Modeling a multi-objective green closed loop supply chain for tire industry

The ever-increasing concerns of the growth in the volume of waste tires and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. In the real world, the CLSC network design is subject to a variety of uncertainties, such as random and fuzzy (epistemic) uncertainties. Designing a reliable and environmentally cautious CLSC with consideration of risks and the uncertainty of the parameters in the network is necessary for a successful supply-chain network. This study proposes a sustainable and environmentally cautious closed-loop supply-chain network for the tire industry, by considering several recovery options, including retreading, recycling, and energy recovery. This study aims to design and develop a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty. There are two types of uncertainties associated with the parameters in the network. There is an uncertainty associated with the demand, which is expressed in some future scenarios according to the probability of their occurrences, as well as fuzzy-based uncertainty associated with return rates, retreading rates, recycling rates, procurement, and production costs, which are expressed with possibilistic distributions. In order to deal with this hybrid uncertainty, a robust fuzzy stochastic programming approach has been proposed, and the proposed mixed integer programming model is applied to a case study in the tire industry to validate the model. The result indicates the applicability of the proposed model and its efficiency to control the hybrid uncertainties and the risk level in the network.

Designing a closed-loop supply chain network for citrus fruits crates considering environmental and economic issues

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.

Resilient closed-loop supply chain network design considering quality uncertainty: A case study of stone quarries

The mathematical model of a multi-product multi-period multi-echelon closed-loop supply chain network design under uncertainty is designed in this paper. The designed network consists of raw material suppliers, plants, warehouses, distribution centers, and customer zones in forward chain and collection centers, repair centers, recovery/decomposition center, and disposal center in the reverse chain. The goal of the model is to determine the quantities of products and raw material transported between the supply chain entities in each period by considering different transportation mode, the number and locations of the potential facilities, the shortage of products in each period, and the inventory of products in warehouses and plants with considering discount and uncertainty parameters. The robust possibilistic optimization approach was used to control the uncertainty parameter. At the end to solve the proposed model, five meta-heuristic algorithms include genetic algorithm, bee colony algorithm, simulated annealing, imperial competitive algorithm, and particle swarm optimization are utilized. Finally, some numerical illustrations are provided to compare the proposed algorithms. The results show the genetic algorithm is an efficient algorithm for solving the designed model in this paper.

The aim of this paper is to present a new closed-loop supply chain (CLSC) network design model for multi-component and multi-product systems. This integrated model is more representative of industrial operations and takes into account the following important design factors: multi-component products, design for disassembly, existence of a secondary market for refurbished products, bill of material for product decomposition into parts, reliability and greenness levels of parts/products. A mixed-integer programming model is developed for a forward/reverse logistic network that includes suppliers, customer zones, inspection, repair and disassembly centres (IRDC), and a recycling centre. A wide range of numerical experiments are conducted and sensitivity analyses are carried out on various parameters yielding valuable managerial insights. The model is used to expose the effects of parts reliability and product greenness on the reverse flow and the economical operating market range for refurbished products in the reverse flow.

With two chains cooperation, Hinfin control methods of bullwhip effects in closed-loop supply chain (CLSC) networks are studied. In each chain, the products which hold by the consumer part are considered as the virtual inventory and the remanufacturing part is defined as a rate to the virtual inventory. The inventory state difference equations of the manufacturers, retailers and consumers are built respectively. Considering CLSCs based on remanufacturing, the model of CLSC networks is established. Bullwhip effects are described as the variance proportion. In the worse case of the demand fluctuation, Hinfin control methods of bullwhip effects in the system are presented with two chains cooperation. To prove it, some number examples are given. The essence of the method is that in the worse case the optimum strategy of the normal manufacturing and order are made by cooperation control of node enterprises in the CLSC networks to restrain bullwhip effects. As a result, it presents a new method to study the stability of CLSC networks and control corporately their bullwhip effects.

This article proposes a novel methodology for multistage, multiproduct, multi-item, and closed-loop Supply Chain Network (SCN) design under uncertainty. The method considers that multiple products are manufactured by the SCN, each composed by multiple items, and that some of the sold products may require repair, refurbishing, or remanufacturing activities. We solve the two main decisions that take place in the medium-/short-term planning horizon, namely partners’ selection and allocation of the received orders among them. The partners’ selection problem is solved by a cross-efficiency fuzzy Data Envelopment Analysis technique, which allows evaluating the efficiency of each SCN member and ranking them against multiple conflicting objectives under uncertain data on their performance. Then, according to the estimated customers’ demand, the order allocation problem is solved by a fuzzy bargaining game problem, where each SCN actor behaves to simultaneously maximize both its own profit and the service level of the overall SCN in terms of efficiency, costs, and lead time. An illustrative example from the literature is finally presented. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —We present a decision tool to address the optimal design, performance evaluation, and continuous improvement of modern cooperative SCNs. We propose an effective method to jointly solve the members’ selection and the orders’ allocation, considering the complex structure of modern SCNs, the multiobjective nature of the problems, and the uncertainty characterizing economic markets. Competition within SCNs stages and cooperation along the chain are considered, with the aim to improve both financial and environmental sustainability, while ensuring the highest service levels to customers.

