Supply Chain Network Model Research Articles

Cost-effective supply chain for electric vehicle battery remanufacturing

Large-scale adoption of electric vehicles can reap significant energy and environmental benefits while also reducing reliance on fossil fuels. Nonetheless, accompanying the benefits of electric vehicles, several economic and ecological challenges arise from the production of Lithium-ion batteries, which are currently the most popular type of batteries used in electric vehicles. Remanufacturing is a promising end-of-life strategy and can lead to more sustainable Lithium-ion battery supply chains to support large-scale adoption of electric vehicles. Several factors will dictate the feasibility and effectiveness of remanufacturing, including economic viability, production capability, and battery demand and supply. Unfortunately, while there exists significant research efforts on remanufacturing at the laboratory scale, there lacks research that investigates Lithium-ion battery remanufacturing at the enterprise scale. Motivated by this, in this paper, a state-of-the-art closed loop supply chain network model for Lithium-ion battery remanufacturing considering different quality levels of spent battery returns is proposed. An optimization model is developed to maximize the network profit and a sensitivity analysis is performed to determine the impact of several important model parameters on the profitability of the proposed supply chain network. A numerical case study is implemented which shows that 9.81–30.93% increase in profit can be achieved if remanufacturing is integrated in Lithium-ion battery supply chain networks. Moreover, the sensitivity analysis shows that careful implementation of the proposed algorithm coupled with understanding of battery parameters are the keys to implementing cost-effective electric vehicle Lithium-ion battery supply chains. In all, this research will help stimulate the implementation of remanufacturing, promote economically and environmentally sustainable supply chain management in the electric vehicle battery industry, and support the transportation sector in reducing environmental burdens.

Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach

Closed loop supply chain network design (CL-SCND) is a critical economic and environmental activity. The closing of the loop to handle return, uncertainty in business environment, various supply chain risks, impact network design processes and performance of the firm in the long term. Thus, it is important to design robust and reliable supply chain structures and obtain network configurations which can always outperform the other configurations under the worst cases of risks and uncertainty. A generic closed-loop supply chain network based on mixed integer programming formulation is proposed with direct shipping to the customer from manufacturing plants as well as shipping through distribution centers under supply risks, transportation risk and uncertain demand using a robust optimization (RO) approach. A large number of numerical tests are carried out to test the performance of the model by considering a total of four levels of uncertainty for four different network structures types. The results of the tests confirm that the risk and uncertainty based integrated supply chain network models are more efficient (cost effective) than the other set of network configurations which treats the supply chain risks and uncertainty post-ante. To demonstrate the applicability of the proposed model, the case of an Indian e-commerce firm which wants to redesign its supply chain structure is presented. The results of case study show that the topology obtained from integrated treatment of risk and uncertainty called as RORU model, outperform other supply chain networks on various network performance indicators such as supply chain costs, the number of facilities open or close and the amount of products flowing through supply chain echelon. Thus, RO based mathematical modeling to address risks and its applicability for SCND for close loop supply chain is proposed, demonstrated and applied in practical cases.

A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study

Biodiesel has recently attracted much interest as an appropriate alternative for the fossil diesel which is mostly consumed in the transportation sector. Meanwhile, Jatropha Curcas L. has emerged as one of the most promising biofuel feedstocks due largely to its salient features such as compatibility with arid and semi-arid lands. In this regard, this paper unveils a two-stage stochastic programming model for the design of an integrated green biodiesel supply chain network from Jatropha Curcas feedstocks. The concerned biodiesel supply chain design is an environmentally friendly mixed-integer linear programming, multi-period and multi-product model. Furthermore, a flexible stochastic programming approach is developed and applied to the supply chain network model. This proposed approach is indeed an extension of a two-stage scenario-based stochastic programming approach which incorporates min-max relative regret in a soft worst-case framework. Given the natural variability of long-term decision-making, fuel demand and Jatropha Curcas trees yielding are hemmed in by uncertainty in this model. The performance of the proposed framework and biodiesel supply chain network design is corroborated through ten realizations and a myriad of various analyses in a real case study of Iran. The derived results and their analyses acknowledge the efficiency and applicability of the model.

