Variational Genetic Algorithm for NP-hard Scheduling Problem Solution

Diveev, Askhat; Bobr, O.V.

doi:10.1016/j.procs.2017.01.010

Cited by 22 publications

(8 citation statements)

References 3 publications

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“…A. I. Diveev and O.V. Bob (2017) [25] conducted an in-depth study on a metaheuristic method for solving VRP and introduced the successful application of genetic algorithms in solving vehicle routing problems. The research results showed that compared with existing linear programming methods, the genetic algorithm has better optimization ability and efficiency in finding the optimal path and calculating the lowest distribution cost.…”

Section: Literature Reviewmentioning

confidence: 99%

“…* ∼ (34) Formula ( 21) indicates the number of services, that is, a refrigerated vehicle serves one demand point at a time; formula (22) indicates the relationship between the route and the vehicle, that is, the number of vehicles is greater than or equal to the number of routes; formula (23) indicates that the distribution center is the starting point of the refrigerated vehicle; formula (24) and formula (25) mean that each vehicle leaves after unloading; formula (26) and formula (27) indicate that the delivery frequency is one time; formula (28) indicates the departure time constraint of the refrigerated vehicle; formula (29) means to ensure that the refrigerated vehicle must meet the customer time window; formula (30) indicates the vehicle load limit; formula (31) indicates to ensure that the customer demand satisfaction rate is met; formula (32) and ( 33) represent the 0-1 decision variable; formula (34) indicates that the customer demand obeys the random distribution F.…”

Section: ) Shortage Costmentioning

confidence: 99%

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An Improved Distribution Cost Model Considering Various Temperatures and Random Demands: A Case Study of Harbin Cold-Chain Logistics

Deng

Wang

et al. 2021

IEEE Access

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This paper explores a more applicable distribution scheme to reduce the distribution cost from the perspective of various influencing factors in the distribution process. We comprehensively considers the effect of temperature changes on the decay rate of fresh products during unloading, the carbon emission costs during transportation and cold storage, customer satisfaction, as well as the traffic situation of the actual distribution route. On this basis, a distribution cost model is constructed. And the improved genetic algorithm is used to solve the problem. In addition, we also conducted sensitivity analysis on different customer demands, so as to put forward some management enlightenment to business managers. An analytical investigation of a case study in Harbin indicates that reasonable transportation path planning can effectively reduce the total distribution cost. Hence, the proposed distribution scheme can serve as an effective and socially feasible method in cold chain logistics management to reduce logistics costs.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) Shortage Costmentioning

confidence: 99%

An Improved Distribution Cost Model Considering Various Temperatures and Random Demands: A Case Study of Harbin Cold-Chain Logistics

Deng

Wang

et al. 2021

IEEE Access

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“…In real-life situations, management tends to prefer efficient and reliable approaches to obtain optimal solutions, thus giving rise to evolutionary optimisation techniques over exact algorithms. A genetic algorithm (GA) is a meta-heuristic technique widely used for solving NP-hard optimisation problems to obtain optimal, or near-optimal, solutions [36]. us, a GA can be considered a reasonable tool to resolve the NP-hard nursing staffing optimisation problem (NSOP) within a short computational time while maintaining the quality of an optimal solution.…”

Section: Existing Approaches Tomentioning

confidence: 99%

Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Tsang

Leung

et al. 2021

Journal of Healthcare Engineering

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Due to the global ageing population, the increasing demand for long-term care services for the elderly has directed considerable attention towards the renovation of nursing homes. Although nursing homes play an essential role within residential elderly care, professional shortages have created serious pressure on the elderly service sector. Effective workforce planning is vital for improving the efficacy and workload balance of existing nursing staff in today’s complex and volatile long-term care service market. Currently, there is lack of an integrated solution to monitor care services and determine the optimal nursing staffing strategy in nursing homes. This study addresses the above challenge through the formulation of nursing staffing optimisation under the blockchain-internet of things (BIoT) environment. Embedding a blockchain into IoT establishes the long-term care platform for the elderly and care workers, thereby decentralising long-term care information in the nursing home network to achieve effective care service monitoring. Moreover, such information is further utilised to optimise nursing staffing by using a genetic algorithm. A case study of a Hong Kong nursing home was conducted to illustrate the effectiveness of the proposed system. We found that the total monthly staffing cost after using the proposed model was significantly lower than the existing practice with a change of −13.48%, which considers the use of heterogeneous workforce and temporary staff. Besides, the care monitoring and staffing flexibility are further enhanced, in which the concept of skill substitution is integrated in nursing staffing optimisation.

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“…Scheduling issues have several types, such as sport events scheduling, staf scheduling, course scheduling, laboratory work scheduling, etc. Scheduling problems are represented as combinatorial optimization problem commonly referred to as NP-hard [4]. …”

Section: Schedulingmentioning

confidence: 99%

Hospital Nurse Scheduling Optimization Using Simulated Annealing and Probabilistic Cooling Scheme

Chahyadi

Kurniawan

2018

Indonesian J. Comput. Cybern. Syst.

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Variational Genetic Algorithm for NP-hard Scheduling Problem Solution

Cited by 22 publications

References 3 publications

An Improved Distribution Cost Model Considering Various Temperatures and Random Demands: A Case Study of Harbin Cold-Chain Logistics

An Improved Distribution Cost Model Considering Various Temperatures and Random Demands: A Case Study of Harbin Cold-Chain Logistics

Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Hospital Nurse Scheduling Optimization Using Simulated Annealing and Probabilistic Cooling Scheme

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