TABLE OF CONTENTS This section discusses a broad set of computational tests intended to evaluate our upper bounding and heuristic solution approaches. Our results focus on gauging both the ability of the different LP relaxations presented in Section 3.1 to provide tight upper bounds on optimal profit, and the performance of the heuristic procedures discussed in Section 3.2 in providing good feasible solutions. Section 4.1 next discusses the scope of our computational tests, while Sections 4.2 and 4.3 report results for the OSP, OSP-NDC, and OSP-AND versions of the problem.
This section presents the approach we used to create a total of 3,240 randomly generated problem instances for computational testing, which consist of 1,080 problems for each of the OSP, OSP-NDC, and OSP-AND versions of the problem. Within each problem version (OSP, OSP-NDC, and OSP-AND), we used three different settings for the number of orders per period, equal to 25, 50, and 200. In order to create a broad set of test instances, we considered a range of setup cost values, production capacity limits, and per unit order revenues. [b] Table 3 provides the set of distributions used for randomly generating these parameter values in our test cases. The total number of combinations of parameter distribution settings shown in Table 3 equals 36, and for each unique choice of parameter distribution settings we generated 10 random problem instances. This produced a total of 360 problem instances for each of the three values...
