Minimum Cost Research Articles

Average Cost Minimization Problems Subject to State Constraints

Average Cost Minimization Problems Subject to State Constraints

Efektivitas Algoritma Kruskal dalam Mengoptimalkan Jalur Terpendek pada Jaringan Intranet

This research takes a case study at the Communication and Information Service, Bondowoso Regency, which is one of the media for conveying information and connecting interactive communication between the government and the community. In the process of conveying information to the public, they utilize information technology, the Intranet Network. The problem found was that there were obstacles in ineffective cable installation. This relates to non-optimality in determining the shortest path between two points in an intranet network, and how to determine the correct wiring position, so that the amount used is minimal but can still function optimally and be connected to all computers. To overcome the problems above, in this research the Kruskal Algorithm will be applied which is one of the network optimization methods in Graph Theory. Graph Theory is one of fields in Mathematics that studies the relationships between objects and how these objects interact with each other. Meanwhile, the Kruskal algorithm is an algorithm used to find the shortest path between two points in a graph. It is hoped that this research can optimize the amount of cabling used in designing intranet network topologies so that we can design optimal intranet routes with minimal costs.

Comparative clinical efficacy of Murraya koenigii gel and chlorhexidine gluconate gel for improvement of oral health

Comparative clinical efficacy of Murraya koenigii gel and Chlorhexidine gluconate gel for improvement of oral health Introduction: Gingivitis is a non-destructive, plaque induced disease that causes inflammation of the gums. Chlorhexidine is regarded as the gold standard agent due to its chemical substantivity and clinical efficacy in chemical plaque control. Nowadays, herbal products are widely substituting synthetic antimicrobials due to their minimal side effects and cost effectiveness. Murraya koenigii (curry leaf) is an easily available green leaf in the Indian subcontinent, known for its antimicrobial, antioxidative, and cytotoxic activity. Objective: This study was aimed to evaluate the effectiveness of M. koenigii gel in reduction of plaque and gingivitis when compared with commercially available Chlorhexidine (Hexigel) gel. Materials and Method: This single center, randomized controlled clinical trial was carried out among 40 participants with moderate gingivitis. After scaling, they were divided into two groups: Group I (control) and Group II (Test) [n=20] and were instructed to use Chlorhexidine gel and M. koenigii gel respectively. Clinical parameters (PI & GI) were recorded on baseline and 15 days later. Student’s paired t‑test and independent t-test were used for intra and inter group comparison of clinical parameters. Results: On intra-group comparison, both the groups showed a statistically significant difference (P = 0.001). Inter-group comparison, showed no statistically significant difference between the two groups. (P > 0.05) Conclusion: M. koenigii gel is equally effective as Chlorhexidine gel, in treating plaque induced gingivitis.

Efisiensi Perolehan Pendapatan Antara Beban Yang Dikeluarkan Dengan Pendapatan Terhadap Keuntungan Usaha Menggunakan Metode Target Costing (Studi Pada Usaha Kuliner Jajanan Najla Di Purwakarta)

The magnitude of profit obtained is highly dependent on production cost efficiency, thus requiring special attention to achieve the company's target. Cost control must align with the company's objectives, including achieving maximum profit with minimal production costs. This study aims to analyze the relationship between expenses, income, and business profits in UMKM Jajanan Nazla, as well as identify key factors influencing the efficiency of revenue generation and expense expenditure. Based on the analysis of production costs, UMKM Jajanan Nazla faces a deficit in net profit due to the inability to cover production costs. Therefore, this research applies the target costing method to improve its profitability. The steps of implementing target costing involve determining market prices, calculating target profits, and identifying target production costs. By implementing value engineering, such as reducing packaging costs, labor costs, and even eliminating rental costs, UMKM successfully reduces production costs to meet the set target cost. As a result, production costs decreased from Rp. 7,880,000 to Rp. 5,240,000 per month, resulting in savings of Rp. 2,640,000. Thus, the initially deficit net profit increased to Rp. 1,510,000 per month after the application of target costing. In conclusion, it can be inferred that the target costing method is effective in improving the production cost efficiency of UMKM Jajanan Nazla, helping them strengthen their position in the competitive market.

