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6 results for “pickup and delivery”
Instances for the pickup and delivery problem with alternative locations
<p>570 Instances for the pickup and delivery problem with alternative locations in a .txt file format.</p> <p>1 Complementary material providing computational results of those instances as a .pdf file format.</p> <p> </p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 4. The solution of C6 found by CEACO
<p>Furthermore, the GA has not been able to find the best solutions in thirteen of the fourteen examples. Therefore, it is the weakest algorithm among the five presented algorithms. However, SS_ACO has been able to find better solutions than the GA and has come up with the best solution in 12 examples. Among remaining five algorithms, PSO has failed in improving the solutions in 10 examples and has come up with solutions similar to the ones found by GA. From the comparison between GAPSO and CEACO, it can be seen that GAPSO in six examples has been able to find better solutions than the proposed algorithm. However, the CEACO has found better solutions than this algorithm for one example. For example, the solution of C6 is shown in Figure 4 which is the best found solution until now by other algorithms. </p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 3. The pseudo of CEACO
<p>The idea here is that a better solution may have a better chance to find a global optimum. After each iteration, if the best solution is not changed during 20 times, the algorithm finished and the best found solution is reported. Otherwise, the algorithm goes to transition rule step. Figure 3 shows the pseudo code of the proposed algorithm. </p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 1. The ACO for the TSP
<p>Ant colony optimization (ACO) is one of the most popular meta-heuristic algorithms inspired by the behavior of real ants seeking a path between their colony and a source of food. For the first time, this algorithm was used to solve the traveling salesman problem (TSP) as shown in Figure 1</p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 2. Insert (left), swap (middle) and 2-opt exchanges (right)
<p> The literature on meta-heuristics indicates that a promising approach for obtaining highquality solutions is to couple a local search algorithm with a mechanism to generate initial solutions. So, after all ants have constructed their routes and before updating global pheromone, three types of local search schemes including 2- opt scheme, insert and swap moves are performed to further reduce the routes length (Figure 2). In insert algorithm, a customer is moved to another route but in swap algorithm a customer in a certain route is swapped with another customer from a different route. One of the most commonly encountered moves is the 2-opt which starts with a feasible tour and continues by omitting two arcs of the same route, which are not adjacent and then connects them again by another method in such a way that the new tour length is shorter. In multiple routes, two edges belong to different routes, which form a criss-cross, are selected and two new edges are replaced. It also should be noted that the new solution will be only accepted in state that first, the constraints are not violated specially about each vehicle’s capacity. It can be noted that there are several routes for connecting nodes and producing the tour again, but a state that satisfies the problem’s constraints is acceptable</p>
Example Solution for the Pickup an Delivery Problem with Online Transfers
<p>This is an example solution of our model for the pickup-and-delivery problem that utilizes quite a few online transfers.<br> </p>
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OpenNeuro
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