Kousha Khatooni, Farhad Hooshyaripor, Bahram MalekMohammadi
et al.
Abstract Assessing urban flood risk is crucial for resilience analysis, particularly in cities like Karaj, which face significant flooding challenges. This study advances the Urban Flood Resilience Index (UFRI) by introducing the Improved UFRI (IUFRI), which incorporates a novel Flood Adaptation Index to evaluate flood preparedness capacity more comprehensively. A key component of the IUFRI is the concept of “Living With Flood” (LWF), which emphasizes the importance of individual attitudes toward flooding, public awareness, education, environmental protection, and social vulnerability. These factors have often been overlooked in traditional flood management strategies, yet they play a critical role in enhancing community resilience. The analysis of flood modeling in Karaj reveals the city’s vulnerability, particularly in older neighborhoods with aging infrastructure. By integrating LWF into flood management strategies, urban planners can foster a more holistic approach to resilience. The findings demonstrate that employing the IUFRI and Flood Adaptation Index significantly enhances the assessment of flood resilience across various districts. The study evaluates and compares IUFRI results from 12 districts in Karaj City, utilizing grey clustering technique for effective spatial analysis. This approach not only identifies areas with similar flood performance but also aids urban managers in prioritizing targeted infrastructure projects and proactive risk management initiatives. Ultimately, this research underscores the necessity of incorporating LWF into urban flood risk assessment frameworks to improve resilience and better prepare communities for future flooding events.
Ananda Tiwari, Kristiina Valkama, Adriana Krolicka
et al.
Abstract Understanding the coverage and treatment processes of wastewater treatment plants (WWTPs) is essential not only for assessing system readiness for wastewater surveillance (WWS) and nutrient recovery but also supporting both regulatory compliance and sustainable sanitation goals. The recast Urban Wastewater Treatment Directive (UWWTD), effective in early 2025, mandates enhanced urban wastewater management across the European Union (EU) and European Economic Area (EEA), including routine antimicrobial resistance (AMR) surveillance of both influent and effluent. Beyond regulatory compliance, these measures enhance environmental health, public health monitoring and resource recovery. This study examined the coverage, treatment processes and operational challenges of WWTPs in the Nordic countries (Finland, Sweden, Norway, Denmark, Iceland and their autonomous regions). A Webropol survey targeting environmental authorities, wastewater experts and policymakers was conducted and complemented by a systematic review of peer-reviewed literature and grey literature, primarily government documents. According to the findings, ~ 85–90% of the Nordic population is connected to centralized WWTPs, highlighting the feasibility of WWS for public health monitoring. Treatment processes vary depending on the population density, coastal or inland location and sensitivity of the recipient waters body. Secondary treatment is nearly universal in Sweden and Finland but occurs in only about 4% of WWTPs in Iceland. Finland, Sweden and Denmark enforce strict effluent standards, while Norway and Iceland face challenges due to the harsh terrain, cold climates and the option to discharge into the turbulent, oligotrophic Atlantic Ocean, in which limited effluents volumes are considered rapidly diluted with minimal aesthetic impact. Overall, the Nordic region is generally well positioned for WWS under the new UWWTD, but diverse conditions, such as cold climates, challenging terrain and legacy discharge practices, require flexible, locally adapted strategies. This study highlighted both regional strengths and implementation gaps, emphasizing the need for resilient infrastructure, advanced treatment technologies and integrated policies to achieve sustainable sanitation and protect public health. Graphical abstract
Water supply for domestic and industrial purposes, Environmental sciences
Abstract The studied region is located in the southwestern Iran and on the border of Iran and Iraq. In the past, this region had dense palm groves and abundant plants. However, due to the decrease in upstream discharge, in recent years, saline and sodium seawater has intrusion in the river and affected the agricultural lands along its sides. This event has caused irreparable and serious damage to the agricultural industry in the region, turning this area into a graveyard of date palm trees. Understanding the characteristics of agricultural soils for their improvement and/or planting appropriate plants is one of the goals of sustainable agriculture. Considering the damage of the studied area from the intrusion of salt water in the Arvand River, this study investigated important characteristics of soil salinity including EC, pH, C.E.C, SAR and ESP. In this research, sampling of agricultural soils along the riverside was carried out in three different horizons and two line parallel to the river and at two different distances. Statistical methods of correlation coefficient, hierarchical analysis and factor analysis were used to identify the factors affecting soil quality and the relationships between parameters. The results showed that due to the intrusion of sodium seawater, the soils of the studied area have become saline-sodium, and the salinity level in the soils near the river mouth is higher than that in the soils on the upstream side of the river. In terms of fertility, the cation exchange capacity is in the medium range, and the clay texture and abundant organic matter of the soil as a result of the remaining plant and tree residues have an important effect on this parameter.
