Abstract Recent advances in wireless networking technologies, the reliable application of fault diagnostic and prognostic techniques, and rapid advances in machine learning algorithms have led to widespread use and benefits for Internet-of-Things applications. These advances have also made it possible for devices, previously used for monitoring at enterprise levels, to be applied to monitoring at the individual equipment level. Devices such as networked sensors and radio frequency identification (RFID) tags are increasingly used to detect, track, and monitor equipment in a variety of applications. While the benefits are quite promising, there are significant challenges in the adoption and deployment of such networked sensors in terms of design, operation, and economic justification. Selecting sensors with appropriate specifications is imperative for capturing high-quality information. This article presents a goal programming approach that utilizes sensor manufacturers' datasheets to specify and select a heterogeneous set of sensors. The usefulness of the proposed goal programming approach is demonstrated through its ability to concurrently handle the specification and selection of heterogeneous sensors in intricate systems. This differs from prior approaches, which treat sensor selection and specification disjointly during system design. The main contribution of this study is the development of a mathematical model to support flexible design needs and distinguishes itself from previous approaches in the field, and the use of dynamic specification bounds allowing users to explore design trade-offs and adjust system targets as needed. As a result, the proposed goal programming approach enables a more thorough and integrated design strategy for selecting sensors for intricate equipment.

Abstract Identification of firefighting strategies (i.e., which endangered units to suppress or cool first) in chemical and process plants falls under the domain of multi‐objective decision‐making (MODM), where not only the safety and integrity of the affected process plant but also the safety of on‐site and off‐site vulnerable targets matter. The importance of identifying effective firefighting strategies becomes more crucial when potential domino effects (i.e., escalation of a primary fire to secondary fires) can quickly increase the number of endangered units and targets beyond what could initially be handled by firefighters. While modelling and risk assessment of domino effects have gained increasing attention over the past decade, developing methods for risk management and firefighting of domino effects has lagged. In this regard, combining the domino effect models with MODM techniques has been proposed as a viable solution for identifying effective firefighting strategies. In the present, by developing an innovative multi‐attribute utility function (MAUF), it will be shown that influence diagrams—an extension of Bayesian networks—can be applied both for modelling domino effects and for identifying multi‐objective firefighting strategies within an integrated framework. The results of the developed method are shown to be consistent with those obtained from other MODM techniques, such as goal programming.

Institutions of higher education must balance multiple, often conflicting objectives when setting admission targets for their academic programs. In this paper, we introduce a recommendation system that integrates Constraint Satisfaction Problem (CSP) techniques, goal programming, and Equity Theory to optimize student assignments. Our model strictly enforces hard constraints—such as faculty-hour limits and classroom capacities—while accommodating soft constraints—such as government quotas and institutional preferences—through adjustable penalty functions. Evaluations against static and heuristic benchmarks show that our approach maintains enrollment at 85–90% of total capacity, markedly reducing both the frequency and severity of constraint violations. Furthermore, an average Gini coefficient of 0.067 demonstrates a fairer distribution of seats across programs. Over five simulated admission cycles, institutions employing this recommender achieve substantial compliance improvements within four years, striking an effective balance between rapid constraint adherence and stable enrollment figures. These results confirm that our system offers a practical, data-driven solution for flexible and equitable enrollment management in resource-limited higher-education settings.

Purpose This study examines how the adoption of operations research models (ORMs) affects the performance of small and medium enterprises (SMEs) in Lagos State. It explores the combined impact of various ORMs on both financial and non-financial performance in a dynamic economic environment. Design/methodology/approach Using a descriptive survey design, data were collected from 382 SME owners across 20 local government areas in Lagos through stratified random sampling. structural equation modeling analyzed the relationships between ORM adoption and SME performance. Findings SMEs in Lagos State show strong adoption of ORMs, significantly improving financial (R2 = 0.838) and non-financial (R2 = 0.839) performance. Decision theory, inventory and probabilistic programming yield the highest impact. Forecasting, linear and network models also contribute positively. ORM adoption enhances productivity, efficiency, customer satisfaction and strategic alignment. However, integer programming, queuing and goal programming show no significant effect. Originality/value This study integrates multiple ORMs within dynamic capability theory to provide empirical evidence of how SMEs can leverage operational capabilities to remain competitive and adaptable. The findings offer practical insights for policymakers, SME owners and support agencies to foster environments that encourage ORM adoption.

