Formulations for joint order picking problems in low-level picker-to-part systems

Cano, José Alejandro

doi:10.11591/eei.v9i2.2110

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The cross-docking problem is considered NP-Hard since it includes the generalized assignment problem [22], therefore, two metaheuristics called simulated annealing (SA) and particle swarm optimization (PSO) are implemented for solving the cross-docking problem for the consumer goods sector to provide high-quality solutions in short computing times [23,24]. SA is classified as a trajectory method meanwhile PSO is a population-based method.…”

Section: Methodsmentioning

confidence: 99%

Improving cross-docking operations for consumer goods sector using metaheuristics

Gómez-Montoya

Cano

Campo³

et al. 2021

Bulletin EEI

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This paper aims to model a consumer goods cross-docking problem, which is solved using metaheuristics to minimize makespan and determine the capacity in terms of inbound and outbound docks. The consumer-goods cross-docking problem is represented through inbound and outbound docks, customer orders (products to be delivered to customers), and metaheuristics as a solution method. Simulated annealing (SA) and particle swarm optimization (PSO) are implemented to solve the cross-docking problem. Based on the results of statistical analysis, it was identified that the two-way interaction effect between inbound and outbound docks, outbound docks and items, and items and metaheuristics are the most statistically significant on the response variable. The best solution provides the minimum makespan of 973.42 minutes considering nine inbound docks and twelve outbound docks. However, this study detected that the combination of six inbound docks and nine outbound docks represents the most efficient solution for a cross-docking design since it reduces the requirement of docks by 28.6% and increases the makespan by only 4.2% when compared to the best solution, representing a favorable trade-off for the cross-docking platform design.

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Section: Methodsmentioning

confidence: 99%

Improving cross-docking operations for consumer goods sector using metaheuristics

Gómez-Montoya

Cano

Campo³

et al. 2021

Bulletin EEI

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“…Single Picker Routing Problem: Single picker routing problem (SPRP) is the application of the shortest routing method in the warehouse to retrieve products ordered by customers and stored in certain storage locations in the warehouse [27], [28], [29], [30]. The purpose of the Single picker routing problem (SPRP) is to determine the path with the least cost that starts and ends at the same point when collecting all the stock-keeping units (SKUs) contained in the pick list from the storage locations in the warehouse.…”

Section: )mentioning

confidence: 99%

Intelligent Warehouse Picking Improvement Model for e-Logistics Warehouse Using Single Picker Routing Problem and Wave Picking

Damayanti

Novitasari²,

Setyawan³

et al. 2022

JOIV : Int. J. Inform. Visualization

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Abstract— The development and use of technological innovations have changed people's behavior from an industrial society to an information society. It can be seen in the increase in people's consumption patterns from trading through physical stores (offline) to trading through electronic systems, often referred to as e-commerce. Logistics services are distribution actors in the downstream line which are tasked with delivering products from the fulfillment center from e-commerce to the end customer. The uncertainty of the number of requests is the biggest challenge for logistics service players. The growth of e-commerce has also led to an increase in sales volume in e-commerce which has given rise to a new generation of warehouses that are specifically tailored to the special needs of online retailers who directly serve the demands of end-customers in the business-to-consumer (B2C) segment. Traditional warehousing systems cannot handle orders with the characteristics of many transactions but smaller sizes. In addition, warehouses that handle e-commerce are also required to have a fast process in the warehouse because shipments must be made on the same day. In this study, the author aims to perform calculations to find the optimal order picking time in the warehouse, so orders in e-commerce can be processed faster by comparing the picking process time using ordinary Single Picker Routing Problem (SPRP) and combined with the concept of wave picking using Genetic Algorithm (GA). Based on a theoretical study in this paper, the combination between SPRP and wave picking can reduce 42.28% picking time.

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“…Constraints in (12) and (13) determine the non-negativity and domain of the variables. The order batching problem is considered NP-Hard when the number of customer orders per batch is greater than two [15], therefore any extension of this problem is considered NP-Hard as well [13,27,28]. Then, the OBSPMP is considered an NP-hard problem, so it cannot be solved using exact solution methods at least for large instances [25], thus metaheuristics like genetic algorithms can provide satisfactory solutions in short computing times for combinatorial problems related to logistics and operations management [29,30].…”

Section: Obspmp Features and Assumptionsmentioning

confidence: 99%

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

Cano

Cortés

Campo³

et al. 2021

IJECE

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This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

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Formulations for joint order picking problems in low-level picker-to-part systems

Cited by 6 publications

References 25 publications

Improving cross-docking operations for consumer goods sector using metaheuristics

Improving cross-docking operations for consumer goods sector using metaheuristics

Intelligent Warehouse Picking Improvement Model for e-Logistics Warehouse Using Single Picker Routing Problem and Wave Picking

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

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