Nazish Mazhar Ali, Muhammad Kamran Khan, Bushra Mazhar
et al.
Abstract Water pollution is a major global issue due to the entrance of hazardous waste from industrial, agricultural, and urban activities. Several environmental elements, including precipitation, climate, and soil quality, contribute to this problem, which has serious ramifications for human health. Contaminated water is a major cause of waterborne infections, especially in underdeveloped nations where poor sanitation and water management worsen the problem. Every year, millions of people suffer from gastrointestinal infections and other disorders caused by microbial pollutants such bacteria (Escherichia coli, Vibrio cholerae, Salmonella spp.), viruses, and protozoa (Giardia lamblia, Cryptosporidium parvum). Despite advances in water treatment and sanitation in developed countries, outbreaks continue in places with little resources. Industrial pollution, agricultural runoff, and untreated sewage are common sources of pathogenic microorganisms, which harm both surface and groundwater. This review investigates the causes of water pollution, the many types of pollutants (biodegradable, non-biodegradable, and persistent organic pollutants), and the effects on human health. It emphasizes the presence of bacterial pathogens such as Legionella spp., Pseudomonas aeruginosa, and Campylobacter jejuni, as well as protozoan and viral agents that cause serious illnesses. The assessment emphasizes the importance of better water management, stricter pollution regulations, and the development of innovative treatment technologies to reduce waterborne diseases. Addressing these concerns is critical for protecting public health, especially in areas prone to water scarcity and microbiological pollution.
Water supply for domestic and industrial purposes, Environmental sciences
Imranur Rahman, Yasemin Acar, Michel Cukier
et al.
While providing economic and software development value, software supply chains are only as strong as their weakest link. Over the past several years, there has been an exponential increase in cyberattacks, specifically targeting vulnerable links in critical software supply chains. These attacks disrupt the day-to-day functioning and threaten the security of nearly everyone on the internet, from billion-dollar companies and government agencies to hobbyist open-source developers. The ever-evolving threat of software supply chain attacks has garnered interest from the software industry and the US government in improving software supply chain security. On September 20, 2024, three researchers from the NSF-backed Secure Software Supply Chain Center (S3C2) conducted a Secure Software Supply Chain Summit with a diverse set of 12 practitioners from 9 companies. The goals of the Summit were to: (1) to enable sharing between individuals from different companies regarding practical experiences and challenges with software supply chain security, (2) to help form new collaborations, (3) to share our observations from our previous summits with industry, and (4) to learn about practitioners' challenges to inform our future research direction. The summit consisted of discussions of six topics relevant to the companies represented, including updating vulnerable dependencies, component and container choice, malicious commits, building infrastructure, large language models, and reducing entire classes of vulnerabilities.
Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.
Elizabeth Lin, Jonah Ghebremichael, William Enck
et al.
