Green Supply Chain Network Design Research Articles

Green design of a cellulosic butanol supply chain network: A case study of sorghum stem bio-butanol in Missouri

Cellulosic butanol is a very promising renewable fuel to consider for the future transportation market. However, the seasonal availability of the raw materials, high maintenance cost, and high logistical cost of the biomass energy supply chain are the main factors impeding the commercialization and large-scale-production of this energy source. Furthermore, research focusing on an environmental or green supply chain network design of cellulosic butanol has been insufficient. This study focused on designing a green supply chain network for cellulosic butanol. A life cycle analysis was integrated into a multi-objective linear programming model to optimize the cellulosic butanol supply chain network. With the objectives of maximizing the economic profits and minimizing the greenhouse gas emissions, the proposed model can optimize the location and size of a bio-butanol production plant. The mathematical model was applied to a case study in the state of Missouri, and solved the tradeoff between the feedstock and market availabilities of sorghum stem bio-butanol.

A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions

Abstract This paper presents a fuzzy bi-objective bi-level model with a price-dependent demand for the network design of a closed-loop supply chain in the presences of random disruptions at suppliers. The environmental issues resulted in considering two strategies, namely: adding a reverse flow to the supply chain and controlling the amount of CO2 emissions. In this model, the uncertain demand is assumed as a function of price offered to the customers by the supply chain and its rival. The proposed model determines outsourcing strategies, pricing decisions that maximize the total profit in such a competitive situation and minimizes the amount of CO2 emissions by production processes. A hybrid approach combining Karush–Kuhn–Tucker (K-K-T) conditions and possibilistic method was developed to solve the fuzzy bi-level model. Then the e-constraint method is used to convert the integrated bi-objective model to a single-objective one. Finally, important managerial insights are obtained from an empirical case study of a filter industry.

A three echelon revenue oriented green supply chain network design

Green supply chain network designing has been studied during last decades. As carbon emissions considered as a major index in today's activities around the world, here a three echelon-multi product network including manufacturer, distributor, retailer have been provided and tried to minimize the pollution gathered from manufacturing and distribution of products all over the chains which causes extra costs as penalty to the system. As we faced with these penalties, the model determines selling prices of products for manufacturer and distribution center simultaneously by locating these centers in order to maximize the profits all around the network. Finally, the proposed model is solved through the numerical examples and the sensitivity analysis and important parameters are reported to find some management insights.

Extending the scope of eco-labelling in the food industry to drive change beyond sustainable agriculture practices

New consumer awareness is shifting industry towards more sustainable practices, creating a virtuous cycle between producers and consumers enabled by eco-labelling. Eco-labelling informs consumers of specific characteristics of products and has been used to market greener products. Eco-labelling in the food industry has yet been mostly focused on promoting organic farming, limiting the scope to the agricultural stage of the supply chain, while carbon labelling informs on the carbon footprint throughout the life cycle of the product. These labelling strategies help value products in the eyes of the consumer. Because of this, decision makers are motivated to adopt more sustainable models. In the food industry, this has led to important environmental impact improvements at the agricultural stage, while most other stages in the Food Supply Chain (FSC) have continued to be designed inefficiently. The objective of this work is to define a framework showing how carbon labelling can be integrated into the design process of the FSC. For this purpose, the concept of Green Supply Chain Network Design (GSCND) focusing on the strategic decision making for location and allocation of resources and production capacity is developed considering operational, financial and environmental (CO2 emissions) issues along key stages in the product life cycle. A multi-objective optimization strategy implemented by use of a genetic algorithm is applied to a case study on orange juice production. The results show that the consideration of CO2 emission minimization as an objective function during the GSCND process together with techno-economic criteria produces improved FSC environmental performance compared to both organic and conventional orange juice production. Typical results thus highlight the importance that carbon emissions optimization and labelling may have to improve FSC beyond organic labelling. Finally, CO2 emission-oriented labelling could be an important tool to improve the effects eco-labelling has on food product environmental impact going forward.

Fuzzy multi-objective sustainable and green closed-loop supply chain network design

This article addresses a design problem of a closed loop supply chain, including suppliers, manufacturers, distribution centers, customers, warehouse centers, return centers, and recycling centers. The problem entails three choices regarding recycling, namely, product recycling, and components recycling raw material recycling. Modeling this chain is carried out by accounting for environmental considerations, total profit optimization, and reduction of lost working days due to occupational accidents, we well as maximizing responsiveness to customer demand. In order to solve the model, genetic algorithm has been used and multiple scenarios with different aspects have been studied. Solving this model provides decisions regarding opening or closing of each of the components of the network and the optimal product flow among them. The results prove the feasibility of the presented model and the applicability of the developed solution methodology.

