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This study investigates a multi-echelon closed-loop supply chain model integrating learning impact with a single producer, single retailer, and single collector. Retail pricing, green innovation, and marketing efforts are assumed to have a linear relationship with end-user demand. The return of rejected goods occurs at random and is subject to an imperfect inspection procedure that is vulnerable to two different types of classification error. An S-shaped learning curve is considered to soothe the inspection process which is subject to inaccuracy. Numerical examples are used to illustrate the implications of the suggested model in order to achieve a goal that benefits both the consignor and the consignee. It is investigated whether the learning effect can outweigh the loss even when the inspection error has a detrimental effect on supply chain earnings. To identify the crucial parameters that can spark some truly exceptional managerial ideas, a sensitivity analysis is carried out. [ABSTRACT FROM AUTHOR]

The paper considers a three-echelon supply chain which consists of one supplier, one manufacturer and one retailer for trading a single product. The market demand at the retailer is influenced by the retail price and the quality of the product. The quality of the finished product at the manufacturer depends on the supplier’s raw material quality. We analyze the model for both deterministic and stochastic demand patterns. We first study the centralized and decentralized systems, and then the decentralized system with a sub-supply chain coordination strategy (where the manufacturer chooses to merge with either the supplier or the retailer and then acts as a single entity) and the two-level retail fixed mark-up (RFM) strategy. In the case of the two-level RFM strategy, the manufacturer and the retailer use fixed mark ups over the supplier’s wholesale price. The proposed models are demonstrated through numerical examples. It is observed from the numerical study that the two-level RFM strategy is superior to the sub-supply chain coordination strategy. Further, the two-level RFM strategy in the stochastic demand scenario is not as effective as in the deterministic demand scenario. [ABSTRACT FROM AUTHOR]

The paper considers a two-echelon supply chain inventory model with one manufacturer and multiple buyers in which each buyer’s demand is dependent on the selling price of the product. The manufacturer’s lead time is composed of several components and each component can be reduced by an additional crashing cost. We develop the proposed model assuming that the lead time demand follows a normal distribution or it is distribution free. The optimal decisions are obtained by maximizing the total expected profit of the supply chain. It is observed from numerical study that the lead time reduction has minor effect on the selling price of the product, but it enhances the profit of the supply chain system. [ABSTRACT FROM AUTHOR]

• Considers a novel closed-loop supply chain with heterogeneous fleet. • Develops and analyses carbon emission regulations mandated by Government. • Analyses the conflict between transportation cost and carbon emission cost. • Evaluates the performance of the multi-objective model with a case study. The paper studies a closed loop supply chain (CLSC) focusing on managing, scheduling and routing problems to achieve economical and environmental sustainability. Generally, a logistic based green supply chain is studied to help multiple stakeholders. Here, we investigate the CLSC to reduce the total transportation cost including carbon emission from a heterogeneous fleet with limited capacity. The forward chain includes manufacturers, depots, distributors/collectors and customers. In the reverse chain, the distributors collect the returned items and sort into three groups through quality-screening and send them to the secondary chain facilities (emerging markets or decomposition centres) which one is fitted. This investigation is contributed by all the demonstrating issues through considering customers' demand, an extensive emission display and the vulnerability of an expanded convey of vehicles. The proposed model addresses four distinct environmental policies viz. cap, carbon tax, cap-and-purchase and cap-and-sale for carbon emission regulations. The model is formulated in the framework of mixed integer non-linear programming (MINLP), and an exact approach is employed to obtain the optimal solution. The theoretical results of the model are verified through numerical examples. A comprehensive analysis is presented to monitor the rate of return products and various fleet transporting parameters. [ABSTRACT FROM AUTHOR]

• put forward, solve, and compare three different cyclic IRP for perishable products. • assume the demand for perishable products is dependent on price and stock. • propose a hybrid heuristic algorithm and verify its efficiency. • deal cyclic IRP that includes the cost of manufacturer. • implement sensitivity analysis to study the impact of parameters on optimal solution. The VMI supply chain can bring benefit to concerned parties. The paper studies cyclic inventory-routing problem (IRP) under VMI policy. Cyclic IRP as a variant of the IRP belong to long-term decision. It means once the replenishment policy and vehicle routing are determined, they will stay the same in the following periods. This paper considers the loss cost caused by perishability of perishable products and assumes the demand is dependent on price and stock. Based on the above consideration, three different cyclic IRP models for perishable products with stock and price dependent demand in VMI supply chain are put forward. They are IRP model ending with shortage, IRP model starting with shortage, and IRP model with no shortage. These models are composed of a single manufacturer and multiple retailers. The objective is to minimize the average total cost. The total cost includes not only fixed and transportation cost of vehicles, inventory (order and holding) and shortage cost of retailers, but also startup and holding cost of manufacturer. The proposed models are nonlinear mixed integer programming models and NP-hard problem. In order to solve these models, a hybrid heuristic algorithm that is developed by combining cuckoo algorithm with improved Clarke-Wright savings algorithm is developed. In computational experiments, the proposed algorithm is compared with the optimization solver. The results demonstrate the proposed algorithm outperforms the optimization solver. In addition, the results show the average total cost may be reduced by using the strategy of stockout in some cases. Sensitivity analysis is also implemented to study the influence of parameters on optimal solution. The results of computational experiments validate the applicability of the proposed models and the effectiveness of hybrid heuristic algorithm. The study also provides policymakers with management implications. Finally, the conclusions and future research directions are given. [ABSTRACT FROM AUTHOR]