Supply Chain Network Design Model Research Articles

Trade credit and loan in capital-constrained supply chain network design model

Trade credit and loan in capital-constrained supply chain network design model

Optimization of pediatric vaccines distribution network configuration under uncertainty

Millions of young people are not immunized in low income (LI) and lower middle income (LMI) countries because of low vaccine availability resulting from inefficiencies in cold supply chains. We create supply chain network design and distribution models to address the unique characteristics and challenges facing vaccine supply chains in LI and LMI countries. The models capture the uncertainties of demand for vaccinations and the resulting impacts on immunization, the unique challenges of vaccine administration (such as open vial wastage), the interactions between technological improvements of vaccines and immunizations, and the trade-offs between immunization coverage rates and available resources. The objective is to maximize both the percentage of fully immunized children and the vaccine availability in clinics. Our research examines how these two metrics are affected by three factors: number of tiers in the supply chain, vaccine vial size, and new vaccine technologies. We tested the model using Niger’s Expanded Program on Immunization, which is sponsored by the World Health Organization. We make many observations and recommendations to help LMI countries increase their immunization coverage.

A sustainable circular supply chain network design model for electric vehicle battery production using internet of things and big data

AbstractDesigning and developing sustainable circular supply chain networks for electric vehicle (EV) lithium‐ion battery recycling and production requires complex environmental sustainability and economic viability assessment. EVs use a lot of data for battery management and delivering optimum performance, and the Internet of Things (IoT) plays a major role in managing this data. This study develops a bi‐objective mixed‐integer linear programming model for designing a sustainable circular supply chain to manage the manufacturing, remanufacturing, and distribution of EV lithium‐ion batteries under uncertainty using the IoT and big data. The proposed model simultaneously minimizes total costs and CO2 emissions and uses IoT to improve network performance and create a traceable and secure environment. A fuzzy multi‐objective method solves the bi‐objective optimization model under uncertainty, and a simulation algorithm examines the effectiveness of the proposed model through simulated problems.

RETRACTED ARTICLE: Sustainable and optimal design of Chinese herbal medicine supply chain network based on risk dynamic regulation mechanism

We propose a robust fuzzy design model for a sustainable closed-loop supply chain network. The model is based on a risk dynamic regulation mechanism. In this way, we can solve the problem of sudden disruptions and uncertain demand in the supply chain of Chinese herbal medicines. We also develop a hybrid algorithm solution to solve the model and design a resilient supply chain network. The specific steps are as follows: (1) The risk dynamic regulation mechanism is created with strong risk resistance by considering the information sharing platform, facility defense, drying station scheduling, safety stock, and shared inventory. (2) Based on the dynamic risk regulation mechanism, we establish a sustainable Chinese herbal medicine supply chain network design model. Then, we use the robust fuzzy method and the epsilon constraint to deal with the uncertainty and integrate the model. (3) We introduce opposition-based learning, cosine convergence factor, and levy flight to the original Whale and Grey wolf algorithms to obtain the Hybrid algorithm, which is used to solve the processed model. The results show the model and algorithm proposed in this paper have strong applicability and advantages in designing closed-loop supply chain networks for Chinese herbal medicine and provide references for relevant decision-makers.

Development of a blood supply chain network design model by considering lateral transshipment and blood substitution

blood supply chain network design is an approach to design an efficient blood supply chain network. This study developed a model for determining the best Blood Supply Chain Network Design solution that considering costs and social sustainability. The developed model was divided into four echelons: blood donation centers, blood banks, regional hospitals, and demand points. The objective of this research was to minimize total supply chain costs while maximize job opportunities. This study considered the concept of lateral transshipment and blood substitution at the third echelon, namely the regional hospital, to ensure that demand was met. The concept of lateral transshipment enabled regional hospitals that having excess of blood unit to satisfy blood supplies to other regional hospitals. Furthermore, blood substitution was a response to emergency conditions, specifically a shortage of appropriate blood product units. Blood substitution scheme is allowed in this research, that is, each type of blood product with a certain group can not only meet its own demand, but can also be used as a replacement for the same product with other compatible groups. a multi-objective possibilistic mixed integer linear programming model was developed. According to the obtained results, the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created can be discovered by the model.

A Flexible Robust Possibilistic Programming Approach for Sustainable Second-Generation Biogas Supply Chain Design under Multiple Uncertainties

The goal of this research is to develop a novel second-generation-based biogas supply chain network design (BG-SCND) model that takes into account the triple bottom line approach. Biogas is a promising renewable energy source that can be obtained from a variety of easily accessible second-generation wastes, including animal manure, municipal waste, and agricultural leftovers. Integrated optimization of the biogas generation system is essential for a speedy and environmentally friendly transition to sustainable biodiesel production. The dynamic environment of the energy market significantly impairs the decisions of the BG-SCND model; therefore, a hybrid solution approach using flexible programming and possibilistic programming is suggested. To verify the suggested model and approach for solving the problem, a thorough computational analysis of a case study is conducted. The case study findings demonstrate that considerable investment is necessary to attain social and environmental well-being goals and safeguard decisions against epistemic uncertainty. Policymakers involved in the planning of biogas production and distribution projects may find the proposed approach useful.