A green supply chain network design framework for the processed food industry: Application to the orange juice agrofood cluster

Food production has put enormous strain on the environment. Supply chain network design provides a means to frame this issue in terms of strategic decision making. It has matured from a field that addressed only operational and economic concerns to one that comprehensively considers the broader environmental and social issues that face industrial organizations of today. Adding the term “green” to supply chain activities seeks to incorporate environmentally conscious thinking in all processes in the supply chain. The methodology is based on the use of Life Cycle Assessment, Multi-objective Optimization via Genetic Algorithms and Multiple-criteria Decision Making tools (TOPSIS type). The approach is illustrated and validated through the development and analysis of an Orange Juice Supply Chain case study modelled as a three echelon GrSC composed of the supplier, manufacturing and market levels that in turn are decomposed into more detailed subcomponents. Methodologically, the work has shown the development of the modelling and optimization GrSCM framework is useful in the context of eco-labelled agro food supply chain and feasible in particular for the orange juice cluster. The proposed framework can help decision makers handle the complexity that characterizes agro food supply chain design decision and that is brought on by the multi-objective nature of the problem as well as by the multiple stakeholders, thus preventing to make the decision in a segmented empirical manner. Experimentally, under the assumptions used in the case study, the work highlights that by focusing only on the “organic” eco-label to improve the agricultural aspect, low to no improvement on overall supply chain environmental performance is reached in relative terms. In contrast, the environmental criteria resulting from a full lifecycle approach is a better option for future public and private policies to reach more sustainable agro food supply chains.

Green Supply Chain Network Design under Multi-mode Production and Uncertainty

Green Supply Chain Network Design under Multi-mode Production and Uncertainty

Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk

The main objective of this research is to introduce the concept of Green Opportunistic Supply Chain (GrOSC) and to design it in a lean and agile manufacturing setting under uncertain and risky environment. The model considers the uncertainties of the market-related information, i.e. the demand and transportation and shortage costs, under Vendor Managed Inventory (VMI) strategy. Addressing the retailer’s risk aversion level through Conditional Value at Risk (CVaR) to deal with these uncertainties leads to a bi-level programming problem. The Karush-Kuhn-Tucker (KKT) conditions are adopted to transform the model into a single-level mixed integer linear programming problem. Since, the realization of the uncertain parameters is the only information available, a data-driven approach is employed to avoid distributional assumptions. The effectiveness of the model is finally demonstrated through a numerical example.

A multi-objective multi-echelon green supply chain network design problem with risk-averse retailers in an uncertain environment

In this paper, the multi-objective multi-echelon supply chain network design problem is investigated. The resulted problem considers uncertainty of single product demand and the downstream risk attitude simultaneously and integrate it into the greenness concept. That is, makes the model more realistic in comparison with the others. According to the related literature study, there is no specific study to design the green supply chain network based on the risk attitude of the retailer. This paper aims to deal with this research gap by formulating robust counterpart of the main proposed uncertain problem. To do this, the conditional value at risk (CVaR) method through the data-driven approach is applied to deal with demand uncertainty to makes the model to be convex and sensitive to the risk averseness level in a robust manner. In addition, uncertainty sets approach is employed to deal with the uncertainty of other three parameters according to CO2 emission. The augmented e-constraint method is used to transform the resulted multi-objective mathematical programming problem into the single objective one, to obtain the global optimal solution through the exact mathematical solution method. Finally, the model is successfully simulated and its results are illustrated through a numerical example.

A Review on Strategic, Tactical and Operational Decision Planning in Reverse Logistics of Green Supply Chain Network Design

The environmental impact has been a critical issue in supply chain network research. Handling it efficiently will help the firm reduce pollution and save cost. In order to improve the environmental effect on this supply chain network, recovery activity becomes a significant part in the reverse logistics network, due to complexity and heterogeneity. In a reverse logistics network, there are many decision problems to be taken into consideration such as facility location, capacity allocation, production planning and vehicle routing. Each of these problems can be categorized into three types of decision planning: strategic, tactical or operational, based on the time planning horizon. In this paper, we review the literature of supply chain network in an effort to achieve green through implementing reverse logistic practices, and classify the defined problems by the group of decision planning. In general, most of the literatures are focused on the strategic and tactical decision planning, only a few are operational-based. The objective of this paper is to analyze the decision planning being done in this area of research and provide future insights on how to design the reverse logistics network with different decision planning which will reflect the real life scenario.

