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Journal articles on the topic 'Optimisation de route'

Author: Grafiati

Published: 25 January 2025

Last updated: 19 July 2025

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Worimegbe, P., and N.C Sylvanus. "Route Optimisation and Operational Efficiency in Selected Manufacturing Firms in Shagamu, Ogun State, Nigeria." Caleb Journal of Social and Management Sciences 9, no. 1 (2025): 13–27. https://doi.org/10.5281/zenodo.15433584.

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<strong>Abstract</strong> Route optimisation has the potential to significantly enhance operational efficiency within Nigeria's manufacturing sector, offering an opportunity for firms to improve productivity, reduce costs, and strengthen competitiveness. However, the sector has faced persistent challenges such as inadequate infrastructure, high production costs, and logistical inefficiencies, all of which hinder performance and global competitiveness. This study seeks to address this gap by examining the effect of route optimisation on operational efficiency in selected manufacturing firms in Shagamu, Ogun State, Nigeria. Using a quantitative research approach, data were collected through structured questionnaires from employees in logistics, supply chain, procurement, and operations departments of six manufacturing firms. The findings revealed that route optimisation has a positive and significant impact on operational efficiency ((β = 0.440, t = 14.849, R² = 0.356, p < 0.05), with a moderate to strong correlation (R = 0.597) and an R² value of 0.356, indicating that approximately 35.6% of the variance in operational efficiency can be explained by route optimisation. The regression analysis further revealed that for every one-unit increase in route optimisation, operational efficiency is expected to increase by 0.440 units (β = 0.440). The study concludes that route optimisation plays a vital role in enhancing operational efficiency in manufacturing firms. It is recommended that manufacturing firms invest in advanced logistics technologies, including AI-driven route planning, real-time GPS tracking, and IoT-enabled fleet management systems, to further streamline logistics operations and improve overall efficiency. Further research should explore longitudinal studies and comparative analyses across different regions and sectors.

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Li, Jialu. "Research on Route Optimization of RO-Ro Ship Based on Ant Colony Algorithm." Applied and Computational Engineering 110, no. 1 (2024): 201–11. https://doi.org/10.54254/2755-2721/2024.melb18117.

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This study is based on an ant colony algorithm for route optimisation of roll-on roll-off (RO-RO) vessels, aiming to minimise the total cost of RO-RO shipping routes. The model takes into account factors such as port berthing cost, fuel consumption and sailing time, and ensures the stability and efficiency of the optimisation results by adjusting the algorithm parameters such as pheromone initialisation, pheromone evaporation rate and ant colony size. The results of the study provide a practical solution for ro-ro ship route optimisation, which significantly reduces the operating costs and also provides key technical support for shipping companies to compete in the global market. In addition, the study demonstrates the potential of the optimisation model to reduce transport costs, improve shipping efficiency and reduce carbon emissions, providing an effective technical support for shipping companies in an increasingly competitive global market. Through this optimisation approach, shipping companies are able to better manage their route selection, resource allocation and operational processes, enhancing market competitiveness and promoting sustainable development of the shipping industry.

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Gigih, Priyandoko, Siti Nur Arofah, Nan Mad Sahar, and Istiadi Istiadi. "Donor Collectors Route Optimisation using Genetic Algorithm Method." International Journal of Electrical, Energy and Power System Engineering 3, no. 1 (2020): 1–5. http://dx.doi.org/10.31258/ijeepse.3.1.1-5.

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Searching for a route to take donation in terms of time and the shortest route is a must for the management because it can save time and effort. In order to make donations more efficient, a system is needed to provide recommendations for taking donation routes, one of which is using a Genetic Algorithm (GA) method. The GA can be applied in optimizing schedules, routes, and spaces. The results show that after testing two different routes with different maximum generation values of 50, 100, 500 and 1000, a maximum generation value of 50 can give optimal results.

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Ibrahim, A., B. Michael, Z. A. T. Suleyman, and O. B. Akintuyi. "Geospatial Approach to Route Optimisation for Security Architecture Improvement." Environmental Technology and Science Journal 14, no. 1 (2023): 75–82. http://dx.doi.org/10.4314/etsj.v14i1.8.

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Understanding the complexity of road networks is tiring, especially in finding the optimum routes for emergency response. Dynamic route optimisation is a decision-making process powered by mathematical models to formulate a strong basis for predicting the best path between any two points in a road network. Exploring the numerous interconnected routes in and out of a road network provides a strong foothold for securing lives and properties. This study presents the findings of route optimisation at the Federal University of Technology Minna, Gidan Kwano Campus. High Target Differential Global Positioning System receiver (DGPS), Handheld GPS (Garmin GPSmap 60Cx), Quick Bird Satellite Imagery, and Google Earth Images were used for data acquisition purposes. An ArcGIS network analyst for optimum route identification based on Dijktra’s algorithm was utilised for analysis. Five different optimisation tests were done and the optimised routes were compared with alternative routes from the network. The total travel distances for alternative and optimum routes are 11852.414 m and 16614.156 m respectively while the total travel times for alternative and optimum routes are 2135.35 s and 2993.25 s respectively. The study revealed all optimum routes are faster cumulatively by (11.7%) and are best for security and emergency responses. Ten unauthorised access roads to the campus were identified. The number of routes paving access to the campus shows that efforts towards ensuring security must be increased.

