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This paper deals with the problem of optimising the network of a Third-Party Logistics (3PL) company, placing emphasis on the distinct characteristics of the 3PL environment. The goal is to minimise the warehousing and distribution costs for both forward and reverse product flows. A new Mixed Integer Linear Program (MILP) model is proposed to address this interesting problem. The related decisions include: selection of (a) warehouse(s) and the related product inventory quantities, (b) product unit loads transported, (c) truck combinations employed to perform the corresponding forward and reverse trips. The model was applied in a case study of a 3PL company to optimise part of its forward supply chain that comprises three warehouses, 23 suppliers and 53 customers. The results obtained were very encouraging, since overall warehousing and distribution costs were lowered by 10.8%. Sensitivity analysis indicated that additional savings may result from optimising warehousing capacities. The main contributions of this work are the inclusiveness of key aspects of the 3PL environment in the proposed model, as well as placing emphasis on warehousing costs and on management of truck capacities. A secondary contribution is the validation of the model's value through a comprehensive case study. [ABSTRACT FROM AUTHOR]

The study focuses on optimizing urban distribution in the dairy industry, specifically at Bhopal Sanchi dairy, to enhance efficiency and minimize costs. It evaluates existing routes, identifies inefficiencies, and proposes new distribution strategies. By implementing these changes, significant improvements are achieved, reducing total distribution costs and dead kilometers while improving vehicle capacity utilization. The study emphasizes the importance of addressing urban distribution challenges to meet increasing milk demand efficiently. It contributes to developing sustainable dairy distribution systems by optimizing costs and minimizing losses. ArcGIS software is utilized for route and fleet optimization analysis. Overall, the research addresses the critical need for efficient urban dairy distribution, particularly in India's context as a major milk producer. [ABSTRACT FROM AUTHOR]

In recent years, the community group buying market has been developing rapidly, and the group buying categories are mainly fresh food, so one of the keys to the success of the community group buying platform lies in the supply chain logistics, in which the cold chain logistics is the key to guarantee the fresh food products for the time and quality. In this paper, on the combing research of the theories and problems of community group purchase, cold chain logistics, and vehicle path planning, in view of the existing problems of cold chain logistics and distribution business in Jinan distribution center of J company's community group purchase, taking into account the actual demand of the grid station and the characteristics of the fresh products to be stored at low temperatures, we constructed the optimization model of the cold chain logistics and distribution path that minimizes the total cost under the demand of the fuzzy time window of the grid station, and designed the distribution area is divided by using K‐means clustering, and then, the distribution path in each sub‐distribution area is optimized and solved by genetic algorithm, and then, the validity of the optimization scheme is verified, the number of distribution trucks used is reduced by two, and the distribution cost is reduced by 12.77%, and optimization measures such as promoting the standardization of warehousing and distribution, and creating a digital and integrated logistics information platform are proposed. [ABSTRACT FROM AUTHOR]

Using a unique data set of Chinese firm-product-destination level export on the period 2000–2013, this paper investigates the effect of real exchange rate changes on multi-product firms' price setting decisions on export product with heterogeneous levels of quality. We find less exchange rate pass-through (ERPT) for higher-quality products. Meanwhile, the ERPT is higher when the Renminbi (RMB) appreciates than when it depreciates, and product quality has a greater effect on the ERPT during RMB depreciation. Furthermore, higher distribution costs lead to more pricing-to-market (PTM) and less pass-through. This heterogeneity in ERPT is also robust in different samples and econometric specifications. Combined with the existing studies on the effects of firm heterogeneity on the ERPT, our study helps piece together a more complete picture of the heterogeneity across firms in their reaction to export prices when the exchange rate changes. [ABSTRACT FROM AUTHOR]

In this paper, a model with integration of interval programming (IP), chance-constrained programming (CCP), and fuzzy random variables (FRVs), termed as an inexact fuzzy random chance-constrained programming (IFRCCP) model, was proposed to deal with the fuzzy and random uncertainties in optimization of booster cost for a water distribution system (WDS) under uncertainty. The IFRCCP model was applied to a WDS to verify the efficiency of the method. After formulating the IFRCCP model, the booster cost intervals were obtained under various violation levels and confidence levels. The results indicated that the lower and upper booster costs increased with the confidence levels of the lower limits, and decreased with the violation levels of the lower limits. Moreover, the nodal chlorine concentrations are more uniform with the increase of booster numbers. The booster costs under trapezoidal distribution FRVs are greater than that under triangular distribution FRVs. Moreover, the uniformity of nodal chlorine concentration under triangular fuzzy distributions is larger than that under trapezoidal fuzzy distributions. The results obtained can help managers to make schemes on booster optimization under fuzzy and random uncertainties. [ABSTRACT FROM AUTHOR]

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global "double carbon" objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China. [ABSTRACT FROM AUTHOR]

Nowadays, sodium‐ion batteries are considered the most promising large‐scale energy storage systems (EESs) due to the low cost and wide distribution of sodium sources as well as the similar working principle to lithium‐ion batteries (LIBs). Therefore, screening suitable materials with high abundance, low cost, and excellent reliability and modified with different strategies based on them is the key point for the development of sodium‐ion batteries (SIBs). In addition, the ideal anodes with high abundance, and low cost elements also greatly influence the cost of SIB systems, determining the large‐scale application. Herein, recent advances in carbon, iron, manganese, and phosphorus‐based anodes of various types, such as hard carbon, iron oxides, manganese oxides, and red phosphorus, are highlighted. The various sodium storage mechanisms and structure‐function properties for these four types of materials are summarized and analyzed in detail. Considering the commercial profits that the EESs can bring and their suitability for mass electrode manufacturing, the participation of high‐abundance and low‐cost elements such as Fe, Mn, C, and P is convincing and encouraging. [ABSTRACT FROM AUTHOR]

