A multi-objective mathematical model to solve a closed-loop pistachios supply chain network problem considering purchasing substitution and discount levels: multi-objective algorithms

PurposeThe purpose of this paper is to assist a manufacturing firm in designing the closed-loop supply chain network under risks that are affecting its supply quality and logistics operations. The modeling approach adopted aims at the embedding supply chain risks in a closed-loop supply chain (CLSC) network design process and suggests optimal supply chain configuration and risk mitigation strategies.Design/methodology/approachThe method proposes a closed-loop supply chain network and identifies the network parameter and variables required for closing the loop. Mixed-integer-linear-programming-based mathematical modeling approach is used to formulate the research problem. The solutions and test results are obtained from CPLEX solver.FindingsThe outcomes of the proposed model were demonstrated through a case study conducted in an Indian hospital furniture manufacturing firm. The modern supply chain is mapped to make it closed loop, and potential risks in its supply chain are identified. The supply chain network of the firm is redesigned through embedding risk in the modeling process. It was found that companies can be in great profit if they follow closed-loop practices and simultaneously keep a check on risks as well. The cost of making the supply chain risk averse was found to be insignificant.Practical implicationsAlthough the study was conducted in a practical case situation, the obtained results are not indiscriminate to the other circumstances. However, the approach followed and proposed methodology can be applied to many industries once a firm decides to redesign its supply chain for closing its loop or model under risks.Originality/valueBy using the identified CLSC parameters and applying the proposed network design methodology, a firm can design/redesign their supply chain network to counter the risk and accordingly come up with planned mitigation strategies to achieve a certain degree of robustness.

ABSTRACTOne of the basic requirements of the companies to survive in real-world competitive environments is to make their supply chains as efficient as possible. Due to recent governmental regulations, environmental issues, and the development of the concept of social responsibility, the closed-loop supply chain management has been focused by many researchers. A closed-loop supply chain includes both forward and reverse supply chain networks with the purpose of combining environmental considerations with the traditional supply chain network designs through the collection of used products and activities related to their reuse. In this paper, a bi-objective, multi-period, multi-product, closed-loop supply chain network is designed under environmental considerations, discounts, and uncertainties. The deterministic model of the chain is first solved by three multi-objective decision-making methods. Then, based on real-world uncertainties involved in some of the parameters, a robust optimization model is proposed and solved using decision-making methods. At the end, the best deterministic and robust models are selected based on the displaced ideal solution.

Fuzzy multi-objective approach for optimal selection of suppliers and transportation decisions in an eco-efficient closed loop supply chain network

PurposeThis study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.Design/methodology/approachIn dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.FindingsThe research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.Research limitations/implicationsThis study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.Originality/valueThis research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

ABSTRACTThe increase in the development of proper channels for recycling and disposal of the manufactured products have motivated the study of closed loop supply chains. The closed loop supply chain networks can be considered as a strong tool for attaining the goals of sustainable development. The customers are not the terminating destination for the products in closed loop supply chain networks. However, after some recycling or refurbishing processes the product once again enters the supply chain networks. The involvement of forward and reverse flow of the products makes the closed loop supply chain networks very complex. As, there may be several conflicting objectives related to the closed loop supply chains, in this paper, we have proposed a bi-level multi-objective programming model for these networks. Bi-level programming problems deal with the situations where decisions are to be taken at two different hierarchical levels. We have considered three objective functions which are distributed among these two levels in such a manner that the decision makers solves for a single objective at each level. A solution procedure is also discussed for solving the proposed model.

The growing importance of the online channel and the increased deployment of new technologies such as smart mobile devices and social networks create new opportunities and challenges for traditional retailers. The retailing industry is moving to a new phase, in which the distinctions between traditional and online channels disappear, namely omni-channel (OC) retailing. The new challenge is to understand how multiple channels can be synergistically managed to provide a seamless customer experience. In OC retailing, logistics represents a key success factor due to its impact on both customer service and total costs. Retailers need to define the distribution configuration for serving the online demand, making decisions on the integration level between online and traditional channels. Companies can set an ad hoc network for the online channel, or use the same network for both online and traditional channels at warehouse and/or transport levels. In this paper, we developed an assessment model of the operational costs for three distribution configurations in OC retailing. The model was also applied to a real Italian case operating in the consumer electronics industry. Results highlighted that the search for synergies between online and traditional flows in both warehouse and transport activities is important for the economic sustainability of OC systems.

