Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
TA10 - RT5: Online retail
Time:
Tuesday, 28/June/2022:
TA 8:30-10:00

Session Chair: Fábio Neves-Moreira
Location: Forum 14


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Presentations

Pricing and delivery lead time policies for online retailers

Saeed Poormoaied, Zumbul Atan, Tom van Woensel

Eindhoven University of Technology, the Netherlands

We consider an online retailer and characterize policies, which specify the options (selling price and delivery lead time) to be offered to customers. In the static M-option policy M options are set at the beginning of the planning horizon and the decisions on when to offer them are made dynamically. In dynamic single-option policy the retailer offers a single dynamic option. We propose algorithms to optimize the policies and evaluate their benefits.



The impact of committing to customer orders in online retail

Goncalo Figueira, Willem van Jaarsveld, Pedro Amorim, Jan Fransoo

Eindhoven University of Technology, Netherlands, The

Online customers like to receive baskets of grocery orders at a confirmed time. Online retailers increasingly offer customers a choice of leadtime, while actively backordering missing items from the baskets. This fundamentally changes strategic inventory management. We develop new allocation policies that commit to an order upon arrival rather than at the moment the order is due. We give analytical results for the performance of these policies and evaluate them with e-tailer data.



Playing hide and seek: tackling in-store picking operations while improving customer experience

Fábio Neves-Moreira, Pedro Amorim

University of Porto and INESC TEC, Portugal

Recently, several omnichannel retailers face the growth of online sales through in-store picking. We tackle a new relevant problem where a picker picks online orders while minimizing customer encounters. The problem is modelled as a Markov decision process and solved using a Q-learning approach. Results on a real retail store suggest that retailers should scale in-store picking without jeopardizing offline customers' experience. However, choosing simplistic picking policies is not sufficient.



 
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