To ensure the safety of fresh food, the cold chain logistics is widely utilised, which can provide a low temperature environment. However, due to the high energy consumption, how to reduce carbon emission of cold chain logistics to develop sustainable distribution has become the research focus. In response, this study proposes an intelligent distribution system for cold chain logistics to achieve green operations. Specifically, the framework of the intelligent system based on blockchain technology is proposed to improve operational efficiency and enhance food traceability. Meanwhile, an electric vehicle routing optimisation model taking into account carbon emission is deployed in the intelligent system to plan the distribution routes. The suggested optimisation model seeks to reduce the total cost, including fixed, spoilage, refrigeration, punishment, queuing, charging and carbon emission costs. Furthermore, an ant colony algorithm is embedded in the intelligent system to help with distribution routes design. Finally, this study combines a real case to discuss the performance of the intelligent system, and the results show that the proposed system can significantly reduce distribution cost and carbon emission. Managers can use the suggested intelligent system to design distribution routes and monitor the distribution process. [ABSTRACT FROM AUTHOR]

Background and Aims Design Setting Participants Measurements Findings Conclusions During the COVID‐19 pandemic, there was a surge in opioid overdose deaths (OODs) in Massachusetts, USA, particularly among Black and Hispanic/Latinx populations. Despite the increasing racial and ethnic disparities in OODs, there was no compensatory increase in naloxone distributed to these groups. We aimed to evaluate two community‐based naloxone expansion strategies, with the objective of identifying approaches that could mitigate mortality and racial and ethnic disparities in OODs.Individual‐based simulation model. We measured naloxone availability using naloxone kits per OOD and evaluated scenarios of achieving higher benchmarks for naloxone availability (i.e. 40, 60 and 80 kits per OOD) from 2022 levels (overall: 26.0, White: 28.8, Black: 17.3, Hispanic/Latinx: 18.9). We compared two naloxone distribution strategies: (1) proportional distribution: achieving the benchmark ratio at the overall population level while distributing additional kits proportional to the 2022 level for each racial/ethnic group (at 40 kits per OOD benchmark: overall: 40, White: 44.3, Black: 26.6, Hispanic/Latinx: 29.1), and (2) equity‐focused distribution: achieving the benchmark ratio among each racial/ethnic group (at 40 kits per OOD benchmark: 40 for all groups).Massachusetts, United States.People at risk of OOD.Annual number and rate of OODs, total healthcare costs of increasing naloxone availability.Both naloxone distribution strategies yielded comparable predicted reductions in total OODs in 2025 and incurred similar incremental costs. However, the relative reduction in the rate of OODs differed across groups. For achieving an 80 kits per OOD benchmark, proportional distribution resulted in a projected 6.7%, 6.5% and 7.1% reduction in annual OODs in 2025 among White, Black and Hispanic/Latinx populations, respectively. In contrast, equity‐focused distribution achieved a reduction of 5.7%, 11.3% and 10.2% in the respective groups. In all scenarios, the cost per OOD averted was lower than the generally accepted thresholds for cost per life saved.An equity‐focused naloxone distribution strategy designed to reduce racial and ethnic disparities in naloxone availability could improve health equity among racial and ethnic groups while potentially improving overall population health at lower healthcare costs per opioid overdose death averted than a proportional distribution strategy. [ABSTRACT FROM AUTHOR]

Data standards play a key role in the governance of commercial actors on the market for health data. This commentary explores expectations for standard-setting in the European Health Data Space (EHDS), a pivotal legal initiative for facilitation of cross-border health data exchange. While EHDS aims to establish shared standards for interoperability and innovation in the European Union (EU) and affiliated countries, the analysis reveals some of the inherent complexities. We draw on insights from policy analysis and Science and Technology Studies (STS) that underscore how standards are performative and have unpredictable and uneven implications. To exemplify this, the commentary delves into the intricate issues surrounding the harmonization of diagnostic codes and pharmaceutical prescriptions. In conclusion, it asserts that while standard setting has unpredictable effects, it still involves prioritizing some purposes and benefits over others. We raise questions about the distribution of costs and benefits among stakeholders in the evolving landscape of health data governance and call for further empirical investigation. [ABSTRACT FROM AUTHOR]

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users' electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users' electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account. [ABSTRACT FROM AUTHOR]

In the Internet era, the e‐commerce industry has risen, its development scale continues to expand, cross‐border e‐commerce (CBEC) has also been born, and it is now in the stage of sustainable development. The rapid development of CBEC also needs the strong support of logistics, the two are inseparable, and today, the development scale of CBEC is constantly expanding. The existing e‐commerce logistics (ECL) model is also gradually unable to meet the increasingly diverse needs of users, and new logistics models need to be actively explored. To change this situation, this paper carried out a specific analysis of CBEC logistics model, and applied embedded technology to ECL, which also built a logistics tracking system. At the same time, combined with the ant colony algorithm, the paper carried out experimental research on the logistics package distribution route planning problem. From the experimental results, in terms of average delivery time, the algorithm's result was 25.95 hr, while the traditional algorithm was 32.53 hr; in terms of average distribution freight cost, the algorithm's result was 163.3 yuan, while the traditional algorithm was 257.7 yuan; in terms of average distribution cost, this algorithm's result was 131.53 yuan, while the traditional algorithm was 211.68 yuan. To sum up, this algorithm could effectively optimize the distribution route of logistics packages and improve the efficiency of package transportation. [ABSTRACT FROM AUTHOR]

