scholarly journals Integrated zone picking and vehicle routing operations with restricted intermediate storage

OR Spectrum ◽  
2021 ◽  
Author(s):  
Manuel Ostermeier ◽  
Andreas Holzapfel ◽  
Heinrich Kuhn ◽  
Daniel Schubert
Keyword(s):  
Vehicle Routing ◽  
Distribution System ◽  
Neighborhood Search ◽  
Integrated Planning ◽  
Distribution Centers ◽  
Routing Problem ◽  
Order Processing ◽  
Planning Approach ◽  
Intermediate Storage ◽  
The Impact

AbstractThe competitiveness of a retailer is highly dependent on an efficient distribution system. This is especially true for the supply of stores from distribution centers. Stores ask for high flexibility when it comes to their supply. This means that fast order processing is essential. Order processing affects different subsystems at the distribution center: orders are picked in multiple picking zones, transferred to intermediate storage, and delivered via dedicated tours. These processing steps are highly interdependent. The schedule for picking needs to be synchronized with the routing decisions to ensure availability of orders at the DC’s loading docks when their associated tours are scheduled. Concurrently, intermediate storage represents a bottleneck as capacity for order storage is limited. The simultaneous planning of picking and routing operations with restricted intermediate storage is therefore relevant for retail practice but has not so far been considered within an integrated planning approach. Our work addresses this task and discusses an integrated zone picking and vehicle routing problem with restricted intermediate storage. We present a comprehensive model formulation and introduce a general variable neighborhood search for simultaneous consideration of the given planning stages. We also present two alternative sequential approaches that are motivated by the prevailing planning situation in industry. Numerical experiments and a case study show the need for an integrated planning approach to obtain practicable results. Further, we identify the impact of the main problem characteristics on overall planning and provide valuable insights for the application of these findings in industry.

scholarly journals Workstation-oriented distribution optimization of shipbuilding materials

2019 ◽  
Vol 272 ◽  
pp. 01014
Author(s):  
Gang Chen ◽  
Yan Jiang ◽  
Xing Sheng ◽  
Jingqian Wang ◽  
Hui Jia
Keyword(s):  
Vehicle Routing ◽  
Distribution System ◽  
Time Windows ◽  
Production Capacity ◽  
Material Distribution ◽  
Distribution Centers ◽  
Routing Problem ◽  
Rule Method ◽  
Population Genetic Algorithm ◽  
The Impact

The optimization of material distribution is of great importance on shipbuilding project, which determines whether the production capacity of the ship is fully embodied. A workstation-oriented material distribution problem is formulated with reference to the production characteristics of shipyards. This problem can be considered as a complex vehicle routing problem (VRP) with capacity constraints, time windows and multiple distribution centers. In order to minimize the impact of distribution problems on production, a multi-population genetic algorithm (MPGA) that can minimize the sum of earliness and tardiness penalties is proposed in this paper. The proposed algorithm looks for near-optimal solutions for assigning distribution tasks and optimizing vehicle routing. Then, the evaluation of the solutions generated with MPGA is achieved with a priority-based heuristic algorithm. Simulation results of different cases show that the proposed MPGA allows logistics distribution system to operate more efficiently and solutions can be improved by 71% on average compared to those obtained with the traditional priority rule method.

scholarly journals Collaborative Vehicle Routing Problem in the Urban Ring Logistics Network under the COVID-19 Epidemic

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wenjia Zheng ◽  
Zhongyu Wang ◽  
Liucheng Sun
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Multicommodity Flow ◽  
Integer Programming Problem ◽  
Distribution Centers ◽  
Routing Problem ◽  
Logistics Network ◽  
The City

This paper explored the problem of collaborative vehicle routing in the urban ring logistics network (Co-VRP-URLN) during the COVID-19 epidemic. According to the characteristics of urban distribution and the restriction of traffic all over China during this period, this study mainly considers a common distribution mode of order exchange through the outer ring of the city and then solves the vehicle routing problem of distribution, which belongs to a special multidepot vehicle routing problem with time windows. According to the definition of the problem, the corresponding mixed-integer programming problem of multicommodity flow is established, and the variable neighborhood search algorithm is designed in detail to solve it. The effectiveness of the algorithm is verified by a standard example, and the benefits of joint distribution are revealed through the improved standard example. At last, the influence of different distribution centers is compared. The results show that this model can significantly improve the distribution efficiency within the city under the restriction of traffic.

scholarly journals A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ha-Bang Ban ◽  
Phuong Khanh Nguyen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Natural Extension ◽  
Solution Space ◽  
Neighborhood Search ◽  
Np Hard ◽  
Routing Problem ◽  
Hybrid Metaheuristic ◽  
Asymmetric Distance ◽  
Constrained Vehicle Routing

AbstractThe Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is NP-hard as it is a natural extension of the NP-hard Vehicle Routing Problem. In ADVRP problem, each customer is visited exactly once by a vehicle; every tour starts and ends at a depot; and the traveled distance by each vehicle is not allowed to exceed a predetermined limit. We propose a hybrid metaheuristic algorithm combining the Randomized Variable Neighborhood Search (RVNS) and the Tabu Search (TS) to solve the problem. The combination of multiple neighborhoods and tabu mechanism is used for their capacity to escape local optima while exploring the solution space. Furthermore, the intensification and diversification phases are also included to deliver optimized and diversified solutions. Extensive numerical experiments and comparisons with all the state-of-the-art algorithms show that the proposed method is highly competitive in terms of solution quality and computation time, providing new best solutions for a number of instances.

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