Green Supply Chain Network Design with Economies of Scale and Environmental Concerns

This study considers a design problem in the supply chain network of an assembly manufacturing enterprise with economies of scale and environmental concerns. The study aims to obtain a rational tradeoff between environmental influence and total cost. A mixed-integer nonlinear programming model is developed to determine the optimal location and size of regional distribution centers (RDCs) and the investment of environmental facilities considering the effects of economies of scale and CO2 emission taxes. Numerical examples are provided to illustrate the applications of the proposed model. Moreover, comparative analysis of the related key parameters is conducted (i.e., carbon emission tax, logistics demand of customers, and economies of scale of RDC), to explore the corresponding effects on the network design of a green supply chain. Moreover, the proposed model is applied in an actual case—network design of a supply chain of an electric meter company in China. Findings show that (i) the optimal location of RDCs is affected by the demand of customers and the level of economies of scale and that (ii) the introduction of CO2 emission taxes will change the structure of a supply chain network, which will decrease CO2 emissions per unit shipment.

A new fuzzy mathematical model for green supply chain network design

A new fuzzy mathematical model for green supply chain network design

Low carbon chance constrained supply chain network design problem: a Benders decomposition based approach

This paper proposes a chance constrained based green supply chain network design model addressing carbon emissions and carbon trading issues. The model determines the optimal flow of materials as well as emissions across the supply chain network. The basic model has been further extended into two models addressing different carbon emission issues. This study has contributed to the body of existing green supply chain literature through addressing uncertainties of suppliers’ capacities, plants’ capacities, warehouses’ capacities and demand for sustainable supply chain network design problem. This study applies Benders decomposition algorithm to handle chance constrained sustainable supply chain network design problem. The proposed models are illustrated with suitable examples and results are carefully analyzed and discussed. The results demonstrated that the flow of materials across the supply chain network varies with the change of the probability as well as carbon credit price. The number of openings of the plants is also influenced with the change of carbon credit price. Similarly, variable cost and variable emissions have been found increased and decreased, respectively with the increase of carbon credit price for the base model. The model is also equipped with dissimilar carbon prices for handling cap and trade scenario. This paper may help managers to deal uncertainties as well as managing emissions of a supply chain network.

Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry

The forward/reverse logistics network design is an important and strategic issue due to its effects on efficiency and responsiveness of a supply chain. In practice, it is needed to formulate and solve real problems through efficient algorithms in a reasonable time. Hence, this paper tries to cover real case problem with a multi-objective model and an integrated forward/reverse logistics network design. Further, the model is customized and implemented for a case study in gold industry where the reverse logistics play crucial role. A new solution approach is applied for the proposed 7-layer network of the case study and the solutions are achieved in order solve the current difficulties of the investigated supply chain. This paper seeks to address how a multi objective logistics model in the gold industry can be created and solved through an efficient meta-heuristic algorithm. A green approach based on the CO2 emission is considered in the network design approach. The developed model includes four echelons in the forward direction and three echelons in the reverse. First, an integer linear programming model is developed to minimize costs and emissions. Then, in order to solve the model, an algorithm based on ant colony optimization is developed. The performance of the proposed algorithm has been compared with the optimum solutions of the LINGO software through various numerical examples based on the random data and real-world instances. The evaluation studies demonstrate that the proposed model is practical and applicable and the developed algorithm is reliable and efficient. The results prove the managerial implications of the model and the solution approach in terms of presenting appropriate modifications to the mangers of the selected supply chain. Further, a Taguchi-based parameter setting is undertaken to ensure using the appropriate parameters for the algorithm.

A Hybrid Territory Defined evolutionary algorithm approach for closed loop green supply chain network design

The Closed loop Supply chain network distribution is one of the most important problems with much real world application in supply chain management area. Presently climate change problem is one of the major concerns for Researchers. Closed loop green supply chain (GCLSC) problem is the extension of closed loop supply chain problem. Semiconductor industries are one of the major industries and a number of waste products in semiconductor industries are quite high. We have considered reducing the waste in semiconductor by recycling the useful waste electronic equipment. In GCLSC, we consider to maximize the profit in forward supply chain whereas we attempt to minimize the Carbon footprints at the same time. In this paper we used a hybrid of Estimation of distribution algorithm (EDA) and Territory Defined multi-objective algorithm to select the optimum number of facilities in the closed loop supply chain network. To examine the effectiveness of our Hybrid Territory Defined algorithm (EDATDEA), we compare the results with those obtained by NSGA II on a same GCLSC problem with different problem sizes and the same data sets.

