Worst-case analysis for new online bin packing problems

We consider two new online bin packing problems, the online Variable Cost and Size Bin Packing Problem (o-VCSBPP) and the online Generalized Bin Packing Problem (o-GBPP). We take two well-known bin packing algorithms to address them, the First Fit (FF) and the Best Fit (BF). We show that both algorithms have an asymptotic worst-case ratio bound equal to 2 for the o-VCSBPP and this bound is tight. When there are enough bins of a particular type to load all items, FF and BF also have an absolute worst-case ratio bound equal to 2 for the o-VCSBPP, and this bound is also tight. In addition, we prove that no worst-case ratio bound of FF and BF can be computed for the o-GBPP. Therefore, we consider a natural evolution of these algorithms, the First Fit with Rejection and the Best Fit with Rejection, able to reject inconvenient bins at the end of the process. Similarly, we prove that no worst-case ratio of these algorithms can be computed for the o-GBPP. Finally, we give sucient conditions under which algorithms do not admit any performance ratio, and conclude that the worst-case results obtained for the o-VCSBPP and the o-GBPP also hold for the oine variant of these two problems.