A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints

Abstract Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS’s solution is higher than that of HGA’s solution, whereas HGA is faster than HHS.

Dynamics of quality as a strategic variable in complex food supply chain network competition: The case of fresh produce.

In this paper, we construct a competitive food supply chain network model in which the profit-maximizing producers decide not only as to the volume of fresh produce produced and distributed using various supply chain network pathways, but they also decide, with the associated costs, on the initial quality of the fresh produce. Consumers, in turn, respond to the various producers' product outputs through the prices that they are willing to pay, given also the average quality associated with each producer or brand at the retail outlets. The quality of the fresh produce is captured through explicit formulae that incorporate time, temperature, and other link characteristics with links associated with processing, shipment, storage, etc. Capacities on links are also incorporated as well as upper bounds on the initial product quality of the firms at their production/harvesting sites. The governing concept of the competitive supply chain network model is that of Nash Equilibrium, for which alternative variational inequality formulations are derived, along with existence results. An algorithmic procedure, which can be interpreted as a discrete-time tatonnement process, is then described and applied to compute the equilibrium produce flow patterns and accompanying link Lagrange multipliers in a realistic case study, focusing on peaches, which includes disruptions.

Modeling and analysis of under-load-based cascading failures in supply chain networks

The phenomena of cascading failures often happen in complex networks. In most infrastructure networks, the subsequent failures of nodes are caused by overload and many overload cascading failure models are developed. Recently, some of these models are adopted to investigate the cascading failure phenomenon in supply chain networks, which cannot capture the real case very well. The subsequent failures of upriver/downriver firms in supply chain networks are triggered by the decreased product demand/material supply, i.e., under-load cascading failures take place. Based on the under-load failures, this paper proposed a more realistic cascading failure model for supply chain networks. In this model, the node firms are characterized by capacities with upper bound parameter $$\alpha $$ and lower bound parameter $$\beta $$ . Results showed that $$\alpha $$ has a negative relationship with cascading size, while $$\beta $$ has a positive relationship with cascading size. In addition, cascading size is mainly determined by $$\beta $$ , and $$\alpha $$ helps mitigate the cascading propagation. In reality, $$\alpha $$ is correlated with the spare production capacity of firms, the holding cost of which is high under stable operation of the market. $$\beta $$ is related to the core competence of firms, which is hard to improve in the short term. Our work may be helpful for developing the cascade control and defense strategies in supply chain networks.

Discrete Switched Model and Fuzzy Robust Control of Dynamic Supply Chain Network

Supply chain network is more complex and dynamic under the uncertain demand and the lead time. Robustness is a key index of the stable operation for the supply chain network. We investigate a fuzzy robust strategy to realize the robust operation of the supply chain network with the production lead times and the ordering lead times under the uncertain customer demand. A discrete switched model of the dynamic supply chain network with the lead times and the uncertain customer demand is established based on T-S fuzzy systems. Then a fuzzy switched strategy is proposed to control the switching actions among subsystems. Furthermore, by introducing the inhibition rate γ, a fuzzy control strategy for the dynamic supply chain network is put forward to suppress the impacts of the lead times and the uncertain customer demand on the operation of the dynamic supply chain network. The fuzzy robust strategy composed of the fuzzy switched strategy and the fuzzy control strategy can guarantee the robust operation of the supply chain network at low cost. Finally, the simulation researches show the advantage of the proposed fuzzy robust strategy through the comparisons with the common robust strategy.

The equilibrium model of dual channel closed-loop supply chain network based on carbon trading and carbon tax

In this paper, on the basis that carbon trading and carbon tax are brought into the category of the carbon management, an equilibrium model of a dual channel closed-loop supply chain network is developed. Many decision-makers and their independent behaviours are fully processed based on the variational inequality theory and Lagrange duality theory. Based on the above analysis, a finite-dimensional variational inequality formulation is established. Besides, qualitative properties of the equilibrium model and a modified projection algorithm are given.

