Evaluating battery charging and swapping strategies in a robotic mobile fulfillment system

Zou, Bipan; Xu, Xianhao; Gong, Yeming; Koster, René de

doi:10.1016/j.ejor.2017.12.008

Cited by 112 publications

(51 citation statements)

References 34 publications

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“…For a more detailed literature review, refer to Tan et al [6] and references therein. Recently, the application of robotic mobile fulfillment systems to the operation of BSCSs has been presented [14]. In this case, batteries are stored on movable shelves and movable robots are deployed to transport the shelves.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Choi

Lim

2020

Sustainability

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This study analyzes the performance of a queue length-dependent overload control policy using a leaky bucket (LB) scheme. This queueing model is applied to the operation of a battery swapping and charging station for electric vehicles (EVs). In addition to the LB scheme, we propose two congestion control policies based on EV queue length thresholds. With these policies, the model determines both EV-arrival and battery-supply intervals, and these depend on the number of EVs waiting in the queue. The queue length distributions, including those at arbitrary epochs, are derived using embedded Markov chain and supplementary variable methods. Performance measures such as blocking probability and mean waiting time are investigated using numerical examples. We study the characteristics of the system using numerical examples and use a cost analysis to investigate situations in which the application of each congestion control policy is advantageous.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Choi

Lim

2020

Sustainability

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“…The authors find that the random policy is significantly outperformed by the proposed handling-speeds-based assignment rule when facing varying service rates of the pickers. [20] build a semi-open queueing network for evaluating the effects of battery management in RMFS. The strategies of battery swapping, automated plug-in charging and inductive charging at the pick station are compared.…”

Section: Related Workmentioning

confidence: 99%

Decision rules for robotic mobile fulfillment systems

Merschformann

Tessensohn

Koster

et al. 2019

Operations Research Perspectives

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The Robotic Mobile Fulfillment Systems (RMFS) is a new type of robotized, parts-to-picker material handling system, designed especially for e-commerce warehouses. Robots bring movable shelves, called pods, to workstations where inventory is put on or removed from the pods. This paper simulates both the pick and replenishment process and studies the order assignment, pod selection and pod storage assignment problems by evaluating multiple decision rules per problem. The discrete event simulation uses realistic robot movements and keeps track of every unit of inventory on every pod. We analyze seven performance measures, e.g. throughput capacity and order due time, and find that the unit throughput is strongly correlated with the other performance measures. We vary the number of robots, the number of pick stations, the number of SKUs (stock keeping units), the order size and whether returns need processing or not. The decision rules for pick order assignment have a strong impact on the unit throughput rate. This is not the case for replenishment order assignment, pod selection and pod storage. Furthermore, for warehouses with a large number of SKUs, more robots are needed for a high unit throughput rate, even if the number of pods and the dimensions of the storage area remain the same. Lastly, processing return orders only affects the unit throughput rate for warehouse with a large number of SKUs and large pick orders.

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“…The objective function (6) minimizes the makespan of all the robots, which indicates that the finishing time of the last task is the makespan of the system. Equation (7) shows that the transfer time of resource "robots" between task i and task j is the travel time of task j in step 1, and 1 is variable depending on task i. Constraints (8)- (11) are the constraints on the starting time of the task so that the finishing time of the task can be calculated according to (2)-(4) after its starting time has been determined.…”

Section: Model Of the Robots Allocation Problemmentioning

confidence: 99%

“…They also build a semiopen queuing network to estimate the performance of the RMFS and used a two-phase approximation method to solve the SOQN. Zou et al [7] investigated plug-in battery charging, inductive battery charging, and battery swapping strategies in the RMFS regarding system throughput time and cost and built a SOQN to estimate the performance of the RMFS with battery recovery consideration. Most of the studies in RMFS mainly focus on performance estimation; some of them tackled the inventory shelves selection problems and order allocation problems.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Weng

2019

Mathematical Problems in Engineering

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Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

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Evaluating battery charging and swapping strategies in a robotic mobile fulfillment system

Cited by 112 publications

References 34 publications

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Decision rules for robotic mobile fulfillment systems

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

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