Optimal dispatch strategy for grand base wind-solar-energy storage systems using machine learning and goal programming

Developing an agro-eco-industrial park based on the tofu industry must consider the balance of each agro-industry's production capacity. This research aims to (1) develop an optimization model for material flow between industries and (2) explain the material flow and provide waste utilization recommendations for other industries to create a closed loop. The industries involved in this agro-eco-industrial park consist of the tofu industry, fertilizer producers, tempeh gembus producers, cattle farms, biodigesters, paddy farmers, soybean producers, and nata de soya producers. The model was created using a goal programming approach. Traditional tofu processing generally generates a variety of waste with high nutritional value, but it is detrimental to the environment if disposed of directly. The flow of materials among the related industries is based on the tofu industrial cluster, which consists of 30 industries in Grobogan, Central Java, Indonesia. The expected output comprises eight decision variables representing the production amount of industries. Data analysis reveals that the model outperforms the current conditions, with the waste recycling rate increasing from 14 % to 97 %. This model converts waste into valuable resources such as fertilizer and gas energy through biodigester processing and other economically viable methods.

BackgroundPromoting sustainable food systems is essential to ensuring both public health and environmental protection. University dining halls, as institutional food environments, provide a strategic opportunity to implement and evaluate sustainable dietary practices.ObjectiveThis study aimed to assess and optimize the sustainability of food menus in three military universities in Tehran by evaluating nutritional adequacy, environmental footprints (water and carbon), and economic cost.MethodsIn this cross-sectional analytical study, one-month food menus from three universities were analyzed. Nutrient content, cost, water footprint, and carbon emissions were calculated for each menu. Three optimization approaches—differing in menu design constraints—were developed and implemented using Linear Programming (LP) and Goal Programming (GP) to improve the sustainability of the menus while maintaining cultural acceptability.ResultsThe original menus were high in energy and fat, with considerable variation across universities. Optimization led to significant improvements, especially in the third approach, which combined menu reformulation and new food item integration. Compared to baseline, this approach reduced the water footprint by 30%, carbon emissions by 36%, and food cost by 32%, while increasing the Nutrient Rich Food (NRF) index by 25%. Micronutrient analysis confirmed that optimized menus provided adequate intake of key nutrients, including vitamin D, iron, calcium, and fiber, while significantly reducing sodium and saturated fat.ConclusionStrategic optimization of institutional food menus can substantially enhance nutritional quality, reduce environmental impact, and maintain affordability. Military universities and other public institutions can serve as platforms to promote sustainable eating habits and support national health and climate goals.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-24664-2.

ABSTRACT Accurately estimating rolling and air resistance is essential for predicting the energy consumption of vehicles. This study presents a field-based approach using a rolldown test to simultaneously determine rolling and air resistance coefficients. Unlike prior methods that frequently used simulations or models, we employ a goal programming methodology to improve precision and evaluate the actual vehicle and environmental conditions. Our methodology was tested using a Class 8 Freightliner eCascadia on a surveyed road section, ensuring controlled conditions for data collection. By analyzing the time–velocity relationship across multiple test runs, we derived resistance coefficients for both loaded and unloaded conditions. The study confirms that rolling resistance is largely independent of velocity at low speeds but exhibits a nonlinear dependency at higher speeds. Additionally, road surface conditions, tire condition, axle configuration, aerodynamic properties, and weather conditions significantly impact resistance values, emphasizing the need for real-world testing rather than relying solely on standardized projections. Our results align with existing literature while demonstrating the efficacy of the goal programming approach in refining resistance estimates. This work contributes to improved vehicle energy modeling, offering practical insights for fleet operators and policymakers seeking accurate energy consumption predictions for electric trucks operating under varying environmental conditions.

Purpose In today’s volatile business environment, firms are required to balance cost efficiency, sustainability and operational resilience to sustain competitiveness. Supplier selection plays a pivotal role in this context; however, traditional models often fail to capture disruption risks caused by geopolitical tensions, health crises or natural disasters. This study aims to address this gap by developing a framework that explicitly incorporates disruption probabilities into sustainable supplier selection. Design/methodology/approach A chance-constrained goal programming (GP) model, enriched with Value-at-Risk (VaR) metrics, is proposed. The framework integrates disruption probabilities, demand variability, procurement costs and sustainability performance indicators to jointly optimize supplier choice. A real-world case from the automotive sector is used to demonstrate the applicability and robustness of the model under varying risk tolerance levels. Findings The empirical results reveal that the model consistently selects resilient supplier portfolios even under high uncertainty. It ensures cost control, safeguards sustainability thresholds and prevents excessive dependence on a small number of suppliers. The analysis highlights that incorporating probabilistic disruption modeling significantly improves procurement stability compared to conventional approaches. Originality/value Unlike prior studies that consider sustainability and disruption risk separately, this research offers an integrated framework that simultaneously accounts for financial, environmental and disruption-related concerns. By embedding disruption probabilities and diversification constraints into supplier selection, the study addresses a critical gap in procurement planning and contributes a novel methodological advancement for sustainable sourcing under uncertainty.