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Hou, Rong, and Pengfei Zhang. "A-star Ant Colony Fusion Optimisation for Unmanned Ground Vehicle Route Planning." Journal of Physics: Conference Series 2891, no. 11 (2024): 112001. https://doi.org/10.1088/1742-6596/2891/11/112001.

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Abstract Route planning technology of Unmanned Ground Vehicle (UGV) is a key issue in robotics, with the goal of avoiding obstacles and moving quickly to the target point. Aiming at the problem of easy collision with obstacles when utilizing the conventional A-star algorithm for global route planning, an improved turning mechanism is proposed to avoid collision. Aiming at the problems of longer routes and insufficiently smooth routes introduced by it, an improved A-star ant colony fusion algorithm for UGV route planning is proposed by combining the local planning capability of the ant colony algorithm. The A-star algorithm with an improved turning mechanism is fused with an ant colony algorithm to iteratively optimize the route length and route smoothness progressively. Simulation results show that the improved A-star ant colony fusion algorithm is able to avoid obstacles, reduce the length of the planned route by at least 2.34%, and improve the route smoothness by at least 5.62% compared to the A-star algorithm. It is beneficial for UGV to accomplish the corresponding tasks quickly on the basis of ensuring its obstacle avoidance.

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WANG, Zeyu, Fujian CHEN, and Chengcheng MO. "Optimisation Methods for Cold Chain Logistics Path Considering Carbon Emission Costs in Time-Varying Networks." Promet - Traffic&Transportation 36, no. 6 (2024): 1103–19. https://doi.org/10.7307/ptt.v36i6.735.

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With the escalating global climate change, the cost of carbon emissions has become a crucial metric for evaluating the sustainability of logistics systems. This study addresses the optimisation of cold chain logistics routes in a time-varying network environment, considering the carbon emission cost factor, and proposes an enhanced particle swarm optimisation algorithm to solve this optimisation problem. Firstly, we establish a cold chain logistics optimisation model that incorporates the time-varying network, integrating logistics route planning with carbon emission costs. Subsequently, we design an improved particle swarm optimisation algorithm suitable for time-varying networks. This algorithm optimises vehicle routes and adjusts delivery times to minimise the total cost incurred during distribution. Finally, through simulation experiments, we analyse the impact of vehicle speeds and carbon trading mechanisms on optimisation outcomes. The results demonstrate that this method effectively optimises cold chain logistics routes, considering real network conditions and environmental factors, thereby reducing delivery costs and carbon emissions.

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Ramtake, Dhammpal, Sanjay Kumar, and V. K. Patle. "Route Optimisation byAnt Colony OptimisationTechnique." Procedia Computer Science 92 (2016): 48–55. http://dx.doi.org/10.1016/j.procs.2016.07.322.

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Mezitis, Mareks, Arpabekov Muratbek, Zura Sansyzbayeva, and Alimzhan Assiltayev. "Enhancing effectiveness of use of the rolling stock through route optimisation." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 12 (2018): 808–10. http://dx.doi.org/10.24136/atest.2018.502.

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The article deals with issues of enhancing efficient use of the rolling stock of urban passenger transport through route optimisation. The authors offer to upgrade urban route network opt for: optimisation of routing system. In order to streamline passenger transport performance, the authors propose to calculate transport route schemes by applying economically mathematical methods of planning.

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Bychkova, A. A. "Optimisation of Russian railways’ high-speed routes in the regions." Vestnik Universiteta 1, no. 7 (2022): 82–89. http://dx.doi.org/10.26425/1816-4277-2022-7-82-89.

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Rail transport is constantly in development and improvement, as it is one of the most important strategic means of transportation. That is why the optimization of Russian railway routes in this case is an integral element of development. The introduction of a high-speed passenger transportation route makes it possible to increase throughput capacity, improve service conditions, and strengthen the position in the industry. The article points out the necessity of optimising the route from the Ural to the Volga federal districts. The relevance of the topic under study lies in the advantage of railway transport in interspecific competition in the market for the provision of passenger transportation services. The author used the local Moran index in the analysis, studied regional interconnections, and identified clusters. The results of the study are visually represented in the figure, where the dotted line highlights the potential route of high-speed traffic Yekaterinburg–Kazan. The article describes the possible risks of implementing this route, cites optimization expediency factors (increase of interspecific competition).

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Siti Hafawati Jamaluddin, Nurul Alyaa Zulkefli, Norwaziah Mahmud, and Nur Syuhada Muhammat Pazil. "Optimising Routes Scheduling of Garbage Truck for Waste Collection using Artificial Bee Colony Algorithm." Applied Mathematics and Computational Intelligence (AMCI) 12, no. 3 (2023): 40–50. http://dx.doi.org/10.58915/amci.v12i3.317.

