scholarly journals Capacitated location routing problem with ‎simultaneous pickup and delivery under the risk of ‎disruption

2021 ◽  
Author(s):  
Milad Dehghan ◽  
Seyed Reza Hejazi ◽  
Maryan Karimi Mamaghan ◽  
Mehrdad MOHAMMADI ◽  
Amir Pirayesh
Keyword(s):  
Mixed Integer ◽  
Terrorist Attacks ◽  
Pickup And Delivery ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Mip Model ◽  
Simultaneous Pickup And Delivery ◽  
The Impact ◽  
Capacitated Location

This paper develops a new mathematical model to study a location-routing problem with simultaneous pickup and delivery under the risk of disruption. A remarkable number of previous studies have assumed that network components (e.g., routes, production factories, depots, etc.) are always available and can permanently serve the customers. This assumption is no longer valid when the network faces disruptions such as flood, earthquake, tsunami, terrorist attacks and workers strike. In case of any disruption in the network, tremendous cost is imposed on the stockholders. Incorporating disruption in the design phase of the network will alleviate the impact of these disasters and let the network resist disruption. In this study, a mixed integer programming (MIP) model is proposed that formulates a reliable capacitated location-routing problem with simultaneous pickup and delivery (RCLRP-SPD) services in supply chain distribution network. The objective function attempts to minimize the sum of location cost of depots, routing cost of vehicles and cost of unfulfilled demand of customers. Since the model is NP-Hard, three meta-heuristics are tailored for large-sized instances and the results show the outperformance of hybrid algorithms comparing to classic genetic algorithm. Finally, the obtained results are discussed and the paper is concluded.

scholarly journals Presenting a Multi-Start Hybrid Heuristic for Solving the Problem of Two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD)

2020 ◽  
Vol 2020 ◽  
pp. 1-24 ◽  
Author(s):  
Houming Fan ◽  
Jiaxin Wu ◽  
Xin Li ◽  
Xiaodan Jiang
Keyword(s):  
Path Relinking ◽  
Mixed Integer ◽  
Computational Time ◽  
Pickup And Delivery ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Hybrid Heuristic ◽  
Location Routing ◽  
Constructive Heuristics ◽  
Simultaneous Pickup And Delivery

This study proposes a three-index flow-based mixed integer formulation to solve a two-echelon location routing problem with simultaneous pickup and delivery. In this formulation, pickup and delivery demands can be addressed using the same vehicle in each echelon of the network to reduce costs and increase logistics efficiency. We solve such NP-hard problem by developing a multistart hybrid heuristic with path relinking (MHH-PR) which is composed of local search and a variable neighbourhood descent algorithm. In the algorithm, three constructive heuristics are applied to generate diversified initial solutions, and path relinking is introduced for intensification and postoptimisation. Results indicate that MHH-PR can reduce the gap between the near optimal and global optimal solutions by 1%-2%. The proposed algorithm significantly improves computational efficiency by reducing the computational time of more than 10 min for existing cases involving 20 nodes to less than 10 s.

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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