Influence on Sales Channels of Supply Chain Structure under Power Balance

Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry

A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount

Supply chain network design is an important decision-making problem affecting the long-term profitability of firms. Evaluating the performance of supply chain network designs can help decision-makers to select the network configuration that meets the business specifications while operating at a reasonable cost. In this study, Social Network Analysis (SNA) metrics are used to evaluate the performance of closed-loop supply chain (CLSC) Network designs in terms of resilience when exposed to disruptions and the balance of flows. CLSC Networks accommodate the flow of returned products from the customers for recycling, remanufacturing, or disposal, increasing the design complexity compared to traditional supply chain networks. The proposed approach involves custom-designed network-level SNA metrics and random forest (RF) feature selection which are computationally low-cost approaches. The proposed metrics are implemented in an R package titled NetworkSNA and shared on GitHub, and RF feature selection method is performed in python. The optimal and near-optimal network designs from a CLSC Network based on real data are used as a case study. The metric values are interpreted into practical recommendations to compare the alternative CLSC Networks.

A Green Dual-Channel Closed-Loop Supply Chain Network Design Model

Due to the significant role of the reverse supply chain (RSCs) in collecting used products and achieving a sustainable environment, both scholars and industries have paid close attention to pricing in reverse and closed-loop supply chains (CLSCs). Moreover, with the rapid development of the Internet and e-commerce in the latest decades, researchers have examined the impact of constructing online return channels based on customer behavior. In this article, a game-theoretic approach was applied to find the optimal economic and environmentally sustainable solutions in a two-level CLSC with a dual collecting channel including the retailer’s traditional channel and the manufacturer’s online channel. The purpose of the current study is to optimise the selling price, acquisition prices, market demand, channels return rate, the portion of manufacturing new products, and cost-sharing contract (CSC) participation shares for each player. For this purpose, various policies, such as centralised and decentralised modes, different structures such as Nash bargaining power, manufacturer-leader Stackelberg, and retailer-leader Stackelberg have been considered. However, the main contribution of this work compared to the existing literature is considering two CSCs from both retailer and manufacturer points of view, with a real case analysis from an emerging economy. In addition, a comprehensive sensitivity analysis has been carried out to enhance the validation of the proposed model. The results indicated that the manufacturer-leader Stackelberg strategy leads to the lowest profit for the SC in both decentralised and cooperative policies. However, when the retailer and manufacturer have equal decision-making power (Nash strategy) and the retailer participates in the remanufacturing cost (i.e. cost-sharing type-2) both the economic and environmentally sustainable goals of CLSC were met.

In this paper, we study a supply chain that consists of a manufacturer and a value-adding retailer that sell a product to customers through dual channels, i.e. a traditional channel (TC) and an online channel (OC). Observing that in practice, the manufacturer may or may not offer an OC guide price to the retailer and/or act as the leader in the supply chain, we discuss and compare two practical pricing strategies, with and without an OC guide price, under two different power configurations based on which member of the supply chain acts as the leader. Our results show that if the manufacturer does not provide a guide price, the retailer might/might not set a higher TC price than the two OC prices, depending on the level of migration effectiveness and the potential market demand. However, if the manufacturer does provide a guide price, the retailer will always charge a higher TC price than the guide price (or the two OC prices) when the retailer acts as the leader. Moreover, we show that the two players in the supply chain might or might not prefer the pricing strategies with an OC guide price. Our results also indicate that migration effectiveness harms the retailer’s profit, and the manufacturer may benefit from mild competition between the two channels. Finally, we show that regardless of whether the manufacturer chooses to offer an OC guide price or not, both the manufacturer and the retailer prefer to act as the follower for high migration effectiveness and the profit of the supply chain will increase when the retailer acts as the leader (for low migration effectiveness.

How does the added new online channel impact the supporting advertising expenditure?

Strategies of pricing based on Stackelberg competition between manufacturer and distributor are discussed under the condition that both traditional and online channels exist. A system consisting of a manufacturer, a distributor and customers is considered, in which the manufacturer can sell products to the distributor, who, in turn, sells the products to customers through traditional channel, or the manufacturer can transact directly with the customers in an electronic manner. The manufacturer and the distributor establish models with the respective objection of maximizing expected profit. The manufacturer regards the price of products sold to distributor through traditional and online channels as decision variables, while the distributor’s decision variable is the price of products sold to customers, and the distributor’s decision variable is the function of the manufacturer’s decision variable. At last, the numerical examples are used to show the application of pricing models.