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Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers' objectives and nurses' preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics -a differential evolution algorithm (DE) and a greedy randomised adaptive search procedure (GRASP) -to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP. OPSOMMINGVerpleegsterskedulering is 'n mannekragtoedelingsprobleem wat deur regverdige skofskedules beide hospitaalbestuurders se teikens en verpleegpersoneel se voorkeure bevredig. Dié artikel handel oor 'n verpleegsterskeduleringprobleem wat gebaseer is op 'n werklike gevallestudie en stel twee metaheuristieke voor om die probleem op te los -'n differensiaal evolusionêre metode en 'n gulsige, ewekansige, aanpasbare soekprosedure. Om die doeltreffendheid van die voorgestelde algoritmes te ondersoek word twee probleme opgelos. Verder word vergelykende maatstawwe gebruik om die betroubaarheid van die voorgestelde algoritmes te ondersoek. Die berekende resultate in die artikel toon dat die differensiaal evolusionêre algoritme beter vaar as die gulsige, ewekansige, aanpasbare soekprosedure.

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In green logistics, environmentally-friendly vehicles are strongly recommended as a transportation option. One of the green logistics vehicles is the electric vehicle which is a good selection to reduce greenhouse gas emissions. The present paper focused on the location-routing problem in electric vehicles by considering multi-depots and hard and soft time windows in uncertain conditions. We proposed a fuzzy bi-objective mathematical model for electric vehicles with a limitation in charge stations, the dependence of energy consumption to vehicle load, and a simultaneous delivery and pick-up. We used the multi-objectives particle swarm meta-heuristic algorithms based on the Pareto archive and the NSGA-II algorithm to solve this model. To evaluate the validity of the proposed model and algorithms, sample problems of EVRPTW were selected and solved using Gomez software and proposed meta-heuristic algorithms. The validation results for the model and algorithm confirmed that the model is valid, and the salving algorithms can solve the model efficiently and converge to an optimal answer. The comparing results of solving algorithms performance showed that, compared to the NSGA-II algorithm, the MOPSO algorithm has a higher ability in all states to generate higher quality responses and more diversity.