A knowledge-based multi-objective evolutionary algorithm for solving home health care routing and scheduling problems with multiple centers

Ma, Xiaomeng; Fu, Yaping; Gao, Kaizhou; Zhang, Hui; Mou, Jianhui

doi:10.1016/j.asoc.2023.110491

Cited by 11 publications

(2 citation statements)

References 59 publications

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“…Constraint (19) sets the departure time of each vehicle from the depot, the waiting time, and the service time in the depot to zero. Constraint (20) ensures that the arrival time of vehicle k at customer j is less than the predicted arrival time at that node, while constraint (21) ensures that the sum of the arrival time and waiting time for each vehicle is greater than or equal to the earliest allowed arrival time at customer i and less than or equal to the latest allowed arrival time at customer i.…”

Section: Mathematical Formulation Of the Model For Two Phase F = F1 + F2mentioning

confidence: 99%

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Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

2024

Teh. vjesn.

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This paper examines the challenge of integrated production and distribution, aiming to deliver products to customers precisely on time. Customers, situated within the transportation network, have predefined requirements regarding demand volume and time frames. In the first phase (F1), the problem of planning and allocation of resources is presented as FJSP, while the second phase (F2) addresses the vehicle routing problem as CVRPTW. The first phase, F1, aims to optimize manufacturing processes by appropriately scheduling production tasks to maximize productivity and minimize the time of task execution on machines. Phase 2, F2, encompasses the process of distribution to customers, seeking to minimize the number of vehicles, delivery time, and overall distance travelled. As both problems are among the most challenging in combinatorial optimization, integrating these phases into a single supply chain system poses a significant challenge in problem-solving. A mathematical formulation has been developed to include planning and task allocation in production, as well as vehicle routing, to obtain an optimal solution to the integrated problem. The input data used in the observed case study represent real data in both the first and second phases, forming one integrated supply chain system. Experimental results support the applied methodology.

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Section: Mathematical Formulation Of the Model For Two Phase F = F1 + F2mentioning

confidence: 99%

“…In the paper [20], the authors discuss the significant drawback of a lack of information exchange between key entities in supply chains. The authors propose a new concept for solving the problem of integrated production and routing with reverse logistics and remanufacturing.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

2024

Teh. vjesn.

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Home healthcare routing and scheduling: operations research approaches and contemporary challenges

Masmoudi,

Euchi,

Siarry

2024

Ann Oper Res

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A modified metaheuristic algorithm for a home health care routing problem with health team skill levels

Somar,

Urazel,

Buruk Sahin

2023

Applied Soft Computing

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A knowledge-based multi-objective evolutionary algorithm for solving home health care routing and scheduling problems with multiple centers

Cited by 11 publications

References 59 publications

Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

Home healthcare routing and scheduling: operations research approaches and contemporary challenges

A modified metaheuristic algorithm for a home health care routing problem with health team skill levels

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