This study employed participatory methods to identify breeding objectives and define desired genetic gains for economically important traits in the Russian sturgeon (Acipenser gueldenstaedtii). Two structured questionnaires were distributed to all Russian sturgeon farmers in Iran. The first questionnaire collected farm management information and asked farmers to prioritise five important traits from a list of thirteen. The top-ranked traits were ovarian fat lobe weight (OFW), total caviar weight (TCW), body weight of broodstock (BWB), larval body area at hatching (LBA), and yolk sac area (YSA). In the second questionnaire, pairwise comparisons were applied to derive individual trait preferences through the Analytical Hierarchy Process (AHP). Social group preference (Soc-p) values were computed for each social group using the weighted goal programming (WGP) model implemented in LINGO software. The greatest disagreement in Soc-p values emerged between the commercial product and water temperature categories. Subsequently, the extended WGP models were employed to derive consensus preference (Con-p) values for these categories. The average of the Con-p values was 0.28 (OFW), 0.22 (BWB), 0.14 (TCW), 0.13 (LBA), and 0.05 (YSA). These Con-p values were then used to determine the desired genetic gains, which were highest for TCW (1.39%) and lowest for YSA (0.34%). The use of AHP and WGP, rather than economic indices, was justified by the limited availability of reliable economic data in Iranian sturgeon aquaculture and the need for farmer-driven, consensus-based breeding goals. This research demonstrates that participatory approaches can successfully define genetic priorities, improve consensus among diverse farmer groups, and guide sustainable breeding strategies for Russian sturgeon in Iran.

Uncertainty is the biggest issue when modeling real-world multi-level fractional optimization problems. In this paper, a fully intuitionistic fuzzy multi-level multi-objective fractional programming problem (FIF-MLMOFPP) is tackled via two different approaches. Because of the ambiguity introduced in the model, all the parameters and decision variables in each objective function and feasible domain are intuitionistic fuzzy numbers (IFNs). Firstly, FIF-MLMOFPP is converted into a non-fractional fully intuitionistic fuzzy multi-level multi-objective programming problem (FIF-MLMOPP) utilizing a series of transformations. The accuracy functions and ordering relations of IFNs are employed to transform the non-fractional FIF-MLMOPP into a deterministic variant. An interactive approach is first applied to solve the problem by transforming it into discrete multi-objective optimization problems (MOOPs). Each separate MOOP addresses the ϵ-constraint methodology and the goal of satisfactoriness. Neutrosophic fuzzy goal programming (NFGP) is the second approach applied to solve the FIF-MLMOFPP, as the marginal evaluations of predetermined neutrosophic fuzzy objectives for all functions at each level are attained through various membership functions, including degrees of truth, indeterminacy, and falsehood, within neutrosophic uncertainty. The NFGP algorithm is presented to achieve optimal levels for each marginal evaluation objective by minimizing their deviation variables, thus yielding a suitable solution. To confirm and approve the two suggested approaches, a numerical example and a comparison between them are presented. Finally, recommendations for additional research are given.

This research aims to determine the House of Representatives of the Republic of Indonesia's (Dewan Perwakilan Rakyat Republik Indonesia/DPR RI) parliamentary diplomacy in supporting the Sustainable Development Goals (SDGs) program in Indonesia during the 2014–2019 period. Although the SDGs are often viewed as an executive-dominated international agenda, this research highlights the DPR RI’s vital role in advancing their implementation. Using a qualitative method with descriptive and interpretative approaches based on the phenomenographic model, the study found that DPR RI strengthened its role through three parliamentary functions: budgeting, legislating, and monitoring. In addition, it expanded international cooperation via the Inter-Parliamentary Union, Asia-Pacific Parliamentary Forum, and the Inter-Parliamentary Cooperation Agency. These platforms enabled DPR RI to exchange experiences with parliaments worldwide and apply lessons learned to Indonesia’s SDG implementation, particularly in overseeing government programs. The findings emphasize that parliamentary diplomacy significantly contributes to Indonesia’s progress toward achieving the SDGs.