The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution. [ABSTRACT FROM AUTHOR]

Drawing on feminist political economy and social reproduction theory, we propose an accounting framework for understanding the distributional role of household production, employment, remittances and government social transfers in the social reproduction of the Cuban people. We apply this quantitative framework to available data and produce estimates for 2016. Our findings demonstrate that households – both domestic and diasporic – were the largest contributors to social reproduction in Cuba. Our empirical exercise reveals how the actual distributional arrangements underlying Cuban social reproduction differ from the official commitments and goals of the Cuban Revolution. The relative contributions in 2016 signal several potentially unsustainable self-reinforcing dynamics that undermine efforts to achieve gender and racial equality on the Island. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a methodology designed to reduce cost, improve demand coverage, and ensure equitable vaccine distribution during the initial stages of the vaccination campaign when demand significantly exceeds supply. We formulate an enhanced maximum covering problem as a mixed integer linear program, aiming to minimize the total vaccine distribution cost while maximizing the allocation of vaccines to population blocks under equity constraints. Block‐level census data are employed to define demand locations, identifying gender, age, and racial groups within each block using population data. A Lagrangian relaxation technique integrated with a modified Voronoi diagram is proposed to solve the location–allocation problem efficiently. Empirical case studies in Pennsylvania, using real‐world data from the Centers for Disease Control and Prevention and health department websites, were conducted for the first 4 months of the COVID‐19 vaccination campaign. Preliminary results show that the proposed solution algorithm effectively solves the problem, achieving a 5.92% reduction in total transportation cost and a 28.15% increase in demand coverage. Moreover, our model can reduce the deviation from equity to 0.07 (∼50% improvement). [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the high penetration of renewable energy, the addition of a large number of energy storage units, and flexible loads, the source-load-storage structure of active distribution networks is becoming increasingly complex, making optimization and scheduling more challenging. In response to issues as difficult global information acquisition, less consideration of flexible load and energy storage unit access, individual deception, and insufficient security in the optimization scheduling process of active distribution networks, this paper constructed a distribution network optimization scheduling model that includes sources, loads, and storage. It proposed a distributed optimization scheduling strategy for source-load-storage distribution networks, combined with alliance chains. This strategy is based on the FISCO BCOS consortium chain platform, with blockchain multi-agent nodes forming a consortium chain network. The consistency variables are the incremental cost of distributed power generation and the incremental benefits of flexible loads. Distributed scheduling calculations were carried out using a consensus algorithm that includes leadership nodes. By combining the data storage mechanism and consensus algorithm advantages of the consortium chain, the centrality of leadership nodes is eliminated, achieving optimal power allocation in the distribution network at a minimum economic cost. The simulation results show that the distributed optimization scheduling strategy proposed in this paper can achieve optimal allocation of minimum cost in the distribution network and converge quickly in various scenarios such as non-flexible load fluctuations, leader node switching, node joining or leaving, and changes in power exchange instruction in the distribution network. It demonstrates good robustness and stability. [ABSTRACT FROM AUTHOR]

Toronto has for 70 years been one of the fastest growing city-regions in North America, and over the last two decades has seen booming high-rise intensification. A distinctive land value capture (LVC) system and practice has emerged. This paper traces the contested evolution of Toronto LVC since 1946, including recent major changes to the rules, and makes three main contributions: First is the suggestion that at its core, LVC is an attempt to purposefully alter the distribution of the costs and benefits of urban property development between public and private actors, and should be considered a key metric to evaluate the social equity of property development processes. Second, conjunctural analysis has recently emerged as an approach to reading the intersection of political, economic, social, and ideological processes that come together to produce historical changes in specific cases. I argue that Historical Institutionalism (HI) is a valuable complement to the conjunctural analysis approach in its focus on the genesis of and contestation over specific sets of institutions. HI provides a set of midlevel conceptual frameworks and research methods to identify major institutions, the actors involved, and institutional change mechanisms, processes, and patterns. I bring together two hitherto separate traditions of thought about conjunctures, that arising from Gramsci through Stuart Hall, and that developed in HI, arguing that HI brings analytical clarity to the comparative analysis of conjunctures. Third, the Toronto case is revealing of processes of institutional change, mechanisms supporting path dependence, and the urban impacts of globally hegemonic ideological conjunctures. [ABSTRACT FROM AUTHOR]

The massive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations. To accurately assess the flexible regulation capabilities of distribution stations, a multi-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics, considering different geographical locations, coverage areas, and response capabilities. Firstly, the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed, and a resource regulation capability model is established to quantify the adjustable range of different regulation resources. On this basis, considering the limitations of line transmission capacity, a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities. Secondly, considering different geographical locations and coverage areas, a comprehensive performance index based on electrical distance modularity and active power balance is established, and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters. Simultaneously, an economic optimization model with the objective of minimizing the economic cost of the distribution station is established, comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources. This model can provide scientific guidance for the economic dispatch of the distribution station area. Finally, case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations, facilitate the consumption of distributed photovoltaics, and enhance the economic efficiency of the distribution station area. [ABSTRACT FROM AUTHOR]

In this paper, we prove BV regularity on the transport density in the mass transport problem to the boundary in two dimensions under certain conditions on the domain, the boundary cost and the mass distribution. Moreover, we show by a counter-example that the smoothness of the mass distribution, the boundary and the boundary cost does not imply that the transport density is W 1 , p , for some p > 1 . [ABSTRACT FROM AUTHOR]

We propose an empirical framework for asymmetric Cournot oligopoly with private information about variable costs. First, considering a linear demand for a homogeneous product with a random intercept, we characterize the Bayesian Cournot–Nash equilibrium. Then we establish the identification of the joint distribution of demand and firm‐specific cost distributions. Following the identification steps, we propose a likelihood‐based estimation method and apply it to the global market for crude oil and quantify the welfare effect of private information. We also consider extensions of the model to include product differentiation, conduct parameters, nonlinear demand, or selective entry. [ABSTRACT FROM AUTHOR]

Forestry in Japan and Austria share many similarities in their natural and social conditions. However, the Family Forest Owners (FFOs) in Japan seem not to be active and sustainable. To understand the factors affecting activeness and sustainability in family-owned forests in Japan, in 2021 and 2022, questionnaire surveys were done with members of Forest Owners' Cooperatives (FOCs) in Aso, Japan, and Styria, Austria. Survey responses were comparatively analyzed via correlation analysis and binary logistic regression. Timber production was found to be more active in FOC Styria than in FOC Aso. One reason for this was the high-income dependence on forestry in Styria. Higher income was realized by self-harvest and the larger size of forest holdings and forest stands. The younger age of the members in FOC Styria, strongly affected by the pension system, leads to a higher self-harvest ratio. The culture of a sole child inheriting the family forest maintains the general size and scale of owned forests and stands in Styria. High distribution costs in FOC Aso reduced forestry income. As a result, sustainability was reduced in Aso because the availability of successors was low, and elderly forest owners who were once motivated by forestry tended to quit forestry. [ABSTRACT FROM AUTHOR]

This article introduces the concept of joint distribution context into the location-routing problem (LRP) to tackle the issue of resource waste and high costs in distribution centers (DCs) for multiple companies. Fixed costs of constructing DCs, fixed costs of hiring vehicles, and routing costs are taken into account when constructing the mixed integer programming (MIP) model. A two-stage algorithm is designed to solve this problem. In the first stage, K-means clustering is used to group demand nodes with vehicle capacity constraints. In the second stage, the simplified LRP model is solved by Lingo, and locations of DCs and routing schemes are obtained. Additionally, a cost-sharing model based on the desirability function is developed to address the cost allocation problem among companies. The results and sensitivity analysis demonstrate that the joint distribution can effectively reduce costs. [ABSTRACT FROM AUTHOR]

Agricultural machinery has been widely used in large-scale operation and wide distribution in recent years. However, the returning duration of refueling and queuing has seriously limited the operational efficiency of agricultural machinery, particularly in tens of acres or even hundreds of acres of land. Apparently, the return refueling cannot fully meet the requirements for the continuous operation of agricultural machinery in the context of large-scale production. It is very necessary to guarantee the fuel rationing and supply of agricultural machinery with less time and high yield in the field. Fuel rationing in the field for agricultural machinery refers to the fuel supply point distributing the required amount of fuel to the farmland, where the agricultural machinery is located within the required time and completing fuel refueling. Fuel rationing in actual agricultural production includes fuel delivery into the field via filling buckets, and mobile refueling service by mobile refueling trucks. The rationing relies mainly on unsafe, untimely, and manual decision-making with a low level of digitization. It is still lacking in the systematic decision-making on rationing, in order to improve the efficiency of fuel rationing and distribution cost-saving. In this study, a fuel rationing in-field strategy was proposed using an intelligent management platform. The fuel demand of agricultural machinery was combined to utilize the agricultural Internet of Things (IoT), taking the synergistic advancement of "demand analysis-order allocation-route planning" as the core. Firstly, a two-stage mathematical model of fuel rationing in agricultural machinery was established using real-time data and the theory of trustworthiness in the intelligent management platform. In the first stage of pre-optimization, a multi-objective vehicle route planning model was established with the fuzzy demand and fuzzy time window using the platform data. In the second stage, the distribution route planning was re-routed to update the relevant information for undelivered orders using the dynamic events in agricultural production. Secondly, the solution was designed after the two stages. Taking the lowest total distribution cost and the highest total satisfaction as the objectives, a multi-objective cooperative co-evolutionary genetic method was applied to realize the distribution route planning using an order analysis. The dynamic optimization was realized to obtain the dynamically optimal scheme of fuel rationing. Finally, an empirical study was conducted with the Nenjiang Farm. An intelligent management platform was selected to implement the scheduling of agricultural machinery. According to the Beidou navigation satellite system terminal carried by the agricultural machinery, the operation data was acquired in the field, such as the location, the amount of operation, and the status information. The production data of the farm was also adopted to conduct simulation experiments. A comparison was then made between the distribution cost and current indicators to verify the feasibility and practicality. The function of fuel rationing was realized in field planning. The results show that high satisfaction and distribution cost-saving were achieved in the fuel rationing in the field for the agricultural machinery, according to the real-time data from the intelligent platform. The final scheme of dynamic optimization was reduced to 14 distribution routes, compared with the current one. The total distribution cost was reduced by about 15 times. The satisfaction rate of 72.85% was slightly lower than the actual one. The relevant functions were promoted in the intelligent platform for agricultural machinery. The finding can provide a scientific basis for the intelligent management of fuel rationing in the field of agricultural machinery. [ABSTRACT FROM AUTHOR]

Power system researcher have turned to Micro-Grids (MG) for higher reliability, greater flexibility, lower operating costs and losses, and lower CO2 emissions at the distribution system. This paper presents a single-level stochastic optimization framework for planning and partitioning of a distribution system including Multiple Micro-Grids (MMGs). The main objective is to minimize the total cost of the system including investment, operation, total losses and reliability costs of the distribution network. The proposed model takes into account the viewpoints of MG owners and distribution system operators, simultaneously. The voltage stability index is introduced to identify the optimal site of MG investment. To deal with uncertainties caused by renewable generations, the Firefly Algorithm (FA) and probability-tree method is used to create various operation scenarios of Photo-Voltaic (PVs) and Wind Turbines (WTs). This model is solved through the genetic algorithm in MATLAB, and to evaluate its effectiveness, numerical studies have been carried out on the experimental IEEE distribution network with four specified locations for investing MGs and seven Tie Switches (TS) for network partitioning. Simulation results reveal that optimal locations for MG investment are determined in such a way all MGs connect to the buses near the beginning of the feeder and as a result, load point reliability is improved, total active power losses are reduced, and the energy program becomes more optimized. [ABSTRACT FROM AUTHOR]

The paper addresses the overlooked interaction between power-to-gas (P2G) devices and carbon capture and storage (CCS) equipment, along with the stepwise carbon trading mechanism in the context of current multi-park integrated energy microgrids (IEMGs). Additionally, it covers the economic and coordinated low-carbon operation issues in multi-park IEMGs under the carbon trading system. It proposes a multi-park IEMG low-carbon operation strategy based on the synchronous Alternating Direction Method of Multipliers (ADMM) algorithm. The algorithm first enables the distribution of cost relationships among multi-park IEMGs. Then, using a method that combines a CCS device with a P2G unit in line with the tiered carbon trading scheme, it expands on the model of single IEMGs managing thermal, electrical, and refrigeration energy. Finally, the comparison of simulation cases proves that the proposed strategy significantly reduces the external energy dependence while keeping the total cost of the users unchanged, and the cost of interaction with the external grid is reduced by 56.64%, the gas cost is reduced by 27.78%, and the carbon emission cost is reduced by 29.54% by joining the stepped carbon trading mechanism. [ABSTRACT FROM AUTHOR]

End-to-end disparity estimation algorithms based on cost volume deployed in edge-end neural network accelerators have the problem of structural adaptation and need to ensure accuracy under the condition of adaptation operator. Therefore, this paper proposes a novel disparity calculation algorithm that uses low-rank approximation to approximately replace 3D convolution and transposed 3D convolution, WReLU to reduce data compression caused by the activation function, and unimodal cost volume filtering and a confidence estimation network to regularize cost volume. It alleviates the problem of disparity-matching cost distribution being far away from the true distribution and greatly reduces the computational complexity and number of parameters of the algorithm while improving accuracy. Experimental results show that compared with a typical disparity estimation network, the absolute error of the proposed algorithm is reduced by 38.3%, the three-pixel error is reduced to 1.41%, and the number of parameters is reduced by 67.3%. The calculation accuracy is better than that of other algorithms, it is easier to deploy, and it has strong structural adaptability and better practicability. [ABSTRACT FROM AUTHOR]

This paper addresses the diversified needs of urban cold chain distribution and proposes innovative solutions based on storage type multi-temperature co-distribution and mechanical type multitemperature co-distribution modes. We present an electric vehicle path optimization model aimed at minimizing total costs, taking into account time-varying speed in accordance with urban traffic patterns. Additionally, a genetic algorithm is designed to solve the multi-temperature co-matching optimization path. The study's results reveal that the storage type multi-temperature co-distribution transport mode offers superior economic efficiency, product security, safety, and resource utilization. By comparing and analyzing the results of model solving under different battery capacities, loads, and distributions speeds, the total cost of distribution is optimal when the battery capacity is 120 kWh, the maximum load is 100 kg, and the normal driving speed is 60 km/h. The mechanical multi-temperature co-distribution mode is optimal for the total cost of distribution at a battery capacity of 100 kWh, a maximum load of 100 kg, and a normal driving speed of 50 km/h. The study aims to provide reference significance for logistics companies when making route selection. [ABSTRACT FROM AUTHOR]

The variables acceptance sampling plan and variables acceptance sampling system (VASS) based on process capability indices (PCIs) have gotten greater attention recently because they can efficiently validate product quality in variables measurement scales. In the VASS, the variables quick-switch sampling system (VQSS) and variables tightened-normal-tightened sampling system (VTSS) are the basic two-plan sampling systems. Compared to the VQSS, the VTSS is more flexible and adaptive because it empowers practitioners to adjust the rule-switch mechanism. Recent studies on the PCI-based VTSS have only focused on the sample size type, denoted as VTSS-I. However, VTSS-I requires a large sample size in the tightened inspection, leading to increased inspection costs and delays in distribution. In this article, we proposed an acceptance criterion-type VTSS, denoted as VTSS-II, based on unilateral PCIs. By comparing the proposed VTSS-II with the existing VTSS-I, the VTSS-II can significantly reduce the inspected sample size and has superior discrimination power. Because the rule-switch mechanism of the VTSS-II is adjustable, we created a cloud-computing app to provide an easy way for practitioners to obtain the optimum system design. [ABSTRACT FROM AUTHOR]

Urban terminal logistics and distribution enterprises face the problems of duplicated network layouts, high costs, and inefficient urban environments. Recently, collaborative distribution using unmanned aerial vehicles (UAVs) and ground vehicles has been considered as a means of reducing costs and enhancing efficiency, thus overcoming the issues created by the high-density layout of enterprises' relatively independent networks. This essentially involves constructing an air–ground collaborative common distribution network. To optimize the economic cost and distribution time of network operations, we established a site selection planning model for air–ground cooperative urban co-distribution centers and designed a solution method based on gray wolf optimization with K-means clustering. Taking the Wangsheren area of Jinan City, China, as an example, 15 UAV co-distribution centers and 13 vehicle co-distribution centers were identified. Although the average distribution cost of the network rose by 35–50% compared to traditional terminal distribution, the time saving was 80–85%, greatly improving the high-value-added service capacity. The end-distribution efficiency and customer satisfaction were also enhanced, which fully verifies the feasibility, validity, and applicability of the proposed model. Our approach can be applied to landing sites and the planning and optimization of large-scale commercial operations using logistics UAVs in urban areas. [ABSTRACT FROM AUTHOR]

The globalization of free trade has caused imported food to become an alternative consumption because of the ease of consumption, such as fast food. Wheat demand fluctuates due to increased demand for making wheat flour by factories around the port area. Indonesia, which does not have productive wheat plantations, imports to meet national needs. The stakeholders in the wheat supply and distribution chain are the ports where transport ships from abroad dock. In the Banten region, the Cigading port is a loading and unloading port for wheat, corn and other agricultural grain commodities. Partner companies manage the delivery of farm commodities from loading and unloading with the help of transportation modes such as trucks, trains and conveyors for factories around the port. Imported wheat from country A is docked at the port, the loading and unloading process and delivered to several warehouses between the dry warehouse at the port and the surrounding factory warehouse. The method of transporting wheat commodities is by trucks and trains when shipping. The research aimed to optimize the number of grain truck transportation modes so that minimum distribution costs per truck can be obtained using the integer linear programming method. Minimize total distribution costs of $435.5, with truck routes sending wheat to dry warehouses inside the port and factory warehouses outside the port 12 times back and forth. That is caused by weather, long loading and unloading times, limited transportation capacity, and irregular truck queues.

Resilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post‐disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple‐microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33‐node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network. [ABSTRACT FROM AUTHOR]

This paper analyzes the case study of the optimization of the cold chain distribution path of cold and fresh meat of enterprise H, and obtains the optimized optimal distribution path and the optimal distribution cost by bringing in the case data and using the software running program, and the research conclusion has certain reference value for the planning and development of the related cold chain logistics and transportation industry. [ABSTRACT FROM AUTHOR]

We measure the performance of a real-world triadic supply chain (SC) network consisting of suppliers, 3PL, and retailers. Three-stage network slacks-based measure (NSBM) and multi-parametric bias correction generalised directional distance function (MPBC-NGDDF) approach are developed to deal with conventional and non-discretionary inputs, desirable and undesirable linking activities and outputs. The sample consists of 786 combinations of triad members. Using both NSBM and MPBC-NGGDF facilitates a sound SC performance evaluation and identification of optimal inputs and outputs. We also compare the NSBM and MPBC-NGDDF estimates against the network epsilon-based measure (NEBM) efficiencies. This comparative analysis reveals the weaknesses and strengths of each method in the context of a complex SC network. Identifying weaknesses and strengths draws on the appropriateness of optimal solutions and practical implications. The MPBC-NGDDF approach meets both appropriateness criteria. According to this approach, considerable reductions are needed in the number of employees used by suppliers, the production agility, the origin port distance from the suppliers, the destination port distance from the retailers, the time from placing an order to delivery to the retailers' premises, and distribution costs. [ABSTRACT FROM AUTHOR]

To meet the growing global demand for minerals and new energy sources, governments in the Global South advance policy interventions to improve the unequal distribution of the cost and benefits of resource extraction. This paper explains the politics behind the implementation of the Closing Development Gaps (CDG) Plan, a new redistributive plan on behalf of Amazonian Indigenous peoples near the oil circuit in the Loreto region of Peru. It emphasizes the long-lasting impact of mobilizing strategies of indigenous organizations, which relayed critical information to policymakers about the claims both old and new of Indigenous peoples neighboring the oil circuit. It also draws attention to the permeability of state institutions, which allowed newer state agencies with distinct policy streams to advance new solutions to old problems. While the CDG Plan seeks to improve resource governance by focusing on infrastructure gaps (e.g., water and sanitation, electrification), it excludes the "political gaps" and the most contentious claims related to the environment that have moved Amazonian Indigenous peoples into struggle in recent years. [ABSTRACT FROM AUTHOR]

This paper constructs a data-driven demand purchasing confidence rule-based model based on the theory of evidential reasoning to address the problem of purchasing decision bias in which the decision maker anchors the prior demand. First, we apply a synthetic algorithm to reason about the confidence structure distribution of the purchasing decision and then iteratively optimize the purchasing quantity according to the confidence structure distribution to minimize the cost loss function. Finally, we choose the average cost loss and service level value as the evaluation indexes to explore the purchasing confidence rule model. The simulation shows that compared with the expected purchasing decision based on the stochastic cumulative distribution function, the decision based on the confidence structure distribution can reduce the purchasing decision bias, manifested in the lower mean cost loss and a higher mean value of service level. Sensitivity analyses of the number of rules, product shelf life, and critical values show that the purchasing confidence rule base is highly adaptable. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to define the distribution of direct healthcare costs for people with diabetes treated in two healthcare regions in Italy, based on number of comorbidities and treatment regimen. Methods: This was a retrospective analysis using data from two local health authority administrative databases (Campania and Umbria) in Italy for the years 2014–2018. Data on hospital care, pharmaceutical and specialist outpatient and laboratory assistance were collected. All people with diabetes in 2014–2018 were identified on the basis of at least one prescription of hypoglycemic drugs (ATC A10), hospitalization with primary or secondary diagnosis of diabetes mellitus (ICD9CM 250.xx) or diabetes exemption code (code 013). Subjects were stratified into three groups according to their pharmaceutical prescriptions during the year: Type 1/type 2 diabetes (T1D/T2D) treated with multiple daily injections with insulin (MDI), type 2 diabetes on basal insulin only (T2D-Basal) and type 2 diabetes not on insulin therapy (T2D-Oral). Results: We identified 304,779 people with diabetes during the period for which data was obtained. Analysis was undertaken on 288,097 subjects treated with glucose-lowering drugs (13% T1D/T2D-MDI, 13% T2D-Basal, 74% T2D-Oral). Average annual cost per patient for the year 2018 across the total cohort was similar for people with T1D/T2D-MDI and people with T2D-Basal (respectively €2580 and €2254) and significantly lower for T2D-Oral (€1145). Cost of hospitalization was the main driver (47% for T1D/T2D-MDI, 45% for T2D-Basal, 45% for T2D-Oral) followed by drugs/devices (35%, 39%, 43%) and outpatient services (18%, 16%, 12%). Average costs increased considerably with increasing comorbidities: from €459 with diabetes only to €7464 for a patient with four comorbidities. Similar trends were found across all subgroups analysis. Conclusion: Annual cost of treatment for people with diabetes is similar for those treated with MDI or with basal insulin only, with hospitalization being the main cost driver. This indicates that both patient groups should benefit from having access to scanning continuous glucose monitoring (CGM) technology which is known to be associated with significantly reduced hospitalization for acute diabetes events, compared to self-monitored blood glucose (SMBG) testing. [ABSTRACT FROM AUTHOR]

We study the endogenous participation problem when bidders are characterized by a two-dimensional private information on valuations and participation costs in first-price auctions. Bidders participate whenever their private costs are less than or equal to the expected revenue from participating. We show that there always exists an equilibrium in this general setting with two-dimensional types of ex-ante heterogeneous bidders. When bidders are ex-ante homogeneous, there is a unique symmetric equilibrium, but asymmetric equilibria may also exist. We provide conditions under which the equilibrium is unique (not only among symmetric ones). In the symmetric equilibrium, we show that the equilibrium cutoff of participation costs described above which bidders never participate, is lower when the distribution of participation costs is first-order stochastically dominated. [ABSTRACT FROM AUTHOR]

The objective of the green logistics distribution model is to minimise environmental pollution and energy usage by employing clean energy, optimising transport routes and enhancing transport efficiency. Nonetheless, current studies on green logistics distribution models and warehousing planning exhibit certain drawback, such as imprecise location accuracy and decreased distribution revenues. To overcome these challenges, this paper proposes a novel approach that combines inertial measurement unit (IMU) and binocular vision, leveraging multisource information positioning. Specifically, the method integrates data collection and preprocessing modules to gather crucial logistics distribution task information, encompassing IMU data, image data and vehicle data. The visual and inertial positioning module consists of two components: visual positioning based on the grey centre method and IMU positioning based on the integral essence. Finally, an adaptive Kalman filter is employed to merge the results of visual positioning and IMU positioning, thus producing the ultimate logistics vehicle positioning result. The proposed method effectively addresses existing challenges in the green logistics distribution model and warehouse planning. In particular, the experimental results demonstrate that the algorithm proposed in this study reduces the location error by 8%. Furthermore, logistics and distribution costs are reduced by 11 %, contributing to the promotion of sustainable and environmentally friendly logistics operations. [ABSTRACT FROM AUTHOR]

Residential owned solar panels mounted on roof-tops generated 1.4% of the U.S. electricity supply in 2022. Utilities compensate residential customers using a net metering system by which each kilowatt-hour generated by the customer reduces the customer's bill by the amount the utility charges for one kilowatt. Despite the small impact of roof-top solar on the total market, in 2022, thirty-two states conducted ninety-four proceedings investigating whether to change the net metering system. In these proceedings, utilities advocated changing the rates to discourage residential investment in solar panels. Utilities argue that net metering provides a subsidy to solar customers because the rate the utility pays is greater than the avoided cost of energy. That excess payment allows solar customers to avoid paying for the distribution network. The cost of the distribution network is shifted to non-solar customers. This article will evaluate these claims. It will show that if the social cost of carbon were in the calculation, it would generate a Kapp cost shift from solar customers to non-solar customers. The size of the Kapp cost shift is greater than the cost of distribution networks, reversing any private cost shift that utilities have estimated. [ABSTRACT FROM AUTHOR]

Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers' immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit. [ABSTRACT FROM AUTHOR]

This paper proposes an estimation approach following the constructive identification strategy of Athey and Haile, and Gentry and Li with adaption in the context of ascending auctions with selective entry. Our estimators are shown to be consistent in a large sample and to perform well in a finite sample by a simulation study. We apply our estimation approach to the Alibaba online judicial auctions of used cars to recover the bounds of conditional value distribution and the entry cost. The bounds estimates of both conditional value distribution and entry cost are quite tight (resp., relatively wide) for middle‐valued (resp., low‐valued or high‐valued) signal, and the cumulative distribution functions of conditional value distribution given signal comply with the law of ordered dominance. Finally, our counterfactual analysis indicates that (i) the ascending auction yields a higher revenue than the first‐price sealed bid auction, and (ii) the revenue can be improved significantly when the entry cost is cut by half. [ABSTRACT FROM AUTHOR]

