The Centralized Sequential Investment Problem with Regional Characteristics Effect by Using Genetic Algorithms
نویسنده
چکیده
This research aims to find a non-recurring directed path that will enable all investment regions to achieve the target revenues within the shortest period of time. We call this optimization problem the centralized sequential investment problem (CSIP). “Regional characteristics effect (RC effect)” is assumed to be the major factor that affects the expected time to achieve the target revenue for an investment region. Using the RC effect definition, we introduce the concepts of path effectiveness and sequential effectiveness. Furthermore, we define a completely memoryless property of the RC effect and consider the scenario that the RC effect of a prior investment region on a subsequent investment region follows a linear fashion. Accordingly, this paper explores CSIP’s structural properties and constructs a binary integer programming model for transnational sequential investment. Also, a simple method of estimating model parameters and a GA-based solution procedure are proposed for solving CSIP.
منابع مشابه
Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملA green transportation location-inventory-routing problem by dynamic regional pricing
Non-uniform distribution of customers in a region and variation of their maximum willingness to pay at distinct areas make regional pricing a practical method to maximize the profit of the distribution system. By subtracting the classic objective function, which minimizes operational costs from revenue function, profit maximization is aimed. A distribution network is designed by determining the...
متن کاملProject resource investment problem under progress payment model
As a general branch of project scheduling problems, resource investment problem (RIP) considers resource availabilities as decision variables to determine a level of employed resources minimizing the costs of the project. In addition to costs (cash outflows), researchers in the later extensions of the RIP took payments (cash inflows) received from clients into account and used the net present v...
متن کاملAERO-THERMODYNAMIC OPTIMIZATION OF TURBOPROP ENGINES USING MULTI-OBJECTIVE GENETIC ALGORITHMS
In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...
متن کامل