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Optimizing delivery routes for sustainable food delivery for multiple food items per order

Kaushika Reddy ThipparthyDepartment of Computer Science, Fairleigh Dickinson University, Teaneck, NJ, USAMohammed KhalafDepartment of Computer Science, Al Maarif University College, Al Anbar, 31001, IraqKottala Sri YogiFaisal Yousef AlghayadhComputer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi ArabiaAkhmadbek YusupovHigher Attestation Commission under the Ministry of Higher Education, Science and Innovation of the Republic of Uzbekistan, Tashkent, UzbekistanLakshmana Phaneendra MaguluriDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, IndiaPaul Ofori-AmanfoUniversity of Energy and Natural Resources, Sunyani, Ghana
Discover Sustainabilityjournal2024en
ABI

Аннотация

Abstract The diversity of consumer demand for take-out food has led to the characteristic structure of one order with multiple items, where the different food items in a single order are provided by two or more merchants. In the context of multi-item delivery for take-out orders with time windows, this study investigates vehicle routing for order delivery. This research aims to improve the service level of merchants and the efficiency of delivery vehicles. Food vendors receive orders from consumers through online platforms, then package the food items according to the orders. This method is a preliminary exploration of the issue of fulfilment of takeout orders on online platforms, and can provide preliminary theoretical support for decision-making on the delivery process of takeout orders on online platforms. In the context of online catering sales platforms and offline food sales merchants, this paper studies the delivery problem of takeaway orders with a time window and the characteristics of one order and multiple items, and constructs a method that takes the order delivery time requirements into account and minimizes the total order fulfilment cost. Taking into account the time window constraints of both physical restaurants and consumers, genetic algorithms are utilized to solve the order delivery problem. Finally, through case studies and experiments, the effectiveness and feasibility of the mathematical model are validated. Practical recommendations and insights are provided from the perspective of management and route planning.

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