A Methodology Based on Evolutionary Algorithms to Solve a Dynamic Pickup and Delivery Problem Under a Hybrid Predictive Control Approach

2015 ◽  
Vol 49 (2) ◽  
pp. 239-253 ◽  
Author(s):  
Diego Muñoz-Carpintero ◽  
Doris Sáez ◽  
Cristián E. Cortés ◽  
Alfredo Núñez
Keyword(s):  
Evolutionary Algorithms ◽  
Predictive Control ◽  
Pickup And Delivery ◽  
Control Approach ◽  
Delivery Problem ◽  
Pickup And Delivery Problem

Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem

2009 ◽  
Vol 43 (1) ◽  
pp. 27-42 ◽  
Author(s):  
Cristián E. Cortés ◽  
Doris Sáez ◽  
Alfredo Núñez ◽  
Diego Muñoz-Carpintero
Keyword(s):  
Predictive Control ◽  
Pickup And Delivery ◽  
Delivery Problem ◽  
Pickup And Delivery Problem ◽  
Adaptive Predictive Control

Optimization of Many Objective Pickup and Delivery Problem with Delay Time of Vehicle Using Memetic Decomposition Based Evolutionary Algorithm

2020 ◽  
Vol 29 (01) ◽  
pp. 2050003 ◽  
Author(s):  
Adeem Ali Anwar ◽  
Irfan Younas
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Delay Time ◽  
Large Scale ◽  
Objective Evaluation ◽  
Pickup And Delivery ◽  
Delivery Problem ◽  
Pickup And Delivery Problem ◽  
Large Scale Problems

The pickup and delivery problem (PDP) is a very common and important problem, which has a large number of real-world applications in logistics and transportation. In PDP, customers send transportation requests to pick up an object from one place and deliver it to another place. This problem is under the focus of researchers since the last two decades with multiple variations. In the literature, different variations of PDP with different number of objectives and constraints have been considered. Depending on the number of objectives, multi and many-objective evolutionary algorithms have been applied to solve the problem and to study the conflicts between objectives. In this paper, PDP is formulated as a many-objective pickup and delivery problem (MaOPDP) with delay time of vehicle having six criteria to be optimized. To the best of our knowledge, this variation of PDP has not been considered in the literature. To solve the problem, this paper proposes a memetic I-DBEA (Improved Decomposition Based Evolutionary Algorithm), which is basically the modification of an existing many-objective evolutionary algorithm called I-DBEA. To demonstrate the superiority of our approach, a set of experiments have been conducted on a variety of small, medium and large-scale problems. The quality of the results obtained by the proposed approach is compared with five existing multi and many-objective evolutionary algorithms using three different multi-objective evaluation measures such as hypervolume (HV), inverted generational distance (IGD) and generational distance (GD). The experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the obtained solutions.

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