همکاری برای یک زنجیره تامین موجودی حلقه بسته رو به وخامت همراه با خرده فروش چندگانه و تقاضای حساس به قیمت

In this paper, a closed-loop logistics system with manufacturing and remanufacturing cycle is analyzed using three optimization methods. They are: (i) sequential optimization, (ii) centralized optimization without benefit sharing, and (iii) centralized optimization with benefit sharing. In the first method, the decisions of sequential optimization are made initially by the retailers, and then by the manufacturer. In the second method, the decision of the centralized optimization is made simultaneously by the whole logistics system. The second method is not favorable to the retailers because the retailers' profit will become smaller as a result of collaboration. Therefore a third method with benefit sharing is investigated. The logistics system includes a manufacturer and multi-retailer considering price-sensitive demand and deterioration. Industrial examples of such products are IC chips, computers and mobile phones that usually outdate or decrease in value due to technological innovation. The decrease in value is considered as a form of deterioration. In order to add value, these outdated products can be remanufactured and resold to market. Four key effects are investigated in this study: (i) recycling in supply chain, (ii) deterioration, (iii) collaboration effect between manufacturer and retailers using three optimization methods, and (iv) price sensitive demand.

One of the keys to successful supply chain management is system integration. Clark and Scarf (1960) presented a concept of serial multi-echelon structures to determine the optimal policy. Banerjee (1986) derived a joint economic lot size model for a single vendor, single buyer system when the vendor has a finite production rate. Goyal (1988) extended Banerjee's model by relaxing the lot-for-lot production assumption. Wee and Jong (1998) studied the integration between parts and finished product with multi-lot size and deterioration. Yang et al., 2007 and Yang et al., 2008 derived a collaborative vendor–buyer inventory system with different patterns of purchase cost, products price and market situations. Wee (1993) considers product deterioration as decay, damage, spoilage, evaporation, obsolescence, pilferage, loss of utility or loss of marginal value of a commodity that results in decreasing usefulness from the original one. Ghare and Shrader (1963) were the first authors to consider on-going deterioration of inventory. Other authors such as Kang and Kim (1983) and Raafat et al. (1991) assumed either instantaneous or finite production with different assumptions on the patterns of deterioration. Many enterprises have focused their attention on reverse supply chain to meet environmental concerns/regulations and social liability. Product remanufacturing such as transforming used items into marketable products through refurbishment, repair and upgrading can also yield substantial cost benefits. Schrady (1967) was the earliest author to propose a deterministic model with instantaneous production rate for manufacturing and remanufacturing. Schrady (1967) argued that optimal lot sizes for manufacturers and remanufacturers can be determined by the classical EOQ formula. Teunter and Laan (2002) used an average cost approach to derive inventory models with remanufacturing. Chung et al. (2008) developed a closed-loop model with single manufacturing and single remanufacturing cycles. Jaber and Saadany (2009) developed a manufacturing and remanufacturing inventory system under the condition of lost sale. Hsu et al. (2010) considered preservation technology investment for deteriorating inventory. Wee et al. (2011) and Chung and Wee (2011) considered deteriorating green products and supply chains inventory systems. Feng and Viswanathan (2011) derived a new lot-sizing heuristic with remanufacturing. Recently, Widyadana and Wee (2012) developed an economic production quantity model for deteriorating items with multiple production setups and rework, and later considered vendor–buyer inventory model with discrete delivery order, random machine unavailability and lost sales (Wee and Widyadana, 2011). Cárdenas-Barrón et al. (2012) developed an improved algorithm and solution on an integrated production-inventory model in a three-layer supply chain. This study extends the paper of Chung et al. (2008) by considering multi-retailer, price-sensitive demand and the effect of deterioration to simulate the real business environment. This paper is organized as follows. Section 2 shows assumptions, notations and model development. Solution procedure with three cases is derived in Section 3. Numerical analysis and sensitivity analysis are validated in 4, 5 and 6. Finally, Section 7 concludes this paper.

This paper develops a closed-loop deteriorating supply chain inventory considering price-sensitive demand and multi-retailer. The three cases consisting of a sequential optimization, a centralized optimization without benefit sharing and a centralized optimization with benefit sharing are compared. The results of the closed-loop supply chain system show that: (i) the centralized optimization method results in a 2.35% higher joint profit than the sequential optimization method; (ii) when the return rate increases, the retail price decreases, the retailers' and the manufacturer's profits increase; (iii) when the return rate, the deterioration rate, the holding cost and the price sensitive parameters increase, the value of the PTPI increases. It implies that collaboration effect is more significant for products with higher return rate, deterioration rate, holding cost and price sensitive parameters. There are two key observations: (i) the recycling is beneficial to both the manufacturer and retailers, and (ii) there is a significant increase in the joint profit for the centralized optimization method as compared with the sequential optimization method. The benefit of recycling decreases for: (i) low purchase cost to the supplier, (ii) low return rate, and (iii) high selling price to the collector. The benefit of collaboration decreases under the following conditions: (i) low return rate, (ii) low price-sensitive parameter and high scale parameter, (iii) low setup parameters, and (iv) low deterioration rate. The above observations and results provide managerial insights in decision making.