International Journal of Industrial Optimization

Fuzzy spatial assignment and NSGA-II approach to multi-objective covering problems in substation expansion planning

2025-07-31T06:36:20+00:00

This study addresses a large-scale Multi-Objective Covering Problem (MOCP) in urban electricity distribution, involving 416.472 customers and 732 substations. The main objective is to optimize the allocation of customers to substations, taking into account trade-offs between three objectives: minimizing average service distance, balancing load, and minimizing substation investment costs. To achieve this, we propose a hybrid framework that combines a constrained fuzzy C-means algorithm for customer assignment, NSGA-II algorithm for multi-objective optimization, and K-means clustering for facility expansion. The results show a trade-off between the objectives, with the composite evaluation identifying k=7 as the best compromise. Statistical validation confirms the significance of these results. Significantly, the proposed framework can generate scalable solutions for MOCP in the real world. By integrating construction cost data and spatial data, the model shows a 15% reduction in substation investment costs and an 8% increase in service reliability. This research provides practical insights for distribution network planners, by offering a data-driven approach to determine the optimal number and strategic placement of additional substations, considering trade-offs between cost, performance, and spatial constraints.

Production cost minimization through optimization

2026-02-25T04:04:17+00:00

Delayed raw material delivery is a persistent supply chain problem in apparel manufacturing, often causing material shortages, production disruptions, productivity losses, and increased operating costs. Although various supplier selection approaches have been developed, relatively limited research has integrated the economic consequences of supplier performance with multidimensional supplier evaluation to support cost-oriented procurement decisions. This study therefore proposes and validates an integrated supplier selection framework that combines Cost Ratio Analysis (CRA) and Dimensional Analysis (DA) to improve supply chain performance and minimize production losses associated with delayed material delivery. A quantitative industrial case study was conducted using operational data from two apparel manufacturing companies in Bangladesh. CRA was employed to quantify the financial impact of supplier-related delivery delays, while DA was used to rank supplier alternatives according to multiple operational performance criteria. The integrated framework was subsequently applied to identify the most appropriate suppliers for timely raw material procurement. The results demonstrate substantial improvements following supplier selection, with the benefit-to-cost ratio increasing by 50% and on-time raw material delivery improving from 60% to 94%. These improvements contributed to an approximately 20% increase in production efficiency and reduced productivity losses attributable to material shortages. The principal novelty lies in integrating economic impact quantification through CRA with multidimensional supplier ranking through DA within a single, practical decision-support framework validated using real industrial data. The framework provides apparel manufacturers with a transparent and cost-oriented approach for supplier evaluation, strategic sourcing, and production planning, thereby strengthening supply chain reliability, operational efficiency, and competitiveness.

Portofolio optimization with cardinality and minimum return constraints on the LQ45 index using genetic algorithm

2025-09-01T01:23:12+00:00

Traditional mean-variance portfolio optimization often faces limitation in accommodating practical investment constraints, particularly in restricting the number of selected assets while ensuring a minimum expected return. To address these limitations, this study proposes a Genetic Algorithm (GA)-based portfolio optimization framework that simultaneously incorporates cardinality and minimum return constraints for constructing efficient portfolios. The main contribution of this study is the integration of these practical investment constraints into a GA framework to improve portfolio optimization under realistic investment scenarios using LQ45 stocks. Historical daily stock price data of LQ45 constituents were collected from Yahoo Finance and used to estimate portfolio returns, portfolio risk (standard deviation), and Sharpe ratios. The optimization process minimizes portfolio risk while enforcing cardinality and minimum return constraints through penalty function. Experimental results show that the proposed framework successfully generated efficient portfolios under various parameter settings. The best portfolio was obtained using a cardinality constraint of three stocks and a minimum return target of 10%. This portfolio achieved a return of 14.17%, a risk (standard deviation) of 0.1670, and a Sharpe ratio of 0.4867. In comparation, the equal-weighted LQ45 portfolio produces a return of -16.64% with a Sharpe ratio of -1.2710, indicating substantially lower investment performance. These findings demonstrate that the proposed GA-based framework provides an effective and practical portfolio optimization strategy under realistic investment constraints while highlighting the potential of evolutionary optimization methods for improving risk-adjusted portfolio performance in dynamic market environments.

Decision support for sustainable household waste processing: a comparative evaluation of ahp and fuzzy ahp

2026-08-10T07:13:54+00:00

Urban household waste management in developing countries faces increasing complexity due to population growth, land constraints, and socio-environmental considerations. This study proposes a comparative evaluation of the Analytic Hierarchy Process (AHP) and Fuzzy AHP to support decision-making in selecting sustainable household waste treatment alternatives in Umbulharjo Sub-district, Yogyakarta City, Indonesia. Five sustainability criteria and five alternative treatment methods were identified through expert consultation and literature review. Primary data were collected from 20 household respondents using structured questionnaires. The AHP method applied a crisp pairwise comparison scale, while Fuzzy AHP incorporated linguistic assessments converted into Triangular Fuzzy Numbers to better handle uncertainty and subjectivity. The results demonstrate that while AHP prioritizes the burning method due to perceived simplicity and feasibility, Fuzzy AHP reveals a stronger preference for “Sorting, Hoarding, and Selling,” reflecting the community’s implicit environmental and economic concerns. The divergence in outcomes highlights the limitations of deterministic assessments in complex socio-technical systems and underscores the added value of fuzzy logic in capturing nuanced stakeholder preferences. By integrating quantitative and qualitative criteria within a hierarchical decision framework, this study contributes to the development of more context-sensitive and adaptive decision-support tools. The findings offer practical guidance for municipal authorities and urban planners seeking to align waste management strategies with cleaner production principles. The proposed methodological framework enhances transparency, inclusiveness, and responsiveness in sustainability assessments, supporting the transition toward more resilient and community-driven urban waste systems.