Non-Preemptive Goal Programming

In the non-preemptive GP model, the buyer sets goals to achieve for each objective and preferences in achieving those goals expressed as numerical weights. Here, the buyer has three goals as follows  

The weights Wy Wz, and W3 can be obtained using the methods discussed in Section 6.3. The non-preemptive GP model can be formulated as 

In the aforementioned model dt,d2, and ds represent the overachievement of the stated goals. Due to the use of the weights the model needs to be scaled. The weights W2 and W3 can be varied to obtain different goal programming optimal solutions. 

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Because the multiple objectives stated by supply chain managers are formulated as goal constraints with allowable deviations in the tactical submodel, the objective function is formulated to minimize some function of the deviations. Here, two different goal programming techniques are considered preemptive and non-preemptive goal programming. With non-preemptive... 

The objectives considered in the multi-criteria model conflicted with one another. Non-preemptive goal programming with ideal values as targets was used to obtain the best possible solution that represented the decision makers preferences. Because the criteria were conflicting, it was not possible to achieve any of the ideal values. Table 5.6 shows how much of each criterion was achieved and the percent difference from the ideal values for Scenarios 6 and 8. As can be observed, in both scenarios, the power objective was almost fully met but the credit performance was not met very much at all. This happened because very few customers had low ratings (two customers had power ratings below 5 out of 10 and two had credit performance ratings below 5 as well) and two of them were selected. 

This section discusses the solution techniques for solving the proposed multicriteria optimization model. To solve the multiple and conflicting objectives model, we use preemptive goal programming (P-GP), non-preemptive goal programming (NP-GP), and the interactive method. 

Krikke et al. (2003) Minimize cost, nunimize energy use, nimimize waste MILP, solved using non-preemptive goal programming Tactical... 

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