MULTI-OBJECTIVE GROUPING GENETIC ALGORITHM FOR THE JOINT ORDER BATCHING, BATCH ASSIGNMENT, AND SEQUENCING PROBLEM

2021 ◽  
pp. 1-17
Author(s):  
Jose Alejandro Cano ◽  
Pablo Cortés ◽  
Emiro Antonio Campo ◽  
Alexander A. Correa-Espinal
Keyword(s):  
Genetic Algorithm ◽  
Sequencing Problem ◽  
Order Batching ◽  
Multi Objective ◽  
Grouping Genetic Algorithm

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

2021 ◽  
Vol 11 (3) ◽  
pp. 2516
Author(s):  
Jose Alejandro Cano ◽  
Pablo Cortés ◽  
Emiro Antonio Campo ◽  
Alexander Alberto Correa-Espinal
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Load Capacity ◽  
Operating Time ◽  
Distribution Centers ◽  
Operations Planning ◽  
Sequencing Problem ◽  
Order Batching ◽  
Feasible Solutions ◽  
First Time

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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