Software supply chains, while providing immense economic and software development value, are only as strong as their weakest link. Over the past several years, there has been an exponential increase in cyberattacks specifically targeting vulnerable links in critical software supply chains. These attacks disrupt the day-to-day functioning and threaten the security of nearly everyone on the internet, from billion-dollar companies and government agencies to hobbyist open-source developers. The ever-evolving threat of software supply chain attacks has garnered interest from both the software industry and US government in improving software supply chain security. On Thursday, March 6th, 2025, four researchers from the NSF-backed Secure Software Supply Chain Center (S3C2) conducted a Secure Software Supply Chain Summit with a diverse set of 18 practitioners from 17 organizations. The goals of the Summit were: (1) to enable sharing between participants from different industries regarding practical experiences and challenges with software supply chain security; (2) to help form new collaborations; and (3) to learn about the challenges facing participants to inform our future research directions. The summit consisted of discussions of six topics relevant to the government agencies represented, including software bill of materials (SBOMs); compliance; malicious commits; build infrastructure; culture; and large language models (LLMs) and security. For each topic of discussion, we presented a list of questions to participants to spark conversation. In this report, we provide a summary of the summit. The open questions and challenges that remained after each topic are listed at the end of each topic's section, and the initial discussion questions for each topic are provided in the appendix.
Stavros Vatikiotis, Ioannis Avgerinos, Stathis Plitsos
et al.
Water scarcity and the low quality of wastewater produced in industrial applications present significant challenges, particularly in managing fresh water intake and reusing residual quantities. These issues affect various industries, compelling plant owners and managers to optimise water resources within their process networks. To address this cross-sector business requirement, we propose a Decision Support System (DSS) designed to capture key network components, such as inlet streams, processes, and outlet streams. Data provided to the DSS are exploited by an optimisation module, which supports both network design and operational decisions. This module is coupled with a generic mixed-integer nonlinear programming (MINLP) model, which is linearised into a compact mixed-integer linear programming (MILP) formulation capable of delivering fast optimal solutions across various network designs and input parameterisations. Additionally, a Constraint Programming (CP) approach is incorporated to handle nonlinear expressions through straightforward modeling. This state-of-the-art generalised framework enables broad applicability across a wide range of real-world scenarios, setting it apart from the conventional reliance on customised solutions designed for specific use cases. The proposed framework was tested on 500 synthetic data instances inspired by historical data from three case studies. The obtained results confirm the validity, computational competence and practical impact of our approach both among their operational and network design phases, demonstrating significant improvements over current practices. Notably, the proposed approach achieved a 17.6% reduction in freshwater intake in a chemical industry case and facilitated the reuse of nearly 90% of wastewater in an oil refinery case.
We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.
Liina Mutilifa, Bushira Kedir, Benjamin Mapani
et al.
The Groot Aub (GA) settlement depends entirely on groundwater for domestic purposes and other development activities. Development activities are mainly agriculture, such as animal husbandry, chicken farming and crop farming using irrigation. However, these activities have the potential to contaminate the groundwater system. A total of 87 groundwater samples were collected from the boreholes in the study area during the dry and wet season. Water quality parameters (pH, salinity, total dissolved solids (TDS), electrical conductivity (EC) and hydrochemistry (Na+ > Ca2+ > Mg2+ > K+, and HCO3− > SO42− > Cl−) of 5 boreholes and 1 reservoir tank were analysed. The dominant cations were attributed to cation exchange processes as a coupled exchange of Na+ + K+ replacing Ca2+; and Na+ replacing Mg2+ ions in the rock–water interactions. The HCO3− anion is attributed to underlying rich carbonate rocks in the study area and chemical reactions between groundwater and silicate minerals. TDS ranged from 639.01 to 1 998.96 mg/L. The microbial levels as indicated by heterotrophic plate count (HPC) values exceed the limits imposed by the local Namibian and WHO drinking water regulations. The Piper diagrams showed a mixed water type of mainly HCO3− and SO42− anions and Na+ and Ca2+ > Mg2+ cations. The study shows that the elemental composition of the groundwater is impacted significantly by primarily anthropogenic activities and secondarily geogenic processes. Based on the results of this study it is recommended that a buffer zone be created between human activities and production boreholes to avoid further groundwater contamination.