A Grey-Fuzzy Programming Approach towards Socio-Economic Optimization of Second-Generation Biodiesel Supply Chains

This study aims to develop a multi-objective second-generation-based socially responsible supply chain (SGB-SRSC) network design model that considers all dimensions of sustainability: economy, environment, and social. The dynamic nature of the biodiesel supply chain (SC) impairs the SGB-SRSC model decisions; thus, a grey-fuzzy solution approach is developed. Biodiesel is a promising renewable energy resource produced from a variety of easily accessible domestic wastes. For a swift transition towards commercially feasible biodiesel production, integrated optimization of the biodiesel SC system is critical. Using the latest social impact assessment tools, this study provides a decision-support system for developing a biodiesel SC network. A comprehensive computational analysis is performed on a case study to validate the proposed model. The results show that significant investment is required to achieve social well-being goals and secure decisions against uncertainty associated with SGB-SRSC model parameters. Further, it is observed that the expenses of biodiesel production and biodiesel plant installation accounted for a large portion of the overall SC cost. As a result, finding more cost-effective biodiesel production methods is critical to the industry’s economic viability. Regulators and policymakers involved in biodiesel production projects may find the framework useful in obtaining a compromise solution for socio-economic goals based on their preferences.

A Green Dual-Channel Closed-Loop Supply Chain Network Design Model

Environmental considerations have become a significant issue in the design of supply chain networks due to today's increasing globalization trends. Therefore, supply chain network design needs to be managed in an efficient way to deal with the complex networks involved. The aim of this article is to present a multi-objective optimization model for a green dual-channel supply chain network that handles economic and environmental objectives to optimize network flow. A complex mixed-integer linear programming model (MILP) has been proposed in a green dual-channel and closed-loop supply chain (CLSC) network design. The main objective of the generated MILP model is to investigate the optimal selection of echelons and the optimal selection of transportation alternatives between these echelons in a CLSC network that includes an e-commerce channel structure based on economic and environmental considerations. Environmental aims are achieved by decreasing CO2 emissions and by reducing PM (particulate matter) concentration throughout the network. In addition, economic aims are also met by minimizing the overall cost. The validity of the presented model is supported by a case study in the home appliances industry. The results indicate that this model provides valuable knowledge and various alternatives to managers and policymakers depending on the different weight combinations.

A two-stage stochastic model for co-firing biomass supply chain networks

A sustainable alternative to fossil fuels is biomass. The design of cost-efficient biomass networks is necessary to compete with nonrenewable resources. The creation of integrated biomass supply chain (BSC) network design models and solution procedures can contribute to the achievement of this goal. In particular, the emerging bioenergy industry must take advantage of the economies of scale in transportation to minimize the final product’s cost. Hub-and-spoke networks have been proposed as a modeling approach to design large scale BSCs. The majority of these models are deterministic and do not consider the inherent variability in the biomass feedstock, such as physical and chemical properties that affect transportation, the effects of future climate on the biomass supply, overall cost, and production operations. Levels of ash and moisture are directly related to the quality of the feedstock, which negatively affects production of biofuels, increasing transportation and handling costs as well as the burden on the BSC’s efficiency. Varying weather affects biomass yield, which creates a fluctuation in the incoming supply into the network, creating a complex large scale Newsvendors Problem. In this paper, a stochastic hub-and-spoke network model and an efficient solution are proposed to minimize logistical costs by finding an optimal production and distribution network as well as optimal transportation while reducing computational burden by using metaheuristics, such as Simulated Annealing, when solving large NP-hard instances. The proposed solution procedure was compared to recent benchmark models, i.e. Bender’s Decomposition, yielding an overall solution difference of less than 7.71% and average reduction time difference of 91.4%. Case studies with several scenarios based on varying weather conditions were created using realistic data from the northeast region of the U.S.

공급자와 경로 붕괴를 고려한 공급망 네트워크설계모델: 혼합유전알고리즘 접근법

In this paper, a supply chain network design (SCND) model considering supplier disruption and route disruption simultaneously is proposed. Since most of conventional studies have focused either on supplier disruption only or on route disruption only, the simultaneous consideration of supplier disruption and route disruption in the SCND model can reinforce its flexibility and efficiency. The SCND model is represented as a mathematical formulation and implemented using a hybrid genetic algorithm (HGA) approach, one of the meta-heuristic approaches. In numerical experiment, the performance of the HGA approach is compared with those of some conventional approaches using various-sized SCND models, and a sensitivity analysis considering i) the change of the number of main suppliers and ii) that of the number of the backup routes at main suppliers is done. Experimental results show that the HGA approach outperforms the conventional competing approaches, and the flexibility and efficiency of the SCND model proposed in this paper is also proved. Finally, the significance of this paper is summarized and a future research direction is remarked in conclusion.

