جستجوی مقالات مرتبط با کلیدواژه "stochastic optimization" در نشریات گروه "صنایع"

Stochastic and robust optimizations have been considered as two different views of stochastic problems. While robust optimization takes optimization in the worst case, stochastic optimization regards no conservative view and merely focuses on expected value. However, a unilateral view of stochastic problems does not apply to most real problems. In this article, a hybrid robust and stochastic approach is proposed for optimization problems under uncertainty. Our major contribution is presenting different conservative levels in solving an optimization problem using a Hybrid Robust and Stochastic Optimization approach. To this end, we cluster uncertain parameters into different clusters using Latin Hypercube Sampling and k-Means clustering tools; having established various numbers of clusters of uncertain parameters, different clustering criteria and a Multi-Criteria Decision Making (MCDM) tool is employed to determine the optimal number of clusters of uncertain parameters. Then, a hybrid energy optimization model under uncertainty is applied to coordinate the scheduling of natural gas-fired electricity generation units and gas supply units (gas refinery) under natural gas and electricity demand uncertainty, with known probability distribution and uncertain parameters having different levels of conservatism. The results indicate that while no special trend is evident in the execution time as the number of clusters increases, the optimal value is decreased.

As global trade flourishes, terminals endeavor to get higher income while adapting to an expanded intricacy concerning terminal administration tasks. Perhaps the most common issues such terminals encounter is the Berth Allocation Problem (BAP), which involves allotting vessels to a bunch of berths and time allotments while at the same time limiting goals, for example, total stay time or total assignment cost. Complex formats of actual terminals present spatial constraints that restrict the mooring and departure of vessels. In spite of the fact that significant research has been carried out with regard to the BAP, these real-world limitations have not been considered in an overall manner. In this paper, a stochastic Multi-Period Berth Allocation-Scheduling Problem in Different Terminals with Irregular Layouts (SBAP) considering multi-Period modes, generalized precedence relations are developed. To solve the (SBAP), a solution approach based on Stochastic Chance Constraint Programming (SCCP) and a solution approach based on Two-Stage Stochastic Linear Programming with Recourse (TSSLPR) is proposed. A mathematical model is solved to show the applicability of the suggested model and solution approach.

Stochastic programming is often used to solve optimization problems where parameters are uncertain. In this article, we have proposed a mathematical model for a three-stage transportation problem, where the parameters, namely transport costs, demand, unload capacity and external purchasing costs are uncertain. In order to remove the uncertainty, we have proposed a new transformation technique to reformulate the uncertain model deterministically with the help of Essen inequality. The obtained equivalent deterministic model is nonlinear. Furthermore, we have provided a theorem to ensure that the deterministic model gives a feasible solution. Finally, a numerical example, following uniform random variables, is presented to illustrate the model and methodology.

This paper considers a three-stage fixed charge transportation problem regarding stochastic demand and price. The objective of the problem is to maximize the profit for supplying demands. Three kinds of costs are presented here: variable costs that are related to amount of transportation cost between a source and a destination. Fixed charge exists whenever there is a transfer from a source to a destination, and finally, shortage cost that incurs when the manufacturer does not have enough products for supplying customer’s demand. The model is formulated as a mixed integer programming problem and is solved using a multicriteria scenario based solution approach to find the optimal solution. Mean, standard deviation, and coefficient of variation are compared as the acceptable criteria to decide about the best solution.