This study aims to design vehicle routes based on cost minimisation and the minimisation of greenhouse gasses (GHG) emissions to help companies solve the vehicle routing problem with pickup and delivery (VRPPD) via particle swarm optimisation (PSO). An effective metaheuristics search technique called particle swarm optimisation (PSO) was applied to design the optimal route for these problems. Simulated data from Li and Lim (2001) were used to evaluate the PSO performance for solving green vehicle routing problems with pickup and delivery (Green VRPPD). The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to customers living in a specific area called a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed. PSO has been confirmed for solving VRPPD effectively. The study compared the results based on the use of two different objective functions with fuel consumption from diesel and liquefied petroleum gas (LPG). It indicates that solving VRPPD by considering the emissions of direct greenhouse gases as an objective function provides cleaner routes, rather than considering total cost as the objective function for all test cases. However, as Green VRPPD requires more vehicles and longer travel distances, this requires a greater total cost than considering the total cost as the objective function. Considering the types of fuels used, it is obvious that LPG is more environmentally friendly than diesel by up to 53.61 %. This paper should be of interest to a broad readership, including those concerned with vehicle routing problems, transportation, logistics, and environmental management. The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed.
In response to the growing problem of unscheduled flows, more and more transmission system operators in Europe provide their systems with phase shifting transformers (PST). However, the operations of several PSTs deployed close to each other must be coordinated for them to be effective and to avoid their harmful interactions. Coordination of a group of such devices leads to a problem of multidimensional optimisation. This paper presents a method of optimal PST setting based on the particle swarm optimisation (PSO) algorithm. As an optimisation criterion the minimization of unscheduled flow through the given system has been applied. The impact of the number of particles in the swarm and their maximum permissible velocity on the optimisation algorithm’s efficiency was analysed. Results are presented for a 118-node test grid.
PL
W odpowiedzi na rosnący problem przepływów nieplanowych coraz większa liczba operatorów systemów przesyłowych w Europie wyposaża swoje systemy w przesuwniki fazowe (PST). Jednakże użycie kilku PST zainstalowanych geograficznie lub elektrycznie blisko siebie musi być skoordynowane w celu skutecznego wykorzystania tych urządzeń i uniknięcia ich niekorzystnych interakcji. Koordynacja grupy takich urządzeń prowadzi do problemu optymalizacji wielowymiarowej. W artykule przedstawiono metodę optymalizacji nastaw PST opartą na algorytmie roju cząstek (PSO). Jako kryterium optymalizacji zastosowano minimalizację przepływu nieplanowego przez dany system. Przeanalizowano wpływ liczby cząstek roju oraz ich maksymalnej dozwolonej prędkości na efektywność algorytmu optymalizacji. Przedstawiono wyniki dla sieci testowej zawierającej 118 węzłów.
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To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.
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