Cross-docking truck scheduling with product unloading/loading constraints based on an improved particle swarm optimisation algorithm

Cross-docking is a very useful logistics technique that can substantially reduce distribution costs and improve customer satisfaction. A key problem in its success is truck scheduling, namely, decision on assignment and docking sequence of inbound/outbound trucks to receiving/shipping dock doors. Th...

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Published inInternational journal of production research Vol. 56; no. 16; pp. 5365 - 5385
Main Authors Ye, Yan, Li, Jingfeng, Li, Kaibin, Fu, Hui
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 18.08.2018
Taylor & Francis LLC
Subjects
Algorithms
cross docking
Crossovers
Customer satisfaction
Distribution costs
Docking
Genetic algorithms
inbound and outbound trucks
Integer programming
Logistics
particle swarm optimisation
Particle swarm optimization
product handling constraint
Scheduling
Shipping
Solution space
Statistical tests
Strategy
Taguchi methods
Trucks
Weight
Online AccessGet full text
ISSN0020-7543
1366-588X
DOI10.1080/00207543.2018.1464678

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Abstract Cross-docking is a very useful logistics technique that can substantially reduce distribution costs and improve customer satisfaction. A key problem in its success is truck scheduling, namely, decision on assignment and docking sequence of inbound/outbound trucks to receiving/shipping dock doors. This paper focuses on the problem with the requirement of unloading/loading products in a given order, which is very common in many industries, but is less concerned by existing researches. An integer programming model is established to minimise the makespan. An improved particle swarm optimisation (ωc-PSO) algorithm is proposed for solving it. In the algorithm, a cosine decreasing strategy of inertia weight is designed to dynamically balance global and local search. A repair strategy is put forward for continuous search in the feasible solution space and a crossover strategy is presented to prevent the algorithm from falling into local optimum. After algorithm parameters are tuned using Taguchi method, computational experiments are conducted on different problem scales to evaluate ωc-PSO against genetic algorithm, basic PSO and GLNPSO. The results show that ωc-PSO outperforms other three algorithms, especially when the number of dock doors, trucks and product types is great. Statistical tests show that the performance difference is statistically significant.
AbstractList Cross-docking is a very useful logistics technique that can substantially reduce distribution costs and improve customer satisfaction. A key problem in its success is truck scheduling, namely, decision on assignment and docking sequence of inbound/outbound trucks to receiving/shipping dock doors. This paper focuses on the problem with the requirement of unloading/loading products in a given order, which is very common in many industries, but is less concerned by existing researches. An integer programming model is established to minimise the makespan. An improved particle swarm optimisation (ωc-PSO) algorithm is proposed for solving it. In the algorithm, a cosine decreasing strategy of inertia weight is designed to dynamically balance global and local search. A repair strategy is put forward for continuous search in the feasible solution space and a crossover strategy is presented to prevent the algorithm from falling into local optimum. After algorithm parameters are tuned using Taguchi method, computational experiments are conducted on different problem scales to evaluate ωc-PSO against genetic algorithm, basic PSO and GLNPSO. The results show that ωc-PSO outperforms other three algorithms, especially when the number of dock doors, trucks and product types is great. Statistical tests show that the performance difference is statistically significant.
Author Li, Kaibin
Li, Jingfeng
Ye, Yan
Fu, Hui
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  organization: Department of Industrial Engineering, School of Electromechanical Engineering, Guangdong University of Technology
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Snippet Cross-docking is a very useful logistics technique that can substantially reduce distribution costs and improve customer satisfaction. A key problem in its...
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SubjectTerms Algorithms
cross docking
Crossovers
Customer satisfaction
Distribution costs
Docking
Genetic algorithms
inbound and outbound trucks
Integer programming
Logistics
particle swarm optimisation
Particle swarm optimization
product handling constraint
Scheduling
Shipping
Solution space
Statistical tests
Strategy
Taguchi methods
Trucks
Weight
Title Cross-docking truck scheduling with product unloading/loading constraints based on an improved particle swarm optimisation algorithm
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