Counting the number of soil particles in grain size studies is of great importance, especially in geological, agricultural, environmental, and engineering sciences. It can be used for detailed analysis of soil properties, determining soil structure, and environmental analysis. The main goal of this research is to evaluate the grain size and shape recognition of soil particles using image processing techniques. In this study, initially, color images of soil grain size were acquired. Then, they were processed using Python programming language and the Scikit-Image library. Finally, for model validation, 17 rice grains and 8 coins were used. The results demonstrated that this method was able to accurately detect the number and shape of these particles. It also performed well in identifying the number and shape of soil particles. Furthermore, when compared to several other software tools in the same field, it provided better results. This approach can be utilized to plot the soil grain size curve, ultimately leading to reduced computational costs and time.
Environmental sciences, Water supply for domestic and industrial purposes
Abstract Estimating runoff at ground-mounted solar photovoltaic (PV) installations is challenging because of the disconnected nature of impervious solar panels and the pervious ground surface underneath and between panel rows. There is a need for improved tools to estimate how low impact development practices at these solar installations affect stormwater runoff. The objective of this study was to develop an innovative spreadsheet-based runoff calculator that rapidly estimates stormwater runoff from ground-mounted solar PV sites. The calculator is built on a 2-D hydrologic model (Hydrus-2D/3D) calibrated and validated using experimental data from five commercial solar farms in Colorado, Georgia, Minnesota, New York, and Oregon. The Hydrus-2D/3D hydrologic model was then used to generate nomographs for stormwater runoff that were incorporated into an easy-to-use Excel-based solar farm runoff calculator. This calculator allows for rapid estimation of NRCS stormwater runoff curve number (CN) values at solar farms by considering several complex factors unique to PV installations including: soil and topographic characteristics, surface cover, disconnected impervious surface factors associated with various solar panel designs, and climatic factors. The solar farm runoff calculator quickly estimates runoff CN for pre- and post-construction scenarios, and can estimate actual depth of runoff based on a user-specified 24-h design storm depth. Factors that have the most significant impact on stormwater runoff include design storm return frequency, soil texture, soil bulk density, and soil depth. Ground surface cover has a moderate impact on stormwater runoff, and factors that have a lesser impact on stormwater runoff include slope and array size, spacing and orientation on the landscape. The runoff calculator allows for accurate estimates of runoff generated by disconnected impervious surfaces and low impact development practices at solar farms as affected by a wide range of site-specific conditions.
Water supply for domestic and industrial purposes, Environmental sciences
Juan D. Moreno-Ternero, Tim Pawlowski, Shlomo Weber
This paper examines the interdependence of international success and competitive balance of domestic sports competitions. More specifically, we apply the notion of the Herfindahl-Hirschman index to examine the effect of international rewards on distortion of competitive balance in domestic competitions and derive conditions under which the level of domestic competitive balance raises or falls. Our results yield interesting policy implications for the regulation of prize schemes in international competitions.
Accurately predicting water table dynamics is vital for sustaining groundwater resources that support ecological functions and anthropogenic activities. This study evaluates a statistical model (BigVAR) that handles three major flexibilities: (a) prediction under a sparsity assumption in coefficients, (b) consideration of a time series autoregression framework, and (c) allowance for lags in both dependent and independent variables for estimating water table depth using daily hydroclimatic data from the USDA Forest Service Santee Experimental Forest (SC) and a site in NC. Data from 2006--2019 (SC) and 1988--2008 (NC) were used, with key predictors including soil and air temperature, precipitation, wind, and radiation. For WS80, RMSE during the dormant season was 10.09 cm, with a daily testing phase RMSE of 14.94 cm. The model achieved an R^2 of 0.93 for 2019 (a dry year) and 0.96 for 2016 (a wet year). Solar radiation, rainfall, and wind direction were among the most influential variables. This predictive model aids in managing wetland hydrology and supports decision-making for forest managers and hydrologists.
