In the integrated uncapacitated lot sizing and bin packing problem, we have to couple lot sizing decisions of replenishment from single product suppliers with bin packing decisions in the delivery of client orders. A client order is composed of quantities of each product, and the quantities of such an order must be delivered all together no later than a given period. The quantities of an order must all be packed in the same bin, and may be delivered in advance if it is advantageous in terms of costs. We assume a large enough set of homogeneous bins available at each period. The costs involved are setup and inventory holding costs and the cost to use a bin as well. All costs are variable in the planning horizon, and the objective is to minimize the total cost incurred. We propose mixed integer linear programming formulations and a combinatorial relaxation where it is no longer necessary to keep track of the specific bin where each order is packed. An aggregate delivering capacity is computed instead. We also propose heuristics using different strategies to couple the lot sizing and the bin packing subproblems. Computational experiments on instances with different configurations showed that the proposed methods are efficient ways to obtain small optimality gaps in reduced computational times.
The Fiber Installation Problem (FIP) in Wavelength Division Multiplexing (WDM) optical networks consists in routing a set of lightpaths (all-optical connections) such that the cost of the optical devices necessary to operate the network is minimized. Each of these devices is worth hundreds of thousands of dollars. In consequence, any improvement in the lightpath routing may save millions of dollars for the network operator. All the works in the literature for solving this problem are based on greedy heuristics and genetic algorithms. No information is known on how good are the solutions provided by these heuristics compared to the optimal solution. Besides, no proof that the problem is NP-Hard can be found. In this paper, we prove that FIP is NP-Hard and also present an Integer Linear Programming (ILP) formulation for the problem. In addition, we propose an implementation of the Iterated Local Search (ILS) metaheuristic to solve large instances of the problem. Computational experiments carried out on 21 realistic instances showed that the CPLEX solver running with our ILP formulation was able to solve 11 out of the 21 instances to optimality in less than two minutes. These results also showed that the ILS heuristic has an average optimality gap of 1% on the instances for which the optimal solution is known. For the other instances, the results showed that the proposed heuristic outperforms the best heuristic in the literature by 7%.
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