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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]

In this paper, a two-echelon closed-loop supply chain with one manufacturer and one retailer is considered, and two game theoretic models are presented in which the first model (Model I) considers demand dependent on selling price and warranty period while the second model (Model II) considers demand dependent on greening level in addition to the selling price and warranty period. During the warranty period offered by the manufacturer, a portion of the returned items is refurbished and sent back to the customer while the remaining portion is remanufactured and sold in the secondary market, and the same portion is replaced by the new products in the market. Both the models are solved under centralized, decentralized, and revenue sharing contract scenarios. In the decentralized scenario, a Stackelberg game is considered between the manufacturer and the retailer in which the manufacturer is assumed to be the leader and the retailer as the follower. Through analytic and numerical analyses, it is seen that Model II gives better response on all the key decisions of the supply chain than Model I. The centralized scenario achieves higher greening level and warranty period compared to the decentralized scenario. Sensitivity analysis is performed to investigate the effect of key-model parameters on optimal decisions. [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]

This paper presents a two-period supply chain model which is comprised of one manufacturer and one retailer who are involved in trading a single product. The demand rate in each period is dependent on the selling prices of the current period and the previous period. We assume that the manufacturer acts as the Stackelberg leader and declares wholesale price(s) to the retailer who follows the manufacturer’s decision and sets his selling prices for two consecutive periods. The manufacturer adopts one of the two pricing options: (1) setting the same wholesale price to both the selling periods (2) setting different wholesale prices to two different selling periods. Based on these pricing options, we develop four decision strategies of the manufacturer and the retailer and compare them. For a numerical example, we study the effects of these decision strategies on the optimal results of the supply chain. Further, we graphically analyze under what circumstances a particular decision strategy plays a dominant role. [ABSTRACT FROM AUTHOR]

The paper considers a closed-loop supply chain inventory system involving a single-manufacturer and a single-retailer with stochastic market demand and random return of used item. The manufacturer produces the finished product by manufacturing from raw materials as well as remanufacturing from used product obtained from a collector. Used product's yield rate is assumed to be random. The raw materials may be sourced from two suppliers. The primary supplier is cheaper but unreliable in the sense that supply from this source may disrupt, whereas the secondary supplier is perfectly reliable but more expensive. The objective of this study is to determine the optimal quantity of finished product which is to be manufactured from raw materials so as to maximize the total profit of the integrated system. Results of numerical experiments are presented and sensitivity analysis is carried out to investigate the influence of model-parameters on the optimal solution. [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]

The article determines pricing and order-up-to level S inventory decisions over an infinite planning horizon from the point of view of a risk-averse decision maker. The demand is assumed to be stochastic but influenced by the selling price which is a decision variable. Shortages are allowed and backordered partially. We calculate the present value of the cash flow over the entire planning horizon and incorporate the notion of risk aversion into the model using a concave utility function. We numerically demonstrate the model and investigate the impact of different model-parameters on the optimal decisions. It is observed that the optimal selling price for a risk-averse decision maker is not less than the optimal selling price of a risk-neutral decision maker while the optimal order level for the risk-averse decision maker is always less than that of the risk-neutral decision maker. [ABSTRACT FROM PUBLISHER]

In this paper, an attempt is made to improve profit through a retailer-Stackelberg game in a multi-echelon supply chain involving one supplier, one manufacturer, and one retailer having deterministic price-sensitive demand. Inventory in each echelon is subject to deterioration. We assume that the supplier and the manufacturer follow a multi-shipment policy to deliver to the manufacturer and the retailer, respectively. The proposed model is developed first under centralized decision policy which is the benchmark case. Then the decentralized model is developed and profit improvement is done through a retailer-Stackelberg game. The optimal results are demonstrated and a sensitivity analysis is performed for a numerical data-set. [ABSTRACT FROM AUTHOR]

This article develops a single-manufacturer single-retailer production-inventory model in which the manufacturer delivers the retailer’s ordered quantity in unequal shipments. The manufacturer’s production process is imperfect and it may produce some defective items during a production run. The retailer performs a screening process immediately after receiving the order from the manufacturer. The expected average total cost of the integrated production-inventory system is derived using renewal theory and a solution procedure is suggested to determine the optimal production and shipment policy. An extensive numerical study based on different sets of parameter values is conducted and the optimal results so obtained are analysed to examine the relative performance of the models under equal and unequal shipment policies. [ABSTRACT FROM AUTHOR]

This article develops a single-manufacturer and single-retailer supply chain model under two-level permissible delay in payments when the manufacturer follows a lot-for-lot policy in response to the retailer's demand. The manufacturer offers a trade credit period to the retailer with the contract that the retailer must share a fraction of the profit earned during the trade credit period. On the other hand, the retailer provides his customer a partial trade credit which is less than that of the manufacturer. The demand at the retailer is assumed to be dependent on the selling price and the trade credit period offered to the customers. The average net profit of the supply chain is derived and an algorithm for finding the optimal solution is developed. Numerical examples are given to demonstrate the coordination policy of the supply chain and examine the sensitivity of key model-parameters. [ABSTRACT FROM PUBLISHER]

Abstract: In this article, we develop an imperfect economic manufacturing quantity (EMQ) model for an unreliable production system subject to process deterioration, machine breakdown and repair and buffer stock. The basic model is developed under general process shift, machine breakdown and repair time distributions. We suggest a computational algorithm for determination of the optimal safety stock and production run time which minimize the expected cost per unit time in the steady state. For a numerical example, we illustrate the outcome of the proposed model and perform a sensitivity analysis with respect to the model-parameters which have direct influence on the optimal decisions. [Copyright &y& Elsevier]

• 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]