Multi-objective fuzzy programming model to design a sustainable supply chain network considering disruption

Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming

Transportation planning for sustainable supply chain network using big data technology

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply chain networks change continuously due to varying objectives, requirements as well as restrictions. The ability of supply chain networks to adapt to changing requirements, especially in fast moving industries, is decisive. As sustainability issues in supply chain management are increasingly concerned among companies, adequate approaches for redesign and replanning considering sustainability become essential. This paper proposes a fuzzy-based approach to support the dynamic design and planning of sustainable supply chain networks from a stakeholder's perspective. An approach is introduced to define and select sustainability measures. In addition, a continuous monitoring of stakeholder requirements, supply chain performance and market trends accounts for the necessity of redesign and replanning. The approach will qualify companies to select sustainability measures to handle rising complexity within sustainable supply chain decision-making context. The findings contribute to the literature on stakeholder management within sustainable supply chain management (SSCM) research.

This chapter introduces an integrative framework coupling GIS tools, biophysical model, enterprise budgeting tools, GHG emission models, and optimization mechanism in determining a sustainable switchgrass biofuel supply chain network. The integrative framework is applied to a case study of replacing 30% of gasoline used in transportation in Tennessee. Using high-resolution spatial data in a multiobjective mixed-integer programming model, we find that land use choice makes substantial impacts on the deployment of the supply chains under different objectives. When considering private cost alone, hay and pasture land concentrated in the east and central Tennessee will be the major source for switchgrass production. If targeting GHG emission minimization solely, more than 500 thousand hectares of the state’s cropland is converted to switchgrass for biofuel production. Moreover, the trade-off between cost and GHG emissions in the supply chains shows that the marginal rate of substitution between total cost and GHG emissions on the frontier curve increases at an accelerating rate. Our findings illustrate the importance of land resource management on the sustainability of a dedicated energy crop supply chain.

The development of a carbon-neutral production and distribution method that minimizes the consumption of input resources and energy and facilitates resource recycling is an important global issue. Therefore, this study aimed to explore a new type of sustainable supply chain network (SSCN) that focuses on carbon neutrality and personalization. The first significance of the SSCN model is that it combines carbon neutrality and personalization problems into one research framework for the sustainable development of corporate management environments. In addition, evaluation and optimization mechanisms are crucial for decision-making in an SSCN. Thus, in this study, a creative evaluation and optimization mechanism was developed as a second significance for the sustainable development of the supply chain. The model used two evaluation indices (EIs) to measure the performance of the entire network in terms of the degree of personalization and carbon neutrality. The proposed SSCN is formulated as a mathematical model using mixed-integer nonlinear programming, and it is implemented by combining two types of approaches: hypergeometric distribution and a genetic algorithm. The results show the evaluation values and comparisons by the average values of transportation cost, handling cost, fixed costs, carbon dioxide emission cost, total cost, and average processing time. In addition to providing help for mass-customized production enterprises, this study also provided constructive suggestions for the conversion of small or venture enterprises to multi-variety and small-scale production, and it suggests the direction of job creation for such enterprises.

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment.

With the issues of climate change continuously taking center stage, there is thus a pressing need for the pharmaceutical companies to assess the sustainability of their supply chains. However, the highly regulated nature of the industry has prompted various supply chain decisions such as material sourcing, siting of plants, and distribution network configuration to be locked into the registration of the new drug. This could consequently lead to inefficient supply chain operation during the drug commercial stage, yielding high greenhouse gas emissions per unit of drug product. Previously, we have proposed a systematic framework for design of a sustainable commercial-stage pharmaceutical supply chain network that can be driven from the early drug development stages. In this paper, we extend the framework by incorporating more functionalities. We also introduce a novel decision support system, called PharmaSC, which implements the framework. PharmaSC system has been developed by integrating two computer tools—an Excel-based analytic hierarchy process and LLamasoft’s Supply Chain Guru—into one coherent framework for selecting the most suitable suppliers and manufacturers, mapping a supply chain superstructure network, and optimizing the network to identify and resolve trade-offs between economic and environmental factor. To illustrate the system, an industrially motivated case study involving a paclitaxel production is solved.

There is a growing concern over environmental pollution resulting from the production. Moreover, the expansion of various industries has led to an increased demand for raw materials. To address these challenges, this paper aims to investigate the bi-objective optimization of a sustainable reverse supply chain network while considering two key sources of uncertainty in the returned product quality and the remanufacturing capacity. Additionally, the study considers the effects of carbon tax policies and government subsidies on remanufactured products, while also focusing on three important sustainability aspects - economic, social, and environmental. The study uses two quality thresholds at inspection centers to sort products, and the epsilon constraint and NSGA-II are applied to solve the model. Through numerical analysis, the research demonstrates that objective functions are sensitive to uncertain parameters and minimum acceptable quality levels. Furthermore, the study reveals that government subsidies can offset the negative effects of carbon tax policies.

