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This chapter focuses on the potential of urban agriculture to support progress in SDG targets 2.1, 2.2, 2.3, and 2.4 in Ho Chi Minh City (HCMC), Vietnam. The chapter integrates findings from the British Council-funded project, ‘Urban Resilience from Agriculture through Highly Automated Vertical Farming in the UK and Vietnam’, undertaken in collaboration with Middlesex University, Van Lang University, and local agricultural stakeholders in HCMC. Food security in the city faces multiple challenges ranging from significant in-migration, decreasing area of cultivated land, the impact of the Covid-19 pandemic that continues to depress the economy and disrupt food supply chains, and climate change impacts affecting the environment and people throughout the city. HCMC accommodates a substantial agricultural sector, which is evolving from traditional to modern production practices. City’s leaders established numerous policies that emphasise green, circular economies, climate change resilience, and low carbon emissions fuelling demand for agricultural solutions that integrate traditional and modern technologies that can be embedded in the local topography, soil types, architectural space, and native culture. Findings from greenhouse trials, community awareness surveys, and stakeholder-led workshops point to a range of high-technology-supported agriculture models that, if applied flexibly throughout the varying context of the urban area, have good scope to help Ho Chi Minh City and meet its growing need for food as well as its sustainability aspirations.

Most of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and project characteristics into account. This study aims to present a novel approach called the “hybrid opposition learning-based Aquila Optimizer” (HOLAO) for optimizing TCQET decisions in generalized construction projects.

In this paper, a HOLAO algorithm is designed, incorporating the quasi-opposition-based learning (QOBL) and quasi-reflection-based learning (QRBL) strategies in the initial population and generation jumping phases, respectively. The crowded distance rank (CDR) mechanism is utilized to rank the optimal Pareto-front solutions to assist decision-makers (DMs) in achieving a single compromise solution.

The efficacy of the proposed methodology is evaluated by examining TCQET problems, involving 69 and 290 activities, respectively. Results indicate that the HOLAO provides competitive solutions for TCQET problems in construction projects. It is observed that the algorithm surpasses multiple objective social group optimization (MOSGO), plain Aquila Optimization (AO), QRBL and QOBL algorithms in terms of both number of function evaluations (NFE) and hypervolume (HV) indicator.

This paper introduces a novel concept called hybrid opposition-based learning (HOL), which incorporates two opposition strategies: QOBL as an explorative opposition and QRBL as an exploitative opposition. Achieving an effective balance between exploration and exploitation is crucial for the success of any algorithm. To this end, QOBL and QRBL are developed to ensure a proper equilibrium between the exploration and exploitation phases of the basic AO algorithm. The third contribution is to provide TCQET resource utilizations (construction plans) to evaluate the impact of these resources on the construction project performance.