Programming Approach Research Articles

Enhanced Dynamic Programming Approaches for Efficient Solutions to the Traveling Salesman Problem

This study aims to enhance dynamic programming techniques for efficiently solving the Traveling Salesman Problem, a fundamental combinatorial optimization challenge. Given its NP-hard classification, traditional exact algorithms become computationally infeasible as the problem size increases. The research revisits foundational dynamic programming principles, notably the Held-Karp algorithm, and identifies existing limitations. The study begins with a comprehensive literature review, followed by an analysis of the dynamic programming challenges specific to TSP. Novel algorithms are then developed, implemented, and rigorously tested against benchmark instances. Performance evaluation is conducted using metrics such as execution time, memory usage, and solution optimality across different problem sizes. Results demonstrate significant improvements in efficiency and scalability, with enhanced algorithms achieving optimal solutions in reduced time and computational resource usage. However, the exponential growth in complexity remains a challenge for larger instances. The study concludes with recommendations for future research, focusing on further algorithmic refinements and exploring hybrid approaches to address large-scale TSPs.

Try to esCAPE from Cybersecurity Incidents! A Technology-Enhanced Educational Approach

AbstractIncorporating gamification elements and innovative approaches in training programs are promising for addressing cybersecurity knowledge gaps. Cybersecurity education should combine hard and soft skill development when building the capacity to manage cyber incidents requiring timely communication, team collaboration, and self-efficacy in risk assessment and incident mitigation. The paper presents a design and evaluation of the technology-enhanced cybersecurity education approach CAPE which works as a hybrid escape room. It combines a virtualized infrastructure simulating the business environment and the physical environment where game participants play the role of the incident response team at the organization’s premises. The CAPE could be applied as a student-centered approach in the educational environment. The work employed a multiple-methods research approach to design a gamified approach and investigate crisis communication, collaboration, self-regulation, and technical competences in incident management scenarios. The design science methodology empowered the game’s construction with an attack surface covering physical and digital security. The undergraduate students participated in the pilot execution and completed the psychological questionnaires. The results were analyzed using statistical methods. Results of the CAPE execution showed a positive impact on student performance and increased interest in cybersecurity. The designed approach promoted field-specific competence development. The results demonstrated the significance of psychological aspects related to incident management. Graphical abstract

Global supply chain network design for shelf-life products under uncertainty: a case study of pistachios

PurposeSupply chain management has become critical in today’s globalized environment, with growingly intense competition on the international level. The particular characteristics of modern trade have led companies to globalize and devise increasingly sophisticated supply chains to meet customer demand worldwide. Motivated by the need to address these challenges, we have developed a new model for a global supply chain that incorporates uncertainties in exchange rates, demand fluctuations, and the quantity of produce.Design/methodology/approachThe objective of the proposed model is to maximize supply chain profitability. Our model optimizes several critical decisions in the proposed global supply chain, including the location of domestic and foreign distribution centers, allocating the centers to customers, transportation mode selection, storage temperature, optimal farm purchase quantities, product flows across the network, and the shelf-life of products. Scenario-based stochastic programming approach is employed to account for the inherent uncertainties within the model. A pistachio supply chain is examined as a case study in this article, and the efficiency of the proposed model is demonstrated through computational results.FindingsThe model was solved using the CPLEX solver in GAMS and the results, the Sirjan DDC and Turkey FDC have been selected. In general, 40% of demand for customers from FDC (turkey) and 60% of demand from DDC (sirjan) is provided. Changes in the demand of foreign customers make the net profit more effective than changes in the demand for domestic customers. The decrease in exchange rate decreases the network profit with a higher slope and the increase in exchange rate will increase network profit with a relatively stable slope.Originality/valueWhile research on GSCs for perishable products has been ongoing for several years, the importance of the subject necessitates continued investigation in this area. This paper aimed to address this gap by presenting an optimization model for designing GSCs for perishable products under uncertainty and with various transportation modes. The proposed model was designed with the aim of improving supply chain performance and real-world applicability.