In this paper, we show how sustainable supply chains can be transformed into and studied as transportation networks. Specifically, we develop a new supply chain model in which the manufacturers can produce the homogeneous product in different manufacturing plants with associated distinct environmental emissions. We assume that the manufacturers, the retailers with which they transact, as well as the consumers at the demand markets for the product are multicriteria decision-makers with the environmental criteria weighted distinctly by the different decision-makers. We derive the optimality conditions and the equilibrium conditions, which are then shown to satisfy a variational inequality problem. We prove that the supply chain model with environmental concerns can be reformulated and solved as an elastic demand transportation network equilibrium problem. Numerical supply chain examples are presented for illustration purposes. This paper, hence, begins the construction of a bridge between sustainable supply chains and transportation networks.

Purpose The purpose of this paper is to design a sustainable supply chain network (SCN) for omnichannel environment in order to provide better service to customers through flexible distribution. Thus, there is a need to incorporate multiple-channel distribution in the network design of supply chains (SCs). Design/methodology/approach A multiple-channel distribution supply chain network (MCDSCN) has been proposed under omnichannel environment. This proposed model integrates online giants with local retailer’s distribution network in an uncertain environment with sustainability. To incorporate sustainability, an objective function is added to reduce carbon content along with other objectives of minimization of SC cost. The model turns out to be mixed-integer linear programming model which is coded in GAMS and solved using CPLEX solver. Findings The proposed MCDSCN model is compared with conventional SCN. Furthermore, it was found that the proposed MCDSCN model has achieved significant saving in SC cost and is also more sustainable than conventional SCN. The proposed model also enables online giants to integrate their distribution network with local retailer’s distribution network. Practical implications Through proposed model, customers are free to access product and services as per their choice of channels which increases their convenience, reach and satisfaction. Originality/value The proposed MCDSCN model is a novel approach to design flexible distribution systems. This would significantly help organizations to design their distribution network more effectively to meet global competition.

Enhancing the supply chain resilience of the information and communications manufacturing industry is closely related to the competitiveness of enterprises and the security of the national economy, and has attracted widespread attention from all sectors of society. This study develops a multidimensional synergistic resilience framework based on the Technology-Organization-Environment theory. Using panel data from 29 Chinese provinces over the 2011–2022 period, it empirically examines the impact of artificial intelligence technology on supply chain resilience in the information and communication technology manufacturing industry and investigates its underlying mechanisms. Research findings: Artificial intelligence significantly enhances supply chain resilience, and this conclusion holds after a series of endogeneity and robustness tests. Mechanism analysis reveals that artificial intelligence promotes the low-carbon transition by optimizing energy intensity and synergistically enhances resilience effects through technological innovation. Heterogeneity analysis shows that the impact of artificial intelligence on supply chain resilience is stronger in eastern China, significantly outperforming western China, but its effect in central China is not significant. Supply chain disruptions weaken the positive effects generated by artificial intelligence when they occur. The research provides a theoretical foundation and practical basis for improving the resilience of information and communications manufacturing supply chain networks, thereby contributing to the development of intelligent, resilient and sustainable supply chain networks.

The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California.All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.

Closed-loop supply chains (CLSCs) are essential for maximising the value creation over the entire life cycle of a product. The design of these networks is increasing due to growing online businesses and rising sustainability awareness. This study develops a multi-objective optimisation model for sustainable CLSC network problem considering supply chain’s inherent complexity (multi-echelon, multi-product, multi-mode and multi-period nature) along with price-sensitive demand, consumer’s incentives and different quality levels of product. The proposed model seeks to optimise total cost and carbon emissions generated by production, distribution, transportation, and disposal activities. A two-stage algorithm, through the integration of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Co-Kriging approach is utilised to determine the trade-off between costs and carbon emissions in the CLSC network. Data collected from a leading European household appliance company was used to analyse and interpret the developed model. The results show that the proposed two-stage approach provides robust outcomes and is computationally less expensive than the epsilon constraint approach. The study evidences the positive effects of incentive pricing on returned goods in the reverse logistics network and provides multiple trade-off solutions for supply chain managers to make informed decisions.

A Stochastic Multi-Objective Model for a Sustainable Closed-Loop Supply Chain Network Design in the Automotive Industry