Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

被引：0

作者：

Kancharla, Surendra Reddy ^{[1
]}

Van Woensel, Tom ^{[2
]}

Waller, S. Travis ^{[1
,3
]}

Ukkusuri, Satish V. ^{[4
]}

机构：

[1] Tech Univ Dresden, FRIEDRICH LIST Fac Transport & Traff Sci, Hettnerstr 1-3, D-01069 Dresden, Saxony, Germany

[2] Eindhoven Univ Technol, Sch Ind Engn & Innovat Sci, POB 513, NL-5600 MB Eindhoven, Netherlands

[3] Australian Natl Univ, Coll Engn Comp & Cybernet, 108 North Rd, Canberra 2601, Australia

[4] Purdue Univ, Lyles Sch Civil Engn, 550 W Stadium Ave, W Lafayette, IN 47907 USA

来源：

NETWORKS & SPATIAL ECONOMICS | 2024年

关键词：

Meal delivery routing; Uncertainty; Sample average approximation; Adaptive large neighborhood search; LARGE NEIGHBORHOOD SEARCH; AVERAGE APPROXIMATION METHOD; PICKUP; WINDOWS; PROFITS; TRAVEL; LINES;

D O I：

10.1007/s11067-024-09643-1

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We investigate the Meal Delivery Routing Problem (MDRP), managing courier assignments between restaurants and customers. Our proposed variant considers uncertainties in meal preparation times and future order numbers with their locations, mirroring real challenges meal delivery providers face. Employing a rolling-horizon framework integrating Sample Average Approximation (SAA) and the Adaptive Large Neighborhood Search (ALNS) algorithm, we analyze modified Grubhub MDRP instances. Considering route planning uncertainties, our approach identifies routes at least 25% more profitable than deterministic methods reliant on expected values. Our study underscores the pivotal role of efficient meal preparation time management, impacting order rejections, customer satisfaction, and operational efficiency.

引用

页码：997 / 1020

页数：24

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