Practical factors in order picking planning: state-of-the-art classification and review

Vanheusden, Sarah; Gils, Teun van; Ramaekers, Katrien; Cornelissens, Trijntje; Caris, An

doi:10.1080/00207543.2022.2053223

Cited by 50 publications

(9 citation statements)

References 142 publications

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“…In conclusion, the present work fits into this line of research, as it explores the application of metaheuristics to solve the order-picking problem. The reader who is interested in a complete review of the literature with gap analysis and future research directions can refer to over 70 papers published in Scopus; the last two [44,45] published during 2023 are recommended.…”

Section: Solution Approaches Proposed In the Literature To Improve Pr...mentioning

confidence: 99%

Enhancing Manual Order Picking through a New Metaheuristic, Based on Particle Swarm Optimization

2023

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This paper proposes a new metaheuristic algorithm called Particle Swarm-based picking time minimization (Pkt_PSO), ideated for picking time minimization in manual warehouses. As the name suggests, Pkt_PSO is inspired by Particle Swarm Optimization (PSO), and it is specifically designed to minimize the picking time in order case picking contexts. To assess the quality and the robustness of Pkt_PSO, it is compared to five alternative algorithms used as benchmarks. The comparisons are made in nine different scenarios obtained by changing the layout of the warehouse and the length of the picking list. The results of the analysis show that Pkt_PSO has a slower convergence rate and suffers less of early stagnation in local minima; this ensures a more extensive and accurate exploration of the solution space. In fact, the solutions provided by Pkt_PSO are always better (or at least comparable) to the ones found by the benchmarks, both in terms of quality (closeness to the overall best) and reliability (frequency with which the best solution is found). Clearly, as more solutions are explored, the computational time of Pkt_PSO is longer, but it remains compatible with the operational needs of most practical applications.

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Section: Solution Approaches Proposed In the Literature To Improve Pr...mentioning

confidence: 99%

Enhancing Manual Order Picking through a New Metaheuristic, Based on Particle Swarm Optimization

2023

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“…The activity of order picking, that is the manual or automated withdrawal of stock keeping units from storage locations to satisfy customer demand, is a frequently object of study in the literature [3,4]. It is in fact among the most labor-intensive and costly activities in any material handling system [1].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Once the items are unloaded from the AMR, the batch is considered completed from the picking-optimization viewpoint, the AMR is equipped with new containers to collect items and can start processing a new batch. Regarding the picking activity, a synchronized Zone Picking strategy [3] The research contributions of the study developed in [21] include the formulation of Mixed Integer Programming (MIP) for the layout depicted in Figure 1. Note that using MIPs to model and solve layout problems is a common practice (see, for example, [27]).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Regarding the picking activity, a synchronized Zone Picking strategy [3] is considered, according to which the items of the same batch are collected simultaneously by different pickers in different zones. If the items of a batch are stored in different aisles, as a result, several operators are involved in retrieving of them.…”

Section: The Two-blocks Warehouse Case Studymentioning

confidence: 99%

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AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse

et al. 2022

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Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, automated and robotized components are increasingly introduced creating hybrid order picking systems where humans and machines jointly work together. This study focuses on the application of a hybrid picker-to-parts order picking system, in which human operators collaborate with Automated Mobile Robots (AMRs). In this paper a warehouse with a two-blocks layout is investigated. The main contributions are new mathematical models for the optimization of picking operations and synchronizations. Two alternative implementations for an AMR system are considered. In the first one handover locations, where pickers load AMRs) are shared between pairs of opposite sub-aisles, while in the second they are not. It is shown that solving the mathematical models proposed by the meaning of black-box solvers provides a viable algorithmic optimization approach that can be used in practice to derive efficient operational plannings. The experimental study presented, based on a real warehouse and real orders, finally allows to evaluate and strategically compare the two alternative implementations considered for the AMR system.

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“…Indeed, considering the available literature from 2015 to date, only a limited number of articles [13][14][15][16] have specifically addressed the direct comparison of pickers-to-parts order picking technologies, while at the same time, a significant number of articles have focused on the study and comparison of different parameters related to the picking layout design, depot location, picking type, picking strategy, storage allocation policies, routing policies, order batching, etc. [17,18].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Completion Time and Perceived Workload in Pickers-to-Parts Order-Picking Technologies: Evidence from Laboratory Experiments

Chondromatidis,

Gialos,

Zeimpekis

et al. 2024

Logistics

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Background: Despite the general impression that digital order-picking supportive technologies can manage a series of emerging challenges, there is still a very limited amount of research concerning the implementation and evaluation of such technologies in manual picker-to-goods order-picking systems. Therefore, this paper aims to evaluate the performance of three alternative picker-to-goods technologies (i.e., Pick-by-Radio Frequency (RF) Scanner, Pick-to-light, and Pick-by-vision) in terms of completion time and perceived workload. Methods: The Design of Experiments (DoE) methodology is adopted to investigate order-picking technologies in terms of completion time. More specifically, a full factorial design has been used (23 × 3 full factorial design) for the assessment of the aforementioned order-picking technologies via laboratory testing. Furthermore, for the comparative assessment of the reviewed order-picking technologies in terms of workload, the NASA Task Load Index (NASA-TLX) is embraced by system users. Results: The results reveal that the best picker-to-goods technology in terms of order-picking completion time and perceived workload under certain laboratory setup is light picking when combined with few items per order line and many order lines per order. Conclusion: The paper successfully identified the best picker-to-goods technology, however it is important to mention that the adoption of such order-picking technology implies certain managerial implications that include training programs for employees to ensure they are proficient in using such technologies, upfront costs for purchasing and implementing the order picking system, and adjustments to existing workflows.

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Practical factors in order picking planning: state-of-the-art classification and review

Cited by 50 publications

References 142 publications

Enhancing Manual Order Picking through a New Metaheuristic, Based on Particle Swarm Optimization

Enhancing Manual Order Picking through a New Metaheuristic, Based on Particle Swarm Optimization

AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse

Investigating the Impact of Completion Time and Perceived Workload in Pickers-to-Parts Order-Picking Technologies: Evidence from Laboratory Experiments

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