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The issue of waste generation has attracted significant public attention in today’s societies, not only because waste production is increasing quantitatively but also because some products and components are becoming more complex. The growing problem of municipal solid waste management has become the most serious environmental issue. Population growth has resulted in the increase in the generation of solid waste, which has become a serious issue that must be resolved. Optimised route planning is one of the most important components of a smart waste management system. Route planning is a technological approach to determining the shortest routes through optimisation. The route optimisation problem commonly referred to as the vehicle routing problem (VRP), has been thoroughly studied to find the best possible route for a vehicle. This study aims to find the optimal routes and reduce the distance travelled by the garbage truck when collecting waste using Artificial Bee Colony algorithm. ABC algorithm is an optimization method based on the clever behaviour of honey bee swarms. The artificial bee colony in the ABC algorithm is divided into three groups which are employed bees, onlookers and scouts. To provide neighbouring solutions that are guaranteed to be possible, swap operator is used. Results show that by using 10 trucks in total as the vehicle, the optimal solution produced a total of optimal distance for 94.9542 km. The findings of this study fulfilled the intended objectives which are to find the optimal route and shortest distance for the vehicles.

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Szlapczynska, Joanna. "Multi-objective Weather Routing with Customised Criteria and Constraints." Journal of Navigation 68, no. 2 (2014): 338–54. http://dx.doi.org/10.1017/s0373463314000691.

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The paper presents a weather routing algorithm utilising a multi-objective optimisation with constraints, namely the Multi-objective Evolutionary Weather Routing Algorithm (MEWRA). In the proposed approach weather route recommendations can be made simultaneously e.g. for passage time, fuel consumption and safety of passage by means of Pareto optimisation. The sets of criteria and constraints in the optimisation process are fully customisable. The algorithm handles static (time-independent) and dynamic (time-dependent) constraints e.g. forecasted high wind speed regions or customisable areas to be excluded from routing (e.g. due to piracy). The paper includes a description of MEWRA as well as examples of its usage for finding Pareto-optimal transoceanic ship routes.

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Chiu, Ching-Sheng, and Chris Rizos. "Towards a Multi Objective Path Optimisation for Car Navigation." Journal of Navigation 65, no. 1 (2011): 125–44. http://dx.doi.org/10.1017/s0373463311000579.

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In a car navigation system the conventional information used to guide drivers in selecting their driving routes typically considers only one criterion, usually the Shortest Distance Path (SDP). However, drivers may apply multiple criteria to decide their driving routes. In this paper, possible route selection criteria together with a Multi Objective Path Optimisation (MOPO) model and algorithms for solving the MOPO problem are proposed. Three types of decision criteria were used to present the characteristics of the proposed model. They relate to the cumulative SDP, passed intersections (Least Node Path – LNP) and number of turns (Minimum Turn Path – MTP). A two-step technique which incorporates shortest path algorithms for solving the MOPO problem was tested. To demonstrate the advantage that the MOPO model provides drivers to assist in route selection, several empirical studies were conducted using two real road networks with different roadway types. With the aid of a Geographic Information System (GIS), drivers can easily and quickly obtain the optimal paths of the MOPO problem, despite the fact that these paths are highly complex and difficult to solve manually.

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PANG, Mingbao, Xing WANG, and Lixia MA. "Transit Route Planning for Megacities Based on Demand Density of Complex Networks." Promet - Traffic&Transportation 34, no. 1 (2022): 13–23. http://dx.doi.org/10.7307/ptt.v34i1.3752.

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The aim of this work is to investigate the simplifica-tion of public transport networks (PTNs) for megacities and the optimisation of route planning based on the de-mand density of complex networks. A node deletion rule for network centre areas and a node merging rule for net-work border areas in the PTN are designed using the de-mand density of complex networks. A transit route plan-ning (TRP) model is established, which considers the demands of direct passengers, transfer passengers at the same stop and transfer passengers at different stops, and aims at maximising the transit demand density of a PTN. An optimisation process for TRP is developed based on the ant colony optimisation (ACO). The proposed method was validated through a sample application in Handan City in China. The results indicate that urban PTNs can be simplified while retaining their local attributes to a great extent. The hierarchical structure of the network is more obvious, and the layer-by-layer planning of routes can be effectively used in TRP. Moreover, the operating efficiency and service level of urban PTNs can be en-hanced effectively.

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Du, Zeyuan, Haoyuan Zheng, Xiangyu Li, Tianyu Zheng, and Sicheng Zhu. "Research on Urban Waste Sorting Transportation Path Scheduling Based on Greedy Algorithm and Clustering Optimization." Frontiers in Computing and Intelligent Systems 12, no. 3 (2025): 92–96. https://doi.org/10.54097/b45exz67.