The equilibrium model of dual channel closed-loop supply chain network based on carbon trading and carbon tax

In this paper, on the basis that carbon trading and carbon tax are brought into the category of the carbon management, an equilibrium model of a dual channel closed-loop supply chain network is developed. Many decision-makers and their independent behaviours are fully processed based on the variational inequality theory and Lagrange duality theory. Based on the above analysis, a finite-dimensional variational inequality formulation is established. Besides, qualitative properties of the equilibrium model and a modified projection algorithm are given.

A competitive multiperiod supply chain network model with freight carriers and green technology investment option

This paper presents a multiperiod supply chain with freight carriers network model. In this model manufacturers, retailers, and carriers maximize the net present value (NPV) of their investments in ecologically friendly technology. Future production, inventory, transaction, and transportation costs savings are used to help fund investments. The environmental impact of production, inventory, transportation, and consumption of products in the supply chain network are all integrated. The tradeoff between the initial technology investment and its ecological footprint effect is considered for the supply chain planning period. We provide variational inequality formulations of the equilibrium conditions and then propose the modified projection method, along with conditions for convergence. Numerical examples are examined with an analysis of the effects of ecologically friendly technology investments on supply chain network production, transportation, and sales.

Optimizing decentralized production\u2013distribution planning problem in a multi-period supply chain network under uncertainty

Decentralized supply chain management is found to be significantly relevant in today’s competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem’s convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.

A Robust Reliable Forward-reverse Supply Chain Network Design Model under Parameter and Disruption Uncertainties

Social responsibility is a key factor that could result in success and achieving great benefits for supply chains. Responsiveness and reliability are important social responsibility measures for consumers and all stakeholders that strategists and company managers should be concerned about them in long-term planning horizon. Although, presence of uncertainties as an intrinsic part of supply chains could adversely affect the best set plans by field experts. Accordingly, uncertainty of parameters and uncertainties caused by disruptions should be regarded in planning process of networks to prevent unpredictable negative consequences of such uncertainties for all echelons of supply chain. Based on enumerated matters, the aim of this paper is to design a reliable multi-echelon closed loop supply chain network model that maximizes social responsibility while minimizing fixed establishing and variable processing costs of network design. To cope with uncertainty of parameters, stochastic programming is applied and an effective reliable modelling method is employed to appropriately control unpleasant economic impacts of disruptions. Notably, an efficient robust programming method is applied to give the decision makers the capability to control level of risk-averseness of decisions while modelling uncertain parameters. Finally, the proposed model is solved and its outputs are analyzed on the basis of generated test problems which shows correct performance and applicability of extended model in real world problems.

An Optimized Supply Chain Network Model Based on Modified Genetic Algorithm

For complex multi-source, multi-product, multi-stage Supply chain network (SCN) design problem, we propose an optimization supply chain network model. We consider cash conversion cycle as an objective to this model and utilize a modified genetic algorithm to solve the problem. To describe the structure of supply chain network, we propose a new encoding method and a genetic algorithm with modified genetic operators. We use the Pareto approach to obtain the set of Pareto-optimal solutions. In order to evaluate the performance of the modified genetic algorithm and validate the model, we conduct comparisons with standard genetic algorithm and the simulated annealing genetic algorithm. Experimental results show that the modified genetic algorithm achieved better CPU time and the accuracy of the Pareto-optimal solutions than the alternative algorithms and the model was effective.

An integrated multi-objective supply chain network and competitive facility location model

In this study, a multi-objective supply chain (SC) network optimization model based on the joint SC network optimization and competitive facility location models is proposed to analyse the results of ignoring the impacts of SC network decisions on customer demand. The objectives utilized in the model are profit maximization, sales maximization and SC risk minimization. The unique unknown variable within the model is the demand. The demand at each customer zone is assumed to be determined by price and the utility function. The utility function is defined as the availability of same-day transportation from the distribution centre (DC) to the customer zone. The application of the proposed model is illustrated through a real-world problem and is solved as single and multi-objective models. The results of single and multi-objective models are subsequently compared. After solving the problem, a sensitivity analysis is also conducted to test the applicability of the model with respect to various parameter coefficients, such as price elasticity, one–day replenishment coverage impact, risk factors (disruption probabilities) and the relative weights of the objectives.

