Data-driven optimization for last-mile delivery

Chu, Hongrui; Zhang, Wensi; Bai, Pengfei; Chen, Yahong

doi:10.1007/s40747-021-00293-1

Cited by 27 publications

(23 citation statements)

References 40 publications

(69 reference statements)

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“…But, they did not assign workers considering the order pickup and delivery locations and failed to estimate the worker service time and cost based on the realtime traffic conditions. A data-driven framework is proposed in [33] to solve the last-mile problem (LMP) in the food delivery system, and the solution is realized based on worker behavior analysis. However, the pickup of the ordered food was not incorporated, making it difficult to solve LMP in reallife complex scenarios.…”

Section: Related Workmentioning

confidence: 99%

Profit and Satisfaction Aware Order Assignment for Online Food Delivery Systems Exploiting Water Wave Optimization

et al. 2022

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Online Food delivery, a specialized application of mobile crowdsourcing, has accelerated its popularity due to rushed urban lifestyle in recent times. The food delivery order assignment to workers that maximizes the service qualities, such as maximization of workers' profit and minimization of order completion time to enhance the customer satisfaction at the same time, is a challenging problem. Existing works in the literature are limited either by focusing solely on minimizing order completion time or reduction of cost incurred in delivery of orders. In this paper, we develop a framework for optimal assignment of food delivery orders to workers as a multi-objective linear programming (MOLP) problem that makes a trade-off in between the worker profit and customer satisfaction. Due to NP-hardness of the above MOLP, a polynomial time solution of the food delivery problem has been developed, namely WWOFooD, exploiting Water Wave Optimization, a meta-heuristic algorithm. The WWOFooD not only explores ways of enhancing service quality of the customers, but also ensures giving additional incentives to the workers providing faster and reliable food delivery services. The results of simulation experiments depict that the WWOFooD offers competitive workers' profit as well as significantly enhances customer satisfaction compared to the state-of-the-art works.

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Section: Related Workmentioning

confidence: 99%

Profit and Satisfaction Aware Order Assignment for Online Food Delivery Systems Exploiting Water Wave Optimization

et al. 2022

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“…As a result of the aforementioned reasoning, it is possible to structure the relevant last-mile problem as a capacitated vehicle routing problem with an on-time delivery goal function. (Chu et al, 2021) is a very critical factor for online food delivery platforms and is also regarded as the main challenge in order assignment and routing service. This method focuses more on the assignment rather than the optimum route to minimize the delivery time.…”

Section: Artificial Bee Colony (Abc) Algorithmmentioning

confidence: 99%

Retail Management of the Food Sector in the Indian Economy during Covid-19: A Systematic Review

Journal¹

2022

IJSREM

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The coronavirus pandemic caused significant changes to businesses worldwide, its rippling effects still being felt today. As businesses adapt to the new restrictions, multiple researchers have analysed the new environment that these organizations operate in. The changes included less footfall in physical stores and an increase in online deliveries leading to stores having to change their entire format of operations. This paper looks to construct a literary review of food retailing and its changes during the COVID-19 pandemic spanning the agriculture and food retail sector supply and demand fluctuations, supply chain disruptions, and the rise of doorstep food deliveries. This paper involves a holistic review of the challenges faced and overcome by food retailing businesses in the past three years, and the prospects of these evolved business models. Our study includes findings from previous research and tries to establish a connection between the problems mentioned above, the increased use of digital services, and the significance of the pandemic as the exacerbator of existing issues in the established infrastructure of the agriculture and food retail sectors. Keywords ; Covid-19 Pandemic; Food supply Chain; Retail; Agriculture

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“…As aforementioned, for the considered problem, we identify two uncertain factors, i.e., the actual order ready time and customer satisfaction level, which are regarded as main challenges in order selection and delivery routing [9]. We employ a data-driven, machine-learning approach to provide more accurate predictions to address these challenges based on historical habit data of stores and customers, i.e., the overdue records of stores and five-star scoring records of customers.…”

Section: Data-driven Machine Learning For Estimating Order Ready Time and Customer Satisfaction Levelmentioning

confidence: 99%

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

Zhang

et al. 2021

Complex Intell. Syst.

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The last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms.

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Data-driven optimization for last-mile delivery

Cited by 27 publications

References 40 publications

Profit and Satisfaction Aware Order Assignment for Online Food Delivery Systems Exploiting Water Wave Optimization

Profit and Satisfaction Aware Order Assignment for Online Food Delivery Systems Exploiting Water Wave Optimization

Retail Management of the Food Sector in the Indian Economy during Covid-19: A Systematic Review

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

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