Integrated Production and Transportation Scheduling for Multi-objective Green Supply Chain Network Design

A green supply chain has been attracting attention as a new approach to minimize a product or service ecological footprint. Our previous researches proposed a basic green supply chain network model consisting of two model components, clients and suppliers. The model provided a numerical method to determine suitable prices and delivery times of ordered products through the modification processes of both production schedules by using genetic algorithms and transportation schedules by heuristic rules and the negotiation processes between suppliers and clients by using an auction based method. The objective of this paper is to propose an integrated scheduling method of production and transportation problems in order to further reduce carbon dioxide emissions without decreasing profits of suppliers. The proposed method makes an efficient transportation schedule that ensures a low vacancy rate and a high loading ratio of transportation vehicles by repeating the modification processes of production and transportation schedules alternately. Experiments were carried by using a developed supply chain simulation system. As the results of the experiments, new method was able to decrease carbon dioxide emissions without decreasing profits.

Development of a Generic decision support system based on multi-Objective Optimisation for Green supply chain network design (GOOG)

Purpose – The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to design their supply chain networks using three key objectives: the lowest cost and environmental impact and the shortest lead time by incorporating the decision maker’s inputs. Design/methodology/approach – GOOG aims to suggest the best-fitted parameters for supply chain partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes for cradle-to-gate. It integrates Fuzzy Goal Programming and weighted max-min operator for trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved using exact algorithm and validated using an industrial case study. Findings – The comparative analysis between actual, three single-objective, and multi-objective decisions showed that GOOG is capable to optimising three objectives namely cost, lead time, and environmental impact. Research limitations/implications – Further, GOOG requires validation for different supply chain scenarios and manufacturing strategic decisions. It can improve by including multi-echelon supply chain networks, entire life cycle and relevant environmental legislations. Practical implications – GOOG helps the decision makers to configuring those supply chain parameters whilst minimising those three objectives. Social implications – Companies can use GOOG as a tool to strategically select their supply chain that reduces their footprint and stop rebound effect which imposes significant impact to the society. Originality/value – GOOG includes overlooked in the previous study in order to achieve the objectives set. It is flexible for the decision makers to change the relative weightings of the inputs for those contradicting objectives.

Green supply chain network design with stochastic demand and carbon price

This paper presents a two-stage stochastic programming model to design a green supply chain in a carbon trading environment. The model solves a discrete location problem and determines the optimal material flows and the number of carbon credits/allowances traded. The study contributes to the existing literature by incorporating uncertainty in carbon price and product demand. The proposed model is applied to a real world case study and the numerical results are carefully analyzed and interpreted. We find that the supply chain configuration can be highly sensitive to the probability distribution of the carbon credit price. More importantly, we observe that carbon price and budget availability for supply chain reconfiguration can both have a positive but nonlinear relationship with greening of the supply chain.

A model proposal for green supply chain network design based on consumer segmentation

The consumption-based economy has created enormous ecological footprint of product and service life cycles. Therefore, the environmental sustainability has become one of the major concerns of today's society and sparked tremendous amount of research. According to the related literature analysis, there is no specific study to design the green supply chain network based on consumers' green expectations. This study aims to contribute to the fulfillment of this research gap by proposing a goal-programming model considering three consumer segments, i.e., green, inconsistent and red consumers. A hypothetical real-life-like example problem is solved to demonstrate clearly the value and applicability of the proposed model. A set of scenarios is also studied to offer an insight on how the consumer determination level of greenness affects the green supply network. The findings of the study present a way to measure the relations between green supply chains and consumer behavior.

Green supply chain network design to reduce carbon emissions

We consider a supply chain network design problem that takes CO2 emissions into account. Emission costs are considered alongside fixed and variable location and production costs. The relationship between CO2 emissions and vehicle weight is modeled using a concave function leading to a concave minimization problem. As the direct solution of the resulting model is not possible, Lagrangian relaxation is used to decompose the problem into a capacitated facility location problem with single sourcing and a concave knapsack problem that can be solved easily. A Lagrangian heuristic based on the solution of the subproblem is proposed. When evaluated on a number of problems with varying capacity and cost characteristics, the proposed algorithm achieves solutions within 1% of the optimal. The test results indicate that considering emission costs can change the optimal configuration of the supply chain, confirming that emission costs should be considered when designing supply chains in jurisdictions with carbon costs.