Design of a dynamic closed-loop supply chain network using fuzzy bi-objective linear programming approach

A multi-period, multi-product, multi-echelon, bi-objective, and dynamic closed-loop supply chain network model with fuzzy parameters is put forward in this article. In order to make the model realistic, the uncertainties of capacity, demand, and price are integrated into the model. Thus, a fuzzy bi-objective mixed integer model is developed to maximize the total profit and minimize the wastes of facilities. To deal with the proposed model, a solution method is put forward by incorporating four methods. Computational experiments demonstrate that the proposed hybrid method can find efficient solutions based on CLSC network managers’ risk preferences. And the proposed models and methods in this paper have important managerial implications for decision-makers.

Hybrid modeling and empirical analysis of automobile supply chain network

Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

Vulnerability of interaction-based supply chain network and its self-organised criticality

With the increasing complexity of the supply chain, it brings benefits as well as increasing vulnerability to the supply chain. Research on the cause and characteristic of vulnerability is prerequisite for effective management measures. First, a supply chain network model based on Poisson distribution and preference selection mechanism is established, of which the interactions are further studied. On this basis, the cascading failure process of supply chain network i analysed, and then the impact of supply chain network topology on the system vulnerability and the determination of node vulnerability are further discussed. Finally, an example of certain automotive group supply chain is used to verify the effectiveness and practicality of this method. The study finds that the root of supply network vulnerability lies in cascading failure and there is a certain power-law relationship between its scale and frequency, namely supply chain network owns self-organised criticality, which affects the vulnerability of supply chain network. Meanwhile, the node vulnerability assessment method could provide an effective basis for taking effective preventive measures.

Multi-objective evolutionary approach for supply chain network design problem within online customer consideration

Supply chain network design is one of the most important strategic decisions that need to be optimized for long-term efficiency. Critical decisions include facility location, inventory, and transportation issues. This study proposes that with a dual-channel supply chain network design model, the traditional location-inventory problem should be extended to consider the vast amount of online customers at the strategic level, since the problem usually involves multiple and conflicting objectives. Therefore, a multi-objective dual-channel supply chain network model involving three conflicting objectives is initially proposed to allow a comprehensive trade-off evaluation. In addition to the typical costs associated with facility operation and transportation, we explicitly consider the pivotal online customer service rate between the distribution centers (DCs) and their assigned customers. This study proposes a heuristic solution scheme to resolve this multi-objective programming problem, by integrating genetic algorithms, a clustering analysis, a Non-dominated Sorting Genetic Algorithm II (NSGA-II), and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach. A scenario analysis is conducted to understand the model’s performance.

Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty

Researchers and practitioners are taking more interest in developing sustainable garment supply chains in recent times. On the other hand, the supply chain manager drops sustainability objectives while coping with unexpected natural and man-made disruption risks. Hence, supply chain managers are now trying to develop sustainable supply chains that are simultaneously resilient enough to cope with disruption risks. Owing to the importance of the considered issue, this study proposed a network optimization model for a sustainable and resilient supply chain network by considering sustainability via embodied carbon footprints and carbon emissions and resilience by considering resilience index. In this paper, initially, a possibilistic fuzzy multi-objective sustainable and resilient supply chain network model is developed for the garment industry considering economic, sustainable, and resilience objectives. Secondly, a possibilistic fuzzy linguistic weight-based interactive solution method is proposed. Finally, a numerical case example is presented to show the applicability of the proposed model and solution methodology.

A Closed-Loop Supply Chain Equilibrium Model with Random and Price-Sensitive Demand and Return

This paper proposes a decentralized closed-loop supply chain network model consisting of raw material suppliers, manufacturers, retailers, and recovery centers. We assume that the demands for the product and the corresponding returns are random and price-sensitive. Retailers and recovery centers face penalties associated with shortage demand and supply, respectively. We derive the optimality conditions of the various decision-makers, and establish that the governing equilibrium conditions can be formulated as a finite-dimensional variational inequality problem. The qualitative properties of the solution to the variational inequality are discussed. Numerical examples are provided to illustrate the effects of demand and return uncertainties on quantity shipments and prices.