15h30 - 15h55
Inventory routing problem with perishable products: Formulations and branch-and-cut algorithms
In this talk, we propose different MIP formulations for an inventory routing problem with perishable products. The perishability is modeled by considering a predefined fixed shelf-life for the product. We present branch-and-cut algorithms to solve the problem and report computational experiments with the algorithms using problem instances from the literature.
15h55 - 16h20
A branch-and-price algorithm to solve a three-level lot sizing problem with a distribution structure
We address a three-level lot sizing problem where a central plant produces items that are sent to warehouses and then to retailers facing a deterministic demand over a finite time horizon. The supply chain considered has a distribution structure and we develop a branch-and-price algorithm to efficiently solve the problem. We also add several improvements and test it on numerous instances.
16h20 - 16h45
Principal role of agent-based approach in further advancements of bioenergy supply chain management
Numerous reports states among all the renewable options biomass is one of the most sustainable alternatives. Yet the heterogeneous nature of biomass along with the complexities risen from seasonality and scattered geographical distribution of biomass sources turns supply chain management into one of the most complex management problems.
16h45 - 17h10
Benders cut-and-solve: A new versatile tool for mixed integer programming problems
Introduced by Climer and Zhang (2006), cut-and-solve has been used to solve well-known optimization problems such as the TSP and facility location to optimality. The cut-and-solve framework can be thought of as a generalized local branching in which at each level of the enumeration tree only two child nodes exist, one corresponding to a smaller "sparse'' problem and the other as its complement known as the "dense'' problem. In this study, we propose the use of Benders-based branch-and-cut as the black box MIP solver for "sparse" problems within the cut-and-solve algorithm. Two important advantages of this are the reduced problem size and the re-usability of the Benders cuts generated in previous sparse problems. We present promising computational results for a naive implementation used to solve the fixed-charge multicommodity network design problem.