ChatBot-based Bus Ticket Booking Prototype Using WhatsApp

Customer relationship management (CRM) is the most critical part of any business's operations. To deploy CRM at PO Harapan Jaya, a ticket ordering system is required to make things easy for customers. Researchers constructed a chatbot prototype using the WhatsApp app with the goal of making it easier and more efficient for PO Harapan Jaya customers to buy bus tickets. The eXtreme Programming (XP) approach is a strategy for creating a ticket-booking chatbot prototype using WhatsApp. In developing the WhatsApp chatbot prototype for this research, a CRM system is proposed that can collect and manage information entered by customers, ticket purchase history, and produce proof of ticket reservations via WhatsApp chat, which will then be shown to the PO Harapan Jaya admin to obtain bus tickets ordered by customers. Researchers conducted a black box test on the prototype. This study intends to demonstrate how deploying chatbots in the WhatsApp application may speed up the bus ticket purchase process, increase customer service quality, and assist PO Harapan Jaya in optimizing CRM tactics. Based on the extensive programming processes, including planning, design, coding, and testing, it is possible to determine that the final WhatsApp chatbot will work properly and may be used by users to buy PO Harapan Jaya bus tickets. Customers may book tickets, examine departure schedules, and contact the PO Harapan Jaya admin if they have any issues when ordering tickets.

Do jumps matter in discrete-time portfolio optimization?

This paper studies a discrete-time portfolio optimization problem, wherein the underlying risky asset follows a Lévy GARCH model. Besides a Gaussian noise, the framework allows for various jump increments, including infinite-activity jumps. Using a dynamic programming approach and exploiting the affine nature of the model, we derive a single equation satisfied by the optimal strategy, and we show numerically that this equation leads to a unique solution in all special cases. In our numerical study, we focus on the impact of jumps and evaluate the difference to investors employing a Gaussian HN-GARCH model without jumps or a homoscedastic variant. We find that both jump-free models yield insignificant values for the wealth-equivalent loss when re-calibrated to simulated returns from the jump models. The low wealth-equivalent loss values remain consistent for modified parameters in the jump models, indicating extreme market situations. We therefore conclude, in support of practitioners’ preferences, that simpler models can successfully mimic the strategy and performance of discrete-time conditional heteroscedastic jump models.

Boosting enjoyment and social inclusion to increase physical activity and reduce sedentary behaviour among older adults: protocol for a feasibility study to test the JOIN4JOY approach in five European countries

IntroductionProgrammes for older people aimed at increasing physical activity (PA) and reducing sedentary behaviour (SB) traditionally focus on achieving functional and health improvements. Focusing on enjoyment and social inclusion could...

A fuzzy rough-interval linear programming model for booster optimization in water distribution systems

In this article, a fuzzy rough-interval linear programming (FRILP) approach is proposed to deal with the uncertainty expressed by the fuzzy rough set in the optimization of booster cost under uncertainty. The FRILP model was applied to two cases to verify its effectiveness in booster optimization under uncertainty. The optimal upper and lower approximation intervals of the injection mass and booster cost in the fuzzy rough set were obtained and the effects of booster number and location were analysed. The results indicated that with an increase in booster number, the injection mass generally decreased, and booster cost decreased or increased. The nodal chlorine concentration also decreased and was distributed uniformly under a scenario with more boosters. Locating the booster far from the source resulted in more uniform chlorine distribution. The results could help managers design booster schemes with consideration of technical and economic factors comprehensively under uncertainty.

Adaptive virtual team planning and coordination: a mathematical programming approach

Purpose The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination. Design/methodology/approach We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver. Findings Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach. Practical implications The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner. Originality/value This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.

Secure beamforming design for MISO URLLC networks in IoT applications

This paper investigates joint beamforming and artificial noise (AN) design for secure multiple-input single-output (MISO) ultra-reliable and low latency communication (URLLC) networks in Internet of Things (IoT) applications. In considered system, a base station (BS) transmits confidential information for individual IoT users using the short-packet communication technique under the wiretap of eavesdroppers. To enhance physical layer security, the BS injects additional dedicated AN symbols to degrade the information retrieval ability at eavesdroppers. In this paper, we aim to joint design the transmit beamforming and AN symbol to maximize the minimum URLLC secrecy rate of IoT user subject to the power budget of the BS. The optimization design problem is highly nonconvex due to coupled variables in the URLLC secrecy rate and channel dispersion expressions, and thus it is mathematically challenging to solve this problem directly. To overcome this issue, we first introduce various convex inner approximations to convexify the nonconvex terms, and then we develop an efficient iterative algorithm based on the sequential convex programming approach. Extensive numerical simulation results are conducted to investigate the URLLC secrecy rate region. In conclusion, the two new URLLC parameters, i.e., the transmit packet blocklength and block error probability, will cause the considerable degradation on the URLLC secrecy rate region when comparing to that of the traditional beamforming design based on the Shannon capacity.