Abstract Reverse osmosis (RO) is widely used in wastewater reclamation to alleviate the increasingly global water shortage. However, it has an inconvenient defect of biofouling. Some disinfection processes have been reported to select certain undesirable disinfection-residual bacteria (DRB), leading to severe long-term biofouling potential. To provide constructive guidance on biofouling prevention in RO systems, this study performed a 32-day experiment to parallelly compared the biofouling characteristics of RO membranes of DRB after five mature water disinfection methods (NaClO, NH2Cl, ClO2, UV, and O3) and two recently developed water disinfection methods (K2FeO4 and flow-through electrode system). As a result, the DRB biofilm of K2FeO4 and O3 caused a slight normalised flux drop (22.4 ± 2.4% and 23.9 ± 1.7%) of RO membrane compared to the control group (non-disinfected, ~27% normalised flux drop). FES, UV, NaClO and ClO2 caused aggravated membrane flux drop (29.1 ± 0.3%, 33.3 ± 7.8%, 34.6 ± 6.4%, and 35.5 ± 4.0%, respectively). The biofouling behaviour showed no relationship with bacterial concentration or metabolic activity (p > 0.05). The thickness and compactness of the biofilms and the organics/bacterial number ratio in the biofilm, helped explain the difference in the fouling degree between each group. Moreover, microbial community analysis showed that the relative abundance of typical highly EPS-secretory and biofouling-related genera, such as Pseudomonas, Sphingomonas, Acinetobacter, Methylobacterium, Sphingobium, and Ralstonia, were the main reasons for the high EPS secreting ability of the total bacteria, resulting in aggravation of biofouling degree (p < 0.05). All types of disinfection except for NaClO and ClO2 effectively prevented pathogen reproduction in the DRB biofilm.
Abstract Agricultural water management, crop modeling, and irrigation scheduling are all dependent on the accurate estimation of reference evapotranspiration (ET0). A satellite image can also compensate for the lack of reliable weather information. So, in this study, stochastic gradient descent (SGD) has been implemented for optimizing multilayer perceptron (MLP) and developing SGD-MLP to estimate daily ET0 in Tabriz (semi-arid climate) and Babolsar (humid climate) stations, Iran, using extracted data from satellite images. The estimated ET0 values were compared to the determined ET0 based on the FAO-Penman–Monteith equation. Based on satellite image data collected from 2003 to 2021, the database was constructed. During the development of the abovementioned models, data from 2003 to 2016 (70%) were used for training purposes, and residual data (30%) were used for testing purposes. Additionally, the input variables, including land surface temperature (LST) day and night, normalized difference vegetation index (NDVI), leaf area index (LAI), and a fraction of photosynthetically active radiation (FPAR) from MODIS sensor, were utilized to estimate the daily ET0. Thus, there are three studied models; first is based on the LST, second on the vegetation indices, and third on the combination of the LST and the vegetation indices. Additionally, four performance indexes, including the coefficient of determination (R 2), the root-mean-square error (RMSE), Willmott’s index of agreement (WI), and Nash–Sutcliffe efficiency, were utilized in order to measure the implemented model’s accuracy. According to the obtained results, the SGD-MLP-3 with input parameters of LSTday&night, LSTmean, LAI, NDVI, and FPAR gave the most accurate results with RMSE and WI values of as 0.417 mm/day, 0.973, for Tabriz and 0.754 mm/day, 0.922 for Babolsar stations, respectively. Conclusively, LST of daytime, nighttime, and average may be suggested as the most influential parameter for ET0 estimation.
Recent global disruptions, such as the pandemic and geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Autonomous supply chains (ASCs) have emerged as a potential solution, offering increased visibility, flexibility, and resilience in turbulent trade environments. Despite discussions in industry and academia over several years, ASCs lack well-established theoretical foundations. This paper addresses this research gap by presenting a formal definition of ASC along with its defining characteristics and auxiliary concepts. We propose a layered conceptual framework called the MIISI model. An illustrative case study focusing on the meat supply chain demonstrates an initial ASC implementation based on this conceptual model. Additionally, we introduce a seven-level supply chain autonomy reference model, delineating a trajectory towards achieving a full supply chain autonomy. Recognising that this work represents an initial endeavour, we emphasise the need for continued exploration in this emerging domain. We anticipate that this work will stimulate further research, both theoretical and technical, and contribute to the continual evolution of ASCs.
