Quantum‑Enhanced Cognitive Modeling for Advanced Logistics Route Optimization

Objectives: A well-known NP-complete problem is the travelling salesman problem (TSP). It has numerous engineering and scientific applications. In this article, we have proposed a multi-conveyance TSP where different conveyances are present to travel from one city to another city. This is an extension to classical TSP. In this TSP, the salesman visits all the cities only once during his/her tour, using different conveyances to travel from one city to another. The cost of travelling between cities using various modes of conveyance varies. The objective of this research is to find the minimum cost tour using an ant colony optimization (ACO) based approach by satisfying the constraints of the proposed multi-conveyance TSP. Method: The considered TSP has been solved using a novel ACO technique. The proposed ACO is adopted with the Roulette-wheel selection and “tuning solution” techniques. We have used a few benchmark datasets from TSPLIB to check the effectiveness of the proposed algorithm. The experimental findings for a few benchmarks TSP instances show that almost always, the proposed ACO is able to find a better result. Then, we used some redefined and randomly generated datasets for experiments. The experimental outcomes for various input datasets are also very encouraging. Findings: The goal of the proposed TSP is to find a complete tour with a minimum cost without exceeding the total travel cost and total travel time. Thus, there are two novel constraints in the classical TSP. Novelty: A unique aspect of the proposed research is the use of several conveyance facilities and a fixed total travel time and cost. This is new, as these three factors are integrated into a single TSP model. Keywords: Travelling Salesman Problem; Travel Cost; Travel Time; MultiConveyance TSP; Ant Colony Optimization

Abstract: This chapter explores the transformative potential of quantum algorithms in solving complex optimization problems across industries such as logistics, finance, and material science. Quantum algorithms, including Quantum Annealing, Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE), offer unprecedented computational capabilities for addressing challenges that are impractical for classical algorithms. In logistics, quantum algorithms optimize supply chains, vehicle routing, and warehouse operations, significantly reducing costs and improving efficiency. In finance, quantum optimization enables better portfolio management, risk assessment, and high-frequency trading strategies, providing faster, more accurate solutions. In material science, quantum algorithms accelerate the discovery and design of new materials, such as superconductors and catalysts, by simulating molecular interactions more efficiently. The chapter discusses real-world implementations, current technical challenges, and the future potential of quantum algorithms to revolutionize industries through faster and more scalable optimization solutions. Keywords: Quantum algorithms, Quantum Annealing, QAOA, VQE, optimization, logistics, finance, material science, portfolio optimization, risk management, supply chain, molecular simulation, quantum computing, quantum optimization, quantum chemistry, high-frequency trading.

The special issue on Managing Artificial Intelligence for Accelerating the Implementation of the United Nations Sustainable Development Goals, positioning it within the urgent global context of advancing the 2030 Agenda. The special issue is closely aligned with the United Nations (UN) Sustainable Development Goals (SDGs) by examining how Artificial Intelligence (AI) can act as a catalyst for comprehensive, sustainable, and data-driven development across critical areas such as poverty reduction, healthcare, education, climate action, and sustainable cities. In the current scenario, which is marked by rapid digital transformation, increasing global inequalities, and escalating environmental challenges, AI presents both unprecedented opportunities and complex ethical considerations. This issue addresses these dynamics by emphasising responsible AI governance, equitable access, and the integration of technological innovation with human-centred development principles. A distinctive feature of this special issue lies in the diversity of its contributions, bringing together interdisciplinary research, policy analyses, and applied case studies from academics, practitioners, and policymakers across different regions and sectors. The collection reflects a balance between theoretical insights and real-world applications, including contributions from both conference presentations and open-call submissions. The uniqueness of this issue is further demonstrated through its comprehensive thematic coverage-spanning governance, business transformation, and environmental sustainability while maintaining a strong focus on actionable outcomes and global collaboration. By bridging gaps between research, policy, and practice, this special issue offers timely and relevant insights for accelerating SDG implementation in an increasingly complex and interconnected world. Keywords: Artificial Intelligence; Sustainable Development Goals; Digital Transformation; Technological Innovation; Human-Centred Development; Ethical Considerations. Citation: Ahmed, A. and Naim, A. (2026): Managing Artificial Intelligence for Accelerating the Implementation of the United Nations Sustainable Development Goals. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, Nos. 1/2/3, pp. 1-10. WASD: London, United Kingdom.

