A Sustainable Supply Chain Network Model Considering Carbon Neutrality and Personalization

Sustainable supply chain networks have attracted considerable attention in recent years as a means of dealing with a broad range of environmental and social issues. This paper reports a multi-objective mixed-integer linear programming (MILP) model for use in the design of a sustainable closed loop supply chain network under uncertain conditions. The proposed model aims to minimize total cost, optimize environmental impacts of establishment of facilities, processing and transportation between each level as well as social impacts including customer satisfaction. Due to changes in business environment the uncertainty existed in the research problem, in this paper the chance constrained fuzzy programming approach applied to cope with uncertainties in parameter of the proposed model. Then the proposed multi-objective model solves as single-objective model using LP-metric method.

Transportation planning for sustainable supply chain network using big data technology

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.

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.

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.

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

A system dynamics analysis of food supply chains – Case study with non-perishable products

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.

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.

Lateral inventory share-based models for IoT-enabled E-commerce sustainable food supply networks

In this research, a novel mathematical model for sustainable supply chain network design is proposed. The main contribution and novelty of this research are considering environmentally friendly facilities and several thresholds for emitted pollution, which bring this research closer to real‐world conditions. Since the amount of pollution produced by different supply chain facilities is not the same, it is better to make different decisions regarding each repair and renovation measure, i.e., environmental decisions should be considered step‐by‐step. In addition, the proposed model was optimized by the whale optimization algorithm (WOA) to find the best solution in large‐scale instances in a short and reasonable time. Finally, the performance of the proposed model and the reduction of environmental impacts to improve the stability of logistics systems are reviewed in a case study. The results of the computational analysis show the efficiency of the proposed model.

A supplier of products and services aims to minimize the capacity investment cost and the operational cost incurred by unwanted byproducts, e.g. carbon dioxide emission. In this paper, we consider a sustainable supply chain network design problem, where the capacity and the product flow along each link are design variables. We formulate it as a multi-criteria optimization problem. A bio-inspired algorithm is developed to tackle this problem. We illustrate how to design a sustainable supply chain network in three steps. First, we develop a generalized model inspired by the foraging behaviour of slime mould Physarum polycephalum to handle the network optimization with multiple sinks. Second, we propose a strategy to update the link cost iteratively, thus making the Physarum model to converge to a user equilibrium. Third, we perform an equivalent operation to transform a system optimum problem into a corresponding user equilibrium problem so that it is solvable in the Physarum model. The efficiency of the proposed algorithm is illustrated with numerical examples.

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.

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.