Relative distances approach for multi-traveling salesmen problem

In this paper, we present a novel approach to addressing the Multi-Traveling Salesman Problem (MTSP), a complex optimization challenge involving the simultaneous fulfillment of multiple tasks such as cargo delivery and warehouse placement by multiple autonomous agents. In general there are two primary objectives to be achieved: minimizing the total path cost and minimizing the maximum cost incurred by agents, also known as makespan. While our primary focus centers on cost minimization, prioritizing this aspect alone often amplifies makespan. Our approach strikes a balance by ensuring that makespan remains within a reasonable threshold. Given the problem’s combinatorial nature, attaining cost-optimal solutions proves infeasible under current constraints. Hence, our emphasis shifts towards swift solution discovery to align with practical applicability. Consequently, we posit that the third pivotal objective is to curtail complexity and expedite solution acquisition, thereby enhancing the problem’s real-world viability. The Multi-Traveling Salesman Problem (MTSP) is typically approached in two distinct phases. In the initial phase, tasks are allocated to salesmen using various methods (such as K-Means or DBSCAN). The second phase involves determining the optimal routes for each salesman, a problem identical to the classic Traveling Salesman Problem (TSP). Our novel heuristic approach integrates these phases into a single method through our relative distance model. This model facilitates the easy addition and removal of tasks within the problem space and enables real-time scheduling.

Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining

Scholars today have been dedicated to researching economically viable and efficient methods to enhance the energy efficiency of manufacturing, aiming for sustainable production. Wire Electrical Discharge Machining (WEDM) holds a significant position in current mechanical processing due to its unique operational principle, which results in notably higher energy consumption compared to conventional processing methods. Various factors influencing machining performance are complex, among which the condition of the cutting fluid plays a crucial role. Effectively controlling the parameters of the cutting fluid to improve its discharge state during machining is vital for enhancing energy and economic efficiency. Specifically, this study analyzes the characteristics of the WEDM process, models its resource and energy consumption, and predicts its economy. A method for optimizing control of cutting fluid parameters is proposed. By correlating different cutting fluid parameters with machining performance using the selected machine tool, and recommending combinations of cutting fluid parameters that ensure quality while improving the material removal rate, the study aims to achieve optimal energy efficiency and minimal cost. The results show that the proposed models for energy efficiency and economic prediction have an average accuracy of 96.64 %. An optimized machining scheme selected reduces machining time and energy consumption by approximately 1255.6 s (about 19.14 %) and 2240112.92 J (about 17.48 %), respectively, and saves about ¥ 6.084 (approximately 14.90 %) in machining costs. Additionally, the energy consumption of the pulse system’s proportion of total energy use increased by 1.63 %. The research demonstrates a general method for specifying combinations of cutting fluid parameters aimed at optimal energy efficiency and economy during the process optimization design stage of machining, providing a reference for improving the energy efficiency of wire cutting operations.

Distributed optimal load frequency control for multi-area power systems with controllable loads

With the proliferation of distributed energy resources, power imbalance may fluctuate rapidly with a large amount, which may make the conventional frequency control scheme not respond fast to the fluctuation and operate in an economically inefficient manner. This paper proposes a distributed optimal load frequency control (DOLFC) scheme for multi-area power systems with controllable loads, which optimally dispatches generators and controllable loads to participate the frequency regulation with cost minimization. It can restore the nominal frequency and only needs local measurements and local communication for implementation. With the DOLFC, we prove that the closed-loop LFC system asymptotically converges to an equilibrium point which minimizes the control cost of generators and controllable loads, and the equilibrium point is unique if and only if the multi-area power network is a tree. Compared with the conventional LFC and economic LFC in literature, the proposed DOLFC has higher economic efficiency and better control performance. Finally, a four-area power system is employed to illustrate the effectiveness of the proposed scheme by numerical simulations.

A novel groundwater monitoring network design framework for long-term and economical data monitoring

