Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse

Abstract

New market developments increase the complexity of managing order picking operations. Integrating order picking planning problems enables warehouse managers to organize order picking operations more efficiently.This paper provides a decision support tool that integrates and solves the three main operational order picking planning problems (i.e., order batching, picker routing, and picker scheduling). Different from other studies, the objective is to increase order picking efficiency while ensuring a high customer service level. The new integrated planning problem accounts for order due times, a limited availability of order pickers, as well as a high-level storage locations, to ensure the applicability in practice. An iterated local search algorithm is introduced to solve the problem effectively and efficiently. Moreover, a real-life case shows the substantial performance benefits gained from integrating batching, routing, and picker scheduling.

Role of artificial intelligence in operations environment: a review and bibliometric analysis

“…Operations management involves strategic level, operational level and tactical level decision-making (Van Gils et al , 2018, 2019).…”

“…Secondly, it evaluates the sustainability of proposed solution. However, Van Gils et al (2018, 2019) argue that genetic algorithms provide biased solution to the problems due to infinite nature of problems. Wang et al (2018a, 2018b) elaborate agent-based system as the process by which a bigger problem is dissected into a smaller one, which can be solved alternatively.…”

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

“…Concerning to achieve picking efficiency, the order batching groups customer orders into batches with a maximum fixed capacity [6], then the batches are assigned to a picking device and batch sequencing determines the picking scheduling and the completion time batches and customer orders [3,7]. Therefore, the joint order batching and sequencing problem with multiple pickers (OBSPMP) is pivotal to enhance the efficiency and customer service [8][9][10]. One of the most important objectives in order picking systems is minimizing the maximal completion time (makespan), which allows reducing working time for order pickers, improving profit margins for warehouse operations, reducing delivery lead times and improving customer services [11].…”

“…To solve the OBSPMP, Henn [8], Scholz et al [25], and Van Gils et al [10] have proposed variable neighborhood descent and variable neighborhood search approaches to minimize tardiness and total order pick time. Matusiak et al [26] proposed an adaptive large neighborhood search algorithm considering pickers with diverse skills to minimize the total order processing time.…”

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

“…In order to optimize order picking operations, several problems like the order batching problem (OBP), batch sequencing problem (BSP), batch assignment problem (BAP), and picker routing problem (PRP) must be solved. The OBP groups or combines several customer orders into ba tches [4], thus reducing the traveled distance and the picking time [3]. The BSP schedules the sequence for batches, while the BA P assigns batches to the available pickers to minimize tardiness [5].…”