Closed-loop supply chain network equilibrium strategy model with environmental protection objectives

With the increasing popularity of ecological civilization and sustainable development,enterprises should consider environmental protection measures in their operations in addition to pursue their economic interests.This paper establsihes a closed-loop supply chain network model composed of multiple suppliers,manufacturers,retailers,recyclers,and demand markets—regarding their dual goals of the profit maximization and the minimization of carbon emissions.The conditions necessary for establishing overall equilibrium and an equilibrium model of the entire closed-loop supply chain network are determined by applying variational inequality and dual theory.A modified projection contraction algorithm is used to design a model-solving program.Finally,using numerical examples,the paper conducts a comparative static analysis on important parameters such as the weight coefficients of environmental protection objectives and consumers'awareness of low-carbon environmental protection and attains some beneficial enlightenment on management.The results indicate that when the environmental protection objectives of a certain type of enterprise increases,both the economic benefits and environmental protection performance will improve;when the environmental protection objectives of all enterprises increases simultaneously,environmental protection performance improves significantly,but the changes in economic benefits of different enterprises are inconsistent and profit coordination is more complex.Although consumers’awareness of low-carbon preference could improve environmental performance,it reduces the overall profits of network members and the entire closed-loop supply chain network as a whole.The above conclusions can be used as a reference for the government in designing low-carbon environmental protection policy and in closed-loop supply chain research.

Improving the Performance of Urban Waste Management Systems in the Context of a Closed-Loop Supply Chain

The saving of non-renewable energies, as well as the reduction of emissions into the environment, are two crucial objectives of industrial production. The recovery of post-consumer products associated with the use of end-of-life products is part of a context of optimization of these objectives. This recovery maximizes the use of resources from end-of-life products in a circular logic while recording the environmental footprint. This study considers a recycling strategy adapted to the need and urgency to reduce greenhouse gas emissions caused by global warming. The proposed model aims to optimize the profits of the circular manufacturing strategies while minimizing operational costs (collection, sorting, recycling), transport, GHG emissions and recycling. In this paper, a compromise between the gains of CM and the costs associated with it was studied. The robustness of the designed model was tested using a case study based on real-world scenarios. A sensitivity analysis was carried out to study the impact of the emission cost on the overall objective, considering the two options currently offered to industries. The obtained results support companies to take the ecological aspect into account and integrate sustainable development into their strategic axes for their logistics supply chains.

Pricing and decision of carbon emission reduction for closed-loop supply chain considering carbon trading mechanism

To determine the optimal pricing and carbon emission reduction decision, a closed-loop supply chain with a manufacturer and a retailer is investigated. In this system, the manufacturer manufactures new products and remanufactures used products while the retailer is responsible for selling new products and remanufactured products. The profit functions of the manufacturer and the retailer are developed, and the corresponding solution formulae for decision variables are given by the Stackelberg game model. Finally, a numerical example is given, and the optimal wholesale price, retail price, carbon emission reduction and others are obtained. Through the sensitivity of the unit carbon allowance price, some significant managerial insights are derived.

Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

Remanufacturing Closed-Loop Supply Chain Model with Radio Frequency Identification Affecting Recycling Rate

In order to construct a resource-saving and environmentfriendly society,the advantages of radio frequency identification(RFID) were considered.And it put forward the idea of introducing RFID in the recycling activities of waste products.Taking into account such elements as the technical level of RFID,cost saving from remanufacturing and the cost of RFID tags,both centralized and decentralized supply chain models with different participants in waste product collection were created,in order to determine the optimal pricing strategy and RFID technical level.In the end,sensitivity analyses were conducted to analyze the impacts of scaling parameter for additional increased recovery rate with RFID on pricing and RFID technology level,and impacts of cost saving on the profits of participants in different remanufacturing closed-loop supply chain models.

Dynamic Pricing Model for the Operation of Closed-Loop Supply Chain System

A class of closed-loop supply chain system consisting of one manufacturer and one supplier is designed, in which re-distribution, remanufacturing and reuse are considered synthetically. The manufacturer is in charge of recollecting and re-disposal the used products. Demands of ultimate products and collecting quantity of used products are described as the function of prices and reference prices. A non-linear dynamic pricing model for this closed-loop supply chain is established. A numerical example is designed, and the results of this example verified the model’s validity to price for the operation of closed-loop supply chain system.

A closed-loop Supply Chain Network Equilibrium Model with Fuzzy Demand under Production Capacity Constraints and Price Rigidities

A closed-loop supply chain network equilibrium problem is examined,which consists of mutual competitive manufacturers with production capacity constraints and retailers with fuzzy market demands,mutual competitive collectors as well as a binding price ceiling of the commodities.By utilizing the credibility measure of fuzzy event,variational inequality and Lagrange dual theory,the optimum behaviors of manufactures,retailers,collectors and consumers are described.Therefore,the supply chain network equilibrium model is established.Numerical examples were given to illustrate the impact of production capacity constraints and price ceiling on the network equilibrium pattern.The results show that the shortage of commodities in consumer market will be more serious,the wholesale price of product and purchase price of waste products rise,the profits of retailers decrease,the profits of manufacturers and collectors increase as the government gradually reduce the binding price ceiling on competitive markets,and the trend will be more obvious in the existence of production capacity constraints at same time.

Research on Alliance Decision of Dual-Channel Remanufacturing Supply Chain Considering Bidirectional Free-Riding and Cost-Sharing

This study delves into the formation dynamics of alliances within a closed-loop supply chain(CLSC)that encom-passes a manufacturer,a retailer,and an e-commerce platform.It leverages Stackelberg game for this exploration,contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models.The non-alliance model acts as a crucial benchmark,enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations.Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships.We thoroughly investigate the consequences of diverse alliance behaviors,bidirectional free-riding and cost-sharing,and the resultant effects on the optimal decision-making among supply chain actors.The findings underscore several pivotal insights:(1)The behavior of alliances within the supply chain exerts variable impacts on the optimal pricing and demand of its members.In comparison to the non-alliance(D)model,the manufacturer-retailer(MR)and manufacturer-e-commerce platform(ME)alliances significantly lower both offline and online resale prices for new and remanufactured goods.This adjustment leads to an enhanced demand for products via the MR alliance’s offline outlets and the ME alliance’s online platforms,thereby augmenting the profits for those within the alliance.Conversely,retailer-e-commerce platform(ER)alliance tends to increase the optimal retail price and demand across both online and offline channels.Under specific conditions,alliance behavior can also increase the profits of non-alliance members,and the profits derived through alliance channels also exceed those from non-alliance channels.(2)The prevalence of bidirectional free-riding behavior largely remains constant across different alliance configurations.Across these models,bidirectional free-riding typically elevates the equilibrium prices in offline channel while negatively affecting the equilibrium prices in other channel.(3)The effect of cost-sharing shows relative uniformity across the various alliance models.Across all configurations,cost-sharing tends to reduce the manufacturer’s profits.Nonetheless,alliances initiated by the manufacturer can counteract these negative impacts,providing a strategic pathway to bolster CLSC profitability.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

International Supply Chain Cooperation and China’s Role

In recent years,the challenges facing the development of international supply chains have been increasing.Major country competition,COVID pandemic and geopolitical conflicts have impacted the development of international supply chains and continue to have negative effects.Against this background,the international community should cooperate to deal with the risks and challenges.

Global Supply Chain Governance and the Future of Globalization

Over the past 30 years or so,globalization has been underpinned by ever-evolving supply chains.The heightened attention to supply chain issues in recent years has been due to a series of global events in the world that have had a significant and profound impact.Factors such as the COVID pandemic,the Ukraine Crisis and increased geopolitical tensions have led to supply chain crises globally or regionally.

