In the article by Ware, Singh, and Banwet (2014), the authors are using the mixed-integer non-linear optimization technique (MINLP) to model the dynamic supplier selection problem. This problem arises whenever there is a need for an organization to secure high-volume supply delivery using different suppliers’ services. This problem is complicated by the amount of information that the organization’s personnel have to take into account. This information includes suppliers’ ability to provide a high-quality delivery, time-management problems, and control of product quality. Furthermore, the problem is complicated by the fact that all of the data has to be analyzed not only for the current period. The data that the organization receives after performing DSSP modeling must also include how the situation will change in the future.
The optimization analysis is used to successfully create a mathematical model for the Dynamic Supplier Selection Problem (DSSP). The purpose of creating this model is to demonstrate the product’s cost for each supplier, supplier’s transportation costs, the quality that the supplier provides for each type of goods, and the time of delay for which the supplier is held responsible. All of the data used in the modeling procedure was generated randomly to demonstrate the abilities of DSSP to provide more accurate information about suppliers. The authors note that DSSP is more precise in terms of describing information regarding suppliers’ ability to provide high-quality services.
The authors use the mixed-integer non-linear program to efficiently simulate the calculations of the suppliers’ service quality taking into account the amount of time required to perform delivery, products’ specifications, transportation costs, and quality levels. All of the information is represented by mathematical formulae and equations that allow calculating the suppliers’ effectiveness. In eight tables the authors present randomly generated data that they used in the process of applying the formulae. The purpose of this optimization technique is also to minimize the total cost. Furthermore, it represents several capacity constraints that every deliverer has for all of the delivered products. Other formulations ensure the satisfaction of demands that an organization will be provided by each supplier, selection of only those suppliers that will provide the required quality of product, and others.
Therefore, it is evident that the optimization technique used by the authors successfully provides the ability to perform a detailed calculation to ensure that every supplier that is involved in the process will demonstrate the ability to support the organization effectively. Furthermore, the product’s quality is also reflected in the equations. Thus, the organization that uses the DSSP will most likely be able always to monitor the current situation regarding supply. The MINLP model that the authors created in their research may also be used by real organizations to calculate their own needs regarding supply efficiently. The randomly generated data then would have to be replaced by the actual data with which the organization is presented. By using the model presented in the article, the organization will be able to evaluate its current state of supply and the issues that may exist in their transportation system or the flaws of product quality that the organization is provided with by the suppliers.
The authors conclude that incorporating the elements of uncertainty in the modeling may also be included in the process of calculation to increase the precision of output data further and deepen the future researches coverage of the topic. Additionally, future research may contain possible solutions for environmental and liability issues.
References
Ware, N. R., Singh, S. P. & Banwet, D. K. (2014). A mixed-integer non-linear program to model dynamic supplier selection problem. Expert Systems with Applications, 41(2), 671-678. Web.