China
Africa
Explore chapters and articles related to this topic
A Simple Algorithm to Estimate the 'Performance Centres' of India and their Movements using Geodetic Coordinates
Published in Purna Chandra Mishra, Muhamad Mat Noor, Anh Tuan Hoang, Advances in Mechanical and Industrial Engineering, 2022
Ignoring this small variation, Euclidean Distance (ED) is used for obtaining an approximate centre instead of Great Circle Distance (GCD). The distance between any two points can be estimated using the Haversine formula [7]. This paper used the following methodology for estimating performance centres.
A data analytic-based logistics modelling framework for E-commerce enterprise
Published in Enterprise Information Systems, 2023
Abhishek Verma, Yong-Hong Kuo, M Manoj Kumar, Saurabh Pratap, Velvet Chen
The merged dataset contained the latitudes and longitudes of sellers and customers along with the values of customer ID, seller ID, order ID, freight value, price and weight. We considered the great-circle distance to conduct the analysis, calculated using geo-locations and haversine formula (Equation 5). The haversine formula calculates the shortest distance between two points on a sphere when their latitude and longitude are provided. The calculated distance is known as the great-circle distance between the points on the map. However, considering the total of the US, driving distances through road are approximately 18% more than the straight-line distances (Shih 2015). The distance between customer and seller of an order ranged from 0.12 miles to an upper limit value of 5434.99 miles.
Cold supply chain inventory models for agricultural products with multi-stage stochastic deterioration rates
Published in Journal of Management Analytics, 2023
Raosaheb Latpate, Maruti Bhosale, Sandesh Kurade
Mejjaouli and Babiceanu (2018) proposed a insights logistic cold supply chain model for perishable items by facing various challenges in the transportation and tackled by these problems by the techniques of Temporary Virtual Machine (TVM). Rafie-Majd et al. (2018) proposed a supply chain model to solve the inventory and routing problem for multi-echelon criteria and for multi-perishable items by considering uncertainty. Zhang et al. (2019) proposed a cold supply chain model to optimize the logistic routing problem of vehicle and solved by using genetic algorithm. Lusiantoro et al. (2018) explained in detail the construction of supply chain for perishable items, challenges in supply chain. Latpate and Kurade (2022) developed a model for import of crude oil and its impact on the economy of the country. For transporting oil from one place to another place there is necessary to optimize their routes and modes of transportation. This article presents a two-stage cost and time minimizing fuzzy multi objective multi index transportation problem. Yu and Xiao (2017) proposed a two stackleberg game model to investigate the pricing and service level decision of a fresh agri-products supply chain including one supplier, one retailer and one third party. Wang et al. (2017) developed a model for optimization of vehicle routing problem with time windows for cold supply chain which is related to carbon emission. Yan (2017) proposed revenue of supply chain model for cold supply chain related to perishable items to increase the revenue of supply chain by the technique of Internet of Thing (IOT). Singh et al. (2018) developed a model for a cold chain location for shippers and customers by considering value deterioration and coordination by using big data approximation. They set the warehouse locations by using the Haversine formula. Haversine formula considers spherical shape of earth, the distance between two points and the radius of the earth.
Optimisation of COVID-19 vaccination process using GIS, machine learning, and the multi-layered transportation model
Published in International Journal of Production Research, 2023
Kenan Mengüç, Nezir Aydin, Mesut Ulu
Some of the most used distance metrics are the Euclidean, Manhattan, and Mahalanobis distance metrics. While Euclidean distance is calculated as a straight line, Manhattan distance is calculated along the axes at right angles between two different points (Pandit and Gupta 2011; Salmanmahiny et al. 2021). Meanwhile, opposite to Euclidean and Manhattan distances, Mahalanobis distance is a data-based metric, whereas Euclidean and Manhattan distances can be calculated for any two data points independently from the dataset it belongs to (Shirkhorshidi, Aghabozorgi, and Wah 2015). Apart from general distance calculation methods, distances can be calculated for geographic locations as well. For instance, the Haversine formula calculates distance between two different geographical locations. The Haversine formula is an important equation in navigation studies that calculates ring distances between two points on a sphere using their longitudes and latitudes (Chopde and Nichat 2013). Different routings that are created according to the distance unit provide formations for different clusters. However, in real-world problems, the distance approach alone may be insufficient for optimal clustering. For more complex problems, which causes by increasing number of parameters, machine learning methods have been used together with vehicle routing methods (Bai et al. 2023). Where a local branching algorithm hybridised with mathematical modelling also resulted in significant time savings in obtaining the optimal result (Demantova et al. 2023). Apart from the hybrid applications, effective solutions for routing problems dominated by constraints were obtained by only using reinforcement learning (Huang et al. 2023). In another study, mathematical modelling, reinforcement learning, and genetic algorithm were used together to solve stochastic distribution problems where demand is not obvious (Achamrah et al. 2022). Determining the number of routes or clusters is an important decision variable for decision makers. Therefore, optimising the number of clusters in cluster analyses is another problem that needs to be addressed. One of the most used methods for this purpose is the elbow method, which provides the potential optimal number of clusters in a dataset with a very little former information regarding the dataset specifications (Shi et al. 2021). Moreover, the elbow method is known as the oldest visual method for determining the optimal number of clusters for a dataset (Syakur et al. 2018).