NSGA-II algorithm for multi-objective generation expansion planning problem

Murugan, Pallikonda Rajasekaran; Kannan, S.; Baskar, S.

doi:10.1016/j.epsr.2008.09.011

Cited by 159 publications

(72 citation statements)

References 12 publications

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“…Genetic Algorithm (GA) is a popular optimization tool, because it avoids the trapping capability for searching for an optimum in local optima [70]. The Non-dominated Sorting GA (NSGA) algorithm, such as NSGA version I and II, are a more effective and efficient algorithm for ranking the solution, assigning ranking fitness, and benchmarking number problems [71].…”

Section: à ámentioning

confidence: 99%

Optimization of foam-filled double circular tubes under axial and oblique impact loading conditions

Djamaluddin

Abdullah

Ariffin

et al. 2015

Thin-Walled Structures

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Section: à ámentioning

confidence: 99%

Optimization of foam-filled double circular tubes under axial and oblique impact loading conditions

Djamaluddin

Abdullah

Ariffin

et al. 2015

Thin-Walled Structures

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“…In the area of energy, the NSGA-II algorithm has been widely used in the reactive power planning problem [15,16,17,18,19], the optimal power flow problem [20,21,22] and the GEP problem [6,23].…”

Section: Methodsmentioning

confidence: 99%

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Palácios¹,

Valdés²,

Batres³

2018

SGCE

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Planning in the energy sector implies multiple and conflicting objectives. Multi-objective models allow the analysis of the inter-relationships and trade-off solutions to be obtained. This paper presents a mixed integer linear model for multi-step multi-objective generation expansion planning (MMGEP). The MMGEP problem is defined as the problem of determining the answers to the following questions: What types of generation technologies are to be added to the grid? What is the capacity of each new generation plant? Where will the plant be located? When will the plant be located? The MMGEP objectives are to minimize the global cost of the system, minimize the environmental impact and maximize the social profits. The proposed model is based on a real power system in Mexico for the planning period between 2017 and 2037. The problem was solved using the NSGA-II algorithm.

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“…Lastly, the NSGA-II has been used to effectively solve a variety of complex optimisation problems, including those of the maritime industry. For example, this algorithm has been used to automatically generate general arrangements of complex ships (van Oers and Hopman 2010), perform aggregate production planning in shipbuilding (Liu, Chua, and Yeoh 2011), develop a decision support system for bulk material handling ports (Pratap, Nayak et al 2015), schedule joint production and maintenance (Berrichi et al 2010), solve the generation expansion planning problem (Murugan, Kannan, and Baskar 2009) and for hydro-thermal power scheduling (Deb and Karthik 2007).…”

Section: Methodsmentioning

confidence: 99%

Automatic generation of a section building planning for constructing complex ships in European shipyards

Rose

Coenen

2016

International Journal of Production Research

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Efficient planning of the section building process is important for European shipyards since delays in this process can disrupt the on-time delivery of a ship. Automatically generating production schedules of the section building process can result in higher quality schedules compared to those created manually. Recently, the production processes of European shipyards have shifted to focus heavily on outsourcing and outfitting, yet existing automatic planning methods for section building fail to sufficiently consider these factors. This paper develops a mathematical model of the section building process which includes the effects of outfitting and outsourcing. The objective of this model is to simultaneously minimise the fluctuations in workload and the number of outsourced man-hours. The mathematical model was solved by implementing the non-dominated sorting generic algorithm-II (NSGA-II) using a custom heuristic as the fitness function. Due to the multi-objective nature of the problem definition and solution approach, a Pareto front of optimal solutions is created instead of a single, best solution. A test case showed that gains in both objectives are achievable compared to the planning developed manually. Implementing the Section Building Planning methodology developed in this paper could potentially improve the efficiency and controllability of the overall shipbuilding process.

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NSGA-II algorithm for multi-objective generation expansion planning problem

Cited by 159 publications

References 12 publications

Optimization of foam-filled double circular tubes under axial and oblique impact loading conditions

Optimization of foam-filled double circular tubes under axial and oblique impact loading conditions

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Automatic generation of a section building planning for constructing complex ships in European shipyards

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