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This study investigates the scheduling of urban waste sorting transportation routes using a greedy algorithm and clustering optimisation to achieve a multi-objective balance between transportation efficiency, cost, and carbon emissions reduction. Firstly, a single-vehicle basic route optimisation model is constructed, and a mixed-integer programming model is solved using a greedy algorithm. In a case study involving 30 collection points and a load capacity of 125 tonnes, the optimal driving distance algorithm is obtained, and the route is optimised through clustering and shortest path strategies. Secondly, in a multi-vehicle collaborative scenario, a multi-objective model incorporating carbon costs is constructed, solved using integer linear programming and genetic algorithms, and it is verified that transportation costs are positively correlated with the distance to collection points. Finally, transfer station location constraints are incorporated to construct a ‘location-route-carbon emissions’ model. K-means clustering is used to divide the collection points into three clusters to determine transfer station locations, and the minimum spanning tree is then used to optimise the routes, achieving a reduction in overall costs. The research findings provide decision-making tools for waste sorting transportation, applicable to collection and transportation system planning, and contribute to the achievement of the ‘dual carbon’ goals.

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Li, Xinlu, Brian Keegan, and Fredrick Mtenzi. "Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs." Sensors 18, no. 10 (2018): 3351. http://dx.doi.org/10.3390/s18103351.

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Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime.

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Ying, Ang Pei, Justtina Anantha Jothi, and Nursakirah ARM. "WeRoute: Route Optimization Web-Based System and Driver Mobile Application." International Journal of Artificial Intelligence 8, no. 2 (2021): 78–87. http://dx.doi.org/10.36079/lamintang.ijai-0802.314.

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This paper intends to conceptualise an optimisation solution for vehicle routing that can get the best routing result and release the most optimal route to the driver, namely WeRoute. The objectives of the paper are to manage the data efficiently, save time, reduce cost, enhance customer satisfaction, and decrease the emission of carbon. Moreover, this is also known as the vehicle routing problem, which deals with a range of variables, including drivers, stops, roads, and customers. The method, Genetic algorithm, was developed to improve the efficiency of generating feasible routes for a project. A team of drivers and several stops are needed to generate the solution of optimising the vehicle routing. It can be said that the more drivers or stops, the more complicated the problem becomes, such as cost controls and vehicle limitations. Thus, a route optimisation tool slowly becomes the key to ensuring the delivery business as efficiently as possible.

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Hopkins, S. C., K. S. Tan, B. A. Glowacki, A. Szulczyk, and H. Krauth. "Computerised optimisation of bronze route Nb3Sn conductors." Journal of Physics: Conference Series 43 (June 1, 2006): 35–38. http://dx.doi.org/10.1088/1742-6596/43/1/009.

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Zheng, Qiangyu, Peijiang Ding, Zhixin Qin, and Zhenguo Yan. "An Investigation into the Rescue-Path Planning Algorithm for Multiple Mine Rescue Teams Based on FA-MDPSO and an Improved Force-Directed Layout." Fire 8, no. 5 (2025): 188. https://doi.org/10.3390/fire8050188.

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It is noted that existing mine emergency-rescue algorithms have overlooked the requirement for multi-route sharing at critical nodes and have offered limited network visualisation. Consequently, a multi-team rescue-path-planning algorithm based on FA-MDPSO (Firefly Algorithm-Multiple Constraints Discrete Particle Swarm Optimisation) was proposed, and a graph-structure optimisation method combining a Force-Directed Layout with Breadth-First Search was introduced for node arrangement and visualisation. Methodologically, the superiority of the improved DPSO (Discrete Particle Swarm Optimisation) in route-planning precision was first validated on the DIMACS dataset. Subsequently, the hyperparameters of MDPSO (Multiple Constraints Discrete Particle Swarm Optimisation) were optimised by means of four intelligent algorithms—ACO (Ant Colony Optimization), FA (Firefly Algorithm), GWO (Grey Wolf Optimizer) and WOA (Whale Optimization Algorithm). Finally, simulations of one to three rescue-team deployments were conducted within a mine-fire scenario, and node-importance analysis was performed. Results indicated that FA-MDPSO achieved comprehensive superiority in route precision, search efficiency and convergence speed, with FA-based hyperparameter optimisation proving most effective in comparative experiments. The graph-structure optimisation was found to substantially reduce crossings and enhance hierarchical clarity. Moreover, the three-team deployment yielded the shortest equivalent path (56,357.02), and node-visitation frequency was observed to be highly concentrated on a small number of key nodes. This not only significantly improves the collaborative rescue efficiency but also provides intuitive and practical technical support for intelligent mine rescue operations. It lays an important foundation for optimising mine emergency rescue plans, ensuring the safety of underground personnel, and promoting the intelligent development of mines.

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John, Jacob, and S. Sakthivel. "Brain Storm Water Optimisation-Driven Secure Multicast Routing and Route Maintenance in IoT." Journal of Information & Knowledge Management 20, Supp01 (2021): 2140010. http://dx.doi.org/10.1142/s0219649221400104.

