A Building‐Block‐Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

Zhang, Jingtian; Ye, Fuxing; Weng, Xun

doi:10.1155/2019/6153848

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…Bogue et al [309] presented and summarized some of the principal warehouse robots, such as the freight robot, the Fetch robot, the Robo-Stow, the LocusBot, and described their applications. Many other works presented models for scheduling robotic solutions in order picking with different applications and technologies to the ones of [310][311][312][313][314][315][316]. Boysen et al [5] analyzed different warehousing systems suitable for e-commerce, i.e., mixed-shelves storage, batching, zoning and sorting, dynamic order picking, AGV-assisted picking, shelf-moving robots and advanced picking workstations, with an increasing automation level, respectively.…”

Section: Warehousing (Wr)mentioning

confidence: 99%

Implementing E-Commerce from Logistic Perspective: Literature Review and Methodological Framework

et al. 2022

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E-commerce is always more diffused as a selling channel around the whole world market, and its importance has increased and continues to increase with the COVID-19 pandemic emergency. It provides enterprises a lot of opportunities, as the importance of physical stores to sell goods is bypassed. However, it has also changed the role of logistics in the supply chain. For this reason, this work aims to identify the main logistics research areas related to e-commerce implementation and the factors and key performance indicators, which should be taken into account for each logistics research area, with particular attention to sustainable aspects. For doing this, a structured and comprehensive literature analysis is carried out. Keywords associated with e-commerce and logistics areas are matched to identify the most interesting works related to its implementation. From the analysis, five main research areas are identified: Supply Chain Network Design (SCND); Outbound Logistics (OL); Reverse Logistics (RL); Warehousing (WR); and IT and data management (E-IT). For each area, key factors, strategies and performance indicators have been identified. Finally, a methodological framework that summarizes the results of the analysis is presented; this is a useful tool for managers to implement or expand their e-commerce business. Many works are focused on one research area, carrying out critical factors, models, and methods to implement that topic. Instead, the methodological framework presented here summarizes multiple research areas from a logistic point of view, identifying for each one input and output variables and how they influence each other.

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Section: Warehousing (Wr)mentioning

confidence: 99%

Implementing E-Commerce from Logistic Perspective: Literature Review and Methodological Framework

et al. 2022

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“…From a different perspective, Zhang et al (2019) define the problem of allocating robots to picking tasks as scheduled at the picking stations. They formulate a resourceconstrained scheduling problem and propose a solving approach using a serial schedule generator and a genetic algorithm.…”

Section: Scheduling and Task Allocationmentioning

confidence: 99%

Supervised learning and tree search for real-time storage allocation in Robotic Mobile Fulfillment Systems

Rimélé,

Grangier,

Gamache

et al. 2021

Preprint

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A Robotic Mobile Fulfillment System is a robotised parts-to-picker system that is particularly well-suited for e-commerce warehousing. One distinguishing feature of this type of warehouse is its high storage modularity. Numerous robots are moving shelves simultaneously, and the shelves can be returned to any open location after the picking operation is completed. This work focuses on the real-time storage allocation problem to minimise the travel time of the robots. An efficient -but computationally costly -Monte Carlo Tree Search method is used offline to generate high-quality experience. This experience can be learned by a neural network with a proper coordinates-based features representation. The obtained neural network is used as an action predictor in several new storage policies, either as-is or in rollout and supervised tree search strategies. Resulting performance levels depend on the computing time available at a decision step and are consistently better compared to real-time decision rules from the literature.

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“…• Defoort and Veluvolu [2014], Herrero-Perez and Martinez-Barbera [2011], Qiu et al [2002], Shahriari and Biglarbegian [2018], Vis [2006], Wang et al [2019], Xue et al [2019], Yu [2016], Zhang et al [2019] for papers dealing with movement issues.…”

Section: Literature Review: Structurementioning

confidence: 99%

Order allocation, rack allocation and rack sequencing for pickers in a mobile rack environment

Valle¹,

Beasley²

2019

Preprint

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In this paper we investigate the problem of simultaneously allocating orders and mobile storage racks to static pickers. Here storage racks are allocated to pickers to enable them to pick all of the products for the orders that have been allocated to them. Problems of the type considered here arise in facilities operating as robotic mobile fulfilment systems.We present a formulation of the problem of allocating orders and racks to pickers as an integer program and discuss the complexity of the problem. We present two heuristics (matheuristics) for the problem, one using partial integer optimisation, that are directly based upon our formulation.We also consider the problem of how to sequence the racks for presentation at each individual picker and formulate this problem as an integer program. We prove that, subject to certain conditions being satisfied, a feasible rack sequence for all orders can be produced by focusing on just a subset of the orders to be dealt with by the picker.Computational results are presented, both for order and rack allocation, and for rack sequencing, for randomly generated test problems (that are made publicly available) involving up to 500 products, 150 orders, 150 racks and 10 pickers.

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A Building‐Block‐Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

Cited by 16 publications

References 21 publications

Implementing E-Commerce from Logistic Perspective: Literature Review and Methodological Framework

Implementing E-Commerce from Logistic Perspective: Literature Review and Methodological Framework

Supervised learning and tree search for real-time storage allocation in Robotic Mobile Fulfillment Systems

Order allocation, rack allocation and rack sequencing for pickers in a mobile rack environment

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