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A mathematical model is presented for the crew scheduling problem, which is subject to complex rule sets for working and resting hours. The optimized crew schedule can reduce crew costs for shipping companies and also help to avoid expensive ship detentions by port state authorities due to incompliances in the crew’s work plan. This encompasses a work plan for the crew, consisting of appropriately qualified seafarers, which also complies with the rules of the Maritime Labour Convention (MLC). des situations où la décision optimale est identique dans tous les scénarios).To ensure safe and efficient ship operations a proper schedule of crew tasks is necessary. L’importance relative des deux objectifs a un effet indirect sur les mesures en augmentant ou diminuant le nombre de certaines décisions (c.-à-d. La résolution du problème d’optimisation bi-objectif qui en résulte par la méthode ε-contrainte a montré que l’information imparfaite était généralement suffisante pour les décisions concernant le moment de la récolte durant l’horizon de planification mais qu’il fallait une information parfaite pour satisfaire la contrainte de la valeur résiduelle. Ces décisions sont évaluées en fonction de deux objectifs : les revenus globaux actualisés pour la durée des périodes de planification et la valeur résiduelle de la forêt à la fin de l’horizon de planification. De plus, on doit décider à quel moment auront lieu la récolte et la prise de mesures. Notre processus de prise de décision inclut trois types de décisions : des décisions d’exploitation, des décisions d’acquisition de mesures et des décisions concernant la qualité des mesures. L’information imparfaite est obtenue avec un scénario de formulation d’arbre spécifique. que le preneur de décision peut choisir de prendre des mesures parfaites ou imparfaites. Dans cette étude-ci, nous introduisons la qualité des données dans le processus de prise de décision, c.-à-d. La qualité des mesures additionnelles n’était pas une variable décisionnelle dans cette étude et la seule alternative consistait à ne prendre aucune mesure ou à mesurer une information parfaite. Dans un article précédent, nous avons démontré que le fait de décider de mesurer des peuplements dont l’exploitation n’est pas certaine en même temps qu’est prise la décision de les récolter peut être très profitable. Plusieurs études récentes se sont penchées sur la valeur de l’information tirée de l’inventaire forestier pour établir un calendrier de récolte. The relative importance of the two objectives affects the measurements indirectly by increasing or decreasing the number of certain decisions (i.e., situations in which the optimal decision is identical in all scenarios). Solving the bi-objective optimization problem formed using the ε-constraint method showed that imperfect information was mostly sufficient for the harvest timing decisions during the planning horizon but perfect information was required to meet the end-value constraint. These decisions are evaluated based on two objectives: discounted aggregate income for the planning periods and the end value of the forest at the end of the planning horizon. In addition, the timing of the harvests and measurements must be decided. Our decision problem includes three types of decisions: harvest decisions, measurement decisions, and decisions about measurement quality. The imperfect information is obtained with a specific scenario tree formulation. In this study, we introduce data quality into the decision problem, i.e., the decisionmaker can select between making imperfect or perfect measurements. In that study, the quality of additional measurements was not a decision variable, and the only options were between making no measurements or measuring perfect information. In a previous paper, we demonstrated that making measurement decisions for stands for which the harvest decision is uncertain simultaneously with the harvest decisions may be highly profitable. In many recent studies, the value of forest inventory information in harvest scheduling has been examined. Download tecplot 360 201712/1/2022 Computational fluid dynamics is the science of predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena by Solve mathematical equations, which express physical laws using a numerical process. All students and mechanical engineers can use this program to do their projects. This software makes the output data of software such as Fluent more tangible by drawing diagrams and contours. In computational fluid dynamics, different methods and algorithms are used to arrive at an answer, but in all cases, they divide the problem domain into a large number of small components and solve the problem for each of these components. After drawing a regular 100 polygon, we will see that the resulting shape is similar to a circle. This similarity will increase as the number of sides increases. In fact, this phenomenon will also make sense in CFD.Ĭomputational fluid dynamics or CFD is one of the largest fields that connects ancient mechanics to computer science and new computational capabilities. is a leading provider of data visualization andanalysis software. Tecplot is actually a tool for visualizing and plotting CFD dataOr it is computational fluid dynamics and has many applications in the field of mechanical and heat and fluid engineering. Fluid dynamics is the name of one of the most widely used and widely used branches of fluid mechanics. The subject of study in this field is the science of how liquids and gases behave when moving under the influence of various factors. ScreenShots: Software Description: Tecplot 360 EX + Chorus + Focus 2017 R3 Build 2017.8 圆4 Keygen Serial Tecplot, Inc. Features and specifications of Tecplot software suite :. AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |