MOEA/D with Uniformly Randomly Adaptive Weights

When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Secondly, the population size becomes not flexible as the number of objectives increases. In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/DURAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems. During the evolutionary process, MOEA/D-URAW adds and removes subproblems as a function of the sparsity level of the population. Moreover, instead of requiring assumptions about the Pareto front shape, our method adapts its weights to the shape of the problem during the evolutionary process. Experimental results using WFG41-48 problem classes, with different Pareto front shapes, shows that the present method presents better or equal results in 77.5% of the problems evaluated from 2 to 6 objectives when compared with state-of-the-art methods in the literature

Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordThe sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city