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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Vydáno v:International journal of production research Ročník 56; číslo 16; s. 5365 - 5385
Hlavní autoři: Ye, Yan, Li, Jingfeng, Li, Kaibin, Fu, Hui
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 18.08.2018
Taylor & Francis LLC
Témata:
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
ISSN:0020-7543, 1366-588X
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Shrnutí: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.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2018.1464678