Abstract Novel African walnut shell (AWS) was treated and improved as an agricultural waste by-product to produce high surface area activated carbon via chemical activation with potassium hydroxide (KOH) to achieve extremely effective adsorptive characteristics for deltamethrin removal. The adsorbent (KOHAWS) was characterized by scanning electron microscopy, Fourier transform infrared (FT-IR) spectroscopy, the Brunauer–Emmett–Teller surface area, and pH point of zero charge (PHPZC). Batch sorption experiments were investigated to study the effect of pH, initial concentration and contact time, sorbent dosage, and agitation speed. The results reveal that the experimental data fitted well with Langmuir isotherm model (R 2 = 0.997) with maximum adsorption capacity at 57.64 mg g−1 and the kinetics of the sorption follows the pseudo-first-order model (R 2 = 0.995). The sorption of deltamethrin onto KOHAWS reveals a high removal efficiency of 98.2%. The findings show that the novel AWS has excellent regeneration and reusability properties and may be used to remove pesticides from aqueous solutions instead of conventional activated carbon.
Samir Amrani, Said Hinaje, Mohamed El Fartati
et al.
Abstract The groundwater in the Timahdite–Almis Guigou area flows through fluvio-lacustrine and volcanic formations of Plio-Quaternary age and Liassic limestone. The groundwater resources in this area are used for drinking water and irrigation of agricultural plots. 18 groundwater samples were collected for this study. The physico-chemical and bacteriological parameters analysed, such as temperature, electrical conductivity, pH, dissolved oxygen, Na+, K+, Ca2+, Mg2+, Cl−, HCO3 −, SO4 2−, NO2 −, NO3 −, faecal Coliforms, total Coliforms and faecal Streptococci, are used to characterise the quality of the groundwater and its suitability for drinking and for irrigation. The Piper and Gibbs diagrams and the saturation index were used to study the hydrogeochemical characteristics of groundwater. The quality of these was assessed on the basis of bacteriological quantification and water quality index (WQI) for drinking, and calculation of sodium percentage (Na%), sodium adsorption ratio (SAR) and permeability index (PI) for irrigation. The mineral saturation index in groundwater indicates that only carbonate minerals tend to precipitate, especially in the form of dolomite. On the other hand, the evaporative minerals are still undersaturated. The bacteriological quality and the water quality index (WQI) of this area are considered to be generally good to poor quality, with the exception of a few points, near the public dump of Almis Guigou, plateau of Lamrijate (Timahdite), Aït Hamza and Aït Ghanem, that show significant bacterial contamination and high concentrations of sodium, chlorides and nitrates. According to the calculation of the (Na%), the (SAR) and (PI), the groundwater samples are suitable for irrigation.
Vinod Kumar Chauhan, Stephen Mak, Ajith Kumar Parlikad
et al.
Supplier selection and order allocation (SSOA) are key strategic decisions in supply chain management which greatly impact the performance of the supply chain. Although, the SSOA problem has been studied extensively but less attention paid to scalability presents a significant gap preventing adoption of SSOA algorithms by industrial practitioners. This paper presents a novel multi-item, multi-supplier double order allocations with dual-sourcing and penalty constraints across two-tiers of a supply chain, resulting in cooperation and in facilitating supplier preferences to work with other suppliers through bidding. We propose Mixed-Integer Programming models for allocations at individual-tiers as well as an integrated allocations. An application to a real-time large-scale case study of a manufacturing company is presented, which is the largest scale studied in terms of supply chain size and number of variables so far in literature. The use case allows us to highlight how problem formulation and implementation can help reduce computational complexity using Mathematical Programming (MP) and Genetic Algorithm (GA) approaches. The results show an interesting observation that MP outperforms GA to solve SSOA. Sensitivity analysis is presented for sourcing strategy, penalty threshold and penalty factor. The developed model was successfully deployed in a large international sourcing conference with multiple bidding rounds, which helped in more than 10% procurement cost reductions to the manufacturing company.