A mathematical programming approach to export pathway planning of distributed hydrogen production in offshore wind farm

Hydrogen production is a vital development trend to improve the economics and penetration rate of offshore wind farms (OWFs), and distributed hydrogen production (DHP) is a preferred solution for deep and far sea OWFs without expensive submarine cables and offshore substations. The export pathway of OWF-DHP consists of collection pipelines, transmission pipelines, and offshore compressor stations. In this paper, mathematical programming is introduced to explore the export pathway planning problem of OWF-DHP. Firstly, the area of OWF is discretized into multiple grids with the center of the grids being the candidate locations of the offshore compressor station. Then, a binary integer quadratic programming problem is established to optimize both the offshore compressor station location and pipeline construction with different topologies. Further, the mathematical model is solved by the branch and cut algorithm integrated with the proposed dimension reduction methods. Finally, the effectiveness of the proposed export pathway planning approach is verified by the actual data of Baltic Eagle OWF in Germany, which would support the construction of the OWF-DHP project.

Optimal synchronization with [formula omitted]-gain performance: An adaptive dynamic programming approach

This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain external disturbance. Under some mild assumptions, Hamilton–Jacobi–Isaacs equation is derived by the performance index function and system dynamics, which serves as an equivalent formulation. Distributed policy iteration adaptive dynamic programming is developed to obtain the numerical solution to the Hamilton–Jacobi–Isaacs equation. Three theoretical results are given about the proposed algorithm. First, the iterative variables is proved to converge to the solution to Hamilton–Jacobi–Isaacs equation. Second, the L2-gain performance of the closed loop system is achieved. As a special case, the origin of the nominal system is asymptotically stable. Third, the obtained control protocol constitutes an Nash equilibrium solution. Neural network-based implementation is designed following the main results. Finally, two numerical examples are provided to verify the effectiveness of the proposed method.

A pilot study in narrative approach of an adolescent parenting program for enhancing parenting competency during the COVID-19 pandemic in Hong Kong

ABSTRACT Since 2019, Hong Kong has been experiencing a social and mental health crisis caused by social unrest and the COVID-19 Pandemic. As a result, there have been reports of increasing family conflicts among secondary students. With the understanding that family support and positive relationships are crucial for maintaining the mental well-being of young people, narrative therapy is a practical approach for helping families shift from problem-focused perspectives to empowering strategies for rebuilding positive relationships. The study aims to report on the effectiveness of a pilot group parent narrative therapy workshop for family relationship harmonisation and enhancing parenting competency using quantitative and qualitative methods. The study participants included 34 parents from five local schools. The results indicated a significantly increased sense of parental competency, general self-efficacy, adult hope, and decreased parental stress. Qualitative reports and participants’ narratives also demonstrated the positive effect of the narrative therapy workshop on their parent–adolescent relationships.

SuperTAD-Fast: Accelerating Topologically Associating Domains Detection Through Discretization.

High-throughput chromosome conformation capture (Hi-C) technology captures spatial interactions of DNA sequences into matrices, and software tools are developed to identify topologically associating domains (TADs) from the Hi-C matrices. With structural information theory, SuperTAD adopted a dynamic programming approach to find the TAD hierarchy with minimal structural entropy. However, the algorithm suffers from high time complexity. To accelerate this algorithm, we design and implement an approximation algorithm with a theoretical performance guarantee. We implemented a package, SuperTAD-Fast. Using Hi-C matrices and simulated data, we demonstrated that SuperTAD-Fast achieved great runtime improvement compared with SuperTAD. SuperTAD-Fast shows high consistency and significant enrichment of structural proteins from Hi-C data of human cell lines in comparison with the existing six hierarchical TADs detecting methods.