Comparative Analysis and Legal Reflection on the Boundaries of Human Rights Due Diligence in the Supply Chain

As global supply chains become increasingly lengthy and complex, human rights due diligence in the supply chain is becoming a controversial focal point in the accountability of multinational corporations. In recent years, legislative practices in the field of human rights due diligence have shown a trend from voluntary soft law toward mandatory hard law, and from corporate due diligence for their own operations towards extended due diligence for the entire supply chain. However, there is a divergence in national practices regarding the extent to which human rights due diligence should extend along the supply chain and the manner in which it should be incorporated into domestic legal policies. International soft law interpretations surrounding the boundaries of human rights due diligence in the supply chain are decentralized, posing risks of interpretation diversification, boundary blurring, and procedural formalization, as well as risks of misinterpretation and misuse. Meanwhile, some countries and regions are vigorously promoting mandatory legislation on human rights due diligence in the supply chain, which has profound implications for the stability of global supply chains and the international economic and trade order. Against this backdrop, it is crucial to explore the reasonable boundaries of human rights due diligence in the supply chain. Instead of applying a one-size-fits-all approach,the rationality of legal factors and the complexity of practical factors should be considered, applying context-specific measures based on the varying degrees of linkage between companies and negative human rights impacts in the supply chain. China should be particularly wary of the “chilling effect” of mandatory legislation on human rights due diligence in the supply chain, attaching great importance to national supply chain security and international supply chain competitiveness.Additionally,China should actively promote the implementation of voluntary human rights due diligence under the United Nations framework, and accelerate the enhancement of China's discourse power in the international rule-making process in the fields of industry and commerce as well as human rights.

Analysis of the Supply Chain in Industrial Companies in Guayaquil,Ecuador-A Secondary Publication

This research clearly outlines the supply chain in a process of evolution and digital transformation,where the collaboration of its members is concentrated,having technological tools to help chain management.To analyze the advantages that this transformation produces in the supply chain,as well as its different processes,this study has a systemic perspective that involves precise elements for organizational development,such as the different phases of each process,operations,logistics,and distribution.It must be borne in mind that any strategy in industrial companies grants the automation of procedures in the supply chain and determines a product in any phase of production,making the organization more sensitive to any variation in orders.The methodology included a bibliographic and non-experimental review that allows a descriptive and analytical study,which details the various characteristics of the fact that is being investigated,collecting information through interviews with different people who are involved with industrial companies.Among the results obtained,it was identified that digital transformation helps reduce costs and generates greater profitability.In conclusion,it was obtained that the digital supply chain helps in each of the phases of the processes,these are supervised by devices that help to have fast and effective information.

Automobile Transportation Logistics Supply Chain Management Strategy

To enhance the management level and quality of the automobile transportation logistics supply chain and promote innovation and development in automobile transportation logistics enterprises,it is essential to strengthen the construction of the automobile transportation logistics supply chain management model.This can be achieved through the gradual improvement of the automobile transportation logistics management process,ensuring that the management of the automobile transportation logistics supply chain proceeds in an orderly manner.The aim is to improve automobile transportation and logistics service levels while meeting the changing market supply needs.This will enable automobile transportation and logistics enterprises to maintain steady economic benefits and enhance their core competitiveness in the market.Therefore,this paper has conducted a comprehensive exploration and research on managing the automobile transportation logistics supply chain.Corresponding management strategies are proposed as a starting point to achieve the aforementioned goals.

Research on the Digital Transformation of the Whole Industry Chain of Guangdong Supply and Marketing Cooperative

Guangdong Supply and Marketing Cooperative serves as an important institution for the“Three Rural Areas”in the Guangdong-Hong Kong-Macao Greater Bay Area.Its digital transformation and optimization serve as powerful supports for rural revitalization.This paper systematically reviews the main practices and effectiveness of the digital transformation within the Guangdong Supply and Marketing Cooperative.It also analyzes the current development dilemmas faced in the construction of digital supply and marketing.Additionally,it proposes targeted solutions,including building a big data resource base,optimizing the digital supply and marketing cloud platform,developing digital publictype agricultural social service applications,establishing a new model of rural e-commerce,enhancing the traceability management system for agricultural products,and strengthening the construction of the digital human resources system.These proposals aim to further promote the strategy for revitalizing the countryside.

