A multi-echelon closed-loop supply chain model with multi-recovery systems and carbon regulation

15 - Modified neutrosophic fuzzy optimization model for optimal closed-loop supply chain management under uncertainty

Technology licensing affects both welfare and financial concepts. In the literature, while the effects of different licensing mechanisms on the decision variables of a manufacturing system are thoroughly investigated, relationships between licensing scenarios and production quantities on a closed-loop supply chain (CLSC) have not been examined completely. To fill out this gap we study three most common types of licensing mechanisms; fixed-fee, royalty cost and two-part tariff licensing scenarios with the quality level of collected second-hand items in a two-period CLSC. The proposed model consists of one manufacturer and one remanufacturer who is permitted to engage in the remanufacturing process just in the second period of the planning horizon. Our main assumption is that a constant demand is to be satisfied within a two-period CLSC. Consumer demand is satisfied in the first period by manufacturing the raw materials and in the second period, both manufacturing and remanufacturing processes are possible due to the demand quantities and quality level of the collected second-hand items. A numerical example and sensitivity analysis are presented along with analytical conclusions to provide insights into the performance of the CLSC parties in each licensing scenario. The findings prove the applicability of the proposed model.

The growing importance of environmental concerns and focus on recycling has encouraged our research efforts to study the bullwhip effect on Closed Loop Supply Chain (CLSC). This article attempts to measure bullwhip effect in a six echelon CLSC with recycling as a reprocessing option for materials like paper and plastics (perhaps the first time in CLSC literature). The proposed model considers the effect of two critical parameters of CLSC: quality of recyclable raw material (in terms of its yield) and degree of segregation at source. It is assumed that the echelons in CLSC employ an Order-Up-To (OUT) inventory policy with Minimum Mean Square Error (MMSE) forecasting scheme, and that the demand forecast is performed via a first order Auto Regressive Moving Average model (ARMA [1, 1]). The model developed is employed to investigate the impact of Auto Regressive (AR) and Moving Average (MA) parameter, yield of recycled material, degree of segregation at source and the lead-time on the bullwhip effect. The model enables the CLSC managers to anticipate the downstream demand. In order to avoid order-process instability, a careful selection of Auto Regressive (AR) and the MA parameters is advised. Sensitivity analysis on replenishment lead-times provides managerial insights for effective design of recycling—distribution system, with constant accumulated lead-time. Further, the analysis reveals that increased degree of segregation at the source reduces the bullwhip effect.

A proposed multi-objective model for cellphone closed-loop supply chain optimization based on fuzzy QFD

Carbon emission is a concern across the industry due to its environmental impact. In order to reduce carbon emissions, industry have to look not only at their processes but also in the generation of emissions in the whole supply chain that it belongs to. This is more necessary when various forms of carbon emissions policies are faced by the industry. The current focus on research is either in the economics of supply chain or mainly on the forward or reverse supply chains when it pertains to the carbon emissions. However, as lifecycle of the product is also becoming important, the isolated treatment of supply chains should be avoided for an integrated (forward and reverse) supply chain, also called the closed loop supply chain (CLSC). The complexity arises when the supply chain is faced with different kind of carbon policies implemented by the governments, for example, carbon tax, carbon cap, and carbon-cap and tax. The design of CLSC may change with the change in the carbon emission policy. In this research, a stochastic model is developed for the design of a CLSC by considering carbon emission policies. This work is one of the first attempts to understand the implication of carbon emission policy on the design of a CLSC. The model is applied to a sample case of aluminum industry with a life cycle assessment of emission.Our preliminary findings include:(1) When the more scenarios are taken into account, the total cost and emission would increase, because more uncertain information is considered in CSSC design.(2) Given the same carbon cap, in a broad sense, the amount of emission credit traded in market changes in line with the total emission. It indicates that the emission trading is determined by the actual emission rather than the carbon price, even the uncertain carbon price is considered. However, a higher emission does not always lead to a bigger amount of emission to be traded.(3) The network structure obtained by the deterministic formulation is not able to be easily adapted to changes in demand and supply.(4) In order to deal with the uncertain demand, the CLSC has to enlarge its handling capacity by using the facilities with a higher capacity or incorporating more facilities with the same function. With the same purpose, the supplier selection decisions are also changed associate the increasing scenarios.(5) The fuel of diesel is selected regardless of the scenarios. It means the current carbon price level would not motivate the firms to gain profit or reduce costs by employing future fuels in transportation. This conclusion is also proved by the further investigation when the total cost and emission are observed under decreasing cap. A further investigation reveals the transportation mode doesn't change when the carbon price increase from 9 to 234. Consequently, the future fuels would be attractive to firms only when the transportation price reduced or other policy environment changes, such as government subsidy. The efforts in reducing the transportation cost with future fuels have never ceased and the advanced technologies in automobile industry provide opportunity for green transportation promoters.