Speeding up Genetic Improvement via Regression Test Selection

Genetic Improvement (GI) uses search-based optimisation algorithms to automatically improve software with respect to both functional and non-functional properties. Our previous work showed that Regression Test Selection (RTS) can help speed up the use of GI and enhance the overall results while not affecting the software system’s validity. This article expands upon our investigation by answering further questions about safety and applying a GI algorithm based on Local Search (LS) in addition to the previously explored Genetic Programming (GP) approach. Further, we extend the number of subjects to 12 by analysing five larger real-world open-source programs. We empirically compare two state-of-the-art RTS techniques combined with GP and LS for these 12 programs. The results show that both RTS techniques are safe to use and can reduce the cost of GI by up to 80% and by 31% on average across programs. We also observe that both search-based algorithms impact the effectiveness gains of GI differently, and that various RTS strategies achieve differing gains in terms of efficiency. These results serve as further evidence that RTS must be used as a core component of the GI search process to maximise its effectiveness and efficiency.

Optimal management plans and initiatives for the valorization and safeguarding of local breeds: a mathematical programming approach

In this study, a parametric programming model was developed in order to deliver optimal management plans for various types of farms rearing endangered Greek sheep breeds under three distinct scenarios. The first scenario investigated the optimal internal organization of farms (i.e., management practices and resource allocation strategies that increase profitability of farms) under current market conditions (e.g., product prices) without, however, considering subsidies and compensations. The second assessed the impact of an agri-environmental support scheme on farm optimal structure and socioeconomic performance. The third investigated the effects of integrating farms into value chains and niche markets where premium prices prevail. For this purpose, a questionnaire survey was conducted in the wider region of Epirus and Thessaly, collecting management data from 16 farms rearing (i) Kalaritiko, (ii) Orino Epiru and (iii) Katsika rare sheep breeds. The main finding of this study was that the optimal internal organization of farms is essential for their self-reliance and viability. Therefore, it should be the basis in the designing of any initiative aimed at the preservation and valorization of local breeds. Furthermore, the analysis showed that a niche marketing strategy (i.e., a premium price strategy) can have a broader positive impact on farms structure and socioeconomic performance, particularly those engaged in cheese production, compared to an agri-environmental scheme. However, due to the fact that both initiatives possess limitations that may render them inefficient under specific external conditions, the development of an integrated incentive mechanism, which will combine both policy schemes and market-based initiatives, appeared to be a more effective strategy for the long-term viability of farms.

Social Processes of Public Sector Collaborations in Kenya: Unpacking Challenges of Realising Joint Actions in Public Administration

AbstractSocial processes behind the success or failure of collaborative implementation frameworks in African public administration contexts are under-researched. This paper addresses this gap by paying particular attention to trust attributes in collaborative implementation arrangements in Kenya. It shows how implementation challenges of policy programs and interventions may be linked to these interventions’ social characteristics in the public sector. The paper draws on a threefold approach of mutual trust and administrative data on public sector collaborative implementation arrangements for Kenyan anti-corruption policy like the Kenya Leadership Integrity Forum. Findings show that despite increased efforts to realise joint actions in public sector collaborative arrangements, they remain primarily symbolic and hierarchical and feature loose social cohesion among actors, producing challenges bordering on deficiencies in social processes of implementation. These include politicised aloofness or lack of commitment, unclear governance structures, coordination deficiencies, inter-agency conflicts, layered fragmentations, and overlapping competencies among different agencies. The paper recommends identifying and nurturing socially sensitive strategies embedded in mutual trust, like informal knowledge-sharing channels, to address primarily mandated public sector collaboration challenges in Kenya. Such efforts should consider systematic training and incentivising public managers to think outside inward-looking organisational cultures, allowing them to devise sustainable collaborative implementation approaches (promote open innovation) for policy programs, particularly anti-corruption policy.

A new Fermatean fuzzy multi‐objective indefinite quadratic transportation problem with an application to sustainable transportation

AbstractIn this work, a Fermatean fuzzy (FF) multi‐objective indefinite quadratic transportation problem (TP) is introduced. Due to some unavoidable reasons, real‐life transportation parameters such as supply, demand and costs are indeterminate in nature and cannot be expressed in crisp terms. We represent these parameters using FF numbers, an extension of fuzzy numbers, which are capable of representing indeterminacy efficiently. A multi‐objective indefinite quadratic TP where each objective is a product of two linear factors (cost functions) is considered. Defuzzification of FF numbers is accomplished by the introduction of ‐cut for the first time. The obtained crisp TP is solved using the intuitionistic fuzzy programming approach and FF programming approach to arrive at a compromise solution. To substantiate the work, solution methodology based on defuzzification using the ranking function is also deliberated. The applicability of the model is demonstrated through a sustainable TP, which simultaneously minimizes transportation cost with depreciation cost and packaging cost with wastage cost. The resulting value of the objective functions and the aspiration levels are compared to depict the efficacy of the proposed method over the ranking function method. The concluding section summarizes the work, and future avenues along with some limitations of the work are also specified.

