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 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 Bi-Objective Optimization for Vehicle Routing Problems with a Mixed Fleet of Conventional and Electric Vehicles and Soft Time Windows

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peixin Zhao ◽  
Fanfan Liu ◽  
Yuanyuan Guo ◽  
Xiaoyang Duan ◽  
Yunshu Zhang
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicles ◽  
Programming Model ◽  
Time Windows ◽  
Nsga Ii ◽  
Weighting Method ◽  
Routing Problem ◽  
Penalty Cost ◽  
Soft Time Windows ◽  
The Impact

With the growing interest in environmental protection and congestion, electric vehicles are increasingly becoming the important transportation means. However, electric vehicles currently face several adoption barriers including high purchasing price and limited travelling range, so the fleets where electric vehicles and conventional vehicles coexist are closer to the current fleet management status. Considering the impact of charging facilities and carbon emission, this paper proposes a vehicle routing problem with a mixed fleet of conventional and electric vehicles and soft time windows. A bi-objective programming model is established to minimize total operational cost and time penalty cost. Finally, the nondominated sorting genetic algorithm II (NSGA-II) is employed to deal with this problem. Furthermore, single-objective optimization is carried out for the two objectives, respectively, and the linear weighting method is also used to solve the problem. Through the contrast of these results and the NSGA-II results, the effectiveness of the algorithm in this paper is further verified. The results indicate that two objectives are contradictory to some extent and decision-makers need a trade-off between two objectives.

scholarly journals Modeling and Solution of Vehicle Routing Problem with Grey Time Windows and Multiobjective Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaojian Yuan ◽  
Qishan Zhang ◽  
Jiaoyan Zeng
Keyword(s):  
Customer Satisfaction ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Algorithm Analysis ◽  
Quantum Evolution ◽  
Delivery Time ◽  
Routing Problem ◽  
The Impact ◽  
Distribution Cost

Purpose. In order to study the impact of grey delivery time uncertainty on customer satisfaction and delivery costs, a vehicle routing problem with grey delivery time windows and multiobjective constraints is defined. Method. The paper first defines the uncertainty of the delivery vehicle’s arrival time to the customer as grey uncertainty and then whitens the grey time windows; at the same time, the customer’s hard time windows is expanded into a soft time windows to measure customer satisfaction when the vehicle arrives. Experiment. In order to verify the validity of the established model, numerical experiments are carried out in two groups based on the Solomon example, and the solution is solved based on the improved quantum evolution algorithm. Analysis. Distribution cost fluctuations and customer satisfaction fluctuations with grey time windows are relatively small; under different satisfaction threshold conditions, the distribution cost is increased gently with the satisfaction threshold. Conclusion. The grey delivery time windows have certain advantages in solving the random travel time vehicle routing problem.

scholarly journals Heuristic algorithms applied to the problems of servicing actors in supply chains

2017 ◽  
Vol 44 (4) ◽  
pp. 25-34 ◽  
Author(s):  
Mariusz Izdebski ◽  
Ilona Jacyna-Gołda ◽  
Katarzyna Markowska ◽  
Jakub Murawski
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Vehicle Routing ◽  
Heuristic Algorithms ◽  
Time Windows ◽  
Production Capacity ◽  
Task Assignment ◽  
Cost Of Transport ◽  
Routing Problem ◽  
Complex Decision

The paper discusses main decision problems analysed in the subject matter of servicing actors operating in the supply chains, i.e. the vehicle routing problem, vehicles-to-task assignment problem and the problem of entities’ localization in the supply chain. The input data used to describe supply chains is given as well as the basic constraints and the criterion functions used in the development of mathematical models describing the supply chains. Servicing actors in supply chains is the complex decision making problem. Operators in the supply chains are constrained by: production capacity of the suppliers, the demand of the customers in particular working days, storage capacities of warehouses, handling capacities of warehouses, suppliers’ and warehouses’ time windows and other. The efficiency of supply chain is described by cost of transport between operators, costs of passing cargoes through warehouses and delivery time to the recipient. The heuristic algorithms, like genetic and ant algorithms are detailed and used to identify issues related to the operation of actors operating in the supply chains are described. These algorithms are used for solving localization problems in supply chains, vehicle routing problems, and assignment problems. The complexity of presented issues (TSP is known as NP-hard problem) limits the use of precise algorithms and implies the need to use heuristic algorithms. It should be noted that solutions generated by these algorithms for complex decision instances are sub-optimal solutions, but nonetheless it is accepted from the practical point of view.

scholarly journals A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

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