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In several Internet of Things (IoT) applications, messages are disseminated to some objects or nodes based on multicast transmissions. However, previous multicast routing schemes in IoT focussed mainly on the ad-hoc sensor network, but they are not robust and responsive in the IoT environment. Hence, this paper introduces the multicast routing protocol based on the proposed optimisation algorithm, named Brain Storm Water Optimisation (BSWO), in the IoT network. By the multicast routing protocol, the multicast path is designed from a multicast source node to various destinations. The multicast source node forwards packet to multiple destinations simultaneously. Initially, the nodes in the IoT network are simulated together and perform the multicast routing process effectively using the proposed optimisation framework. The multicast routing protocol performs the multicast routing mechanism using the multiobjective factors, such as distance, delay, energy, link-quality factor and trust. The multicast routing path is effectively chosen based on the developed BSWO through fitness measures. The proposed BSWO is designed by integrating the Brain Storm Optimisation (BSO) and Water Wave Optimisation (WWO), respectively. The path with the minimum distance is selected as an optimal path using the fitness parameters like delay, distance, trust, energy and link-quality factor. The proposed optimisation algorithm effectively performs the multicast routing mechanism by integrating the parametric features from both the optimisation algorithms. Once the multicast routing mechanism is done, the route maintenance process is carried out in the simulated IoT network to recover the link breakage. The proposed BSWO outperformed other methods with the minimal delay of 0.0682[Formula: see text]s, minimal average routing distance of 178.4[Formula: see text]m, maximal energy of 39.59[Formula: see text]J, maximal throughput of 87.75% and maximal trust of 90%, respectively.

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Li, Chunlei. "A novel framework for discovery and reuse of typical process route driven by symbolic entropy and intelligent optimisation algorithm." PLOS ONE 17, no. 9 (2022): e0274532. http://dx.doi.org/10.1371/journal.pone.0274532.

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Manufacturing enterprises accumulate numerous manufacturing instances as they run and develop. Being able to excavate and apply the instance resources reasonably is one of the most effective approaches to improve manufacturing and support innovation. A novel framework for the discovery and reuse of typical process routes driven by symbolic entropy and intelligent optimisation algorithm so as to scientifically determine reuse objects and raise the reuse flexibility is proposed in this paper. A similarity measurement method of machining process routes based on symbolic entropy is developed in this framework. Subsequently, a typical process route discovery method based on the ant colony clustering model and similarity measurement is devised, and two reuse approaches based on the typical process route are analysed. Finally, three case studies are rendered. These case studies cover the aspects of similarity analysis, mining, and reuse of manufacturing instances, which systematically explains the whole procedure of discovery and reuse based on typical process route. The case studies show that (i) the similarity measurement method based on symbolic entropy can accurately evaluate the similarity among ten machining process routes, (ii) ant colony clustering model can realize adaptive clustering for these ten process routes, and (iii) indirect reuse approach for the typical process route can support the generation of new machining plan effectively. This reveal that the proposed framework comprehensively considers various aspects of retrieval and reuse of manufacturing instances, which can effectively support process instance reuse. Can better support process instance reuse.

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Hutsul, Taras, and Yurii Karpinskyi. "Possibility of applying geoinformation multiagent optimisation for planning the development of road networks." Reports on Geodesy and Geoinformatics 112, no. 1 (2021): 1–8. http://dx.doi.org/10.2478/rgg-2021-0002.

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Abstract In recent years, computational intelligence has been used to solve optimisation problems. An innovative direction in the development of artificial intelligence methods is multiagent methods of intellectual optimisation, which simulate the collective behaviour of insects, animals and other living beings. It indicates the effectiveness of their behaviour, and hence the effectiveness of these methods, and the ability to be involved in solving applied problems. This article is devoted to the study of the development of road transport networks using the metaheuristic ant method of optimisation based on a number of data. The initial data were geospatial layers of information on slope steepness, engineering structures, forests, perennials, land development and hydrographic objects. The parameters of the behaviour of the studied method under different conditions and volumes of input geospatial data are experimentally established. The Max–Min method of multiagent optimisation is modified. The proposed modification takes into account the functional distance – the coefficient of the complexity of the route, which affects its length. This modification had an effective influence on the behaviour of ants and the choice of optimal routes, taking into account the terrain as one of the factors. The result of the advancement is an informational system, which is capable of formulating flexible options for passing optimal alternative routes between specified settlements.

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Li, Qin, Shuangning Lv, Jingya Cui, Jiawei Zhang, and Yijun Liu. "Environmental Perception about Pedestrian Environment on Cultural Visitation Roads." Sustainability 16, no. 16 (2024): 7097. http://dx.doi.org/10.3390/su16167097.