Unpacking the role of primary packaging material in designing green supply chains: An integrated approach

Due to the adverse impact of packaging materials on several ecosystems, the circular and sustainable approaches to manage packaging waste have been receiving increasing attention worldwide. Plastic pails are widely used primary packaging materials that are cost-effective, lightweight, yet long-lasting. In the present study, we propose a closed-loop supply chain (CLSC) network design model to jointly optimize the decisions related to the location of collection, sorting, and recycling centers, and the quantity of plastic pales to be recycled and/or freshly produced and distributed. The proposed model is augmented with an incentive mechanism to acquire used plastic pails from customers as well as a green supplier selection procedure. In addition to the traditional objective of profit maximization, the proposed model also minimizes carbon emission, maximizes the return of used plastic pails, and prioritize suppliers for the procurement of sustainable packaging raw materials. A set of non-dominated solutions to the proposed multi-objective model are obtained using the Augmented ε-constraint method (AUGMECON). The results of AUGMECON are also compared with other methods such as weighted sum method and Augmented Tchebycheff method. It is oberseved that AUGMECON produces diverse set of pareto-optimal solutions for the considered problem. The applicability of the model is explained using an illustrative example of an adhesive manufacturer in India. Further, several managerial insights are drawn by carrying out sensitivity analysis through scenario building.

Costs of resilience and disruptions in supply chain network design models: A review and future research directions

Supply chain network design (SCND) is a key strategic decision in supply chain management (SCM). One particular area of SCND is concerned with disruption risk modelling. This paper presents a systematic literature review of quantitative models of SCND under disruption risks in industrial SCM and logistics. More specifically, our analysis is focused on different costs induced by the planning of proactive investments in robustness and through parametrical/structural adaptation at the recovery stage. This review can be of value for researchers and decision-makers alike for several reasons. First, we categorise the existing knowledge based on decision-making problems, which can be instructive for a convenient association of a particular SCND problem to a modelling domain according to network-wise, supply-side and demand-side perspectives. Second, our analysis focuses on the costs specifically induced by disruption risks and resilience investments. Third, we offer a dedicated section related to disruption probability formulation methods and their impact on resilience costs. Fourth, the integration of different SCM dimensions (i.e., social impact, environmental impact, responsiveness, and risk-aversion) and the associated multi-objective modelling settings are discussed along with disruption risks in SCND models. Finally, we summarize our findings as insights from a managerial perspective. Drawbacks and missing aspects in the related literature are highlighted, and we lay out several research directions and open questions for future research.

A Supply Chain Network Design Model to Improve Response to Market

A Supply Chain Network Design Model to Improve Response to Market

Supply chain network design model to improve response to market

Supply chain network design model to improve response to market

Sustainable supply chain network design problem: Using the integrated BWM, TOPSIS, possibilistic programming, and ε-constrained methods

This paper proposes a two-stage multi-objective possibilistic integer linear programming sustainable supply chain network design model, minimizing the economic, environmental goals and maximizing the social sustainability goals. The proposed model determines the openings of facilities and the amount of flow of goods across the supply chain. It introduces supplier green image factors in the design of the supply chain network. The model has considered epistemic uncertainty to model the unknown capacity, cost, and demand. The proposed study has been carried out in two stages. In the first stage, BWM (Best-Worst method) and TOPSIS are applied to evaluate the green image weights of suppliers. Further, these green weights are being used in the second phase for the supply chain network design. The study has adopted combined possibilistic programming and Epsilon (ε) constraint method, which was reported infrequently in the literature. Epsilon (ε) constraint method generates distinct Pareto-optimal solutions, which provided a large combination of the trade-off between the cost, emission, and social sustainability. The results facilitate decision-makers to take the decision in an uncertain environment.

Development of a reliable and flexible supply chain network design model: a genetic algorithm based approach

Enhancing the proactive strategic capabilities to withstand the most unfavourable circumstances is always appreciated as a long-term policy rather than incident-based responses. The present research is positioned on this fundamental notion of supply chain risk management with a particular focus on strategic capabilities like reliability and flexibility that often conflict with cost. Accordingly, the authors propose a multi-objective mathematical model for designing a four-echelon supply chain that optimises cost, reliability, and volume flexibility. Interestingly, this research is the maiden effort to optimise the supply chain with these trifold objectives and herein lies the novelty as well as the challenges. Consequently, a genetic algorithm based approach is utilised as the solution methodology. To demonstrate the effectiveness of the proposed method, the small problem instances and the four-echelon problems have also been validated through exact methods and simulated annealing algorithm, respectively. A case study on a footwear supply chain involving three echelons is also presented to showcase the industrial applicability and adaptability of the proposed model. A fuzzy TOPSIS method has been adopted in the case study to incorporate the expert opinion for assigning priorities to the objectives. Supply chain professionals can leverage this methodology to establish a risk resistant supply chain.