Mathematical modeling, sensitivity analysis, and optimal control of students awareness in mathematics education

This article presents a continuous-time mathematical model, EFSMK, integrating various educational categories, serving as a valuable tool for understanding students interactions with mathematics. The paper begins by exploring the necessary preliminaries to describe the model. Subsequently, we examine the model’s well-posedness, focusing on the positivity and boundedness of solutions. The basic reproduction number is then derived using the next-generation matrix method. The model exhibits two stable states: the mathematics-free equilibrium point and the learning equilibrium point for students who struggle with mathematics. The study shows that when the basic reproduction number is less than one, the mathematics-free equilibrium point is globally asymptotically stable. Conversely, when the basic reproduction number exceeds one, the learning equilibrium point for students who struggle with mathematics is globally asymptotically stable. Numerical simulations validate the theoretical findings. In the last part of the paper, we present a structured model involving two distinct strategies aimed at improving the mathematical skills of students who encounter difficulties in this subject. The first strategy consists of offering individualized or small-group tutoring sessions, focusing on challenging topics and using alternative teaching methods. The second strategy entails adjusting the overall program and pedagogical approach to better respond to diverse learning needs. This approach uses Pontryagin’s maximum principle and iterative analysis to determine optimal controls. The effectiveness of these strategies is evaluated using numerical simulations in various scenarios.

A new approach for reliability modeling in green closed-loop supply chain design under post-pandemic conditions: A case study

Climate change, pandemics, and economic crises have created complex challenges for supply chains. Managing such situations requires the development of reliable decision-making frameworks. In this paper, a multi-level, multi-product, and multi-period closed-loop supply chain is studied with environmental considerations. A bi-objective mixed-integer linear programming model is presented for facility location, flow allocation, and transportation mode determination. The objectives of the model are to minimize the total cost and maximize the reliability of suppliers to meet the needs of factories. In the area of reliability engineering, a new approach is defined for modeling the probability of supplier availability considering catastrophic failures caused by pandemics, economic sanctions, and other failure modes. Furthermore, the decision-maker can handle the emission of greenhouse gases by an upper-bound constraint. In order to face the simultaneous uncertainty of demand and the maximum CO2 emission allowed, a scenario-based two-stage stochastic programming approach is proposed. The improved version of the augmented ε-constraint method, known as AUGMECON2, is used to solve the proposed model. The efficiency of the model and the proposed solution approach are investigated through a real-world case study of a battery manufacturing company in Iran.

A Constraint Programming Approach for Discrete Time–Cost Tradeoff Problems in a Time-Constrained Activity Network

The discrete time-cost tradeoff problem (DTCTP) is a well-researched topic in the field of operations research. The majority of existing DTCTP models are based on traditional activity networks, which permit the execution of an activity as soon as all its predecessors have been completed. This assumption is reasonable, but it is important to note that there are always exceptions. The main work of this study was threefold. Firstly, we expanded the analysis of the DTCTP to encompass time-constrained activity networks (DTCTPTC), which encompassed three different types of time constraints. The first constraint was the time-window constraint, which limited the time interval during which an activity could be executed. The second constraint was the time-schedule constraint, which specified the times at which an activity could begin execution. The third constraint was the time-switch constraint, which required project activities to start at specific times and remain inactive during designated time periods. Secondly, a constraint programming (CP) model was developed for the purpose of solving the DTCTPTC. The model employed interval variables to define the activity and its potential time constraints, while CP expressions were utilized to ensure the feasibility of the solution. The objective was to identify the optimal execution mode for each activity, the optimal start times for time-scheduled activities, and the optimal work/rest patterns for time-switch activities, with the aim of minimizing the total cost of the project. Finally, the efficacy of the proposed CP model was validated through two case studies based on two illustrative projects of varying sizes. The outcomes were then compared against existing algorithms. The results demonstrated that time constraints were important factors affecting schedule optimization, and the proposed CP model had the ability to solve large-scale DTCTPTC.