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Cultural visitation routes represent an important opportunity for the public display of the famous historical and cultural city of Beijing, and its pedestrian environment, as a linear spatial carrier linking various historical and cultural attractions, is of great significance for the preservation of the famous historical and cultural city of Beijing through analysis of its spatial quality. At present, Beijing’s cultural visitation routes are in the stage of exploration and improvement, and scholars mainly focus on the selection, integration, and construction of cultural visitation routes in their research on cultural visitation routes, while the amount of research on the quality of the pedestrian environment for visitors is relatively small; in particular, the evaluation methods and indicator systems are still in the exploration stage. In this study, from the perspective of environmental behaviour theory, we took the Forbidden City–The Red House of Peking University–Wangfujing cultural visitation route as the research object and constructed a structural equation model to determine the perception of the pedestrian environment quality of the cultural visitation route in Beijing, starting from the visitors’ feelings of the pedestrian environment of the cultural visiting route. This study found that there is a positive correlation between the quality of spatial behaviours, the quality of the pedestrian environment, and the quality of the facilities on the emotional response of the visitors, with the quality of the facilities having the greatest impact on the emotional response of the visitors; among the observational variables, cultural and scientific facilities, spatial landscapes, street furniture, and safety play a more obvious role, and they are the main factors affecting the emotional response of the visitors. Based on the results of this study, an optimisation strategy for enhancing the pedestrian environments of Beijing’s cultural visitation routes is proposed to provide a reference for their design and optimisation.

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Mitra, Sulata. "Dynamic route optimisation in a multihomed mobile network." International Journal of Computational Vision and Robotics 1, no. 2 (2010): 121. http://dx.doi.org/10.1504/ijcvr.2010.036076.

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Siddam, Samidha. "Route Optimisation for Solid Waste Management Using GeoInformatics." IOSR Journal of Mechanical and Civil Engineering 2, no. 1 (2012): 78–83. http://dx.doi.org/10.9790/1684-0217883.

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Bernardos, Carlos J., Ignacio Soto, María Calderón, Fernando Boavida, and Arturo Azcorra. "VARON: Vehicular Ad hoc Route Optimisation for NEMO." Computer Communications 30, no. 8 (2007): 1765–84. http://dx.doi.org/10.1016/j.comcom.2007.02.011.

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Huang, Zhongxuan, Di Wu, and Chunyan Zhong. "Research on Fresh Produce Delivery Scheduling Problems under the Perspective of Community E-commerce." Journal of Education, Humanities and Social Sciences 37 (August 15, 2024): 53–62. http://dx.doi.org/10.54097/c054f874.

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This paper addresses the challenges associated with the high costs and logistical difficulties in the distribution of fresh agricultural products from the perspective of community e-commerce. The paper takes the perspective of merchant scheduling and integrates a classical vehicle scheduling model. The study meticulously examines the various costs associated with the distribution process, encompassing fixed costs, transportation costs, refrigeration costs, storage costs and time subsidies. A vehicle routing optimisation model was developed with the dual objectives of cost control and product quality assurance. The use of a genetic algorithm for analysis and solution indicates that this approach can achieve superior cost efficiency and more optimal delivery routes. The results not only confirm the validity of the model and the effectiveness of the solution algorithm, but also provide theoretical underpinnings and viable optimisation strategies for analogous route optimisation challenges. This research has significant reference value and practical relevance for improving delivery efficiency and cost management in community e-commerce.

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Aburas, Hala, Isam Shahrour, and Carlo Giglio. "Route Planning under Mobility Restrictions in the Palestinian Territories." Sustainability 16, no. 2 (2024): 660. http://dx.doi.org/10.3390/su16020660.

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This study aims to enhance people’s mobility in the context of mobility restrictions in the Palestinian territories, West Bank. It aims to develop a comprehensive route planning model that prioritises safety and optimises travel time while also considering sustainability issues. Unlike previous research, which has often focused solely on traffic crashes and physical road considerations in safety route planning, this study addresses the gap by developing a comprehensive model that integrates new risk criteria including mobility restrictions and violent events. The methodology involves historical and real-time data collection and processing, machine learning-based travel time prediction, and route optimisation using Dijkstra’s algorithm. The results highlight the significant impact of violent incidents on comprehensive risk scores, offering insights for proactive, sustainable measures. The waiting time prediction model performs strongly, with (R-squared) R2 values ranging from 80% to 92%. The developed route planning model provides three categorised routes under mobility restrictions—safest, fastest, and shortest—offering travellers sustainable and tailored options.

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Liu, Mengya, Vahid Yazdanpanah, Sebastian Stein, and Enrico Gerding. "Sustainability-oriented route generation for ridesharing services." Computer Science and Information Systems, no. 00 (2023): 53. http://dx.doi.org/10.2298/csis221209053l.

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Sustainability is the ability to maintain and preserve natural and man made systems for the benefit of current and future generations. The three pillars of sustainability are social, economic, and environmental. These pillars are interdependent and interconnected, meaning that progress in one area can have positive or negative impacts on the others. This calls for smart methods to balance such benefits and find solutions that are optimal with respect to all the three pillars of sustainability. By using AI methods, in particular, genetic algorithms for multiobjective optimisation, we can better understand and manage complex systems in order to achieve sustainability. In the context of sustainability-oriented ridesharing, genetic algorithms can be used to optimise route finding in order to lower the cost of trans portation and reduce emissions. This work contributes to this domain by using AI, specifically genetic algorithms for multiobjective optimisation, to improve the efficiency and sustainability of transportation systems. By using this approach, we can make progress towards achieving the goals of the three pillars of sustainability.

