Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem

Cheng, Chen-Yang; Chen, Yin-Yann; Chen, Tzu‐Li; Yoo, John Jung-Woon

doi:10.1016/j.ijpe.2015.03.021

Cited by 102 publications

(49 citation statements)

References 14 publications

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“…As batching and routing are both operational decisions, these planning problems are particularly suitable for being solved in an integrated way. Efficient heuristic algorithms have been proposed for the simultaneous construction of batches and picking tours (Kulak et al, 2012;Cheng et al, 2015;Li et al, 2016;Lin et al, 2016), compared to combinations of more straightforward batching and routing policies.…”

Section: Order Batching and Routingmentioning

confidence: 99%

Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review

Gils

Ramaekers

Caris

et al. 2018

European Journal of Operational Research

252

149

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Warehouses deliver labor-intensive services to customers. Underperformance may result in high costs and unsatisfied customer demand. New market developments force warehouses to handle a large number of orders within tight time windows. To cope with this, order picking operations need to be optimized by solving a wide range of planning problems. Optimizing order picking planning problems sequentially may yield a suboptimal overall warehouse performance. Still, previous warehouse planning reviews focus on individual planning problems. This literature review differs by investigating combinations of multiple order picking planning problems. A state-of-the-art review and classification of the scientific literature investigating combinations of tactical and operational order picking planning problems in picker-to-parts systems is presented with the aim of determining how planning problems are related. Furthermore, this literature review aims to find excellent policy combinations and to provide guidelines how warehouse managers can benefit from combining planning problems, in order to design efficient order picking systems and improve customer service. Combining multiple order picking planning problems results in substantial efficiency benefits, which are required to face new market developments.

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Section: Order Batching and Routingmentioning

confidence: 99%

Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review

Gils

Ramaekers

Caris

et al. 2018

European Journal of Operational Research

252

149

View full text Add to dashboard Cite

show abstract

“…New solutions are created by relocating and swapping orders among batches. Cheng et al (2015) use a particle swarm optimization approach to batch orders and an ant colony optimization algorithm to optimize the order picking. To the best of our knowledge, the only exact approach for the JOBPRP so far has been proposed by Valle et al (2017).…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient and general approach for the joint order batching and picker routing problem

Briant

Cambazard

Cattaruzza

et al. 2020

European Journal of Operational Research

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Picking is the process of retrieving products from inventory. It is mostly done manually by dedicated employees called pickers and is considered the most expensive of warehouse operations. To reduce the picking cost, customer orders can be grouped into batches that are then collected by traveling the shortest possible distance.This work presents an exponential linear programming formulation to tackle the joint order batching and picker routing problem. Variables, or columns, are related to the picking routes in the warehouse. Computing such routes is generally an intractable routing problem and relates to the well known traveling salesman problem (TSP). Nonetheless, the rectangular warehouse's layouts can be used to eciently solve the corresponding TSP and take into account in the development of an ecient subroutine, called oracle. We therefore investigate whether such an oracle allows for an eective exponential formulation.Experimented on a publicly available benchmark, the algorithm proves to be very eective. It improves many of the best known solutions and provides very strong lower bounds. Finally, this approach is applied to another industrial case to demonstrate its interest for this eld of application.

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“…Several simulation studies analyze combinations of two operational order picking policies (e.g. storage-routing [16,17], zoning-batching [21], and routing-batching [20,3,4]), while [2,13,12,15] investigate the combination of storage, batching, and routing policies. This paper differs from previous studies by analyzing the four main operational policy decisions simultaneously.…”

Section: Problem Contextmentioning

confidence: 99%

Improving Order Picking Efficiency by Analyzing Combinations of Storage, Batching, Zoning, and Routing Policies

Gils

Braekers

Ramaekers

et al. 2016

Lecture Notes in Computer Science

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In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. The objective of this research is to simulate and evaluate the interaction between several storage, batching, zone picking and routing policies in order to reduce the order picker travel distance. The value of integrating these four operation policy decisions is proven by a real-life case study. A full factorial ANOVA provides insight into the interactions between storage, batching, zoning, and routing policies. The results of the study clearly indicate that warehouses can achieve significant benefits by considering storage, batching, zone picking, and routing policies simultaneously. Awareness of the influence of an individual policy decision on the overall warehouse performance is required to manage warehouse operations, resulting in enhanced customer service.

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Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem

Cited by 102 publications

References 14 publications

Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review

Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review

An efficient and general approach for the joint order batching and picker routing problem

Improving Order Picking Efficiency by Analyzing Combinations of Storage, Batching, Zoning, and Routing Policies

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