Quantum Computing in Logistics and Supply Chain Management an Overview

The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing as key algorithmic approaches. The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems. Notably, most solutions in the literature are hybrid, combining quantum and classical computing. The conclusion highlights the early stage of quantum computing, emphasising its potential impact on logistics and supply chain optimisation. In the final overview, the literature is categorised, identifying quantum annealing dominance and a need for more research in prediction and machine learning is highlighted. The consensus is that quantum computing has great potential but faces current hardware limitations, necessitating further advancements for practical implementation

Protocolo de enrutamiento de optimización TABU de bajo consumo de energía para WSN

Introduction: This article is the result of the research “Energy efficient routing protocols in wireless sensor network: Examine the impact of M-SEEC routing protocols on the lifetime of WSN with an energy efficient TABU optimization routing protocol”developed in the IKG, Punjab Technical University, India in 2019. Problem: The task of finding and maintaining routes in WSNs is non-trivialsince energy restrictions and sudden changes in node status cause frequent and unpredictable changes. Objective: The objective of this paper is to propose an energy efficient heterogeneous protocolwith the help of a hybrid meta-heuristic technique. Methodology: In the hybrid meta-heuristic technique, the shortest route has been selected and the data forwarded to the sink in a minimal time span,savingenergy and making the network more stable. To evaluate the technique, a new hybrid technique has been created where the data transmission is implemented from the beginning under MATLAB 2013a. Results: The proposed technique is better than the existing ones since the remaining energy in the network is increased by 62% compared to normal nodes in MSEEC, 65% compared to advanced nodes in MSEEC and 70% compared to super nodes in MSEEC. The network lifetime was also enhanced by 70.8% compared to MSEEC. Conclusion: The proposed protocol was found to be superior based on the average residual energy.This paper proposes an efficient routing mechanism towards the energy efficient network. Originality: Through this research, a novel version of MSEEC protocol is carried out using the TABU search mechanism to generate the functions of two neighbourhoods to detect the optimum path with the aim of maximizing the network lifetime in an area of 200×200m2. Limitations: The lack of other routing techniques falls under swarm intelligence.Introducción: Este artículo es el resultado de la investigación “Protocolos de enrutamiento energéticamente eficientes en la red de sensores inalámbricos: examine el impacto de los protocolos de enrutamiento M-SEEC en la vidaútil de WSN con un protocolo de enrutamiento de optimización TABU energéticamente eficiente” desarrolladoen el IKG, Punjab Technical University, India en 2019. Problema: La tarea de encontrar y mantene rrutas en WSN no es trivial ya que las restricciones de energía y los cambios repentinosen el estado de los nodos causan cambios frecuentes e impredecibles. Objetivo: El objetivo de este trabajo es proponer un protocoloheterogéneo de eficiencia energética con la ayuda de una técnica metaheurística híbrida. Metodología: en la técnica metaheurística híbrida, se seleccionó la ruta más corta y los datos se enviaron al sumidero en un período de tiempo mínimo, ahorrando energía y haciendo que la red sea más estable. Para evaluar la técnica, se ha creado una nueva técnica híbrida donde la transmisión de datos se implementa desde el principio en MATLAB 2013a. Resultados: La técnica propuesta es mejor que las existentes, ya que la energía restante en la red aumenta en un 62% encomparación con los nodos normales en MSEEC, el 65% encomparación con los nodos avanzados en MSEEC y el 70% encomparación con los nodos en MSEEC. La vidaútil de la red también se mejoróen un 70.8% encomparación con MSEEC. &nbsp

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms

A review of quantum-inspired metaheuristic algorithms for automatic clustering

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clus tering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algo rithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits.Web of Science119art. no. 201

Quantum-enhanced multiobjective large-scale optimization via parallelism

Traditional quantum-based evolutionary algorithms are intended to solve single-objective optimization problems or multiobjective small-scale optimization problems. However, multiobjective large-scale optimization problems are continuously emerging in the big-data era. Therefore, the research in this paper, which focuses on combining quantum mechanics with multiobjective large-scale optimization algorithms, will be beneficial to the study of quantum-based evolutionary algorithms. In traditional quantum-behaved particle swarm optimization (QPSO), particle position uncertainty prevents the algorithm from easily falling into a local optimum. Inspired by the uncertainty principle of position, the authors propose quantum-enhanced multiobjective large-scale algorithms, which are parallel multiobjective large-scale evolutionary algorithms (PMLEAs). Specifically, PMLEA-QDE, PMLEA-QjDE and PMLEA-QJADE are proposed by introducing the search mechanism of the individual particle from QPSO into differential evolution (DE), differential evolution with self-adapting control parameters (jDE) and adaptive differential evolution with optional external archive (JADE). Moreover, the proposed algorithms are implemented with parallelism to improve the optimization efficiency. Verifications performed on several test suites indicate that the proposed quantum-enhanced algorithms are superior to the state-of-the-art algorithms in terms of both effectiveness and efficiency