10 research outputs found
Evolutionary Algorithms to Solve Agricultural Routing Planning
No full textThis doctoral thesis aims to develop effective Evolutionary Algorithms that can be competitively applied to Agricultural Routing Planning (ARP) and to formulate an extension of the ARP. The outcomes of this research will impact on the research community with the development of new algorithms as well as the dissemination of findings. This study is significant as it is expected to improve the management of agricultural machinery, to minimise the total cost and the settling time for completing field operations, and to produce better routing plans
Penyelesaian Masalah Penempatan Fasilitas dengan Algoritma Estimasi Distribusi dan Particle Swarm Optimization
No full textThe layout positioning problem of facilities on a straight line is known as Single Row Facility Layout Problem (PFSB). Categorized as NP-Complete problem, PFSB aim to arrange the layout so that the sum of distances between all facilities’ pairs can be minimized. Estimation of Distribution Algorithm (EDA) improves the solution quality efficiently in first few runs, but the diversity lost grows rapidly as more iterations are run. To maintain the diversity, hybridization with meta-heuristic algorithms is needed. This research proposes EDAPSO, an algorithm which consists of hybridization of EDA and Particle Swarm Optimization (PSO). The objective of this research is to test the performance of EDAPSO algorithm for solving PFSB. EDAPSO’s performance is tested in 10 benchmark problems of PFSB and it successfully achieves optimum solution
Decision making for Farmers: A Case Study of Agricultural Routing Planning
No full textAgricultural business is shifting to a stronger integration of information technology and data analysis to optimise the management and operations of small- and large-scale farms. In particular, computer support for decision-making is critical for farmers who want to decrease the cost of operations and control their (semi-)automated fleet of agricultural machines. This paper develops an optimisation module for decision support in Agricultural Routing Planning (ARP). The output is expected to help farmers to decide on the most efficient route for their harvesting machines. Specifically, the aim of this study is to contribute to optimisation solutions by introducing a new methodology called a Lovebird Algorithm, to address the routing problem. The Lovebird Algorithm acts as an optimisation tool to screen alternatives and focus only on efficient ones. The experimental results show that the proposed algorithm can save 8% of the non-working distance compared to the Genetic Algorithm and Tabu Search
SEGMENTASI TRAFO LISTRIK MENGGUNAKAN ALGORITMA K-MEANS UNTUK MENDUKUNG EVALUASI KAPASITAS GARDU INDUK LISTRIK DI JAWA TIMUR
No full textDISTRIBUTION ROUTE OPTIMIZATION of GALLON WATER USING GENETIC ALGORITHM and TABU SEARCH
No full textDistributions of drinking water in gallons often do not pay attention to the problem of finding the most optimal route,
thus causing inefficiency in the cost of shipping. To minimize incurred costs, it is necessary to minimize vehicle fleet
and amount of travel distance, with the restriction that the vehicle must have sufficient capacity to transport the goods
to be shipped and return it back to the depots. This problem could be framed as a Vehicle Routing Problem with pickup
and delivery (VRPPD).
In this paper, we propose a method to optimize delivery route in a drinking water depot by combining genetic
algorithm (GA) and Tabu search. GA has advantages by providing possible solutions while Tabu covers up its
shortfall in identifying local solutions so that searching will able to avoid loop in the area of the same solution.
Experimental results show that the proposed method is more efficient than a manually predetermined route
Distribution Route Optimization of Gallon Water Using Genetic Algorithm and Tabu Search
No full textAbstractDistributions of drinking water in gallons often do not pay attention to the problem of finding the most optimal route, thus causing inefficiency in the cost of shipping. To minimize incurred costs, it is necessary to minimize vehicle fleet and amount of travel distance, with the restriction that the vehicle must have sufficient capacity to transport the goods to be shipped and return it back to the depots. This problem could be framed as a Vehicle Routing Problem with pick-up and delivery (VRPPD).In this paper, we propose a method to optimize delivery route in a drinking water depot by combining genetic algorithm (GA) and Tabu search. GA has advantages by providing possible solutions while Tabu covers up its shortfall in identifying local solutions so that searching will able to avoid loop in the area of the same solution. Experimental results show that the proposed method is more efficient than a manually predetermined route
The Agricultural Routing Planning in Field Logistics
No full textThe agricultural sector is facing the need to gain a higher yield on their fields while optimising their operations to stay competitive and satisfy the continuously increasing demand for produce. Cost reductions can be achieved by increasing the effective field size and reducing the operations without gain (e.g., driving longer distance to harvest the field). The agricultural routing planning (ARP) problem represents a specialisation of the travelling salesman problem (TSP) or vehicle routing problem (VRP) with focus on the agricultural operations and considerations of the field and vehicles configurations. In addition, various adaptations of the problem can be found in the literature that define a new problem class with specialised optimisation needs. This chapter introduces the ARP and reviews the past and current research and developments
