Supply Chain Network Model Research Articles

The effect of CHINA RAILWAY Express on alleviating the Red Sea Waterway Crisis: A GTAP model-based study

In November 2023, the Red Sea Waterway Crisis broke out, which caused the goods originally shipped to Europe via the Red Sea-Suez Canal Route to be transported around the Cape of Good Hope in Africa, thus increasing the shipping cost and shipping time. Firstly, this paper uses the Supply Chain Network Model and the Optimization Model to theoretically analyze the impact of this incident. Secondly, the Global Trade Analysis Project (GTAP) Model is employed to analyze the impact of the crisis on China. At the same time, it also explains whether the impact of the incident can be alleviated by using CHINA RAILWAY Express (CR Express). Finally, this paper compares the upward pressure changes of domestic commodity prices in China, Germany, France, Netherlands and Italy before and after the adoption of CR Express. The results of the study are as follows. (1) The outbreak of the Red Sea Waterway Crisis has a great negative impact on China's macro economy. When China switches to the use of CR Express, the impact on China's GDP and terms of trade is reduced. (2) The outbreak of the Red Sea Waterway Crisis has a greater impact on China's import trade than on its export trade. (3) The outbreak of the Red Sea Waterway Crisis has a great negative impact on China's textile and clothing industry and electronic equipment industry. (4) CR Express can ease the upward pressure of domestic commodity prices. Taken together, the above results can provide reference for China and other countries to cope with similar impact events.

Evaluating Supply Chain Network Models for Third Party Logistics Operated Supply-Processing-Distribution in Thai Hospitals: An AHP-Fuzzy TOPSIS Approach

Background: This study introduces a novel supply chain management (SCM) model tailored for the hospital industry in Thailand. The model emphasises the integration of third-party logistics (3PL) providers to streamline supply-processing-distribution (SPD) functions. By outsourcing non-core activities like SPD to 3PL providers, hospitals can enhance their operational efficiency, allowing healthcare professionals to focus on core tasks and ultimately improving service delivery. Methods: This research employed a dual methodology, combining an analytic hierarchy process (AHP) with a Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). These approaches evaluated various SCM models based on multiple hospital logistics performance attributes. Results: The AHP results highlighted on-time delivery, patient safety, utilisation rate, and emergency procurement as critical criteria for selecting the optimal model. Fuzzy TOPSIS analysis identified the SCIII: W-G-H model as the most suitable for implementation in Thai hospitals. This model incorporates a centralised warehouse for negotiation leverage, a Group Purchasing Organisation (GPO) for cost efficiency, and regional SPD hubs for effective inventory management and rapid responses to demand fluctuations or emergencies. Conclusions: Adopting this SCM model is expected to significantly enhance supply chain performance, reduce operational costs, and improve the quality and safety of patient care in Thai hospitals.

Analisis Aliran Rantai Pasok Produk Koffie Hiedeung CV Toean Koffie di Kabupaten Karawang

Increasing coffee consumption in Indonesia provides opportunities for the coffee processing industry. CV Toean Koffie is one of the companies in Karawang Regency engaged in coffee processing, which is involved in providing added value and brand branding, the product is known as Koffie Hideung. The supply of coffee beans for Koffie Hideung production is supplied by the farmer communities on the Sanggabuana Mountains in Karawang. The supply chain flow of Koffie Hideung products starts from the farmer level as a supplier to product marketing to consumers. The supply chain concept basically has interconnected aspects including the flow of goods, financial flow, and information flow which will determine the continuity of the supply chain performance mechanism properly. Good supply chain performance is determined by the role of the supply chain members involved. This study aims to analyze the supply chain conditions of Koffie Hideung products using the Food Supply Chain Network (FSCN) model. Based on the results of the analysis, the Food Supply Chain Network (FSCN) model includes 6 aspects consisting of, supply chain structure, supply chain objectives, resources involved, business processes in the supply chain, supply chain management, and supply chain performance. The supply chain structure of Koffie Hideung products involves a certain process from the production of coffee beans in farmers, the manufacturing process by CV Toean Koffie, distribution, to the final consumption by customers.

Overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability factor

At present, the supply chain is multi-layered and complicated, and its failure detection is based solely on the characteristic threshold, which does not lack the ability to describe the complex supply chain. An overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability is proposed. Based on the multi-layer supply chain network including raw material suppliers, manufacturers, distributors and retailers, the overload cascading failure model of the multi-layer supply chain network is constructed through networking, and the overload cascading failure process of the multi-layer supply chain network is described through three aspects: initial load, node capacity and overload node load distribution. By monitoring and analyzing the status update messages of each node in the supply chain network, and considering the delay probability factor, the node and path that may lead to cascading failure are identified by predicting the delay probability of the next message through the historical message delay probability, and the overload cascading failure detection of the multi-layer supply chain network is realized. The experimental results show that the algorithm can effectively detect the failure of each overloaded enterprise node in the multi-layer supply chain network, which is helpful to enhance the early warning and response ability of the cascade failure of the supply chain network. The algorithm can realize the failure detection of supply chain network under different attack conditions and initial failure ratio of nodes, and measure the anti-risk ability of supply chain network. Under deliberate attack and when the initial attack node is a supplier network layer node, it has a greater impact on the multi-layer supply chain network.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

An optimization model for sustainable multi-product multi-echelon supply chain networks with U-shaped assembly line balancing under uncertainty

This study presents an integrated supply chain network with suppliers, manufacturers, assemblers, and customers. The proposed model considers a U-shaped assembly line with three sustainability objective functions. We consider assumptions considering different types of raw materials, multiple products, location selection of manufacturers, location selection of assemblers, and capacity of suppliers. The problem is formulated non-linear and then linearized as a multi-objective model. Some cost and demand parameters are considered uncertain and are represented by fuzzy sets and theory. The proposed uncertain model is first converted to a multi-objective crisp model by applying the modified robust possibilistic programming approach. Then, the obtained crisp multi-objective model is solved by an interactive-fuzzy optimization approach in the literature. For computational study, some test problems are generated and solved using an original deterministic formulation and the crisp form of the uncertain formulation. The obtained results are analyzed and compared according to the objective function values. Finally, an extensive sensitivity analysis is performed on the parameters of the models.

Designing a responsive-sustainable-resilient blood supply chain network considering congestion by linear regression method

Blood Components (BCs) are highly perishable. Perishability, the short lifespan of BCs, and unpredictability in demand volumes make managing the Blood Supply Chain (BSC) more complex. Therefore, it is essential to expand a proper blood network model to tackle uncertainty and minimize the time of delivering blood to patients. This study presents an innovative multi-objective multi-echelon multi-period mixed-integer non-linear model for efficient, responsive, and Green Blood Supply Chain (GBSC) networks considering resilience measures that are divided into three assessment metrics and congestion in Permanent Blood Centers (PBCs). The suggested model aims at significantly reducing prices, Waiting Time (WT) in the system, and environmental damages while simultaneously boosting the supply chain networks' level of resilience. Apart from that, a linear regression is proposed to forecast the demand for blood to decrease the possibility of a shortage of BCs supply in the Blood Supply Chain Network (BSCN). To meet the suggested model, firstly, LP-metric method is used for solving small-sized problem instances. However, this method was inefficient for solving large-sized instances. So, the Lagrangian Relaxation (LR) method is employed. Additionally, the presented decision model's efficacy is evaluated against a case study from real life. Moreover, a sensitivity analysis is undertaken on important problem parameters to offer insightful managerial information.

Model Integrasi Sistem Produksi Single Supllier & Multi Buyer

To compete in a market that continues to grow rapidly, companies need to have the ability to meet market needs. One method that is often used to determine the amount of production in each producer cycle is Economic Production Quantity (EPQ). An integrated inventory model is used to achieve optimal results in the supply chain. In this research, a supply chain network model is developed between one producer and two buyers with the aim of minimizing inventory costs. Producers make simultaneous deliveries to each buyer, and these goods are then sold on the market to consumers. Annual demand from each buyer is assumed to vary, and the inventory cycle is also the same length. The optimal production quantity in one production cycle is found through the use of a simple algorithm. Numerical experiments are carried out to test this algorithm. Numerical experimental results show that the delivery frequency in one production cycle is 4 times. with an optimal production quantity of 1,898 units. The optimal quantity for each shipment to each buyer is 380 units for buyer 1, 285 units for buyer 2, 475 units for buyer 3, 569 units for buyer 4, and 190 units for buyer 5. The total inventory costs in this model, which involving one producer and five buyers, is Rp. 76,757,641.02. This inventory cost is the minimum cost obtained from the numerical experiments that have been carried out.

A Comprehensive Strategic-Tactical Multi-Objective Sustainable Supply Chain Model with Human Resources Considerations

This study proposes a multi-objective Sustainable Supply Chain Network (SSCN) model considering human resources limitations with different levels of expertise. The proposed model includes multiple suppliers, factories, and customers, where the construction of factories is a strategic decision, and determining the amount of production and allocating human resources with different levels of expertise is taken as a tactical decision. Also, the capital recovery factor has been used in the mathematical model to prevent the influence of strategic decisions on tactical decisions. The results from the mathematical models of epsilon limit, Non-dominated Sorting Genetic Algorithm II (NSGA II), and Multi-Objective Particle Swarm Optimization (MOPSO) show that by reducing the amount of shortage, the amount of production has increased, and as a result, the costs of production, supply and distribution and transportation have increased. Also, with the increase in the production and transportation of products, greenhouse gas emissions have also increased. Examining the impact of the uncertainty rate on the Robust Fuzzy Optimization (RFO) model also shows that with the increase of this coefficient, due to the increase in the demand in the network, the total costs of production, distribution, purchase of raw materials, and transportation have increased. Examining different comparison indices between solution methods also shows that heuristic methods have higher efficiency than exact methods. MOPSO is more efficient than NSGA II for the designed mathematical model in these investigations.

Optimizing the emission control policies and trade-in program effects: A carbon-constrained closed-loop supply chain network model

Economic development and human activities affect the natural environment severely. The government has promogulated several policies such as carbon caps and trading scheme, government subsidies, and green credit to control emissions. Accordingly, enterprises should profit on the premise of undertaking social responsibilities, green technology and trade-in programs are two ways to benefit the environment. However, the interaction between regulations and emission abatement measures is largely unknown. A carbon-constrained closed-loop supply chain network model is proposed to discuss the joint effect of the policies and operational measures. The model is constructed under two scenarios, i.e., Manufacturer-led mode and Retailer-led mode. Sensitivity analysis is conducted to compare the scenarios. The results show that although the retailer-led mode promotes resource recovery and supply chain performance, it weakens manufacturers’ profit. We find that the carbon caps and trading scheme is effective in emission reduction when the initial carbon quota is relatively less, which indicates the importance of carbon caps allocation method. The government subsidies stimulate green manufacturers to do more emission reduction efforts enhancing sustainability. The subsidy on green products has more obvious effect on coordinating economic and environmental targets. We also reveal environmental awareness positively affects the emission abatement level. Finally, we extend our models to two special cases with (i) generalization of customers and (ii) different production cost function forms. The main conclusions remain valid in the special cases. The theoretically derived conclusions contribute to the operational research literature and will guide policymakers and enterprises in the manufacturing industry.

FUZZY MULTI-OBJECTIVE NONLINEAR PROGRAMMING PROBLEMS UNDER VARİOUS MEMBERSHIP FUNCTIONS: A COMPARATİVE ANALYSIS

Fuzzy sets have been applied to various decision-making problems when there is uncertainty in real-life problems. In decision-making problems, objective functions and constraints sometimes cannot be expressed linearly. In such cases, the problems discussed are expressed by nonlinear programming models. Fuzzy multi-objective programming models are problems containing multiple objective functions, where objective functions and/or constraints include fuzzy parameters. Membership functions are crucial to obtain optimal solution of fuzzy multi-objective programming model. In this study, a green supply chain network model with fuzzy parameters is proposed. Proposed model with nonlinear constraints is a fuzzy multi-objective nonlinear programming model that minimizes both transportation costs and emissions generated by two vehicle types during transportation. The model is used in Zimmermann's Min-Max approach by considering triangular, hyperbolic and exponential membership functions and optimal solutions are obtained. When optimal solutions are compared, it is seen that optimal solution obtained using the hyperbolic membership function is better than the optimal solutions obtained from triangular and exponential ones. Maximum common satisfaction level calculated using hyperbolic membership function for proposed model is λ=0.97. Sensitivity analysis is also carried out by taking into account distances between suppliers, manufacturers, distribution centers and customers, as well as customer demands.

