Electric Power Generation Expansion Planning (GEP) is the problem of determining an optimal construction and generation plan of both new and existing electric power plants to meet future electricity demand. We consider a stochastic optimization approach for this capacity expansion problem under demand and fuel price uncertainty. In a two-stage stochastic optimization model for GEP, the capacity...

The traveling salesman problem (TSP) is one of the most important combinational optimization problems that have nowadays received much attention because of its practical applications in industrial and service problems. In this paper, a hybrid two-phase meta-heuristic algorithm called MACSGA used for solving the TSP is presented. At the first stage, the TSP is solved by the modified ant colony s...

In this work, we propose a new technique for efficiently exploring a multiobjective design space to find non-dominated solutions in the presence of uncertainty. Our approach uses a two-stage optimization technique. In the first-stage, the design problem is represented by a multi-objective optimization problem considering the performances associated with design parameters. In the second stage, t...

This paper presents a conjectural-variation-based equilibrium model of a single-price electricity market. The main characteristic of the model is that the market equilibrium equations incorporate the effect of the voltage constraints on the companies’ strategic behavior. A two-stage optimization model is used to solve the market equilibrium. In the first stage, an equivalent optimization proble...

Based on the analysis of the properties of tent map, parallel chaos optimization algorithm (PCOA) using logistic map and outlook algorithm, a hybrid global optimization algorithm (PCOOA) is presented. The algorithm is structured in two stages. The first stage uses parallel chaos optimization based on tent map for global search, while outlook algorithm is employed in the second stage for local s...

This paper considers a scheduling problem with uncertain processing times and machine breakdowns in industriall/office workplaces and solves it via a novel robust optimization method. In the traditional robust optimization, the solution robustness is maintained only for a specific set of scenarios, which may worsen the situation  for new scenarios. Thus, a two-stage predictive algorithm is prop...

This paper presents a method for the energy management of a set of smart homes, in which batteries, thermal storage and demand response are considered as flexibilities in order to achieve minimum operation costs. To cope with the uncertainty of load forecast, a two-stage stochastic optimization process is proposed, in which the first stage decision is the committed energy to be purchased, and t...

In the management of container sea-rail intermodal transportation, dynamic pricing problem with uncertain conditions has significant impacts on the benefit and competitiveness of a multimodal transport operator. Based on the revenue management theory and the features of container sea-rail intermodal transport, this paper develops a two stages optimal model which integrates dynamic pricing and s...

This contribution focuses on recent efforts towards coupling building morphology optimization and energy efficiency computation in the context of early stage planning of complex buildings, using the hospital domain as a showcase. In more detail, a Netlogo model was conceived to generate typological design variations based on a grid grammar. Each resulting design was subjected to a fitness test ...

Abstract: Inspections are common activities in most preventive maintenance (PM) programs. The models for optimizing the inspection interval using the two-stage delay time have been presented by many researchers. However, the three-stage failure process introduced by Wang is closer to reality corresponding to the actual industrial applications. When the minor defective stage is identified at an ...