Pricing Decisions in Dual-Channel Closed-Loop Supply Chain Under Retailer’s Risk Aversion and Fairness Concerns

This paper studies the price decisions in a dual-channel closed-loop supply chain with a risk-averse retailer and a risk-neutral manufacturer by modeling and analyzing three cases:(1)the retailer does not have fairness concerns;(2)the retailer has fairness concerns and the manufacturer considers it;and(3)the retailer has fairness concerns and the manufacturer does not consider it.The effects of risk aversion and fairness concerns on the pricing decisions,profits and demand are examined in differing scenarios.

Closed-loop Supply Chain Based Battery Swapping and Charging System Operation: A Hierarchy Game Approach

To realize optimal day-ahead operation of battery swapping and charging systems(BSCSs),a closed loop supply chain(CLSC)based management scheme is proposed,where the game theory is adopted for benefits allocation.The CLSC is used to depict the battery-swapping-charging process between the battery charging stations(BCSs)and battery swapping stations(BSSs).The arrival,departure and swapping service of electric vehicles(EVs)at BSSs is modeled as distinct queues based on the network calculus theory.The depleted batteries(DBs)and well-charging batteries(WBs)based interaction among BCSs and BSSs is formulated as a Stackelberg game.In the game,one BCS acts as the leader and the BSSs act as the followers.The BCS sets optimized prices to maximize its utility and the BSSs optimally demand WBs,supply DBs and provide battery swapping services to maximize their own utilities while guaranteeing the quality of service(QoS)needed for battery swapping.The existence of Stackelberg equilibriums(SEs)of the proposed game is proved.A differential evaluation based hybrid algorithm is proposed to compute an SE.The effectiveness of proposed method has been demonstrated by the simulation results,guaranteeing the QoS and balancing benefits among the BCS and BSSs while maximizing social welfare.

Dynamic decision and its complex dynamics analysis of low-carbon supply chain considering risk-aversion under carbon tax policy

This study establishes a low-carbon supply chain game model under the centralized decision situation and the decentralized decision situation considering the manufacturer risk-aversion behavior, and discusses the influence of the manufacturer risk-aversion behavior on the optimal decision, profit, coordination, and complex dynamics of the supply chain. We find that comparing with the risk-neutral decentralized decision, the increase of manufacturer's risk tolerance attitude can narrow the gap between the supply chain profit and the centralized decision, but it will further reduce the carbon emission reduction level. The increase of risk tolerance of the manufacturer and carbon tax will narrow the stable region of the system. Under this situation, the manufacturer should carefully adjust parameters to prevent the system from losing stability,especially the adjustment parameters for carbon emission reduction level. When the system is in a chaotic state, the increase of carbon tax rate makes the system show more complex dynamic characteristics. Under the chaotic state, it is difficult for the manufacturer to make correct price decision and carbon emission reduction strategy for the next period, which damages its profit, but increases the profit of the retailer and the supply chain. Finally, the carbon emission reduction cost-sharing contract is proposed to improve the carbon emission reduction level and the supply chain efficiency, achieving Pareto improvement. The stability region of the system is larger than that in the centralized decision situation, but the increase of the cost sharing coefficient will reduce the stability of the system in the decentralized decision-making situation.

Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently.The pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient costs.Blockchain(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake circulation.The designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DSCM)method.With this motivation,the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme(BCODL-DSCMRS)for Pharmaceutical Industries.Firstly,Hyperledger fabric is used for DSC management,enabling effective tracking processes in the smart pharmaceutical industry.In addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and consumers.The spotted hyena optimizer(SHO)algorithm is used to optimize the performance of the HDBN model.The design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the work.The presented model is tested on the UCI repository’s open-access drug reviews database.

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain.In an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain.As an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative study.The root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,respectively.Among these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,respectively.The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more accurate.The prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.