Designing a reverse supply chain network with quality control for returned products: Strategies to mitigate free-riding effect and ensure compliance with technology licensing requirements

For agility and cost-effectiveness, remanufacturing was highlighted as a solution to a capacity shortage for ventilators during the Covid-19 pandemic. In post-pandemic times, recycling was introduced as a solution to raw material shortcomings. However, it is recognized that implementing these reverse logistics activities creates operational challenges, including quality control of returned ventilators and product information leakage of product information in the process of passing technical information from the original equipment manufacturer (OEM)s to the reverse logistic service provider. The proposed reverse supply chain network model includes competing collection centers, third party remanufacturers (remanufactures), and recyclers investigating the case of ventilator products in three pandemic scenarios: pre-pandemic, during the pandemic, and post-pandemic. The minimum acceptable quality for remanufacturing is determined. The model is then extended with technology acquisition settings to allow remanufacturers to receive OEM licenses for accessing technical needs. The possible leakage of technical information is then considered, along with incentivizing and penalizing strategies. We found that the penalizing strategy improves the technology acquisition level at the cost of reducing the overall profit, while the incentivizing strategy can maintain the profittextcolorblacks but does not markedly improve the technology acquisition level remarkably.

A Game Theoretic Competitive Supply Chain Network Model with Green Investments and Labour

In light of the recent severe Supply Chain (SC) disruptions that have occurred across multiple industries around the globe, three essential and linked themes have emerged in SC management: the well-being of employees, SC sustainability, and competition between SCs for limited resources. In this paper, we create a game-theoretic SC network model that incorporates together non-cooperative SC competition, employee productivity and engagement, and green investing. Each competing firm within the network seeks to maximise its profit by determining an optimal flow of products and allocation of green investments across the SC according to a predetermined budget. A carbon tax on emissions and consumer sustainability preferences are also included in the model. The model is solved using a Variational Inequality reformulation. The illustrative numerical examples presented in this paper have been inspired by the Maltese dairy industry and demonstrate the applicability of the model to real-world problems. The results highlight the significance of the employee engagement factor in enabling firms to adopt and realise more sustainable SC practices.

Optimization of Closed-loop Supply Chain Network for Vegetable and Fruit Agricultural Products Considering Fuzzy Demand and Purchasing Price

Due to the seasonal and perishable nature of vegetable and fruit agricultural products, which can easily cause sudden changes in demand and prices in a short period of time, a decision model framework was established to address the uncertainty issues related to vegetable and fruit agricultural products. The framework consists of triangular fuzzy numbers, a closed-loop supply chain network model for vegetable and fruit agricultural products, and an optimization model based on chance constrained programming, while considering demand, return rate,utilization rate. And with the goal of minimizing the sum of fixed costs, transportation costs, purchasing costs, packaging costs, storage costs, processing cost and disposal costs in the supply chain network, the model is validated through two scenarios: fuzzy demand and deterministic demand. The model calculation results indicate that under certain other conditions, the return rate is positively correlated with the total cost; The relationship between utilization rate and total cost may be either positive or negative, influenced by the combined effects of transportation costs and disposal costs in reverse logistics activities; The cost increase and facility selection corresponding to adjacent demand proportion nodes are different. Proved the effectiveness and feasibility of the model.

Presenting a Fuzzy Multiobjective Mathematical Model of the Reverse Logistics Supply Chain Network in the Automotive Industry to Reduce Time and Energy

The current research aims to design a fuzzy multiobjective model of the reverse logistics supply chain network in the automotive industry, taking into account the energy and time reduction approach. The automobile industry is one of the industries with a high demand worldwide. To continue the competition, the manufacturers of the leading car equipment should strive for better product quality by continuously improving their production processes, directing the production of greenhouse gases with low carbon levels, and increasing sustainability. In this regard, reverse supply chain networks and closed-loop chains have unique features that are very useful in the industry under review. The goal is to transform this model into a supply chain of a secure link in the automotive industry. Deterministic methods, genetic algorithm, particle swarm algorithm, and several scenarios with different aspects have been used to solve the model. The results show that the effectiveness of the three ways in terms of solution time is higher in the deterministic solution method. Proper use of the proposed process can help managers effectively manage the flow of recycled products concerning environmental considerations, and this process provides a sustainable competitive advantage for companies.

Supply chain network model of cost accounting based on improved particle swarm optimisation algorithm

Abstract This paper examines the current supply chain network model (SCNM) in order to effectively apply cost accounting information to strengthen supply chain management and solve the problem of low overall efficiency of the model. This paper constructs the SCNM of cost accounting, analyses the characteristics of the model and designs the variables of the model. In order to optimise enterprise cost accounting, the corresponding mathematical model is constructed, and the preconditions and constraints for solving the model are set up. In addition, particle swarm optimisation (PSO) algorithm is used to solve the model, and the moving speed of particle swarm is updated by dynamic relaxation factor. Finally, the validity of the model and the accounting cost are simulated. The results show that the model can effectively solve the optimisation problem of SCN in accounting cost.

