Temat: multi-vehicle - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Multicriteria vehicle routing problem solved by artificial immune system
Wielokryterialny problem marszrutyzacji rozwiązywany metodą sztucznego systemu immunologicznego
Autorzy:: Mrówczyńska, B.
Powiązania:: https://bibliotekanauki.pl/articles/375401.pdf
Data publikacji:: 2015
Wydawca:: Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:: vehicle routing problem
multi-vehicle
artificial intelligence
artificial immune system
clonal selection
problem marszrutyzacji
sztuczna inteligencja
system immunologiczny sztuczny
selekcja klonalna
Opis:: Vehicles route planning in large transportation companies, where drivers are workers, usually takes place on the basis of experience or intuition of the employees. Because of the cost and environmental protection, it is important to save fuel, thus planning routes in an optimal way. In this article an example of the problem is presented solving delivery vans route planning taking into account the distance and travel time within the constraints of vehicle capacities, restrictions on working time of drivers and having varying degrees of movement. An artificial immune system was used for the calculations.
Planowanie tras samochodów dostawczych w dużych firmach transportowych, w których kierowcy są pracownikami najemnymi, najczęściej odbywa się na podstawie doświadczeń lub intuicji pracowników. Ze względu na koszty i na ochronę środowiska ważne jest oszczędzanie paliwa, a więc układanie tras w sposób optymalny. W artykule rozwiązano przykładowy problem planowania trasy samochodów dostawczych ze względu na długość drogi i czas przejazdu przy ograniczeniach ładowności pojazdów, ograniczeniach czasu pracy kierowców i przy uwzględnieniu zmiennego natężenia ruchu. W obliczeniach zastosowano sztuczny system immunologiczny.
Źródło:: Transport Problems; 2015, 10, 3; 141-152
1896-0596
2300-861X
Pojawia się w:: Transport Problems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Multi-Criteria Optimization in Fuel Distribution
Autorzy:: Pempera, J.
Żelazny, D.
Powiązania:: https://bibliotekanauki.pl/articles/409566.pdf
Data publikacji:: 2015
Wydawca:: Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:: vehicle routing problem
multi-criteria
discrete optimization
fuel distribution
Opis:: This paper deals with some computational study of multi-criteria optimization in fuel distribution problem. We consider vehicle routing problem, e.g. routing of a fleet of tank trucks, for one of the famous polish petroleum companies. For the purpose of solving this problem, we developed the random search algorithm, which explores the broad range of feasible sets of routes and searches for non-dominated multi-criteria solutions. The problem is examined on real data, which contains distances between 50 petrol stations and one central warehouse (refinery). Obtained results indicate, that it is possible to obtain single solution and satisfy both optimization criteria. Based on the analysis of the collected data, we formulate a number of proposals useful in future for construction of algorithms for multi-criteria fuel distribution optimization.
Źródło:: Research in Logistics & Production; 2015, 5, 1; 77-84
2083-4942
2083-4950
Pojawia się w:: Research in Logistics & Production
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Multi-objective optimization of vehicle routing problem using evolutionary algorithm with memory
Autorzy:: Podlaski, K.
Wiatrowski, G.
Powiązania:: https://bibliotekanauki.pl/articles/305266.pdf
Data publikacji:: 2017
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: vehicle routing problem
time windows
evolutionary algorithms
multi-objective optimization
Opis:: The idea of a new evolutionary algorithm with memory aspect included is proposed to find multiobjective optimized solution of vehicle routing problem with time windows. This algorithm uses population of agents that individually search for optimal solutions. The agent memory incorporates the process of learning from the experience of each individual agent as well as from the experience of the population. This algorithm uses crossover operation to define agents evolution. In the paper we choose as a base the Best Cost Route Crossover (BCRC) operator. This operator is well suited for VPRTW problems. However it does not treat both of parent symmetrically what is not natural for general evolutionary processes. The part of the paper is devoted to find an extension of the BCRC operator in order to improve inheritance of chromosomes from both of parents. Thus, the proposed evolutionary algorithm is implemented with use of two crossover operators: BCRC and its extended-modified version. We analyze the results obtained from both versions applied to Solomon’s and Gehring & Homberger instances. We conclude that the proposed method with modified version of BCRC operator gives statistically better results than those obtained using original BCRC. It seems that evolutionary algorithm with memory and modification of Best Cost Route Crossover Operator lead to very promising results when compared to the ones presented in the literature.
Źródło:: Computer Science; 2017, 18 (3); 269-286
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Agent - based dispatching enables autonomous groupage traffic
Autorzy:: Gath, M.
Edelkamp, S.
Herzog, O.
Powiązania:: https://bibliotekanauki.pl/articles/91721.pdf
Data publikacji:: 2013
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: groupage traffic
vehicle routing problem
VRP
multi-agent system
decision making
intelligent agent
multiagent simulation
Opis:: The complexity and dynamics in groupage traffic require flexible, efficient, and adaptive planning and control processes. The general problem of allocating orders to vehicles can be mapped into the Vehicle Routing Problem (VRP). However, in practical applications additional requirements complicate the dispatching processes and require a proactive and reactive system behavior. To enable automated dispatching processes, this article presents a multiagent system where the decision making is shifted to autonomous, interacting, intelligent agents. Beside the communication protocols and the agent architecture, the focus is on the individual decision making of the agents which meets the specific requirements in groupage traffic. To evaluate the approach we apply multiagent-based simulation and model several scenarios of real world infrastructures with orders provided by our industrial partner. Moreover, a case study is conducted which covers the autonomous groupage traffic in the current processes of our industrial parter. The results reveal that agent-based dispatching meets the sophisticated requirements of groupage traffic. Furthermore, the decision making supports the combination of pickup and delivery tours efficiently while satisfying logistic request priorities, time windows, and capacity constraints.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 27-40
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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