The groundwater monitoring network (GMN) design balancing prediction accuracy with cost minimization has paramount importance in depth to groundwater (DTW) monitoring, modeling, and management. This study demonstrates a robust and transferable GMN design framework for the underexplored statistically homogenous DTW to facilitate appropriate, long-term, cost-effective, and regional-scale groundwater monitoring. The inherent homogeneity in regional scale DTW data was investigated to cluster the observation wells into 18 strongly structured clusters/strata using optimal cluster number (k = 18), corresponding to the highest mean silhouette score of 0.72 and the K-means clustering algorithm. The clustered observation wells were used to evaluate the cluster random sampling (CRS) and stratified random sampling (SRS) for GMN design. The sampling techniques were evaluated to capture the spatial variability of DTW over the whole study region through spatial modeling at an adequate accuracy by the inverse distance weighting interpolation tools. The performance metrics for SRS recommended it as a more robust and appropriate technique for regional-scale GMN design than CRS. Then, the GMN was designed with 540 wells corresponding to 70% sampling under SRS for accurate, cost-effective, and long-term groundwater monitoring. By reducing 30% of observation wells, the cost-saving can contribute to cutting-edge monitoring infrastructure development. Additionally, the need-based balancing of the accuracy in spatial modeling and monitoring cost may further moderate the number of observation wells using the performance metrics of SRS corresponding to sampling percentages. Conclusively, this novel and transferable framework can be adopted globally with internally homogenous DTW data for effective GMN design.

A novel methodology for map-based model fitting: A case study with a Dual Source Heat Pump experimental dataset

This paper presents a new methodology to adjust map-based models to experimental data and reports the main results of a comprehensive experimental campaign of a Dual Source Heat Pump (DSHP) prototype. The prototype tested incorporates variable speed components (compressor, circulation pumps, and fan). The novelty of this prototype lies in its ability to select two possible heat sources: air or ground. Thus, it can operate as a geothermal or aerothermal heat pump, as well as a chiller, thanks to the additional capacity to reverse the cycle. Thanks to this hybrid approach, several advantages can be obtained compared to conventional equipment, such as higher efficiency, the requirement of smaller borehole heat exchangers, or the absence of defrost cycles. In a prior study, polynomial models were developed to accurately characterize the DSHP’s performance (i.e., condenser and evaporator capacities and electrical energy consumption). These models were obtained considering the external variables to the unit as independent variables to facilitate their applicability using variables commonly measured in real installations. Due to the complexity of heat pump performance, which in current equipment can be influenced by up to 5 or 6 independent variables, the search for suitable polynomial models required the availability of a complete working map including more than 3000 working points. Thus, this previous work developed these models based only on simulation results. In this sense, this paper concludes the development of these models by focusing on two critical issues concerning empirical model development. The first aspect involves determining the minimum number and location of testing points needed to define the experimental sample for the model adjustment. The reported experimental data were obtained by analyzing the most suitable experimental design methodology to create the experimental matrices in each operating mode of the DSHP. The second aspect focuses on the final adjustment of models using experimental data. A novel fitting approach for empirical models is introduced in the last part of this study. The developed methodology enables the integration of simulation and experimental results for the final fitting of empirical models through a two-step adjustment. The first step involves analyzing and defining polynomial functionals from the complete working maps generated by simulation. Subsequently, in a second step, the polynomial models are refitted to a suitable experimental sample using the methodology presented in this work. The latter allows for the increase of the accuracy of the models and the minimization of experimental costs. This novel approach ensures a robust characterization of systems with many independent variables using a minimum amount of experimental data. Significant benefits can be obtained from its application, such as the reduction of experimental cost and an increase in the model’s accuracy through an effective combination of both experimental and simulated information. Furthermore, it can be considered of general applicability to other engineering problems where the characterization of physical systems influenced by a high number of independent variables is required.

USING ECONOMETRIC MODELS FOR MANAGING DISTRIBUTION PROCESSES

Introduction. The constant increase in competition and rapid changes in market conditions compel various business entities to seek more efficient ways to manage the distribution of goods. In this context, econometric models become an indispensable tool for analysis and forecasting, enabling informed decision-making, optimization of logistics processes, and cost minimization. Objective. The aim of this article is to substantiate effective econometric models for managing the distribution of goods to optimize supply processes, sales, and maximize profits for manufacturers, retailers, and other participants in distribution channels. Methods. The theoretical and methodological basis of the research consists of works by domestic and foreign scientists on the implementation of econometric models for managing business processes, including distribution. The informational basis includes scientific, economic, and reference literature, works of leading domestic and foreign scientists, methodological materials, information portals, and periodicals of Ukraine. A set of methods and approaches were used: system analysis – for studying the role and significance of econometric models for goods distribution and identifying their features; methods of analysis and synthesis – for analyzing and systematizing econometric models and determining the advantages and limitations of their application for goods distribution. Results. The study investigates the role and significance of econometric models for goods distribution. It examines how these models can become a key tool in solving optimization tasks, adapting to changing market conditions, and ensuring competitiveness in a world where distribution strategies turn into strategic advantages. The advantages and limitations of applying such models are identified: simple linear or nonlinear demand models; time series autoregressive model ARIMA; exponentially smoothed model ETS; transportation problem model; Vehicle Routing Problem (VRP); Traveling Salesman Problem (TSP); orienteering problem; conjoint analysis. Conclusions. Based on a comparative analysis of the key aspects of using econometric models for goods distribution, their specific aspects depending on the implementation of particular tasks and needs of the business entity are detailed. The main advantages of applying econometric models in distribution are highlighted, primarily providing the ability to effectively consider a large number of factors affecting distribution processes, along with certain limitations of their use. Among the main benefits are more accurate demand forecasting, which allows for optimizing inventories and supply chain management costs, as well as strategies for warehouse placement, route selection for delivery, etc.

