Sustainability Assessment of Constructive Solutions for Urban Spain: A Multi-Objective Combinatorial Optimization Problem

Simón Martínez, Cristina González, Antonio Hospitaler and Vicente Albero
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Simón Martínez: Department of Construction and Manufacturing Engineering, Escuela Técnica Superior Ingenieros Industriales, Universidad Nacional Educación a Distancia, 28015 Madrid, Spain
Cristina González: Department of Construction and Manufacturing Engineering, Escuela Técnica Superior Ingenieros Industriales, Universidad Nacional Educación a Distancia, 28015 Madrid, Spain
Antonio Hospitaler: Concrete Science and Technology Institute, ICITECH, Universitat Politècnica de València, 46022 València, Spain
Vicente Albero: Concrete Science and Technology Institute, ICITECH, Universitat Politècnica de València, 46022 València, Spain

Sustainability, 2019, vol. 11, issue 3, 1-16

Abstract: Industrial areas are set up on plots of roads and associated infrastructure. These use materials and machinery that have environmental impacts, and thus require constructive solutions throughout their lifecycles. In turn, these solutions and their components cause environmental impacts that can be measured by sustainability indicators. The concept of sustainability is closely tied to sustainable development, which is defined as “development that meets the needs of the present, without compromising the ability of future generations to meet their own needs”. The large number of possible and available solutions means that identifying the best one for a given road section must employ a set of heuristic techniques, which conceptualize the issue as a combinatorial optimization problem that is purely discrete and non-differential. The system chosen can be based on a genetic algorithm method that differentiates individuals based on three sustainability indicators: CO 2 emissions, embedded energy (also known as embodied energy, defined as the energy expended to manufacture a product), and economic cost. In this paper, we supplement traditional cost analyses using a three-objective multi-objective genetic algorithm that considers the aforementioned criteria, thus addressing sustainability in aggregate planning. The procedure is applied to three objective functions—CO 2 emissions, economic cost and embedded energy—for each possible solution. We used the non-dominated sorting genetic algorithm (NSGA-II) to implement multi-objective optimization in MATLAB. Additional results for a random walk and multi-objective search algorithm are shown. This study involved 26 design variables, with different ranks of variation, and the application of the algorithm generates results for the defined Pareto fronts. Our method shows that the optimal approach effectively solves a real-world multi-objective project planning problem, as our solution is one of the Pareto-optimal solutions generated by the NSGA-II.

Keywords: industrial parks; eco-design; urban planning; sustainability assessment; genetic algorithms; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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