The water‐energy nexus at the hybrid bioenergy supply chain: A sustainable network design model

Nowadays, the fossil fuel price fluctuations are high, their doubtless exhaustion is distressing, air pollution is a severe problem, and these issues seriously threaten the global economy. Bioenergy is a significant alternative source, with positive environmental, social, and economic impacts, that can reduce the world’s heavy dependence on fossil fuels. As a result, the importance of the design, implementation, and management of bioenergy supply chains have increased in recent years. This research, hence, is aimed to develop a bioenergy supply chain network design (BSCND) model to study the hybrid second (i.e., Jatropha; agricultural residues; and livestock manure) and third (i.e., microalgae) generations of biomass. Since water and energy are two momentous sources for sustainable development of societies where the increasing demand has become a worldwide concern, the water-energy nexus concept is another subject discussed in this study. And since sustainability is another issue to be dealt with in modern energy supply chains, a multi-objective sustainable mathematical model is developed in this study to present. A three-echelon sustainable hybrid bioenergy supply chain model has been proposed with four objective functions that minimize the total cost, reduce the environmental impacts, maximize the energy production, and minimize the water consumption to produce bioenergy. The GIS-based model, also, has been used to form a suitability map of Jatropha/microalgae cultivation. Results of a case study conducted to show the model performance using the MINMAX goal programming method. It has revealed that producing energy from microalgae/Jatropha is more viable compared to agricultural residues and livestock manure.

Competitive green supply chain network design model considering inventory decisions under uncertainty: a real case of a filter company

A robust bi-level model of the single-product multi-period network design problem is proposed for a competitive green supply chain considering pricing and inventory decisions under uncertainty and disruption risks. The bi-level programming approach is used through this model to demonstrate the competition among two supply chains; the leader and the follower, respectively. After modelling the competition and applying pricing decisions by defining a price-dependent demand, disruption risks are analysed through the model. The proposed model simultaneously considers demand uncertainty and disruption risks and is capable of dealing with such uncertainties by implementing resilience strategies including, inventory decisions, and having a contract with reliable suppliers. Moreover, to consider the environmental issues, controlling CO2 emissions and managing the reverse flow were added to the model. Our approach to mitigate the problem uncertainties is to use the possibilistic programming method. The Karush-Kuhan-Tucker (K-K-T) optimality conditions are deployed to make a single-level equivalent form. Since the integrated model was bi-objective, the ϵ-constraint method is implemented to make a single objective integrated model. Finally, some managerial implications are discussed through an industrial case example.

Multi-Period Multi-Product Supply Chain Network Design in the Competitive Environment

This paper studies a supply chain network design model with price competition. The supply chain provides multiple products for a market area in multiple periods. The model considers the location of manufacturers and retailers and assumes a probabilistic customer behavior based on an attraction function depending on both the location and the quality of the retailers. We aim to design the supply chain under the capacity constraint and maximize the supply chain profit in the competitive environment. The problem is formulated as a mixed integer nonlinear programming model. To solve the problem, we propose two heuristic algorithms—Simulated Annealing Search (SA) and Particle Swarm Optimization (PSO)—and numerically demonstrate the effectiveness of the proposed algorithms. Through the sensitivity analysis, we give some management insights.

Integrated optimization model for hydrogen supply chain network design and hydrogen fueling station planning

This paper focuses on developing a mathematical model that covers the entire hydrogen supply network. The classical hydrogen supply chain network design (HSCND) model is integrated with the hydrogen fueling station planning (HFSP) model to generate a new formulation. The proposed model considers the feedstock supply, the installation and operation of hydrogen facilities, the operation of transportation technologies, and the carbon capture and storage (CCS) system. Two primary hydrogen fueling technologies, namely on-site fueling (hydrogen is produced on-site) and standard fueling (hydrogen is delivered by road), are considered. The problem is formulated as a mixed-integer linear programming (MILP) model that minimizes the least cost of hydrogen (LCOH). The necessity of considering various components within a single framework is demonstrated through a case study in Franche-Comté, France. The role of each key model component (such as the fueling technology, feedstock transportation, and CCS system) is analyzed. The proposed model is capable of studying the interactions that exist between different parts of a hydrogen supply network. Consequently, more comprehensive construction plans for the HSCN are guaranteed.