Extreme-Point-based Heuristics for the Three-Dimensional Bin Packing problem

One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points at which to place the items inside the bins, because the performance of exact and heuristic solution methods is actually strongly influenced by the choice of a placement rule. We introduce the extreme point concept and present a new extreme point-based rule for packing items inside a three-dimensional container. The extreme point rule is independent from the particular packing problem addressed and can handle additional constraints, such as fixing the position of the items. The new extreme point rule is also used to derive new constructive heuristics for the three-dimensional bin-packing problem. Extensive computational results show the effectiveness of the new heuristics compared to state-of-the-art results. Moreover, the same heuristics, when applied to the two-dimensional bin-packing problem, outperform those specifically designed for the proble

A greedy adaptive search procedure for multi-dimensional multi-container packing problems

Tech. Rep. CIRRELT-2012-10, CIRRELT, Montrea

A cycle-based evolutionary algorithm for the fixed-charge capacitated multi-commodity network design problem

This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through selecting arcs, with an objective of minimizing the fixed costs of the selected arcs plus the variable costs of the flows on each arc. The proposed algorithm evolves a pool of solutions using principles of scatter search, interlinked with an iterated local search as an improvement method. New cycle-based neighborhood operators are presented which enable complete or partial re-routing of multiple commodities. An efficient perturbation strategy, inspired by ejection chains, is introduced to perform local compound cycle-based moves to explore different parts of the solution space. The algorithm also allows infeasible solutions violating arc capacities while performing the "ejection cycles", and subsequently restores feasibility by systematically applying correction moves. Computational experiments on benchmark MCNDP instances show that the proposed solution method consistently produces high-quality solutions in reasonable computational times

The synchronized multi-commodity multi-service Transshipment-Hub Location Problem with cyclic schedules

The synchronized multi-commodity multi-service Transshipment-Hub Location Problem is a hub location problem variant faced by a logistics service provider operating in the context of synchromodal logistics. The provider must decide where and when to locate transshipment facilities in order to manage many customers’ origin–destination shipments with release and due dates while minimizing a total cost given by location costs, transportation costs, and penalties related to unmet time constraints. The considered synchromodal network involves different transportation modes (e.g., truck, rail, river and sea navigation) to perform long-haul shipments and the freight synchronization at facilities for transshipment operations. To the best of our knowledge, this variant has never been studied before. Considering a time horizon in which both transportation services and demand follow a cyclic pattern, we propose a time–space network representation of the problem and an ad-hoc embedding of the time-dependent parameters into the network topology and the arcs’ weight. This allows to model the flow synchronization required by the problem through a Mixed-Integer Linear Programming formulation with a simplified structure, similar to well-known hub location problems and avoiding complicating constraints for managing the time dimension. Through an extensive experimental campaign conducted over a large set of realistic instances, we present a computational and an economic analysis. In particular, we want to assess the potential benefits of implementing synchromodal logistics operations into long-haul supply-chains managed by large service providers. Since flexibility is one of the main features of synchromodality, we evaluate the impact on decisions and costs of different levels of flexibility regarding terminals’ operations and customers’ requirements

The generalized bin packing problem

Tech. Rep. CIRRELT 2011-39, CIRRELT, Montreal, Canad

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