Recent advances in engineering fast-growing cyanobacterial species for enhanced CO2 fixation

Atmospheric CO2 removal (CDR) is a fundamentally endergonic process. Performing CDR or Bioenergy with Carbon Capture and Storage (BECCS) at the gigatonne scale will produce a significant additional burden on the planet’s limited renewable energy resources irrespective of the technology employed. Harnessing photosynthesis to drive industrial-scale CO2 fixation has been of significant interest because of its minimal energy requirements and potential low costs. In this review, we evaluated the thermodynamic considerations of performing atmospheric carbon removal using microalgae and cyanobacteria versus physicochemical processes and explore the implications of these energetic costs on the scalability of each respective solution. We review the biomass productivities of recently discovered fast-growing cyanobacterial strains and discuss the prospects of genetically engineering certain metabolic pathways for channeling the fixed carbon into metabolic ‘carbon sinks’ to further enhance their CO2 capture while concurrently extracting value. We share our perspectives on how new highly productive chassis strains combined with advanced flux balance models, essentially coupling synthetic biology with industrial biotechnology, may unlock more favorable methods for CDR, both from an economic and thermodynamic perspective.

Enhancing pharmaceutical supply chain resilience: A multi-objective study with disruption management

In this study, we tackle the problem of pharmaceutical supply chain optimization using a multi-objective model that simultaneously considers cost minimization, environmental impact minimization, and maximizing of service level equity (minimum ratio). This represents the three alms of sustainability which are key in manufacturing. Furthermore, we developed a disruption model capable of effectively managing disruptions within the supply chain and compared the capabilities with the baseline model.The result shows how the supply chain network behaves under different objectives. Minimizing costs led to maximizing capacity utilization, while environmental objectives result in reduced production levels to meet coverage requirements, and maximizing the minimum ratio expands more facilities. Using an epsilon constraint, the trade-off shows that the environmental budget limits the flexibility between the other total cost achievable and the minimum ratio. Comparing the baseline model and the disruption model underscores the importance of proactive disruption management in maintaining service levels and managing costs effectively. Ultimately, our study offers practical insights for optimizing pharmaceutical supply chains, balancing economic efficiency with social responsibility to navigate disruptions and challenges successfully.

CO119 Indirect Treatment Comparison and Cost-Minimization Analysis between Riociguat and Selexipag to WHO Functional Class III Pulmonary Arterial Hipertension (PAH) Patients

CO119 Indirect Treatment Comparison and Cost-Minimization Analysis between Riociguat and Selexipag to WHO Functional Class III Pulmonary Arterial Hipertension (PAH) Patients

A bi-level optimization framework for the power-side virtual power plant participating in day-ahead wholesale market as a price-maker considering uncertainty

This paper develops a novel bilevel decision-making framework for a power-side virtual power plant (VPP) comprising refined power to gas (P2G), wherein the upper-level is the cost minimization objective of VPP, while the lower-level is social cost minimization objective of wholesale electricity market. Meanwhile, wind power uncertainty is handled by distributionally robust optimization method. By utilizing Karush-Kuhn Tucker optimality condition and strong duality theory, the bilevel model is interpreted as a tractable single-level mixed integer programming model. Simulation results show that: relative to the models with removing uncertainty and refined P2G device, 1) the proposed approach realizes outstanding economic benefit by obtaining an increase with a rate by 7.47 % and 105.09 % in total actual profit, respectively. Regarding environmental benefit, CO2 emission volume experiences a 0.89 % decline and 0.90 % increase, and the CO2 intensity of the proposed strategy is the lowest with the value of 193. 25kg/MWh. 2) The proposed model can achieve the economic and low-carbon dispatching of the VPP. Wind power curtailment in the proposed model witnesses a trivial increase and reduced by 53.19 %, respectively. 3) The model exhibits merits due to its trade-off between robustness and computational complexity.

