It is well known that production, distribution, marketing, inventory control, and financing all/each have a positive impact on the performance of a supply chain. Despite the growing interest in the development of integrated inventory models, the interactions between these elements of a supply chain may not be efficiently included, resulting in a restricted supply chain model presentation. To incorporate this phenomenon, a mathematical model that tackles the interdependent relationships between these aforementioned elements is developed in this paper. This study considers the determination of the optimal pricing, ordering, and delivery policies of a profit-maximizing supply chain system, faced with (1) unit wholesale price of the supplier is set based on unit production cost, (2) unit production cost is taken as a function of demand rate and production rate, (3) the supplier's production rate is adjusted according to market demand, (4) market demand depends upon buyer's selling price, (5) a free freight is offered if the buyer's order exceeds a certain minimum requirement, and (6) a constant credit period is offered by the supplier to stimulate the demand of the buyer. Algorithm for computing the optimal policies is derived. The sensitivity of the optimal results with respect to those parameters which directly influence the production and transportation costs is also examined. 1. Introduction Following the assumption in Harris's model [1], most traditional inventory models assumed that the production rate is constant. However, with advanced manufacturing technologies, such as Computer-aided design/manufacturing , flexible manufacturing system (FMS), and computer-integrated manufacturing system (CIMS), modem manufacturing industries are highly flexible, intelligent, and integrated. As stated by Schweitzer and Seidmann [2], today it is not difficult to adjust the mechanical productivity. Over the years, a number of papers have been published dealing with economic order quantity problems under conditions of variable production rate, such as Goswami and Chaudhuri [3], Balkhi and Benkherouf [4], Goyal and Giri [5], Bhunia and Maiti [6], Kalir and Arzi [7], Rahim and Ben-Daya [8], Giri et al. [9], and ？ner and Bilgi？ [10]. Recently, Li et al. [11] developed an economic production quantity-(EPQ)-based model with planned backorders to evaluate the impact of the postponement strategy on a manufacturer in a supply chain. Transportation cost is another important but often overlooked feature of real inventory systems. Transportation costs are a critical part of the
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