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Zhang, Gailian. "Solving route optimisation problem in logistics distribution through an improved ant colony optimisation algorithm." International Journal of Services Operations and Informatics 8, no. 3 (2017): 218. http://dx.doi.org/10.1504/ijsoi.2017.081511.

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Pyrih, Ya, M. Klymash, Yu Pyrih, and O. Lavriv. "GENETIC ALGORITHM AS A TOOL FOR SOLVING OPTIMISATION PROBLEMS." Information and communication technologies, electronic engineering 3, no. 2 (2023): 95–107. http://dx.doi.org/10.23939/ictee2023.02.095.

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The article focuses on the peculiarities of using the genetic algorithm (GA) for solving optimization problems. It provides a classification of optimization problems and offers a detailed description of the structural elements of the GA and their role in solving the traveling salesman problem. To assess the impact of GA parameters on its effectiveness, a study on the influence of population size on the length of the traveling salesman's route is conducted. Based on the obtained results, it is shown that population size affects the route length, and the optimal population size for this problem is found to be 150. Using the tournament selection operator, the ordered crossover operator, and the inverse mutation operator, we obtained a salesman's route of 9271.735 km, which, based on the results presented in this paper, is optimal for visiting 29 cities.

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Izzah, Nurul, and Asri Barokatul Jannah. "Optimisation of Evacuation Route Determination in an Earthquake Natural Disaster Scenario Using an Excel Solver." Journal of Computer Science Advancements 1, no. 6 (2024): 259–67. http://dx.doi.org/10.70177/jsca.v1i6.1132.

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Earthquake natural disasters can cause significant infrastructure damage, hindering community access and mobility. In such a situation, determining the shortest path for evacuation becomes very important. This research uses linear programming by utilising Excel Solver to determine the shortest evacuation route for earthquake natural disasters. Excel Solver is a feature in Microsoft Excel that serves as an analytical tool to solve linear optimisation problems. Excel solver can be used to find solutions that maximise or minimise the objective function, with respect to predetermined constraints. This research utilises relevant earthquake natural disaster simulation data as constraints in the optimisation model. The results show that the model can generate shortest paths that fulfil various constraints and criteria. The model can also be used to predict the time required to reach the destination. This research makes an important contribution to the development of tools and methods for shortest path optimisation in natural disaster scenarios. The optimisation model developed in this research can be used to assist decision makers in improving the efficiency and effectiveness of evacuation and disaster relief operations.

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Li, Changyu, and Yang Zhao. "Traffic route optimisation based on clouding computing parallel ACS." International Journal of Information and Communication Technology 14, no. 2 (2019): 204. http://dx.doi.org/10.1504/ijict.2019.097693.

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Exposito, Adrian, Dominic Schupke, and Hector Esteban. "Route Optimisation for Maximum Air to Ground Channel Quality." IEEE Access 8 (2020): 203619–30. http://dx.doi.org/10.1109/access.2020.3037075.

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Yang, Yuan, Xiaonan Wang, Qi Sun, and Dong Wang. "A route optimisation scheme for 6LoWPAN nested mobile networks." International Journal of Mobile Network Design and Innovation 6, no. 3 (2016): 131. http://dx.doi.org/10.1504/ijmndi.2016.078979.

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Heyken Soares, Philipp, Christine L. Mumford, Kwabena Amponsah, and Yong Mao. "An adaptive scaled network for public transport route optimisation." Public Transport 11, no. 2 (2019): 379–412. http://dx.doi.org/10.1007/s12469-019-00208-x.

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Nadeem, Imran, Sheik Uduman, and Aijaz Dar. "An integrated bus - based routing and dispatching approach for flood evacuation." Yugoslav Journal of Operations Research 30, no. 4 (2020): 443–60. http://dx.doi.org/10.2298/yjor190415028n.

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Floods are among the most persistent natural disasters that lead to massive economic and human loss globally. In this regard, this study has proposed an optimisation model for evacuating people before the occurrence of the flood using bus transit. The process of evacuation commences with the issue of warning and extends up to a certain time before the flood forecasts to strike. This model provides an optimised framework for 1) Locating pickup points and shelters, 2) Assigning pickup points to the nearest shelter by opting the shortest route among the available routes. On the basis of demand, each pickup point is allocated with a certain number of trips. The modelling of dispatch sequence of public buses, random adventing of evacuees at pickup points and evacuees transit to the shelters are presented by developing a simulation tool. The results of simulation serve for assessing the route design and then, a local search heuristic is suggested to improve the route design during the evacuation.

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Fairil, Ahan, and Imam Tobroni. "Geographic Information System for Shortest Route Search for Clinics in Pamekasan Regency Using the Djikstra Method." Journal of Computer Science Advancements 1, no. 6 (2024): 268–78. http://dx.doi.org/10.70177/jsca.v1i6.1140.