Many theorists advocate for "economic degrowth," which entails "scaling down production and consumption activities," as a radical proposal to achieve environmental sustainability and social justice. This is in response to the increasingly destructive nature of economic growth on a planet with limited resources, as well as the unequal distribution of the costs associated with changes to planetary boundaries. The examination of spatial production and consumption processes holds significant potential for realizing the social transformation required for economic activities to operate within planetary boundaries. However, the scarcity of studies on how the ideas of degrowth can be translated into the material world highlights the need to focus on how degrowth will manifest spatially. The problem of spatializing degrowth will be addressed in this study, with a focus on housing. The injustices inherent to growth-oriented housing production processes make it imperative to discuss the spatialization of degrowth through housing. For degrowth to manifest spatially at home, the spatial modes of production inherent to economic growth, the spatial consumption patterns, and the social relations produced by space need to become compatible with the fundamental goals of degrowth. To provide a compass for aligning housing production and consumption processes with the fundamental objectives of degrowth, it is imperative to identify the toolkit employed by economic growth for spatialization and to analyze criticisms directed against existing housing degrowth experiments. Through the analysis of these two principal themes, commoning of housing emerges as a viable strategy for spatialization of degrowth at home. [ABSTRACT FROM AUTHOR]

We assess the impact of regional differences related to energy transition on average costs and allowed revenues for a panel of Dutch electricity distribution system operators (DSOs) subject to yardstick competition. Yardstick competition entails that the allowed revenues of DSOs are based on the average costs of the entire industry, which requires that these DSOs are comparable. This comparability requirement is challenged by the penetration of distributed generation and other distributed energy resources, which may cause regional differences among DSOs. Estimating an average-cost function for the entire population of Dutch DSOs for the period 2012–2020, we find that the installed capacity of solar PV, installed capacity of on-shore wind and number of public electric-vehicle charging points have a significant effect on unit costs of DSOs. If yardstick competition does not take these effects into account, the allowed revenues for some DSOs (having above-average shares of energy-transition variables) are too low, whereas allowed revenues for other DSOs (having below-average shares of energy-transition variables) are too high. We find that taking the impact into account can change the price caps of individual DSOs with a percentage up to around 20%. [ABSTRACT FROM AUTHOR]

With the evolution of people's consumption habits and the rapid growth of urban logistics, the number of trucks and delivery frequency has increased significantly, exacerbating urban traffic congestion and environmental pollution. Consequently, there is an urgent need to improve the current inefficient and highly polluted distribution mode. Currently, in most cities in China, the metro passenger flow is insufficient, and the capacity is excessive during off-peak hours, resulting in underutilized carriage capacity. The integration of surface and underground transport resources can effectively address these issues and facilitate complementary advantages and win–win cooperation between express companies and metro enterprises. This study proposes an innovative problem of split demand route planning in the cooperative distribution system involving express companies and subways. A cooperative distribution model is developed to minimize the total cost, and solved by the Cuckoo Search algorithm to obtain the optimized solution. The model is applied to the urban rail transit network in Changchun, and the results demonstrate that it is effective in reducing truck mileage and distribution costs. Compared to the single delivery pattern by trucks, the cooperative distribution approach proves to be more cost-effective and environmentally friendly. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic highlighted significant challenges in global vaccine distribution, particularly in India due to its vast geography and high population density. This paper explores sustainability issues within India's vaccine cold supply chain (VCSC), using a multi-criteria framework to assess the economic, social, and environmental factors in this regard. Data was gathered from primary healthcare centers (PHCs), government records, healthcare agencies, and interviews with key stakeholders to evaluate distribution costs and community impact. The findings reveal disparities in vaccination efficiency between urban and rural areas, with urban areas benefitting from better services and lower costs. Social justice issues persist in remote areas, besides irregular coverage. The study identifies the need for improved infrastructure, green logistics technologies, and coordinated stakeholder efforts to enhance sustainability and efficiency in vaccine distribution. With the pandemic over, the focus is now on distribution of vaccines in general, which requires a cold supply chain. Addressing these challenges can improve India's vaccine cold supply chain and public health outcomes. [ABSTRACT FROM AUTHOR]

One of the significant challenges facing cooperative businesses is leveraging networks and connections efficiently. Cooperative Business Networks (CBN) like the Indonesian Jaringan Usaha Koperasi (JUK) aim to integrate buyers and sellers, reduce distribution costs, and strengthen market channels for SMEs. However, JUK has not been fully optimized, with cooperation primarily occurring between secondary forms of cooperatives rather than at a broader network level. This study explores how consumer cooperatives can better serve their members by partnering with producer cooperatives within the cooperative supply chain. It investigates the factors driving these partnerships and the success of cooperative networks. The qualitative methodology used includes case studies and in-depth interviews with key personnel from cooperatives in Depok City, West Java, Indonesia. Findings suggest that successful collaboration in the supply chain requires producer cooperatives to meet specific quality and administrative standards. The research has both theoretical and practical implications, offering insights into improving cooperative relationships and suggesting the need for further quantitative studies to generalize findings across different cultural contexts in Indonesia. [ABSTRACT FROM AUTHOR]