A New Approach Based on Particle Swarm Algorithm to Solve the Location-Routing Problem with Simultaneous Pickup and Delivery

2021 ◽  
Vol 20 (2) ◽  
pp. 213-222
Author(s):  
Lubov K. Ilyashenko ◽  
Aygul Z. Ibatova ◽  
Elena S. Pavlova ◽  
Rustem A. Shichiyakh
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Pickup And Delivery ◽  
Location Routing Problem ◽  
Routing Problem ◽  
New Approach ◽  
Location Routing ◽  
Simultaneous Pickup And Delivery ◽  
Swarm Algorithm

An Location-Routing Problem with Simultaneous Pickup and Delivery in Urban-Rural Dual-Directions Logistics Network

2013 ◽  
Vol 756-759 ◽  
pp. 3423-3429
Author(s):  
Xue Feng Wang
Keyword(s):  
Heuristic Approach ◽  
Pickup And Delivery ◽  
Total System ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Commodity Flow ◽  
Logistics Network ◽  
Location Routing ◽  
Urban Rural ◽  
Simultaneous Pickup And Delivery

The design and optimization of urban-rural dual-directions logistics network is a substantial important issue, which will directly affect the development of the urban-rural integration in China. A reasonable scheme of logistics network will contribute to supply efficient logistics services to customers scattering in urban and rural areas. In this paper, we consider a variant of the Location-Routing-Problem (LRP), namely the LRP with simultaneous pickup and delivery in specially background (LRPSB). The objective of LRPSB is to minimize the total system cost, including depot location cost and vehicle routing cost, and implement and control the effective dual-direct commodity flow to meet customers requirement by simultaneously locating the depots and designing the vehicle routes that satisfy pickup and delivery demand of customer at the same time. A nonlinear mixed integrated programming model is formulated for the problem. Since such integrated logistics network design problems belong to a class of NP-hard problems, we propose a two-phase heuristic approach based on Tabu Search, tp-TS, to solve the large size problem and an initialization procedure to generate an initial solution for the tp-TS. We then empirically evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions or strong lower bounds, and investigate the effectiveness of the proposed heuristic approach. Computational results show that the proposed heuristic approach is computationally efficient in finding good quality solutions for the LRPSB.

scholarly journals Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Yanwei Zhao ◽  
Longlong Leng ◽  
Jingling Zhang ◽  
Chunmiao Zhang ◽  
Wanliang Wang
Keyword(s):  
Learning Strategies ◽  
Computing Time ◽  
Random Permutation ◽  
Pickup And Delivery ◽  
Solution Quality ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Selection Strategies ◽  
Location Routing ◽  
Simultaneous Pickup And Delivery

This paper presents an evolution-based hyperheuristic (EHH) for addressing the capacitated location-routing problem (CLRP) and one of its more practicable variants, namely, CLRP with simultaneous pickup and delivery (CLRPSPD), which are significant and NP-hard model in the complex logistics system. The proposed approaches manage a pool of low-level heuristics (LLH), implementing a set of simple, cheap, and knowledge-poor operators such as “shift” and “swap” to guide the search. Quantum (QS), ant (AS), and particle-inspired (PS) high-level learning strategies (HLH) are developed as evolutionary selection strategies (ESs) to improve the performance of the hyperheuristic framework. Meanwhile, random permutation (RP), tabu search (TS), and fitness rate rank-based multiarmed bandit (FRR-MAB) are also introduced as baselines for comparisons. We evaluated pairings of nine different selection strategies and four acceptance mechanisms and monitored the performance of the first four outstanding pairs in 36 pairs by solving three sets of benchmark instances from the literature. Experimental results show that the proposed approaches outperform most fine-tuned bespoke state-of-the-art approaches in the literature, and PS-AM and AS-AM perform better when compared to the rest of the pairs in terms of obtaining a good trade-off of solution quality and computing time.

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