Abstract
This paper considers a multi-period supplier selection and order allocation problem for a green supply chain system that consists of a single buyer and multiple heterogeneous suppliers. The buyer sells multiple products to end customers and periodically replenishes each item’s inventory using a periodic inventory control policy. The periodic inventory control policy used by the buyer starts every period with an order size determination of each item and the subsequent supplier selection to fulfill the orders. Because each supplier in the system is different from other suppliers in the types of carrying items, delivery distance, item price, and quantity discount schedule, the buyer’s problem becomes a complicated optimization problem. For the described order size and supplier selection problem of the buyer, we propose a nonlinear integer programming model and develop two different algorithms to enhance the usability of the model in a real business environment with a large amount of data. The algorithms are developed to considerably cut computational time and at the same time to generate a good feasible solution to a given supplier selection and order allocation problem. Computational experiments that were conducted to test the efficiency of the algorithms showed that they can cut as much as 99% of the computational time and successfully find feasible solutions, deviating not more than 3.4% from the optimal solutions.
Highlights
In a highly competitive and uncertain business environment, efficient supply chain construction and management becomes an increasingly important issue to maintain a leading edge (Ware et al [1]).Among the various kinds of business decision-making problems occurring in a supply chain, supplier selection and order allocation problems are considered to have a significant impact on the successful operation of a retail business
Many complicated new characteristics have emerged for the supply chains. One such complication is caused by processes related to carbon emissions, such as maximum carbon emission limits, emission capacity trading, and costs related to such activities
Among numerous topics that appear in the SSS & OA area, we narrowly focus our previous research review on SSS & OA problems considering quantity discount and carbon emissions in the mathematical optimization category
Summary
In a highly competitive and uncertain business environment, efficient supply chain construction and management becomes an increasingly important issue to maintain a leading edge (Ware et al [1]). Among the various kinds of business decision-making problems occurring in a supply chain, supplier selection and order allocation problems are considered to have a significant impact on the successful operation of a retail business For this and additional reasons, many previous researchers dealt with similar problems with different particular features of such operations (Fox et al [2], Chen et al [3], Kiesmüller et al [4], Lian et al [5], Kim et al [6]). Kim et al [10] formulated a mixed integer linear programming model for a similar SSS & OA problem for a single buyer and multiple supplier system. They proposed a branch-and-freeze algorithm to handle real problems with millions of constraints and variables. Computer experiments showed that the algorithms are sufficiently fast for these bigger problems and perform to a desired level of accuracy
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