Increasing environmental concerns require businesses to become more responsive to products that either have been returned or that are at the end of their useful lives. Organization's responsiveness and their reactions toward life cycles of products are critical to achieve sustained success once fluctuations are recurrent and the business environments are turbulent. Life cycles are getting shorter, and efficient handling can save large amounts of cash since many materials can be extracted, reused, and redistributed. Reverse Logistics (RL) and Closed Loop Supply Chains (CLSC) have garnered growing interest as a way to manage this reverse flow of products in a cost effective way. It is discernible that the degree of complexity in closed loop networks is usually higher than open networks. In a closed loop supply chain network, the attempt is to ensure a smooth flow of materials as well as extracting the maximum value from returning and end-of-life goods. Reduction of waste and generating profit for enterprises are two paramount achievements through integrating forward and reverse logistics. By considering various proposed conceptual models for CLSC, in this study at first we present a comprehensive applicable conceptual model. Then, we specifically focus on a CLSC network, which includes multiple plants, collection centers, demand markets, and products. To this aim, a generalized proposed mixed-integer linear programming model is presented that minimizes the total cost. The objective is to know how many, which production plants and Return Processors (Collection sites) should be open, which products, and in which quantities should have stuck. The goal of this model is to minimize the supply chain waste and reduce supply chain costs. In such a way, the supply chain system can be agile, integrated, robust, and lean. The proposed model can be extended to consider more environmental factors. Moreover, the efficacy and efficiency of that have been validated by case study and numerical example.

Over the course of the last decades, closed-loop supply chains (CLSC) and reverse logistics issues have attracted increasing attention owing to strict environmental laws, social responsibilities, economic interests, and customer awareness. Hence, the issue of closed-loop supply chain and reverse logistics has emerged as a field of research in the new era. This issue has received much attention because it allows recyclable products to return to their original cycle. Therefore, this study primarily intends to present a mathematical model for designing a supply chain network for recycled products. The multi-stage and multi-period objective function of the closed-loop supply chain is presented to meet that aim. In this chain, dismantling, recycling, and disposal centers are considered. The objective function is to reduce the total cost of the closed-loop supply chain. The results of optimizing the mathematical model demonstrate that this model has the necessary efficiency for use in recycled products.

In the last decade, planning of closed-loop supply chains in different strategic, tactical, and operational levels has attracted many interests due to economic reasons, environmental challenges, and government legislations. This paper presents a novel linear programming model for the integrated production and distribution planning in closed-loop supply chains under uncertainty. The proposed model involves multi-product and multi-period which considers multiple transportation modes, direct or indirect shipments, advertising costs, and several customer zones for different types of products and also attempts to integrate production and distribution plans in the forward and reverse sides of the closed-loop supply chain, simultaneously. To deal with uncertain input data, a robust optimization counterpart based on polyhedral uncertainty set is developed to obtain optimal solutions immunizing the problem for any realization of uncertain parameters in the given polyhedral uncertainty set. Computation results for a numerical example under different scenarios are discussed to give insights about the features of the proposed robust optimization model in handling the uncertainty of parameters. Finally, some sensitivity analyses are performed to show the behaviour of the robust and deterministic models respect to changes of uncertainty levels of parameters as well as the amounts of important parameters such as demands and returns.

Managing channel profits of different cooperative models in closed-loop supply chains

Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management

Closed-loop supply chain is an important path to realize the sustainable development of supply chain, and cap-and-trade and carbon tax policies further promote closed-loop supply chain to invest in low-carbon technologies. This paper combines Economic Production Quantity with Economic Order Quantity models and designs multiple carbon policies. Abatement rate and collection rate are the key variables used to explore the mechanism of the interaction between carbon tax and cap-and-trade, inventory dynamics of closed-loop supply chain and impact of multiple carbon policies on closed-loop supply chain. Construct and analyze benchmark model, investment model under multiple carbon policies, remanufacturing model under multiple carbon policies and mixed strategies model under multiple carbon policies, and the results show that there is a non-linear complementary relationship between carbon price and carbon tax, and both remanufacturing and low-carbon investment can optimize cost and carbon emissions when carbon price is distributed at a certain interval. Furthermore, mixed strategies combining remanufacturing and low-carbon investment can optimize cost and carbon emissions simultaneously. The main contribution of this paper is to build a low-carbon circular system, which can help balance cost and carbon emissions of closed-loop supply chain.

Product disposal penalty: Analysing carbon sensitive sustainable supply chains

CLSC design with simultaneous consideration of product design for manufacturing and remanufacturing

Decentralized Decision System for Closed-Loop Supply Chain: A Bi-Level Multi-Objective Risk-Based Robust Optimization Approach

This study develops a multi-period Mixed Integer Linear Programming model for Closed-Loop Supply Chain (CLSC) network design in the electronics sector. The model explicitly integrates two dynamic drivers of consumer behavior—discounts and environmental awareness—into the return rate formulation. The model defines the flow of production, remanufacturing, shipping, returned product quantity, waste volume, and consumer return rate across multiple planning periods, with the goal of minimizing total costs. The numerical case study is based on synthetic data constructed based on parameter ranges in previous CLSC studies to reflect realistic operational conditions. The numerical results show manufacturing costs as the largest component (68.36%), followed by transportation (8.23%) and distribution handling (6.69%). Sensitivity analysis indicates (i) higher discounts and greater awareness significantly increase the return rate, increase remanufacturing volume, and reduce waste; and (ii) increasing Fd results in significant cost savings, with the highest outcome minimizing combined production and remanufacturing costs. These findings support a targeted strategy of discounts and environmental awareness, aligning remanufacturing capacity with returns, and achieving profitability. For policymakers, this model offers a decisionsupport tool to assess discounts and awareness programs, encouraging sustainable and profitable CLSC operations that align with circular economy principles.