Sensitivity analysis to perturbations of the weight of a subset of items: The knapsack case study

• Sensitivity analysis to perturbations of a subset of weights in knapsacks.
• Dynamic programming establishes new limit values for each sensitivity interval.
• Approximate and optimal limit values are proved for both KP and KSP.

In this paper, we study the sensitivity of the optimum to perturbations of the weight of a subset of items of both the knapsack problem (denoted KP) and knapsack sharing problem (denoted KSP). The sensitivity interval of the weight associated to an item is characterized by two limits, called lower and upper values, which guarantee the optimality of the solution at hand whenever the new weight’s value belongs to such an interval. For each perturbed weight, we try to establish approximate values of the sensitivity interval whenever the original problem is solved. We do it by applying a dynamic programming method where all established results require a negligible runtime. First, two cases are studied when considering an optimal solution of KP: (i) the case in which all perturbations are (non)negatives and (ii) the general case in which the set of the perturbed items is divided into two disjoint subsets (the first subset contains the nonnegative perturbations and the second one represents the subset of negative perturbations). Second, we show how we can adapt the results of KP to the KSP. All established results require a negligible runtime which grows the interest of such a study. Finally, for each of these problems, we will see the impact of the established results on an example while considering the various cases.