The World Health Organization recognizes that physical activity (PA) during childhood is crucial for healthy development, aligning well with the achievement of several United Nations (UN) Sustainable Development Goals (SDGs). This study aimed to explore the associations between 10 key indicators of PA for children and adolescents assessed in the Global Matrix 4.0 project, and the UN SDGs. Data from 57 countries/jurisdictions of the Global Matrix 4.0 project were used. The UN SDG indicators were sourced from the SDG Transformation Center, which publishes each country's performance on each of the 17 SDGs. Given the robust evidence supporting plausible links between PA and SDGs 3 (good health and well-being), 9 (industry, innovation, and infrastructure), 11 (sustainable cities and communities), 13 (climate action), and 16 (peace, justice, and strong institutions), these SDGs were investigated. Countries/jurisdictions with good and moderate performance in achieving SDG 3, SDG 9, SDG 11, and SDG 16 had higher grades than countries/jurisdictions with fair performance in achieving these SDGs for the following indicators: Organized Sports and PA, Community and Environment, and Government Investments and Strategies. However, countries/jurisdictions with good performance in achieving SDG 13 had lower grades than countries/jurisdictions with fair performance in achieving SDG 13 for the following indicators: Organized Sports and PA, Community and Environment, and Government Investments and Strategies. Organized Sports and PA, Community and Environment, and Government Investments and Strategies were the indicators that demonstrated differences between countries/jurisdictions with good and poor performance in achieving the SDGs.

The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of combinatorial optimization problems, it is not clear which classes of problems are best suited for it. One factor in demonstrating quantum advantage is the relationship between a problem instance and the circuit depth required to implement the QAOA method. As errors in noisy intermediate-scale quantum (NISQ) devices increase exponentially with circuit depth, identifying lower bounds on circuit depth can provide insights into when quantum advantage could be feasible. Here, we identify how the structure of problem instances can be used to identify lower bounds for circuit depth for each iteration of QAOA and examine the relationship between problem structure and the circuit depth for a variety of combinatorial optimization problems including MaxCut and MaxIndSet. Specifically, we show how to derive a graph, G, that describes a general combinatorial optimization problem and show that the depth of circuit is at least the chromatic index of G. By looking at the scaling of circuit depth, we argue that MaxCut, MaxIndSet, and some instances of vertex covering and Boolean satisfiability problems are suitable for QAOA approaches while knapsack and traveling salesperson problems are not.

Quantum computing is poised to transform computational paradigms across science and industry. As the field evolves, it can benefit from established classical methodologies, including promising paradigms such as Transfer of Knowledge (ToK). This work serves as a brief, self‐contained reference for ToK, unifying its core principles under a single formal framework. We introduce a joint notation that consolidates and extends prior work in Transfer Learning and Transfer Optimisation, bridging traditionally separate research lines and enabling a common language for knowledge reuse. Building on this foundation, we classify existing ToK strategies and principles into a structured taxonomy that helps researchers position their methods within a broader conceptual map. We then extend key transfer protocols to quantum computing, introducing two novel use cases—reverse annealing and multitasking Quantum Approximate Optimization Algorithm (QAOA)—alongside a sequential Variational Quantum Eigensolver (VQE) approach that supports and validates prior findings. These examples highlight ToK's potential to improve performance and generalisation in quantum algorithms. Finally, we outline challenges and opportunities for integrating ToK into quantum computing, emphasising its role in reducing resource demands and accelerating problem‐solving. This work lays the groundwork for future synergies between classical and quantum computing through a shared, transferable knowledge framework.

