportunities.