Supply Chain Network Optimization Research Articles

Enterprise supply chain network optimization algorithm based on blockchain-distributed technology under the background of digital economy

Demand forecasting is essential for streamlining supply chain operations in the digital economy and exceeding customer expectations. On the other hand, traditional forecasting techniques cannot frequently present real-time data and respond to dynamic changes in the supply chain network, leading to less-than-ideal decision-making and higher costs. This research aims to create a technique for optimizing the supply chain network based on blockchain-distributed technology (SCN-BT) to overcome these drawbacks and fully utilize the potential of the digital economy. The suggested framework uses the hybridized LSTM network and Grey Wolf Optimization (GWO) algorithm to examine demand forecasting in the supply chain network for inventory planning. The SCN-BT framework develops a safe and productive, enabling precise and flexible demand by combining blockchain with optimization techniques. A thorough case study utilized information collected from an enterprise supply chain that operates in the digital economy to show the efficiency of the suggested framework. Compared to conventional approaches, the results show considerable gains in demand forecasting precision, responsiveness of the supply chain, and cost-effectiveness. In the context of the digital economy, demand sensing and prediction enable firms to react to changes swiftly, shorten turnaround times, optimize inventory levels, and improve overall supply chain performance. The results highlight how blockchain technology has the potential to enhance collaboration, trust, and transparency inside intricate supply chain networks working in the digital economy. The experimental results show the proposed to achieve prediction rate of demand prediction rate of 128.93, demand forecasting accuracy ratio of 92.18%, optimum efficiency of 94.25%, RMSE rate of 1841.25, MAE rate of 260.74, and sMAPE rate of 0.1002 compared to other methods.

Optimization and Benefit Assessment of Production Supply Chain Networks Using Graph Neural Network Models

With the flourishing development of global economy, effective management of production supply chains is crucial for the competitiveness of enterprises. Optimizing supply chain networks can not only improve the efficiency of resource allocation but also enhance market responsiveness and systemic risk resistance. Traditional supply chain network optimization methods, focusing mostly on linear models and static analysis, fall short in addressing the growing complexity and dynamism. The emergence of Graph Neural Network (GNN) models in recent years has offered new opportunities to tackle non-linearity and structural dynamism in supply chain networks. However, existing research still faces methodological limitations in supply chain node relationship mining and benefit assessment. This study introduces an optimization and benefit assessment method for production supply chain networks based on GNNs. Firstly, by developing a node role type-aware graph neural network model, this paper achieves in-depth mining and optimization of node relationships within production supply chain networks. Secondly, a hierarchical factor analysis method is used to comprehensively assess the benefits of the production supply chain. This method can dynamically capture changes in node roles and relationships within the supply chain network, optimize the network structure, and provides a multidimensional, multilevel framework for benefit assessment. This study not only expands the application of GNN in the field of supply chain management but also provides a new analytical tool for the comprehensive assessment of supply chain benefits.

Sustainable Municipal Solid Waste Management Strategies and Practices: A review

Purpose: This paper introduces a multi-period, multi-objective model for creating an integrated, sustainable municipal solid waste management supply chain. The model includes source separation and a reward-penalty mechanism, aiming to minimize total system costs, greenhouse gas emissions, and environmental impacts on residential areas. Many countries strive to develop efficient solid waste management systems (SWMS) that handle and dispose of daily waste cost-effectively while maintaining sustainability. These systems include waste sources, collection stations, landfills, incinerators, recycling plants, and a transportation network. Methodology: A systematic literature review was conducted on existing literature from various studies. Recent studies were used for this systematic review of sustainable municipal solid waste management strategies and practices. Findings: Decision-makers must design or reconfigure a sustainable MSWM to determine the optimal supply chain network for treating and disposing of daily waste effectively. This paper suggests sustainable waste management strategies and practices from various countries, aiming to determine the best number and locations for facilities and optimal waste flow within the system to minimize daily costs. The primary contribution of this review is the theoretical development of sustainable MSWM practices applicable worldwide. Unique contribution to theory, policy and practice: Cooperation between the private and public sectors involved in solid waste management and the circular economy is imperative to ensure the technical, financial, and social sustainability of waste management systems. Large-scale awareness programs on solid waste management and the circular economy should be implemented, focusing on fostering environmentally responsible behaviors and attitudes, promoting sorting, selective collection, and the recovery of materials and energy from waste.

Supply chain network optimization for emergency materials considering demand satisfaction

AbstractIn order to effectively guarantee the smooth supply of emergency materials under public health emergencies, first, we predict the number of daily infected people based on the modified SEIRD infectious disease dynamics model and accordingly construct the dynamic demand prediction model of emergency materials so as to catch the change rule of the demand for emergency materials with greater precision. Subsequently, under the premise of demand satisfaction, we optimize the supply chain network of emergency materials and embed two insurance strategies in the supply system in advance: multi‐source purchase and buffer production capacity, to enhance the stability of supply chain by sacrificing part of the cost. Next, a bi‐objective linear programming model based on minimizing system cost and maximizing demand satisfaction is proposed with the intention of realizing the design of emergency materials supply chain network, which balances the risk and cost. Finally, through the analysis of the example, we find that compared with the traditional supply chain network of emergency materials, the total satisfaction rate of the supply chain network optimized by balancing cost and risk is increased by 11.70%, while the cost is increased by only 2.04%, which fully verifies the validity of the optimized design of supply chain network of emergency materials.

A socio-economic and quality-oriented optimal fruit supply chain network design in a multi-market and multi-product environment: A real case study

This paper focuses on developing an optimal sustainable fruit Closed-Loop Supply Chain (CLSC). A novel multi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate a multi-product, multi-market, multi-period, multi-level, multi-mode selling problem which aims to minimize cost and emissions and maximize the whole social segments’ responsiveness to fruit, vinegar, and compost. The model is solved by the LP-metric method and implemented in a real case in Iran. The findings indicate that achieving the highest level of responsiveness comes with a substantial cost for the network. However, this does not apply to environmental considerations. For decision-makers prioritizing sustainability, the proposed decision support system strongly advises against buying fruit from fruit cars. Due to the absence of positive impacts on the level of responsiveness, buying from fruit cars results in increased costs and emissions. For a cost- and environmentally-sensitive decision-maker, it is recommended to provide higher-quality fruits. Because the lower the quality of the fruit, leads to increased waste rates and emissions. Meeting increased vinegar demand, with its elevated costs and emissions, becomes justifiable only when the policymaker is actively seeking to achieve maximum responsiveness.

A multi-objective optimization approach for green supply chain network design for the sea cucumber (Apostichopus japonicus) industry

In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber (Apostichopus japonicus) is an aquatic product of high economic value; however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016-world method were used to assess each aquaculture model's global warming potential (GWP), as the environmental performance indicator. In addition, multi-objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating high-energy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in China.

Optimization algorithm for enterprise supply chain network based on blockchain distributed technology in the context of digital economy

In the era of digital economy, the optimization of enterprise supply chain networks has become a key challenge, while the problems of traditional supply chains, including information asymmetry and lack of trust, seriously hinder the development of enterprise supply chain networks. This paper will use the blockchain distributed technology and the digital economy background to explore how to use the blockchain distributed technology to optimize the existing problems. Firstly, study the supply chain information sharing to develop resources to reduce costs, then use the application of block chain technology and smart contract to establish information sharing mechanism to help the supply chain information more transparent and improve trust; secondly, use the block chain technology decentralized storage model to realize the decentralized supply chain research, and finally use the consensus method to improve the privacy protection of information, to avoid information asymmetry among users. Through experiments, it could be found that the optimization method of enterprise supply chain network based on blockchain distributed technology had a traceability accuracy of over 92.35% for the extracted products, with an average traceability accuracy of 93.791% for 10 products. Research on the transparency of different supply chain information was above 89.73% . By utilizing blockchain distributed technology, information protection in enterprise supply chains could be effectively improved; trust mechanisms could be better established; risk control effectiveness could be improved; optimization of enterprise supply chain networks could be better assisted.

E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border

E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border

From linear to circular sustainable supply chain network optimisation: towards a conceptual framework

Circular economy has the potential to counter sustainability challenges by moving towards a circular flow that enhances the efficiency of resource use. Government directives and stakeholders’ pressures are forcing organisations to redesign their global supply chain network to fit sustainability and circularity aims. To fulfil this need, many models are proposed to integrate sustainability objectives in supply chain optimisation. However, designing a sustainable supply chain within the circular economy framework is lacking. This is a barrier for organisations to provide a circular and sustainable product. To fill this gap, descriptive and content analysis are used in this paper to scrutinise literature and develop a conceptual framework incorporating sustainability objectives and circular economy strategies to assist supply network design. The developed framework is a useful tool that helps in a better transition from a linear to a sustainable circular economy. Based on the results, research opportunities are identified and suggestions for future research are proposed. The findings are useful for researchers and managers to implement circular economy practices in a sustainable supply chain.

Optimization strategy of cross-border e-commerce supply chain network based on machine learning

Abstract The rapid development of cross-border e-commerce increases the complexity of supply chain management, and machine learning-based supply chain network optimization strategies are essential for improving efficiency and reducing costs. The study first analyzes the cross-border supply chain network topology and determines the multilayer network structure. Subsequently, an optimization model based on particle swarm algorithm (PSO) is proposed, including the mathematical model of the algorithm and improvement measures. The practical effect of the optimization strategy was verified through example analysis. It is found that the supply chain optimized with particle swarm algorithm has significant Improvement in terms of shipping accuracy, surface transportation ratio, and unit transportation cost. For example, the shipping accuracy of product A in 2022 increased to 95.3% compared to 2021, the proportion of surface transportation increased to 96.5%, and the unit transportation cost decreased by RMB 2.175 per kilogram. This study shows that the particle swarm algorithm can effectively optimize the cross-border e-commerce supply chain network, which is significant in achieving efficient supply chain management.

A scenario-based robust possibilistic-flexible programming model for responsive supply chain network design with a performance-oriented solution methodology

This study addresses an integrated multi-objective responsive supply chain network optimization problem under hybrid uncertain business environment and optimal scheme selection. In addition to economic performance, delivery time and delivery reliability, as responsiveness indicators, are introduced into the multi-objective mixed-integer linear programming model. To deal with hybrid uncertainties caused by various scenarios, parameters, and flexible constraints, a scenario-based robust possibilistic flexible programming approach is extended. Moreover, a performance-oriented solution method is developed to solve the multi-objective optimization problem. The method integrates meta-heuristic and entropy-weighted TOPSIS to perform performance evaluation together with iteration optimization. The set of non-dominant solutions obtained by evaluation guide the iteration process. The numerical case verifies the effectiveness and efficiency of the proposed model, uncertainty technical and solution method. Interestingly, Pareto optimal solution set and its sort obtained by evaluation provide the optimal network scheme with the best supply chain performance.

Impact of Fixed Cost Increase on the Optimization of Two-Stage Sustainable Supply Chain Networks

Supply chain networks have emerged as the backbone of economic activities in the modern world. Most of the literature on the optimization problem of transportation in supply chain networks (SCNs) considers fixed costs (FCs). Although most researchers have used fixed costs in supply chain design, none have studied their impact on optimal supply chain design. The design of the network can change when we take fixed costs into optimization. However, in the practical application of this problem, it is important to study the effect of FCs. This paper examines the impact of the fixed cost’s increase as well as the variable cost on the optimization of a two-stage SCN. To do so, two mathematical models for mixed-integer nonlinear programming were developed. The first model optimizes fixed and variable costs in addition to the opening cost, whereas the second model only optimizes variable and opening costs. To evaluate the effect of considering the FC on the optimization supply chain problem, four groups of instances were randomly generated and solved using Lingo. The results of the two models are compared using the average percentage deviation. In addition, sensitivity analysis was performed to determine the impact of changes in opening and variable costs on the considered optimization problem. The computational results and the sensitivity analysis show that the first model with minimized FC outperforms the second model, which does not consider the FC in minimization and FC affects the optimization.

Retracted: Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

Retracted: Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

Web Semantic-Based MOOP Algorithm for Facilitating Allocation Problems in the Supply Chain Domain

The facility allocation of the supply chain is critical since it directly influences cost efficiency, customer service, supply chain responsiveness, risk reduction, network optimization, and overall competitiveness. When enterprises deploy their facilities wisely, they may achieve operational excellence, exceed customer expectations, and obtain a competitive advantage in today's volatile business climate. Due to this reason, a multi-objective facility allocation problem is introduced in this research with cooperative-based multi-level backup coverage considering distance-based facility attractiveness. The facility of the coverage is further described as two different layers of the coverage process, where demand can be covered as full, partial, and no coverage by their respective facilities. The main objectives of this facility allocation problem are to maximize the coverage of the facility to maximize overall facility coverage in the supply chain network and simultaneously minimize the overall cost.

Centralized supply chain network optimization with UAV-based last mile deliveries

This paper proposes a centralized supply chain network optimization model that maximizes the total profit obtained by a company that produces and/or outsources production, stores, ships and sells products to customers using a fleet made up of trucks and, in the last mile, also of drones. The model includes realistic features of unmanned aerial vehicles (UAVs) in the form of drones with fundamental limitations such as low battery capacities and short delivery ranges. The constrained nonlinear optimization problem is formulated as a variational inequality. Existence and uniqueness results for the solution of the variational inequality are provided along with the results of detailed numerical simulations that emphasize the advantages of the use of a hybrid fleet from enhanced profits to reduction in air pollution. Our quantitative results reveal great promise and insights for the logistics industry in the use of emerging UAV technologies for last mile parcel deliveries as a practical solution within a holistic supply chain network context.

Identifying the operational status of container terminals from high-resolution nighttime-light satellite image for global supply chain network optimization

Container terminals are cargo gateways in the global maritime supply chain network. Major container terminals generally operate throughout the year, but do not operate at night, when container vessels are not calling at ports, or when there is no need to handle containers. Terminal congestion can delay containers’ shipping schedules, which impacts the supply chain network. To optimize global logistics, it is therefore important to understand fully the daily operational status of container terminals. A vessels’ automatic identification system data are not sufficient to determine whether containers are being handled in container terminals at night. Remote sensing, especially nighttime-light (NTL) imagery, might solve this problem. Recently, high-resolution images for the CE-SAT-IIB satellite with a pixel resolution of 5.1 m became available to observe NTL. This study assessed the operational status of container terminals based on satellite image taken at night. Eight terminals in the Port of Tokyo, Japan, were selected for the study. A Sentinel-2A image recorded during the day on 7 April 2021, and a CE-SAT-IIB image recorded during the night on 6 April 2021, were obtained. The digital numbers (DNs) of each red-, green-, and blue-(RGB) band image were analyzed, revealing that the red, green, and blue bands, in that order, had higher DNs in the Sentinel-2A daytime image and the CE-SAT-IIB NTL image at all terminals. One of the eight terminals had a low DN in the CE-SAT-IIB RGB image because its lights were off at the time the image was taken. The operational status of the terminals could be verified from the CE-SAT-IIB image by setting the DN threshold to the green or red bands. We also found that the CE-SAT-IIB image could distinguish white-light-emitting diode (LED) lamps from high-pressure sodium lamps based on color differences in the DNs of the RGB bands. If high-resolution NTL sensors were placed onboard microsatellites, a high-frequency observation constellation network could be constructed using a combination of NTL data and daytime images. This study showed the benefits and usefulness of NTL images of ports; the results will contribute to the overall optimization of the global maritime supply chain network.

Multi-objective sustainable supply chain network optimization based on chaotic particle-Ant colony algorithm.

For the optimal design of the sustainable supply chain network, considering the comprehensiveness of the problem factors, considering the three aspects of economy, environment and society, the goal is to minimize the establishment cost, minimize the emission of environ-mental pollution and maximize the number of labor. A mixed integer programming model is established to maximize the efficiency of the supply chain network. The innovation of this paper, first, is to consider the impact of economic, environmental and social benefits in a continuous supply chain, where the environmental benefits not only consider carbon emissions but also include the emissions of plant wastewater, waste and solid waste as influencing factors. Second, a multi-objective fuzzy affiliation function is constructed to measure the quality of the model solution in terms of the overall satisfaction value. Finally, the chaotic particle ant colony algorithm is proposed, and the problem of premature convergence in the operation of the particle swarm algorithm is solved. Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.

Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory

Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions.

Multi-period design and optimization of classified municipal solid waste supply chain integrating seasonal fluctuations in waste generation

Municipal solid waste (MSW) management should be consistent with the principles of supply chain management, and the integration of relevant theories in supply chain has been proven to be feasible while contributing positively to the management model of MSW. Waste management operators have developed a multiperiod model that optimizes and designs the MSW supply chain by incorporating seasonal variations in classified MSW supply to obtain the optimal supply chain network for each type of MSW in each season. The optimal supply chain network includes the collection, transportation and treatment of MSW. The results of the case study indicated that seasonal fluctuations in MSW fraction generation during different seasons affected the total amount and generation of the four different MSW waste fraction. Consequently, the optimal supply chain network was altered. The model demonstrated the seasonal impact on the MSW supply chain and determine the optimal supply chain network that integrates seasonal fluctuations in MSW fraction generation. The quarterly cost of the model was the largest in summer at 9.72 × 109 CNY and the smallest in winter at 5.80 × 109 CNY.

Facilities Location in the Supply Chain Network Using an Iterated Local Search Algorithm

The supply chain design problem is one of the important problems in supply chain management. In this problem, the goal is to locate several facilities for distribution centers in different geographical areas to cover customer demands. In the model presented in this paper, customer demand is met through distribution centers, while these distribution centers supply products from manufacturing plants. In this paper, a mathematical model of integer linear programming is presented that, in addition to the optimal supply chain network design, considers the facility's location. This model aims to minimize the costs of sending products from the factory to distribution centers, from distribution centers to customers, and establishing distribution centers. To evaluate the proposed model, different network samples were created randomly on small, medium and large scales for experiments. In order to perform the experiments, a linear programming solver (LP Solver) and an iterated local search algorithm were used to compare the performance of each of these methods to find the optimal solution. These experiments show the superiority of the iterated local search algorithm compared to LP Solver in achieving the optimal solution in the shortest runtime for all instances.