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

Zhang, Minxia; Wu, Junjie; Wu, Xue; Zheng, Yujun

doi:10.1007/s40747-021-00410-0

Cited by 13 publications

(9 citation statements)

References 51 publications

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“…Since its proposal, WWO has attracted considerable attention in both academic and industrial communities. There have been a lot of work on modified WWO algorithms ( Zheng and Zhang, 2015 ; Wu et al., 2017 ; Zhang et al., 2018 ; Zhang et al., 2019 ) and their applications to a variety of engineering optimization problems ( Zheng et al., 2017c ; Fard and Hajaghaei-Keshteli, 2018 ; Shao et al., 2018 ; Shao et al., 2019 ; Zhao et al., 2019 ; Zhou et al., 2019 ; Yan et al., 2021 ; Su et al., 2022 ; Zhang et al., 2022 ).…”

Section: Water Wave Optimization For the Problemmentioning

confidence: 99%

Crop cultivation planning with fuzzy estimation using water wave optimization

Liu

Lv²,

Zheng³

2023

Front. Plant Sci.

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In a complex agricultural region, determine the appropriate crop for each plot of land to maximize the expected total profit is the key problem in cultivation management. However, many factors such as cost, yield, and selling price are typically uncertain, which causes an exact programming method impractical. In this paper, we present a problem of crop cultivation planning, where the uncertain factors are estimated as fuzzy parameters. We adapt an efficient evolutionary algorithm, water wave optimization (WWO), to solve this problem, where each solution is evaluated based on three metrics including the expected, optimistic and pessimistic values, the combination of which enables the algorithm to search credible solutions under uncertain conditions. Test results on a set of agricultural regions in East China showed that the solutions of our fuzzy optimization approach obtained significantly higher profits than those of non-fuzzy optimization methods based on only the expected values.

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Section: Water Wave Optimization For the Problemmentioning

confidence: 99%

Crop cultivation planning with fuzzy estimation using water wave optimization

Liu

Lv²,

Zheng³

2023

Front. Plant Sci.

Self Cite

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“…Moreover, they did not consider the delivery deadline of orders, thus may achieve poor customer satisfaction for delayed delivery. In [25], the authors proposed a hybrid evolutionary algorithm that adapted two metaheuristics to maximize revenue and estimate order ready time. However, instead of assigning an order to a worker, they focused on allowing workers to select an order from the available order list.…”

Section: Related Workmentioning

confidence: 99%

“…Meta-heuristics often identify good solutions with less computation than optimization algorithms by employing iterative approaches or simple heuristics since they search a large set of feasible solutions. The WWOFooD system obtains the near optimal solution of the assignment problem by utilizing water wave optimization (WWO) [24] which is well explored for discrete combinatorial optimization in various domains [22], [23], [25]. The cardinal principle of WWO is to assign each wave (i.e, solution) to a wavelength inversely proportional to its fitness and let it to propagate in a range proportional to the wavelength.…”

Section: Meta Heuristic Order Assignmentmentioning

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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“…In the fourth paper, "A metaheuristic-based framework for index-tracking with practical constraints" [15], the authors introduced a framework with a joint approach based on metaheuristics to solve the comprehensive index tracking problem, which involves the sparsity, weights, assets under management (AUM), transaction fees, the full share restriction, and investment risk diversification. The developed framework enabled the constructed model to fit future data and facilitated the application of various metaheuristics, which were verified by the numerical experiments.…”

Section: Other Data-driven Operations Management Problemsmentioning

confidence: 99%

“…

Data-driven operations managementWe can roughly divide the accepted 15 papers into four groups according to their topics: data-driven supply chain management [1-4], data-driven process scheduling [5-8], data-driven healthcare operations management [9][10][11], and other data-driven operations management problems [12][13][14][15]. In the following, we formally introduce related works in detail.

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mentioning

confidence: 99%