An integrated sustainable partner selection approach with closed-loop supply chain network configuration

The last decade has seen a numerous studies focusing on the closed-loop supply chain. Accordingly, the uncertainty conditions as well as the green emissions of facilities are still open issues. In this paper, a new fuzzy multi-objective programming approach is to present for a production-distribution model in order to develop a multi-product, multi-period and multi-level green closed-loop supply chain network problem, which this model is formulated as multi-objective mixed linear integer programming (MOMILP). In regards to offered fuzzy multi-objective model, three conflicting goals are exited, simultaneously. The objective functions are to minimizing the total cost, minimizing the gas emissions costs due to vehicle movements between centers, and maximizing the reliability of delivery demand due to the reliability of the suppliers. To get closer to real-world applications, the parameters of model are considered by fuzzy numbers. Another novelty of proposed model is in the solution methodology. To solve the model, this study not only uses a well-known Imperialist Competitive Algorithm (ICA) but a number of new modifications of ICA (MICA) also have been provided to address the proposed problem, which is to demonstrate the efficiency and performance of the proposed algorithm with other algorithms included: SA, ICA, ACO, GA, and PSO are compare. Finally, different analyses with a variety of problem complexity in different sizes are performed to assess the performance of algorithms as well as some sensitivity analyses on the efficiency of model are studied.

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.

The research focuses on the optimization of closed-loop supply chain (CLSC) network design in intelligent manufacturing systems, to achieve sustainable development goals by optimizing resource utilization, and reducing environmental impact and operating costs. The research focuses on constructing and optimizing supply chain networks, and the proposed algorithm is applied to multi-objective optimization of intelligent manufacturing supply chains. The experimental results show that the optimized supply chain efficiency has increased from about 81% before optimization to 96.13%. The total cost is controlled between 2.30654 and 3.38452 million yuan, which is 8%-12% lower than traditional methods. The profits are maximized to 1.42387 million yuan. The utilization rates of raw materials and remanufactured materials have increased to 83.78% and 69.21%, respectively. Energy consumption is controlled between 1442.672 and 1524.567 MWh. Carbon emissions are reduced to 126.564 tons. The highest product recycling rate is 74.52%. The total logistics distance of the supply chain is optimized from 1147.932 to 1389.432 km, and the production cycle is shortened from 44.982 to 49.782 h. By modeling and optimizing parameters such as logistics nodes, production cycles, and utilization rates of raw and remanufactured materials, the algorithm proposed by the research performs excellently, with a stable running time ranging from 18.732 to 19.564 s. The significance of the research lies in providing a dynamic, efficient, and sustainable CLSC design model for intelligent manufacturing systems, and demonstrating the significant advantages of this method in improving supply chain efficiency, optimizing cost control, and achieving environmental sustainability.

Small- and medium-sized enterprises (SMEs) are crucial to economic development, yet they face challenges in enhancing supply chain (SC) performance while addressing environmental concerns. This study develops a green closed-loop supply chain (CLSC) model that integrates economic, environmental and operational objectives to support SME decision-making. The model introduces a reselling market to boost profitability by incorporating product reuse in reverse logistics for secondary customers. It also considers the strategic addition of selling agents and customer recategorisation. Three solution methods are proposed: elastic boundary objectives, modified weighted Chebyshev and a customised augmented e-constraint method (AUGMECON2). Among them, AUGMECON2 demonstrates superior performance through comparative analysis. Numerical examples validate the model, and a real-world SME case study reveals that integrating selling agents and multilevel customers leads to a 93% profit increase, a 33% reduction in environmental impact and a 36% improvement in delivery times. These results underscore the effectiveness of the proposed CLSC framework in optimising SME supply chains for both profitability and sustainability.

We observe that between the competitors of technology products, companies such as HP and Xerox or Apple and Google, one competitor (HP and Apple) manages the reverse supply chain to extract value profitably, while the other competitor (Xerox and Google) uses a third-party reverse logistics company to simply recycle consumer returns. In this chapter, we show that, under a given set of supply chain levers, the OEM’s approach to supply chain network design can significantly impact the profitability in the reverse supply chain over the product lifecycle. We propose an integrated multi-period optimization model called the product life cycle optimization model (PLCOM) to design the closed loop supply chain (CLSC) for the OEMs. The PLCOM is a mixed-integer linear program that consists of a demand model based on the extended-Bass’s diffusion model and a pricing model based on a realistic customer willingness to pay (WTP). The PLCOM is applied to a realistic case study using Apple’s iPhone 7 for a product life cycle of 8 years. We show that an integrated approach to designing the supply chain network by the OEMs is more profitable compared to a sequential approach.

Bi-objective stochastic closed-loop supply chain network design under uncertain quantity and quality of returns