A practical and robust approach for solving the multi‐compartment vehicle routing problem under demand uncertainty using machine learning

AbstractThe multi‐compartment vehicle routing problem is an extension of the vehicle routing problem and consists of designing a set of routes to perform the collection or delivery of different product types from customers with minimal costs. The product types are incompatible with each other and must be transported separately in multiple compartments. In practice, several uncertainties such as uncertainty in demands can arise, where the exact demand of customers is not known at the time of planning. To deal with these uncertainties, decision‐makers have to rely on robust solutions. A solution is considered robust when it can resist perturbations in every possible demand scenario as much as possible. In the day to day business of most logistic companies, historical data about each customer can be stored and used to make intelligent decisions regarding the expected demands. In this article, we propose an adaptive large neighborhood search for solving the robust multi‐compartment vehicle routing problem under demand uncertainty and present a robust solution approach for the problem in practical settings by employing machine learning. We show that by using our approach, the solutions obtained have lower recourse costs and have a lower gap between expected and actual costs, which is a favorable outcome to have in practice.

Lead-acid battolysers for hydrogen cooking: A comparison with electric cooking for sub-Saharan Africa

A battolyser combines the function of battery and electrolyser in one device, i.e. it provides both electrical energy storage and a means to produce hydrogen. A battolyser with lead-acid chemistry has recently been proposed, and this has potential as a particularly low-cost solution. Here, the battolyser is considered for the production of hydrogen as a cooking fuel (“hCooking”) in sub-Saharan Africa, a region where cooking typically employs polluting fuels (firewood and charcoal). The more conventional approach for decarbonisation of cooking is the introduction of electric cookers (e.g. hotplate, induction hob, pressure cooker) which can be powered by PV and possibly battery storage; accordingly these electric cooking (“eCooking”) systems are considered as the competing decarbonised technology. Multi-objective optimisation is used to design both battolyser and eCooking systems for a notional off-grid community, with solar PV as the main energy source. Objectives are the minimisation of net present cost and lifetime greenhouse gas emissions, and Pareto frontiers are produced to show the play-off between these. Results show that a battolyser system could eliminate 95.6 % of CO2 emissions when compared with a baseline using charcoal, at an annualised cost of $507 per household, over a system lifetime of 20 years. However, eCooking systems appear superior to the battolyser, with the cleanest battery + eCook system achieving 95.8 % emissions reduction at annualised cost $422/household. More generally, hCooking systems are nearly always Pareto dominated by eCooking systems, even under a realistic range of sensitivity scenarios. This result is due to the inherently higher energy intensity of cooking over a flame compared to the eCooking options. Priorities to make the battolyser a more viable solution include extending its lifetime as far as possible, cheaper PV systems, and improved hydrogen burner efficiencies. We also show that eCooking together with some continued use of charcoal may be the cheapest possible cooking solution, whilst simultaneously curtailing 60 % of lifetime greenhouse gas emissions.

Multi-strategy reference vector guided evolutionary algorithm and its application in multi-objective optimal scheduling of microgrid systems containing electric vehicles

To solve the optimization scheduling problem of microgrids involving Electric Vehicles (EVs), in this paper, the integration of EVs into the microgrid is considered in the microgrid system composed of wind turbine, photovoltaic turbine, micro gas turbine, fuel cell, and storage battery, and the economic dispatch model of the microgrid system containing EVs is established, with the minimization of the operation cost and pollutant treatment cost as the optimization objective. Meanwhile, Chebyshev mapping is introduced into the reference vector guided evolutionary algorithm (RVEA) to initialize the distribution of the population, and then an improved angle-penalized distance (APD) method is used to balance the convergence and diversity of the algorithm. The improved RVEA algorithm is applied to the scheduling model proposed in this paper, Different operational scheduling scenarios are investigated, scheduling results with different optimization objectives are discussed. The results show that the multi-strategy RVEA algorithm proposed in this paper can improve the economical and environmentally friendly protection of the microgrid system, and promote the optimal operation of the microgrid.

