We propose in this work a hybrid improvement procedure for the bin packing problem. This heuristic has several features: the use of lower bounding strategies; the generation of initial solutions by reference to the dual min-max problem; the use of load redistribution based on dominance, differencing, and unbalancing; and the inclusion of an improvement process utilizing tabu search. Encouraging results have been obtained for a very wide range of benchmark instances, illustrating the robustness of the algorithm. The hybrid improvement procedure compares favourably with all other heuristics in the literature. It improved the best known solutions for many of the benchmark instances and found the largest number of optimal solutions with respect to the other available approximate algorithms.

No abstract

No abstract

The bi-objective minimum diameter-cost spanning tree problem (bi-MDCST) seeks spanning trees with minimum total cost and minimum diameter. The bi-objective version generalizes the well-known bounded diameter minimum spanning tree problem. The bi-MDCST is a NP-hard problem and models several practical applications in transportation and network design. We propose a bi-objective multiflow formulation for the problem and effective multi-objective metaheuristics: a multi-objective evolutionary algorithm and a fast nondominated sorting genetic algorithm. Some guidelines on how to optimize the problem whenever a priority order can be established between the two objectives are provided. In addition, we present bi-MDCST polynomial cases and theoretical bounds on the search space. Results are reported for four representative test sets.

After disasters, such as in the aftermath of a major earthquake, the road network can be blocked by debris from collapsed buildings, impacting accessibility to the affected population. In addition, people move and assemble in various points of the city. In this context, road network accessibility becomes an important issue for logistics operations responsible for the relief and the distribution of supplies to the affected population. We propose metaheuristics for the multi-period Work-troops Scheduling Problem (WSP), extending a contribution from the literature. The WSP is an NP-hard problem. This study brings several contributions to the WSP such as a dedicated local search and two metaheuristics: a Greedy Randomized Adaptive Search procedure and an Iterated Local Search. Results are performed on theoretical instances, and on a realistic instance of Port-au-Prince city in Haïti after the 2010 earthquake, where the problem has 16,660 vertices and 19,866 routes representing the urban network, and more than 500 blocked roads. The method developed improved the results from the literature and the results indicate its robustness. Moreover, the best-known results for the case of the Haïti instances are presented in this study.