Abstract:This document presents a methodology for the optimal selection of the vehicle fleet necessary to distribute medical products from the company's storage center to many intermediate transfer nodes, and from these to a group of public hospitals. The problem addressed is a real case presented by a company that provides logistics services in the Mexican Republic. An algorithm that incorporates three modified mathematical models was designed for its solution. A version of the Dijkstra algorithm was modified for the … Show more
“…1. Note that Cadenillas [17] proved the verification theorem (Theorem 3.1 in the literature), which states that the solution to HJBQVI (12) under the QVI control (16) through (18) is in fact the value function (11) and the QVI control is the optimal impulse control. Thus, if we could solve the HJBQVI (12), then we are able to obtain the optimal impulse control.…”
Section: Optimal Controlmentioning
confidence: 98%
“…The boundary condition for the HJBQVI is V (0) = 0, which means that no problem arises when there is no population. The left part in "min" operator in the HJBQVI (12) corresponds to the situation where no intervention should be performed, while the right part corresponds to the situation where the intervention should be performed immediately. The following lemma gives the upper and lower bound of V .…”
“…Stochastic optimal control theory [10] has been applied to population and resource management problems [1,[11][12][13][14]. In reality, there are fixed costs besides proportional costs when some interventions are performed for management of animal population.…”
We formulate a stochastic impulse control model for animal population management and a candidate of exact solutions to a Hamilton-Jacobi-Bellman quasi-variational inequality. This model has a qualitatively different functional form of the performance index from the existing monotone ones. So far, optimality and unique solvability of the Hamilton-Jacobi-Bellman quasi-variational inequality has not been investigated, which are thus addressed in this paper. We present a candidate of exact solutions to the Hamilton-Jacobi-Bellman quasi-variational inequality and prove its optimality and unique solvability within a certain class of solutions in a viscosity sense. We also present and examine a dynamical system-based numerical method for computing coefficients in the exact solutions.
“…1. Note that Cadenillas [17] proved the verification theorem (Theorem 3.1 in the literature), which states that the solution to HJBQVI (12) under the QVI control (16) through (18) is in fact the value function (11) and the QVI control is the optimal impulse control. Thus, if we could solve the HJBQVI (12), then we are able to obtain the optimal impulse control.…”
Section: Optimal Controlmentioning
confidence: 98%
“…The boundary condition for the HJBQVI is V (0) = 0, which means that no problem arises when there is no population. The left part in "min" operator in the HJBQVI (12) corresponds to the situation where no intervention should be performed, while the right part corresponds to the situation where the intervention should be performed immediately. The following lemma gives the upper and lower bound of V .…”
“…Stochastic optimal control theory [10] has been applied to population and resource management problems [1,[11][12][13][14]. In reality, there are fixed costs besides proportional costs when some interventions are performed for management of animal population.…”
We formulate a stochastic impulse control model for animal population management and a candidate of exact solutions to a Hamilton-Jacobi-Bellman quasi-variational inequality. This model has a qualitatively different functional form of the performance index from the existing monotone ones. So far, optimality and unique solvability of the Hamilton-Jacobi-Bellman quasi-variational inequality has not been investigated, which are thus addressed in this paper. We present a candidate of exact solutions to the Hamilton-Jacobi-Bellman quasi-variational inequality and prove its optimality and unique solvability within a certain class of solutions in a viscosity sense. We also present and examine a dynamical system-based numerical method for computing coefficients in the exact solutions.
“…The process becomes iterative until it reaches the convergence. For the design of the clusters the method mentioned in [19] is used. Here, the use of radial distribution was used due to the transportation costs of the product and the characteristics of it.…”
Section: The Clustering and Location Of Biorefineries Modelmentioning
confidence: 99%
“…The variety of applications covers from the location of banks [3,7,10,11,22,23] industrial facilities [2,4,8,19,25]. A review on location of facilities and supply chain can be found in [16].…”
This document presents an economic optimization model which identifies the location, the nominal plant capacity and the operation scheduling for set of biorefineries of second-generation ethanol using the biomass obtained as waste in the sugarcane industry. The model also determines the gasoline volumes that will be mixed with ethanol in order to produce a mixed fuel. Given a planning horizon of the operation of the system, the model obtains its optimal parameters at fixed time intervals (annual) so the global optimum is obtained by minimizing the mathematical expectation of the stochastic process generated when the product demand is assumed random with known density. Partial optimization of the process is achieved using a mixed integer linear programming model. Real information obtained from the Secretariat of Energy for the management of biorefineries in the state of Veracruz of the Mexican Republic is included and numerical results are reported.
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