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Le Nedelec, Martin, Paul Glue, Helen Winter, Chelsea Goulton, and Natalie J. Medlicott. "The effect of route of administration on the enantioselective pharmacokinetics of ketamine and norketamine in rats." Journal of Psychopharmacology 32, no. 10 (2018): 1127–32. http://dx.doi.org/10.1177/0269881118780013.

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Background: Ketamine has been shown to produce a rapid and potent antidepressant response in patients with treatment-resistant depression. Currently ketamine is most commonly administered as a 40-minute intravenous infusion, though it is unknown whether this is the optimal route of administration. Aims: To determine the plasma concentration time course of the R- and S-enantiomers of ketamine and norketamine following administration of ketamine by four different routes of administration. Methods: Plasma from conscious non-anaesthetised rats was collected following administration of ketamine by either subcutaneous (SC), intramuscular (IM), intravenous infusion (IVI) or intravenous bolus (IVB) routes of administration. Concentrations of the enantiomers of ketamine and norketamine were determined by LC/MS. Results: Administration by the SC, IM and IVI routes produced an overall similar drug exposure. In contrast, administration by the IVB route produced approximately 15-fold higher peak plasma concentrations for the enantiomers of ketamine and an approximately four-fold lower AUC for the enantiomers of norketamine. Conclusions: Route of administration can significantly influence ketamine and norketamine exposures. These differences may influence safety and tolerability, and potentially drug efficacy in humans. This knowledge adds to current research into the optimisation of the use of ketamine for the treatment of depression.

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Szłapczyńska, Joanna, Roberto Vettor, Rafał Szłapczyński, et al. "Weather Routing System Architecture Using Onboard Data Collection and Route Optimisation." Polish Maritime Research 29, no. 2 (2022): 87–95. http://dx.doi.org/10.2478/pomr-2022-0020.

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Abstract This paper describes the architecture of a weather routing system consisting of two key elements: onboard monitoring and route optimiser sub-systems. The former is responsible for collecting various onboard measurements, such as current ship position or ship motion variables. These data, when gathered and processed, are then used for fine-tuning a ship model. The model, together with weather forecasts, is utilised by a multi-objective route optimiser to estimate forecasted ship responses during the voyage. The route optimiser has been developed in a client-server architecture to reallocate all necessary high-tech resources to the server side and keep the client software as simple and light as possible. The system also includes a module responsible for optimising transmission costs, to reduce onboard transmission during the voyage. The entire solution has been deployed onboard the demonstrator ship ‘Monte da Guia’ and tested during its operations at sea.

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Suresh, K., and M. Rajasekhara Babu. "SOSIoT: SOS optimisation to leverage the energy efficient internet of things based on route search optimisation." International Journal of Computer Aided Engineering and Technology 10, no. 5 (2018): 530. http://dx.doi.org/10.1504/ijcaet.2018.094331.

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Sfakianaki, E., and V. R. Stovin. "A spatial framework for environmental impact assessment and route optimisation." Proceedings of the Institution of Civil Engineers - Transport 153, no. 1 (2002): 43–52. http://dx.doi.org/10.1680/tran.2002.153.1.43.

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Lu, Yuping. "A multi-objective optimisation algorithm for rural tourism route recommendation." International Journal of Information and Communication Technology 21, no. 2 (2022): 197. http://dx.doi.org/10.1504/ijict.2022.124826.

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E. Cooper, Anne, Douglas Ferguson, and Ken Grime. "Optimisation of DMPK by the Inhaled Route: Challenges and Approaches." Current Drug Metabolism 13, no. 4 (2012): 457–73. http://dx.doi.org/10.2174/138920012800166571.

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Mukai, Naoto, and Toyohide Watanabe. "Route optimisation using evolutionary approaches for on-demand pickup problem." International Journal of Advanced Intelligence Paradigms 2, no. 1 (2010): 19. http://dx.doi.org/10.1504/ijaip.2010.029438.

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Fida, Adnan, Nor Tuah Jaidi, and Trung Dung Ngo. "Coordinated route reconfiguration for throughput optimisation under Rician fading channel." International Journal of Information and Communication Technology 12, no. 3/4 (2018): 299. http://dx.doi.org/10.1504/ijict.2018.090433.

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Dębski, Roman, and Rafał Dreżewski. "Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm." Applied Sciences 14, no. 13 (2024): 5919. http://dx.doi.org/10.3390/app14135919.

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The paper proposes an innovative adaptation of the estimation of distribution algorithm (EDA), intended for multi-objective optimisation of a ship’s route in a non-stationary environment (tidal waters). The key elements of the proposed approach—the adaptive Markov chain-based path generator and the dynamic programming-based local search algorithm—are presented in detail. The experimental results presented indicate the high effectiveness of the proposed algorithm in finding very good quality approximations of optimal solutions in the Pareto sense. Critical for this was the proposed local search algorithm, whose application improved the final result significantly (the Pareto set size increased from five up to nine times, and the Pareto front quality just about doubled). The proposed algorithm can also be applied to other domains (e.g., mobile robot path planning). It can be considered a framework for (simulation-based) multi-objective optimal path planning in non-stationary environments.

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