The problem of computing K shortest loopless paths, or ranking of the K shortest loopless paths between a pair of given vertices in a network is a well-studied generalization of shortest path problem. The K shortest paths problem determines not only one shortest path but the K best shortest paths from s to t in an increasing order of weight of the paths. Yen’s algorithm is known to be the efficient and widely used algorithm for determining K shortest loopless paths. Here, we introduce a new algorithm by modifying the Yen’s algorithm in the following way: instead of removing the vertices and the edges from the graph, we store them in two different sets. Then we modified the Dijkstra’s algorithm by taking these two sets into consideration. Thus the algorithm applies glass box methodology by using the modified Dijkstra’s algorithm for our dedicated purpose. Thus the efficiency is improved. The computational results conducted over different datasets, shows the proposed algorithm has better performance results.

A*(A-Star)演算法透過啟發函式，以減少路徑搜尋過程中所需要計算的網路數量，SMA*(Simplified Memory Bounded A-Star)為A*之變形，目前最廣泛被應用於GPS導航系統之路徑規劃的演算法。尋找路徑的計算過程中，A*與SMA*演算法利用中間節點與目的地的方向(直線距離)作為啟發函式，以預估中間節點到目的地之路徑長度挑選優先搜尋的路段，而SMA*則因記憶體的限制會排除預估長度較長的路段，以減少搜尋的路段數量與記憶體之使用量。當起點與終點中存在障礙地形時或路段較崎嶇時，以方向導引路徑搜尋之準確度便大幅降低，導致A*與SMA*之搜尋數量增加，SMA*甚至會得到較差的路徑。 主幹導引式最短路徑搜尋演算法(Backbone Orientation)以骨幹路徑導引路徑之搜尋，在障礙地形或道路崎嶇的情況下，可有效避免SMA*之缺點，效能較佳。主幹導引式最短路徑搜尋演算法分為二階段，先由原始路網中提取骨幹路網，並計算出最佳骨幹路徑，再利用骨幹路徑引導路徑的搜尋，在骨幹路徑的一定範圍內搜尋最短路徑。 本研究以台灣地區2007年之平面路網圖進行實驗，以三種不同的實驗方式進行實驗，以驗證主幹導引式最短路徑搜尋演算法之效能，證明在SMA*演算法之啟發函式效能低落時，使用主幹導引式最短路徑搜尋演算法可以有效的改善SMA*在障礙地形之效能不彰的問題。

When a large number of base stations fail due to the breakdown of some transmission circuit in a mobile cellular network, base stations located in neighboring areas may take over those malfunctioned base stations and continue to provide the access service of mobile communications for users in surrounding areas, thereby reducing the area in which mobile communications are out of service. Therefore, if leased circuits in base stations could complete the route distribution configuration prior to the onset of malfunction, it could decrease the impact of circuit breakdown and traffic loss. Also, the efficiency would be improved if the circuit assignment personnel could complete the job when the leased lines are less, while avoiding reassignment in the future and enhancing the mobile communications operations. In this study, we use a graph structure to represent the present mobile cellular network and establish the route-selection strategies. We define the "Optimal Route Assignment" for a newly constructed base station, which refers to the route assignment that causes least impact on disconnection area when any circuit in the network is broken. We also propose to use A* algorithm for optimal route assignment. However, the computation for the optimal route is time consuming. Measures such as computation time and least hops are considered in designing other strategies for route assignment. These strategies are parametric and we carried out experiments by adjusting and controlling parameters using real routing data. The experimental results demonstrate that there is no single winner among the proposed strategies. We identify a number of best strategies for different operating regions.

In 2019, 770 million people worldwide lived without electricity. As many as 490 million people could be electrified with 210,000 mini-grids by 2030. Obtaining information for decision-making is crucial to determine the viability of such a project. Currently, it is a major challenge for mini-grid developers to gather this information at the speed and scale necessary to make effective investment choices. Village Data Analytics (VIDA) is a decision-making tool used for mini-grid project planning and site selection. This paper presents a method to estimate the cost of a mini-grid distribution network on a site-by-site basis. This method can estimate the total demand, potential connections, distribution infrastructure components and corresponding costs for each site. The model can make predictions for 50 sites within two hours so the tool is especially useful for preliminary estimates in the planning phase. A more detailed study of the individual sites is recommended. Comparison with a benchmark has shown that on-site conditions often reveal activities that can only be captured by a survey. However, collecting on-site data is time-consuming and costly. Therefore, GIS and modelling tools can serve as a good approximation of the on-ground reality and are relevant to accelerate planning and support timely decision-making. / 2019 levde 770 miljoner människor världen över utan elektricitet. Så många som 490 miljoner människor skulle kunna elektrifieras med 210 000 mininät till 2030. Att få information för beslutsfattande är avgörande för att avgöra om ett sådant projekt är lönsamt. För närvarande är det en stor utmaning för utvecklare av mininät att samla in denna information i den hastighet och skala som krävs för att göra effektiva investeringsval. Village Data Analytics (VIDA) är ett beslutsfattande verktyg som används för projektering av mininät och platsval. Det här dokumentet presenterar en metod för att uppskatta kostnaden för ett distributionsnät för mininät på plats för plats. Denna metod kan uppskatta den totala efterfrågan, potentiella anslutningar, komponenter för distribution sinfrastruktur och motsvarande kostnader för varje plats. Modellen kan göra förutsägelser för 50 platser inom två timmar, så verktyget är särskilt användbart för preliminära uppskattningar i planeringsfasen. En mer detaljerad studie av de enskilda platserna rekommenderas. Jämförelse med ett riktmärke har visat att förhållanden på plats ofta avslöjar aktiviteter som bara kan fångas genom en undersökning. Men att samla in data på plats är tidskrävande och kostsamt. Därför kan GIS- och modelleringsverktyg fungera som en bra approximation av verkligheten på marken och är relevanta för att påskynda planering och stödja beslutsfattande i rätt tid.