Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "14728"
#_source: array:26 [
"id" => "14728"
"slug" => "14728-market-thickness-in-online-food-delivery-platforms-the-impact-of-food-processing-times"
"yearMonth" => "2024-02"
"year" => "2024"
"title" => "Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times"
"description" => "ZHAO, Y., PAPIER, F. et TEO, C.P. (2024). Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times. <i>Manufacturing & Service Operations Management</i>, In press, pp. 1-20. 
ZHAO, Y., PAPIER, F. et TEO, C.P. (2024). Market Thickness in Online Food Delivery Platforms: The Im "
"authors" => array:3 [
0 => array:3 [
"name" => "PAPIER Felix"
"bid" => "B00325218"
"slug" => "papier-felix"
]
1 => array:1 [
"name" => "ZHAO Yanlu"
]
2 => array:1 [
"name" => "TEO Chung Piaw"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "online food delivery"
1 => "market thickness"
2 => "dynamic matching"
3 => "bipartite min-cost matching"
]
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "https://doi.org/10.1287/msom.2021.0354"
"publicationInfo" => array:3 [
"pages" => "1-20"
"volume" => "In press"
"number" => null
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => """
Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the algorithmic matching of drivers to food orders, which is the focus of our study as we aim to optimize this driver-order matching process.\n Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts
We formulate real-time matching algorithms that take into account uncertain food processing times to strategically “delay” the assignment of drivers to orders. This intentional delay is designed to create a “thicker” marketplace, increasing the availability of both drivers and orders. Our algorithms use machine learning techniques to predict food processing times, and the dispatching of drivers is subsequently determined by balancing costs for idle driver waiting and for late deliveries. In scenarios with a single order in isolation, we show that the optimal policy adopts a threshold structure. Building on this insight, we propose a new k-level thickening policy with driving time limits for the general case of multiple orders. This policy postpones the assignment of drivers until a maximum of k suitable matching options are available. We evaluate our policy using a simplified model and identify several analytical properties, including the quasi-convexity of total costs in relation to market thickness, indicating the optimality of an intermediate level of market thickness. Numerical experiments with real data from Meituan show that our policy can yield a 54% reduction in total costs compared with existing policies. \n We formulate real-time matching algorithms that take into account uncertain food processing times to
Our study reveals that incorporating food processing times into the dispatch algorithm remarkably improves the efficacy of driver assignment. Our policy enables the platform to control two vital market parameters of real-time matching decisions: the number of drivers available to pick up and deliver an order promptly, and their proximity to the restaurant. Based on these two parameters, our algorithm matches drivers with orders in real time, offering significant managerial implications. Our study reveals that incorporating food processing times into the dispatch algorithm remarkably im
"""
"en" => """
Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the algorithmic matching of drivers to food orders, which is the focus of our study as we aim to optimize this driver-order matching process.\n Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts
We formulate real-time matching algorithms that take into account uncertain food processing times to strategically “delay” the assignment of drivers to orders. This intentional delay is designed to create a “thicker” marketplace, increasing the availability of both drivers and orders. Our algorithms use machine learning techniques to predict food processing times, and the dispatching of drivers is subsequently determined by balancing costs for idle driver waiting and for late deliveries. In scenarios with a single order in isolation, we show that the optimal policy adopts a threshold structure. Building on this insight, we propose a new k-level thickening policy with driving time limits for the general case of multiple orders. This policy postpones the assignment of drivers until a maximum of k suitable matching options are available. We evaluate our policy using a simplified model and identify several analytical properties, including the quasi-convexity of total costs in relation to market thickness, indicating the optimality of an intermediate level of market thickness. Numerical experiments with real data from Meituan show that our policy can yield a 54% reduction in total costs compared with existing policies. \n We formulate real-time matching algorithms that take into account uncertain food processing times to
Our study reveals that incorporating food processing times into the dispatch algorithm remarkably improves the efficacy of driver assignment. Our policy enables the platform to control two vital market parameters of real-time matching decisions: the number of drivers available to pick up and deliver an order promptly, and their proximity to the restaurant. Based on these two parameters, our algorithm matches drivers with orders in real time, offering significant managerial implications. Our study reveals that incorporating food processing times into the dispatch algorithm remarkably im
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-21T22:21:42.000Z"
"docTitle" => "Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/papier-felix">PAPIER Felix</a>, ZHAO Yanlu, TEO Chung Piaw"
"docDescription" => "<span class="document-property-authors">PAPIER Felix, ZHAO Yanlu, TEO Chung Piaw</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2024</span> <span class="document-property-authors">PAPIER Felix, ZHAO Yanlu, TEO Chung Piaw</span><br><span cla "
"keywordList" => "<a href="#">online food delivery</a>, <a href="#">market thickness</a>, <a href="#">dynamic matching</a>, <a href="#">bipartite min-cost matching</a> <a href="#">online food delivery</a>, <a href="#">market thickness</a>, <a href="#">dynamic matching "
"docPreview" => "<b>Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times</b><br><span>2024-02 | Journal articles </span> <b>Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times</b><br><s "
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1287/msom.2021.0354" target="_blank">Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times</a> <a href="https://doi.org/10.1287/msom.2021.0354" target="_blank">Market Thickness in Online Food Del "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.9643545
+"parent": null
}
