An effective iterated greedy algorithm for solving a multi-compartment AGV scheduling problem in a matrix manufacturing workshop

Zou, Wen-Qiang; Pan, Quan-Ke; Taşgetiren, M. Fatih

doi:10.1016/j.asoc.2020.106945

Cited by 49 publications

(17 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the studies (Chen, Liu, and Langevin 2019), (Chen and Shi 2019), (Kim and Park 2020) and (Chen, Pan, and Yi 2022) used temperature-based multi-compartment vehicles to address the issue. Manufacturing-based examples are discussed in (Pasha et al 2020) and (Zou, Pan, and Tasgetiren 2021). Additionally, liquid or fuel containers based cases are addressed in (Cornillier et al 2008), (Asawarungsaengkul et al 2013), (Lahyani et al 2015), (Febriandini, Sutopo, et al 2020), (Wang, Kinable, and Van Woensel 2020), (Chowmali and Sukto 2021), (Yindong, Liwen, and Jingpeng 2021), (Ramadhani, Masruroh, and Waluyo 2021) and (Guo et al 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Trip Multi-Compartment Vehicle Routing Problem with Backhauls

Ramanand

Kumar

2023

Preprint

View full text Add to dashboard Cite

The Multi-Trip Multi-Compartment Vehicle Routing Problem with Backhauls (MTMCVRPB) is a complex optimization problem that involves finding the most efficient routes for a heterogeneous fleet of multi-compartment vehicles to transport heterogeneous commodities simultaneously. The problem involves making deliveries and pickups while taking into account capacity constraints on compartments and other specific requirements such as limitation on route length. The MTMCVRPB aims to minimize the total cost of the routes, including the cost of vehicles, fuel and other expenses, while maximizing efficiency of vehicles and fulfilling the demand of customers. In order to solve the MTMCVRPB, this article recommends a mixed integer linear programming (MILP) formulation. The proposed model takes into consideration the demands of both linehaul and backhaul customers, ensuring that on each route linehaul deliveries are made before visiting any backhaul customer. The model was tested on benchmark instances from literature and randomly generated data sets and numerical experiments were performed to demonstrate its effectiveness. The proposed model can be beneficial for the industries such as grocery and food delivery, fuel distribution and garbage collection as these are main applications of this model. We have also provided a saving based heuristic by making certain modifications to find the quick and efficient solution to the proposed model.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Multi-Trip Multi-Compartment Vehicle Routing Problem with Backhauls

Ramanand

Kumar

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…After analyzing, it will be found that the y value of any node is less than the threshold, so it is the global optimal solution. Therefore, a greedy strategy can be designed, which only needs to traverse the node whose y value is lower than the global optimal solution to solve the problem more efficiently [14][15][16][17][18].…”

Section: Greedy Algorithm For Solving Problemsmentioning

confidence: 99%

Path Planning of AS/RS Based on Cost Matrix and Improved Greedy Algorithm

Wang

Geng

et al. 2021

Symmetry

View full text Add to dashboard Cite

Logistics plays an important role in the field of global economy, and the storage and retrieval of tasks in a warehouse which has symmetry is the most important part of logistics. Generally, the shelves of a warehouse have a certain degree of symmetry and similarity in their structure. The storage and retrieval efficiency directly affects the efficiency of logistics. The efficiency of the traditional storage and retrieval mode has become increasingly inconsistent with the needs of the industry. In order to solve this problem, this paper proposes a greedy algorithm based on cost matrix to solve the path planning problem of the automatic storage and retrieval system (AS/RS). Firstly, aiming at the path planning mathematical model of AS/RS, this paper proposes the concept of cost matrix, which transforms the traditional storage and retrieval problem into the element combination problem of cost matrix. Then, a more efficient backtracking algorithm is proposed based on the exhaustive method. After analyzing the performance of the backtracking algorithm, combined with some rules, a greedy algorithm which can further improve efficiency is proposed; the convergence of the improved greedy algorithm is also proven. Finally, through simulation, the time consumption of the greedy algorithm is only 0.59% of the exhaustive method, and compared with the traditional genetic algorithm, the time consumption of the greedy algorithm is about 50% of the genetic algorithm, and it can still maintain its advantage in time consumption, which proves that the greedy algorithm based on cost matrix has a certain feasibility and practicability in solving the path planning of the automatic storage and retrieval system.

show abstract

“…ey put forward a particle swarm optimization-based method where an indirect priority-based particle encoding scheme is proposed. For the multi-compartment AGV scheduling problem in matrix workshop, Zou et al [6] addressed an effective iterated greedy algorithm with some advanced techniques including accelerations for evaluating neighborhood solutions, two improved constructive heuristics based on nearest-neighbour and sweep, an improved destruction procedure, and simulated annealing type of acceptance criterion. Zou et al [7] then investigated a new AGV scheduling problem with pickup and delivery that simultaneously optimized two objectives of maximizing the overall customers' satisfaction and minimizing the total distribution costs.…”

Section: Introductionmentioning

confidence: 99%

Nondominated Sorting Differential Evolution Algorithm to Solve the Biobjective Multi-AGV Routing Problem in Hazardous Chemicals Warehouse

Duan

Liu

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For the multiple automated guided vehicle (multi-AGV) routing problems in the warehousing link of logistics, where the optimization objective is to minimize both the number of AGVs used and the maximum pickup time simultaneously, a nondominant sorting differential evolution (NSDE) algorithm is proposed. In the encoding and decoding stages, the pickup point area is divided. AGVs are allocated to each region according to the proposed rule based on avoiding duplicate paths. Meanwhile, the pickup points within the region can be adjusted to optimize the pickup paths and improve the pickup efficiency. The fast nondominated sorting method and elitist selection strategy in the nondominated sorting genetic algorithm II (NSGA-II) are introduced into the differential evolution algorithm, which sorts all the regions to obtain the best Pareto solution set. Lastly, the domination of the proposed NSDE algorithm in Pareto frontier evaluation indicators is verified by some numerical experiments.

show abstract

An effective iterated greedy algorithm for solving a multi-compartment AGV scheduling problem in a matrix manufacturing workshop

Cited by 49 publications

References 41 publications

Multi-Trip Multi-Compartment Vehicle Routing Problem with Backhauls

Multi-Trip Multi-Compartment Vehicle Routing Problem with Backhauls

Path Planning of AS/RS Based on Cost Matrix and Improved Greedy Algorithm

Nondominated Sorting Differential Evolution Algorithm to Solve the Biobjective Multi-AGV Routing Problem in Hazardous Chemicals Warehouse

Contact Info

Product

Resources

About