Self-adaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem

• We study a new multi-objective 2-dimensional vector packing problem.
• We propose several encoding/decoding strategies whose parameters are embedded in the solution encoding.
• We compare the presented strategies using two multi-objective population-based metaheuristics namely NSGA-II and PLS-1.

In this paper, a multi-objective 2-dimensional vector packing problem is presented. It consists in packing a set of items, each having two sizes in two independent dimensions, say, a weight and a length into a finite number of bins, while concurrently optimizing three cost functions. The first objective is the minimization of the number of used bins. The second one is the minimization of the maximum length of a bin. The third objective consists in balancing the load overall the bins by minimizing the difference between the maximum length and the minimum length of a bin. Two population-based metaheuristics are performed to tackle this problem. These metaheuristics use different indirect encoding approaches in order to find good permutations of items which are then packed by a separate decoder routine whose parameters are embedded in the solution encoding. It leads to a self-adaptive metaheuristic where the parameters are adjusted during the search process. The performance of these strategies is assessed and compared against benchmarks inspired from the literature.

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