China joined the Paris Agreement, and the global 2°C and 1.5°C warming targets will be supported by China. In order to achieve these targets, China's CO2 emissions need to be cut deeply by 2050. The present paper presents studies from the integrated policy assessment model for China (IPAC) team about the impact on China's economic development of deep cuts in greenhouse gas (GHG) emissions, in order to realize the Paris climate change targets. With the requirement of deep cuts in GHG emissions in China, China's economic development will also be impacted in moving toward a low‐carbon or zero‐carbon emission‐based economy by 2050. This means the Chinese economy needs a strong transition over the next three decades, a relatively short time. All sectors in the economy need to seek ways to reduce GHG emissions, and this could change activities, industry processes and technologies in order to make the deep cuts in GHG emissions happen. This is the meaning of the economic transition toward to a low‐carbon economy. The findings of the present paper include: a significant transition in the energy supply sector; a high rate of electrification in all end‐use sectors; and a technology transition in the transport sector. Transitions will also occur in the traditional industrial sectors, including steel making, cement manufacture, and the chemical sector. The availability of low‐cost renewable energy could change the allocation of industries, which could potentially have a strong impact on regional economic development. Deep cuts in CO2 emissions in China need not be a burden for economic development, as the IPAC results show there will be a more than 1.5% increase of gross domestic product by 2050 in the deep cut scenario compared with the baseline scenario.

The Traveling Salesman Problem (TSP) is an NP-hard graph search problem. Despite having numerous modifications of the original abstract problem, Time Dependent Traveling Salesman Problem (TD TSP) was one of the most realistic extensions under real traffic conditions. In TD TSP the edges between nodes are assigned higher costs (weights), if they were traveled during the rush hour periods, or crossed the traffic jam regions, such as the city center(s). In this paper we introduce an even more real-life motivated approach, the Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem (IFTD TSP), which is a further extension of the TSP, and also of the classic TD TSP, with the additional notion of using intuitionistic fuzzy sets for the definition of uncertain costs, time, and space of the rush hour—traffic jam region affecting graph sections. In IFTD TSP we use fuzzy memberships and non-memberships sets for estimating the vague costs between nodes in order to quantify the behavior of traffic jam regions, and the rush hour periods. Since intuitionistic fuzzy sets are generalizations of classic fuzzy sets, our approach may be considered an extension and substitution of the original abstract TD TSP problem, even, of the (classic) Fuzzy TD TSP. Lastly, DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm) was applied on the IFTD TSP model, the results of the simulation runs based on some extensions of the benchmarks generated from the original TD TSP data set showed quite good and promising preliminary results.

Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAOA) or quantum annealing (QA). Thus, we consider training neural networks in this context. We first discuss QUBO problems that originate from translated instances of the traveling salesman problem (TSP): Analyzing this representation via autoencoders shows that there is way more information included than necessary to solve the original TSP. Then we show that neural networks can be used to solve TSP instances from both QUBO input and autoencoders' hidden state representation. We finally generalize the approach and successfully train neural networks to solve arbitrary QUBO problems, sketching means to use neuromorphic hardware as a simulator or an additional co-processor for quantum computing.

In view of Traveling Salesman Problem (TSP) is a NP hard problem, Lion Swarm Optimization (LSO) Algorithm is a new swarm intelligence optimization algorithm. Therefore, in this paper, it uses improved hybrid LSO algorithm to solve TSP. Because the basic LSO algorithm is mainly used to solve the continuous problem, and the TSP is a discrete combinatorial optimization problem, this paper improves the LSO algorithm, introduces the greedy strategy, and combines with the Population Competitive Search (PCS) algorithm in the later stage of the search. As a result, the convergence speed is accelerated and the search efficiency is improved. The classic TSP example is tested, and the simulation results show that the algorithm proposed in this paper has strong stability and is an effective method for solving TSP.