Building maintenance is a critical component of ensuring long-term performance, safety, and cost-efficiency in both conventional and critical infrastructures. While traditional contracting approaches have often led to inefficiencies and rigid procurement systems, recent developments in performance-based maintenance, digital technologies, and multi-objective optimization provide opportunities to enhance both operational reliability and energy performance. From a resilience perspective, the ability to sustain functionality, adapt maintenance intensity, and recover performance under resource or operational stress is essential for ensuring infrastructure continuity and resilience. This study develops and validates an optimization model for the operation and maintenance of large campus infrastructures, addressing the persistent imbalance between over-maintenance, where costs exceed optimal levels by up to 300%, and under-maintenance, which compromises performance continuity and weakens resilience over time. The model integrates maintenance efficiency indicators, building performance indices, and energy-efficiency retrofits, particularly LED-based lighting upgrades, within a multi-choice goal programming framework. Using datasets from 15 campuses comprising over 2000 buildings, the model was tested through case studies, sensitivity analyses, and simulations under varying facility life cycle expectancies. The facilities were analyzed for alternative life cycles of 25, 50, 75, and 90 years, and the design life cycle was set for 50 years. The results show that the optimized approach can reduce maintenance costs by an average of 34%, with savings ranging from 1% to 55% across campuses. Additionally, energy retrofit strategies such as LED replacement yielded significant economic and environmental benefits, with payback periods of approximately 2–2.5 years. The findings demonstrate that integrated maintenance and energy-efficiency planning can simultaneously enhance building performance, reduce costs, and support sustainability objectives, offering a practical decision-support tool for managing large-scale campus infrastructures.

Purpose This work introduces a novel model for weighted sum approaches of goal programming that finds the optimum compromise solution for multi-objectives convex programming problem (MOCPP) by minimizing the distance between the ideal solution and the feasible solution space. For any MOCPP, the number of convex objectives must be minimized across a convex set of constraints. When these objectives conflict, several compromise solutions are usually found rather than a single ideal solution. Accordingly, for decision-makers, the best compromise solution is crucial because it takes into account the fundamentals of optimization problems with multiple objectives. Design/methodology/approach To find the best compromise efficient solution to multi-objective convex programming problems, we used the suggested approach to convert MOCPP into a sum of single problem. Then, minimize the distance between the ideal solution and the practical solution. For any number of objectives, the solution determined by the proposed method is valid, which calculates the efficient solution of the given objectives. Findings This technique is demonstrated with examples, and the results are compared with existing works in the literature. Remarkably, the results show how reliable and successful the proposed methodology is at solving these kinds of problems with conflicting objectives. Research limitations/implications Thank you for your comment. The main limitation of the proposed work is when the objectives are non-convex functions, where the proposed method works only for convex cases. Moreover, in multi-objective convex programming problems, it is easy to find the ideal objective vector, which may not always be achievable in real-world situations. This dependence could affect the outcomes if the ideal point is not achievable for feasible solutions. Furthermore, the method’s effectiveness can be sensitive by assigning weights to prioritize objectives, which may vary depending on the problem’s specific characteristics. Originality/value This work is original and not submitted anywhere else.

The study examined population growth, available resources, and quality of life using an optimization tool of goal programming. The decision variable of the goal programming analysis was generated from the 4-year average of key variables of interest, namely population growth, available resource proxied by energy consumption, and quality of life proxied by the human development index, ranging from 2000 to 2023. The optimization technique of simplex goal programming analysis was adopted for the analysis. Based on the decision variables obtained from the averages of the relevant variables, the goal programming solutions could not satisfy any of the aspirational targets; hence, the non-optimization of the objective function after substituting the respective deviations into the objective function. The study, therefore, highlights the endemic flashpoint of the Nigerian economy wherein potentials and resource endowment are not sufficiently harnessed, hence the poor quality of life. The study therefore recommends urgent government intervention in the form of broad-based master plan on how to harness our resource endowment, both human and material, for the sole aim of promoting increased and sustainable improvement in the quality of life.

