This thesis describes a stochastic ship routing problem with inventory management. The problem involves finding a set of least costs routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain. Storage at consumption and supply ports is limited and inventory levels are monitored in the model. Consumer demands are at a constant rate within each time...

Problem statement. Heat supply systems are one of the essential for life support. production and consumption require a rational approach, which implies fulfilling demand with minimal use resources. In that case, science-technical practical increasing energy efficiency country's heat economy is evident, especially in post-war restoration. Ukrainian have multiple unique features issues, meaning s...

Estimation of stochastic demand in physical distribution in general and efficient transport routs management in particular is emerging as a crucial factor in urban planning domain. It is particularly important in some municipalities such as Tehran where a sound demand management calls for a realistic analysis of the routing system. The methodology involved critically investigating a fuzzy least...

abstract introduction: hospital is one of the most important parts of a health- ‎providing network which requiring an experienced and efficacious ‎managing system thus a weak managing system would result in increase in ‎expenses, loss of economic sources, and decrease in optimal usage. ‎therefore current study has performed to evaluate the association between ‎leadership styles of managers and ...

this paper presents a genetic algorithm (ga) for solving a generalized model of single-item resource-constrained aggregate production planning (app) with linear cost functions. app belongs to a class of pro-duction planning problems in which there is a single production variable representing the total production of all products. we linearize a linear mixed-integer model of app subject to hiring...

Stochastic programming problems appear as mathematical models for optimization problems under stochastic uncertainty. Most computational approaches for solving such models are based on approximating the underlying probability distribution by a probability measure with finite support. Since the computational complexity for solving stochastic programs gets worse when increasing the number of atom...