Scheduling and routing of multiple heterogeneous vehicles in a milk collection problem with blending in compartments and time windows. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Gheisariha, Elmira; Etebari, Farhad; Vahdani, Behnam; Tavakkoli-Moghaddam, Reza
• Is Part Of: International journal of systems science. Volume 10:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Scheduling and routing -- multi-compartment vehicles -- blending -- milk collection -- time windows -- meta-heuristics; Operations research -- Periodicals; Logistics -- Periodicals; System analysis -- Periodicals; 003
• DOI: 10.1080/23302674.2023.2190852
• Languages: English
• ISSNs: 2330-2674

Abstract

Abstract : A raw milk collection problem is consistently the focus of the dairy and milk supply chain, wherein a broad range of concerns need to be reflected, including coordinating heterogeneous vehicles and harmonising various activities under miscellaneous time restrictions. So, this study offers a general milk collection network with different milk qualities to formulate a scheduling and routing problem with multiple multi-compartment heterogeneous vehicles, where associated activities are performed under various time constraints, including time windows, maximum available time, and planning horizon. Moreover, the blending process in multi-compartment vehicles is also considered to increase total profit as the primary purpose of the network. Additionally, a novel hybrid meta-heuristic algorithm that encompasses variable neighbourhood search (VNS) and adaptive large neighbourhood search (ALNS) is proposed to solve the proposed model, wherein a new heuristic algorithm for the blending process is developed to accelerate the offered meta-heuristic algorithm. Finally, a case study and extensive numerical experiments are offered to illustrate the applicability and validity of the proposed model and solution approach. The obtained results reveal that the considerations of the blending process and multi-compartment vehicles significantly impact the system's profit.