MILP formulations and an Iterated Local Search Algorithm with Tabu Thresholding for the Order Batching Problem

Öncan, Temel

doi:10.1016/j.ejor.2014.11.025

Cited by 59 publications

(22 citation statements)

References 38 publications

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“…However, to explore the effect of routing policy on the results, the Midpoint policy is assessed in the numerical experiment section as well. While some evidence shows that the S-shape routing policy has anti-congestion properties [7] , [8] , the results of applying the Midpoint policy in the context of the presented problem, does not confirm this hypothesis.…”

Section: Introductionmentioning

confidence: 60%

Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach

Ardjmand

Singh²,

Shakeri

et al. 2021

Applied Soft Computing

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In the aftermath of the COVID-19 pandemic, supply chains experienced an unprecedented challenge to fulfill consumers’ demand. As a vital operational component, manual order picking operations are highly prone to infection spread among the workers, and thus, susceptible to interruption. This study revisits the well-known order batching problem by considering a new overlap objective that measures the time pickers work in close vicinity of each other and acts as a proxy of infection spread risk. For this purpose, a multi-objective optimization model and three multi-objective metaheuristics with an effective seeding procedure are proposed and are tested on the data obtained from a major US-based logistics company. Through extensive numerical experiments and comparison with the company’s current practices, the results are discussed, and some managerial insights are offered. It is found that the picking capacity can have a determining impact on reducing the risk of infection spread through minimizing the picking overlap.

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Section: Introductionmentioning

confidence: 60%

Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach

Ardjmand

Singh²,

Shakeri

et al. 2021

Applied Soft Computing

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“…Matusiak et al [38] introduced a simulated annealing approach that simulates the cooling process of metal to the OBP. Moreover,Öncan [43] introduced a combination of an iterated local search with a tabu threshold to the OBP. A hybrid of a large adaptive neighbourhood search and a tabu search was developed byŽulj et al [56] based on the findings of Henn & Wäscher [25] andÖncan [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Picking location metrics for order batching on a unidirectional cyclical picking line

Hofmann¹,

Visagie²

2019

ORiON

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In this paper order batching is extended to a picking system with the layout of a unidirectional cyclical picking line. The objective is to minimise the walking distance of pickers in the picking line. The setup of the picking system under consideration is related to unidirectional carousel systems. Three order-to-route closeness metrics are introduced to approximate walking distance, since the orders will be batched before the pickers are routed. All metrics are based on the picking location describing when a picker has to stop at a location to collect the items for an order. These metrics comprise a number of stops, a number of non-identical stops and a stops ratio measurement. Besides exact solution approaches, four greedy heuristics as well as six metaheuristics are applied to combine similar orders in batches. All metrics are tested using real life data of 50 sample picking lines in a distribution centre of a prominent South African retailer. The capacity of the picking device is restricted, thus the maximum batch size of two orders per batch is allowed. The best combination of metric and solution approach is identified. A regression analysis supports the idea that the introduced metrics can be used to approximate walking distance. The combination of stops ratio metric and the greedy random heuristic generate the best results in terms of minimum number of total cycles traversed as well as computational time to find the solution.

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“…The performance of an order batching policy is closely related to that of the picking. ¨O ncan [14] presents mixed integer linear programming formulations for the order batching under three different policies: traversal, return and midpoint. He also develops an efficient local search procedure based on tabu search for these problems.…”

Section: Goetschalckx and Ratliffmentioning

confidence: 99%

Mathematical model, heuristics and exact method for order picking in narrow aisles

Coelho

Renaud

Côté

2017

Journal of the Operational Research Society

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Order picking is one of the most challenging operations in distribution center management and one of the most important sources of costs. One way to reduce the lead time and associated costs is to minimize the total amount of work for collecting all orders. This paper is motivated by a collaboration with an industrial partner who delivers furniture and electronic equipment. We have modeled their narrow aisles order picking problem as a vehicle routing problem through a series of distance transformations between all pairs of locations.

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MILP formulations and an Iterated Local Search Algorithm with Tabu Thresholding for the Order Batching Problem

Cited by 59 publications

References 38 publications

Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach

Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach

Picking location metrics for order batching on a unidirectional cyclical picking line

Mathematical model, heuristics and exact method for order picking in narrow aisles

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