Through the Sustainable Development Goals (SDGs) program, the target is to address all forms of malnutrition and reduce stunting and wasting in toddlers by 2030. This movement involves policymakers working together to reduce the prevalence of stunting in Indonesia. The objective of this study was to analyze and prioritize, through the Hierar chical Analytical Process (AHP), public policy strategies to accelerate the reduction of stunting in the city of Tasikmalaya. Methods: This research was conducted using a mixed method, including a qualitative approach for interviews and a quantitative approach for prioritization analysis using AHP. The sampling technique used a purposive sampling, with 14 respondents, with the inclusion criteria were: more than 5 years of experience in nutrition policy, representation from key institutions, and a decision-making level of middle to upper management. Interviews were conducted to obtain information on strategic issues in stunting management in Tasikmalaya City. The selection of these alternative policy strategies will be determined in focus group discussions (FGDs) held at government institutions. AHP analysis is carried out using Expert Choice software version 10. Results: Based on the Analytical Hierarchy Process from respondents, the most important alternative strategy is the macronutrient intake intervention. This was determined through an evaluation based on four key criteria: budgetary readiness, infrastructure, institutional sustainability, and community readiness. For example, providing locally sourced food supplements, such as eggs and milk, to toddlers is considered the most rapid and effective way to address stunting in toddlers. This research found that three months of Food Supplement support resulted in increased weight, as well as improvements in nutritional status. Most toddlers experienced weight gain during the support period. Conclusion: Macro-nutrition interventions are clearly superior to other program strategies because this program can be sustainable. The government has to provide more macronutrient interventions for toddlers .

As the portfolio optimization field grows, classical techniques often notoriously find it difficult to efficiently model how investors decisions, risk tolerances, and asset attributes intertwine. This paper presents an innovation-based hybrid method, where Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) combined with Additive Ratio Assessment (ARAS) for multi-criteria decision making, Goal Programming (GP) and a Genetic Algorithm (GA) for finding constraints are united. The proposed approach enhances the accuracy of ranking and effectiveness of allocation by incorporating asset evaluation, characterization of investors and probabilistic construction of portfolios. The system is tested in view of various performance implications, using the FAR-Trans dataset, a collection of genuine transaction statistics and asset pricing, as well as investor data. The first step involves project transaction capacities partitioning and risk categorization to create a bipartite TOPSIS–ARAS scoring mechanism. The GP part of the model matches investment decisions to the individual return and risk expectations of each investor, and the GA promotes the use of entropy-aware strategies. Important performance metrics are a Sharpe Ratio of 2.241, the annualized return of 4.6% and diversification score of 0.845. The study also reflects a 0.729 correlation between TOPSIS–ARAS rankings, and GP configurations leading to portfolio returns of over 30.0%. The system offers a realistic depiction of the behavior of investors, considering several transaction channels and different risk factors as well as geographies. The comprehensive integration is very flexible, computationally effective and based on realistic investment models while minimizing constraint deviation.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-17604-y.

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential.

Sustainability has recently grown to be a primary focus for transport planning and policies in both developing and developed nations. The paper focuses on the sustainability of a multiobjective linear fractional fixed charge transportation model that utilizes trapezoidal intuitionistic fuzzy numbers to define all the variables and parameters. A novel approach, which has three stages, is presented based on the amalgamation of fuzzy AHP and goal programming techniques. The first stage streamlines the proposed model by employing arithmetic operations for trapezoidal intuitionistic fuzzy numbers, thus converting the fuzzy constraints into crisp ones. In stage two, the model undergoes further transformation into a linear optimization model by utilizing the goal programming approach and linearization technique. The third stage describes how the weights are derived using fuzzy AHP, which are then assigned to objectives. To support the proposed methodology, an application in the sugar mill industry has been illustrated by designing a sustainable transport infrastructure. The solution of the obtained model is computed using easily accessible software. A comparison is drawn between the proposed and existing techniques, and it is concluded that the proposed methodology gives the minimum transportation cost compared to the existing methods.

The transportation of goods in a supply chain must navigate multiple conflicting objectives, including minimizing cost, route risk, and environmental impacts. This study presents the development, formulation, and validation of a Dynamic Fuzzy Risk-Averse Multi-Objective Capacitated Transportation Optimization (DFRAMCTO) model for sustainable logistics planning. The model simultaneously minimizes transportation costs, route-specific risks, and carbon emissions within a fuzzy, risk-sensitive, and capacity-constrained environment. Leveraging triangular fuzzy numbers to capture parameter uncertainty, the model integrates dynamic cost structures, time-varying risk coefficients, and emission penalties into a unified optimization framework. A Triangular Fuzzy Goal Programming (TFGP) approach, based on the max–min compromise strategy, transforms the multi-objective fuzzy problem into a solvable linear program. The methodology includes model formulation, parameterization through simulated yet realistic datasets, defuzzification via the Graded Mean Integration Representation method. A numerical illustration demonstrates model applicability. Results revealed that incorporating dynamic risk-aversion and fuzzy multi-objective trade-offs significantly improves decision robustness in uncertain logistics networks. The DFRAMCTO model contributes to operational research by offering a transparent, adaptable, and sustainability-aware decision-support tool for transportation planners.