The traveling salesman problem (TSP) is one of the most studied problems in combinatorial optimization. Given a set of nodes and the distances between them, it consists in finding the shortest route that visits each node exactly once and returns to the first. Nevertheless, more flexible and applicable formulations of this problem exist and can be considered. The Steiner TSP (STSP) is a variant of the TSP that assumes that only a given subset of nodes must be visited by the shortest route, eventually visiting some nodes and edges more than once. In this paper, we adapt some classical TSP constructive heuristics and neighborhood structures to the STSP variant. In particular, we propose a reduced 2‐opt neighborhood and we show that it leads to better results in smaller computation times. Computational results with an implementation of a GRASP heuristic using path‐relinking and restarts are reported. In addition, ten large test instances are generated. All instances and their best‐known solutions are made available for download and benchmarking purposes.

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.

Inequitable radiotherapy availability in India leads to non-compliance in many cases, as patients need to travel long distances for treatment; this has long-term implications for achieving the United Nations sustainable development goals. Notably, the number of functional radiotherapy units in India is below the limit recommended by the World Health Organization, and most centers in this vast country are located in urban centers. This creates a serious barrier to accessibility for the socioeconomically disadvantaged sections of the rural population. Recent reports suggest that despite the availability of free treatments for a wide variety of cancers, many patients are non-compliant owing to the high costs incurred on travel to distant centers. In view of the current distribution of radiotherapy units, and the low ratio of radiotherapy units serving the vast population, distances traveled for radiotherapy are likely to have considerable impact on realization of the United Nations sustainable development goals. It is also likely to have considerable impact on the existing weak infrastructure of healthcare facilities, as poor cancer control will increase the need for palliative care and support, thereby further reducing resource allocation to cancer control. Policies directed towards reducing travel times for radiotherapy are currently lacking in India. However, this issue needs urgent consideration to ensure optimal utilization of available resources. Until measures to reduce travel time can be implemented, reducing travel-related patient distress may improve compliance in the short term; urgent measures in this regard will help achieve the targets of the United Nations sustainable development goals.

CCS in the European electricity supply system — Assessment of national conditions to meet common EU targets

Purpose This study investigates the alignment of quality management (QM) practices with sustainability dimensions to achieve the United Nations Sustainable Development Goals (UNSDGs) in Indian industries. The study investigates how these industries utilise QM tools to advance sustainability, mainly focusing on environmental, social, governance, economic and operational sustainability. Design/methodology/approach A multiple-case study approach was employed, analysing 41 case studies submitted to the International Academy for Quality (IAQ) for the Quality Sustainability Award. The data were collected from various Indian industries and analysed through within-case and cross-case comparisons, focusing on applying QM practices and their alignment with sustainability dimensions. The research methodology utilised both qualitative and quantitative analysis techniques. Findings The findings reveal that industries predominantly use Six Sigma, lean tools, PDCA and root cause analysis (RCA) to address operational and environmental challenges. The most frequently addressed UNSDGs include Goal 12 (Responsible Consumption and Production), Goal 9 (Industry Innovation and Infrastructure) and Goal 13 (Climate Action). Significant improvements were observed in defect reduction, energy efficiency and cost management. However, the study also highlights gaps in addressing social sustainability, particularly in areas like diversity and inclusion. Practical implications This research provides practitioners with insights on aligning QM tools with sustainability initiatives to ensure operational excellence while addressing environmental and social challenges. For policymakers, the study highlights the need for regulatory frameworks supporting UNSDGs through QM, emphasising social responsibility mandates. At the same time, academicians can leverage the findings to explore new QM-based sustainability models. Originality/value This research contributes uniquely to the literature by conducting a cross-sectoral analysis within a consistent regulatory environment in India. Unlike multinational studies where legal and regulatory variations can affect comparability, this study provides a controlled context that enhances the reliability of cross-sector comparisons. This allows for clearer insights into implementing QM practices to achieve sustainability across various sectors. Additionally, the study highlights the role of advanced technologies in enhancing operational efficiency and sustainability while identifying areas for improvement, such as the broader adoption of I4.0 (Industry 4.0) tools and a more balanced focus on social sustainability.