Multi-objective optimisation of automated guided dispatching and vehicle routing system

Wang, Hsiao Fan; Chan, Chao-Wen

doi:10.1504/ijmom.2014.063585

Cited by 8 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On route optimization, Wang and Chan [4] developed a multi-objective integer programming model for the D-P transport of multiple goods, which optimizes the number of vehicles, finds the most effective routes, and minimizes the energy and operational costs. To satisfy different consumer demands, Villegas et al [5] obtained multiple optimal routes through partitioned scheduling of D-P services.…”

Section: Relevant Studies On the D-p Transportmentioning

confidence: 99%

“…In this case, the single-hub H-S D-P network can no longer satisfy the demand, and should be replaced with a multi-hub network. (2) 1.000000 X (5,9) 0.7000000 P (3) 1.000000 X (6,9) 2.230000 P (8) 1.000000 Y (2,4) 0.7090000 P (9) 1.000000 Y (2,5) 0.7120000 X (1,9) 2.000000 Y (3,3…”

Section: Empirical Analysis On the Model Of Traditional Freight Statimentioning

confidence: 99%

“…1.706000 X (5,2) 0.099 X (5,9) 0.8850000 X (6,9) 2.569000 Y (2,4) 0.6100000 Y (2,5) 1.195000 Y(8,1) 1.530000 Y (8,2) 0.6910000 Y (3,3) 1.513500 Y (9,2) 0.9385000 Y (9,5) 0.1339000 Y (9,6) 4.483100 Total cost RMB 156.35 million yuan Therefore, alternative freight stations 2, 3, 8 and 9 were selected for transport services.…”

Section: Empirical Analysis On the Model Of Shared Freight Station Dementioning

confidence: 99%

See 2 more Smart Citations

Optimization of Drop-and-Pull Transport Network Based on Shared Freight Station and Hub-and-Spoke Network

Feng¹,

Cheng²

2019

JESA

View full text Add to dashboard Cite

The current drop-and-pull (D-P) transport process has many defects, including but not limited to the insufficient information sharing, the private ownership of vehicles and infrastructure, and the mismatch between vehicles and goods. Moreover, the hardware and software of existing freight stations fall short of the demand for D-P transport. To solve these problems, this paper optimizes the design of the D-P transport network based on shared freight station and the hub-and-spoke (H-S) network. The freight stations were taken as the hubs, and the routes between supply/demand point and freight station are treated as spokes. On this basis, an optimization model was established to minimize the total cost of freight stations and maximize the force from freight stations on supply/demand points in the H-S D-P network. In addition, all the supply/demand points in the region are covered by the selected freight stations. The LINGO software was introduced to solve the established model. Taking a region in southern China for example, the proposed shared freight station design was compared with the traditional freight station design. The results show that the single-hub H-S D-P network obtained by the traditional design could meet the demand when the D-P demand was relatively small; however, only the multi-hub H-S D-P network obtained by the shared freight station design could fulfil a large D-P demand in an efficient manner. The research findings show that the shared freight station is the future of D-P transport.

show abstract

Section: Relevant Studies On the D-p Transportmentioning

confidence: 99%

Section: Empirical Analysis On the Model Of Traditional Freight Statimentioning

confidence: 99%

Section: Empirical Analysis On the Model Of Shared Freight Station Dementioning

confidence: 99%

See 1 more Smart Citation

Optimization of Drop-and-Pull Transport Network Based on Shared Freight Station and Hub-and-Spoke Network

Feng¹,

Cheng²

2019

JESA

View full text Add to dashboard Cite

show abstract

“…If the number of AGVs used can be reduced, many costs will be saved [18–20]. Therefore, subsequent studies began to consider the number of AGVs to guide the actual scheduling while minimizing the makespan [21–23]. Moreover, another practical problem of AGV scheduling has been often neglected, namely the charging process of AGV.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm

Liu

Su³

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.

show abstract

“…Makespan minimization keeps the resources utilization rate at a balanced level and results in a better implementation of expensive FMSs [8,10]. In addition, performance of the AGV systems is heavily influenced by the number of vehicles employed, because AGVs are expensive devices that determining the type and the appropriate number of them in an FMS largely influences the profitability of the FMS [9,11,12], and the appropriate use of AGV's battery charge [13,14]. Multi-objective scheduling of AGVs problem is NP hard; thus, a fuzzy hybrid GA-PSO that is a hybrid evolutionary algorithm has been applied to the proposed model in this study.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

Mousavi¹,

Yap²,

Musa³

2017

Int. j. simul. model.

View full text Add to dashboard Cite

An automated guided vehicle (AGV) is a mobile robot with remarkable industrial applicability for transporting materials within a manufacturing facility or a warehouse. AGV scheduling refers to the process of allocating AGVs to tasks, taking into account the cost and time of operations. Multiobjective scheduling is adopted in this study to acquire a more complex and combinatorial model in contrast with single objective practices. The model objectives are the makespan and number of AGVs minimization while considering the AGVs battery charge. A fuzzy hybrid GA-PSO (genetic algorithm -particle swarm optimization) algorithm was developed to optimize the model. Results have been compared with GA, PSO, and hybrid GA-PSO algorithms to explore the applicability of the algorithm developed. Model's feasibility and the algorithms' performance were investigated through a numerical example before and after the optimization. The model evaluation and validation was conducted through simulation via Flexsim software. The fuzzy hybrid GA-PSO surpassed the other methods, although obtaining less mean computational time was the only significant improvement over hybrid GA-PSO.

show abstract

Multi-objective optimisation of automated guided dispatching and vehicle routing system

Cited by 8 publications

References 15 publications

Optimization of Drop-and-Pull Transport Network Based on Shared Freight Station and Hub-and-Spoke Network

Optimization of Drop-and-Pull Transport Network Based on Shared Freight Station and Hub-and-Spoke Network

Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm

A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

Contact Info

Product

Resources

About