A novel supply chain network evolving model under random and targeted disruptions

Due to the fact that there is a lack of comprehensive understanding of how the dynamic nature of supply chain networks (SCNs) interrelates with network structures, particularly network topologies under disruptions. This research employs a novel evolving model of a supply chain network (SCNE model) by modifying the Barabási and Albert (BA) model to capture the phenomenon of regional economy and the factor of firms’ attractiveness, considering the degree, the locality preference, and the heterogeneity of SCN members simultaneously. We then analyze the SCNE model via the mean-field theory and conduct simulation study to identify the scale-free characteristic of the proposed supply chain network model. Additionally, we leverage node and edge removal to emulate random and targeted disruptions. We measure and compare the robustness of four network models, i.e., the SCNE model, the Erdos and Rényi (ER) model, the BA model, and the Watts and Strogatz (WS) model using two essential metrics, i.e., the size of the largest connected component and the network efficiency. We find that the robustness of the SCNE model is better than the BA model and the WS model on the whole in the presence of disruptions. Also, from the node level, the SCNE model maintains resilience, behaving similarly to the ER model against random disruptions while it shows vulnerability under targeted disruptions, responding in line with the BA model and the WS model. From the edge level, the network efficiency of the SCNE model changes slowly, and the topological structure of the SCNE model slightly changes initially but decreases rapidly at some value, as well as the BA model, the WS model, and the ER model. Based on the results, we summarize key points of the implications for research and practice in supply chain management.

Modelling the barriers of rice supply chain in India using the fuzzy logic approach

PurposeRice is an important grain in Indian scenarios, and the purpose of the research work is to identify the attributes which can be the possible barriers in the traditional rice supply chain network.Design/methodology/approachA multilevel conceptual model is developed based on the literature review, and a field study is conducted by administering a questionnaire from the experts. Fuzzy logic methodology and a ranking score method is applied to identify the rice supply chain performance and the barriers of the traditional rice supply chain network.FindingsThe rice supply chain performance index for the traditional rice supply chain network is obtained, and the performance of the existing rice supply chain is found to be “fair”. The “information flow” is the attribute that can be a critical weak attribute in the traditional rice supply chain network. A proposed model of the blockchain technology-enabled rice supply chain network is developed as a solution for the “information flow” barrier.Research limitations/implicationsThe present research work is focussed on the generalized rice supply chain model of the Indian scenario, and more detailed studies can be carried out based on the regional issues.Originality/valueThe rice supply chain plays an important role in Indian economic development, and hence the current research paper focusses on identifying the barriers and the performance of the existing rice supply chain network.

Research on Risk Propagation in Complex Supply Chain Networks Based on SEIR Model

The complexity of supply chain network structure makes the risk propagation among the enterprises in the supply chain more and more complicated. This paper constructs a SEIR risk propagation model for complex supply chain networks based on the analytical idea of contagion model, which reveals the risk propagation mechanism of supply chain networks from a macro perspective. The article verifies the existence of risk propagation threshold and the stability of different equilibrium points by analyzing the basic regeneration number, equilibrium point and stability of the model; explores the effectiveness of different parameters on the basic regeneration number by combining sensitivity analysis; and verifies them by using MATLAB simulation software. The results show that the basic regeneration number is a key factor affecting the risk propagation, and the risk diffusion in this system can be effectively curbed by controlling the basic regeneration number.

Designing a New Location-Allocation and Routing Model with Simultaneous Pick-Up and Delivery in a Closed-Loop Supply Chain Network under Uncertainty

Background: In this paper, a new closed-loop supply chain (CLSC) network model, including economic, social and environmental goals, is designed. This paper’s primary purpose is to meet customers’ uncertain demands in different scenarios where the new robust-fuzzy-probabilistic method has been used to estimate the exact demand. Furthermore, strategic and tactical decisions, such as vehicle routing, facility location and optimal flow allocation in the CLSC network, are considered, and features such as queuing system in product distribution and time window in product delivery are considered. Methods: To solve the problem, NSGA II and MOPSO have been used. Results: The results of solving numerical examples in larger sizes show that as the environmental effects decrease and the social effects increase, the design costs of the total supply chain network (SCN) increase. Moreover, the NSGA II is more efficient than the MOPSO in problem-solving and achieving comparison indicators. Conclusions: The results of sensitivity analysis show that with increasing network uncertainty rate, the total costs of the SCN, the amount of greenhouse gas emissions and the maximum vehicle traffic time increase.