Gasoline and electric vehicle ownership rationing over time: Lottery vs. First-come-first-served schemes

This paper addresses the ownership rationing issues of gasoline vehicles (GV) and electric vehicles (EV) in a given time horizon. A state-owned vehicle manufacturer is assumed to be the producer of these vehicles. Two typical rationing schemes for the GV and EV ownership that have been implemented in some Chinese megacities, namely a simultaneous lottery scheme for both GV and EV and a hybrid scheme consisting of lottery for GV and first-come-first-served (FCFS) for EV, are investigated. Multi-period social cost minimization models are proposed for determining the optimal auto quota (i.e., the number of vehicles to be produced) and the optimal frequency of ownership allocation for each scheme in the given time horizon. In the proposed models, residents’ heterogeneity is considered in terms of their values of time (VOTs). The properties of the proposed models are analytically explored, and a comparison between the lottery mode and the FCFS mode is made. The results show that the lottery mode outperforms the FCFS mode in terms of the total social cost. The VOT threshold of the subsequent participants in auto rationing schemes tends to be higher than that of the preceding participants. The decision of EV subsidy should be cautiously made due to a tug of war between the resultant decreased pollutant emissions and increased traffic congestion. The implementation of the auto ownership rationing schemes can cause inequity issue in terms of the changes of travel costs of different-income residents. A Pareto-improvement strategy is presented to balance equity and efficiency of the rationing schemes.

Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting.

As a pivotal subfield within the domain of time series forecasting, runoff forecasting plays a crucial role in water resource management and scheduling. Recent advancements in the application of artificial neural networks (ANNs) and attention mechanisms have markedly enhanced the accuracy of runoff forecasting models. This article introduces an innovative hybrid model, ResTCN-DAM, which synergizes the strengths of deep residual network (ResNet), temporal convolutional networks (TCNs), and dual attention mechanisms (DAMs). The proposed ResTCN-DAM is designed to leverage the unique attributes of these three modules: TCN has outstanding capability to process time series data in parallel. By combining with modified ResNet, multiple TCN layers can be densely stacked to capture more hidden information in the temporal dimension. DAM module adeptly captures the interdependencies within both temporal and feature dimensions, adeptly accentuating relevant time steps/features while diminishing less significant ones with minimal computational cost. Furthermore, the snapshot ensemble method is able to obtain the effect of training multiple models through one single training process, which ensures the accuracy and robustness of the forecasts. The deep integration and collaborative cooperation of these modules comprehensively enhance the model's forecasting capability from various perspectives. Ablation studies conducted validate the efficacy of each module, and through multiple sets of comparative experiments, it is shown that the proposed ResTCN-DAM has exceptional and consistent performance across varying lead times. We also employ visualization techniques to display heatmaps of the model's weights, thereby enhancing the interpretability of the model. When compared with the prevailing neural network-based runoff forecasting models, ResTCN-DAM exhibits state-of-the-art accuracy, temporal robustness, and interpretability, positioning it at the forefront of contemporary research.

A biologically inspired computational model of human ventral temporal cortex

Our minds represent miscellaneous objects in the physical world metaphorically in an abstract and complex high-dimensional object space, which is implemented in a two-dimensional surface of the ventral temporal cortex (VTC) with topologically organized object selectivity. Here we investigated principles guiding the topographical organization of object selectivities in the VTC by constructing a hybrid Self-Organizing Map (SOM) model that harnesses a biologically inspired algorithm of wiring cost minimization and adheres to the constraints of the lateral wiring span of human VTC neurons. In a series of in silico experiments with functional brain neuroimaging and neurophysiological single-unit data from humans and non-human primates, the VTC-SOM predicted the topographical structure of fine-scale category-selective regions (face-, tool-, body-, and place-selective regions) and the boundary in large-scale abstract functional maps (animate vs. inanimate, real-word small-size vs. big-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity and view-invariant representations). In addition, when the same principle was applied to V1 orientation preferences, a pinwheel-like topology emerged, suggesting the model's broad applicability. In summary, our study illustrates that the simple principle of wiring cost minimization, coupled with the appropriate biological constraint of lateral wiring span, is able to implement the high-dimensional object space in a two-dimensional cortical surface.