Many real-world optimization problems involve conflicting objectives that need to be minimized to reduce cost and/or maximized to increase profit. In this study, a multi-objective social group optimization (MOSGO) is proposed and implemented to solve multi-objective problems and find approximated solutions to the optimal Pareto front. A new mechanism, the dynamic fitness function, is introduced and integrated with non-dominated sorting and crowding distance elimination strategies to enhance the quality of the non-dominated solutions. The dynamic fitness function is designed to select the best solution for each objective at each iteration. Non-dominated sorting is used to dismiss weak solutions, and crowding distance elimination is deployed to achieve the best solution diversity. The suggested algorithm is compared with four competitive algorithms: the multi-objective artificial hummingbird algorithm (MOAHA), the multi-objective particle swarm optimization (MOPSO), the multi-objective ant lion optimizer (MOALO), and the non-dominated sorting genetic algorithm-II (NSGA-II). Computational simulations are performed on well-studied ZDT benchmark test functions. Comprehensive comparisons are carried out regarding convergence, diversity, and solution distribution. Experiment results show that the proposed MOSGO provides, in most problems, significantly better convergence near the true Pareto front, with improved diversity and spread of solutions, compared to other multi-objective algorithms.
Ghazwan Alsoufi, M. A. Zeidan, N. Al-Thanoon et al.· International journal of mat...· 0 citations
In light of the escalating popularity of online shopping and the urgent need to reduce unnecessary driving to mitigate environmental impacts, it has become increasingly important to provide cost-effective solutions for attended home deliveries. Extensive research efforts have been dedicated to addressing challenges related to integrating demand management and vehicle routing with time windows. In this paper, we present two key contributions. Firstly, we propose an enhanced method for estimating opportunity cost, by leveraging a dynamic-routing and distribution approach that incorporates forecast orders. This approach allows for more accurate revenue-loss assessments, ultimately leading to improved decision-making on the delivery charges. Secondly, we introduce a dynamic slot-combination strategy, aiming to fully exploit the flexibility that customers possess in receiving their delivery, which enhances overall route efficiency and customer satisfaction. Importantly, our proposed augmented time-windows approach can be easily implemented within existing systems, employing standard time windows, without necessitating any strategic changes or complex computational modifications to the routing system. To assess the performance of our proposed approach, we conducted exhaustive experiments on real data. The results demonstrate that our generated solution outperforms both recent and current state-of-the-art approaches in terms of profitability and delivery efficiency. This signifies the effectiveness and practicality of our proposed methodology in addressing the challenges associated with attended home deliveries.
M. Abdollahi, Xinan Yang, Michael Fairbank· Annals of Operations Researc...· 0 citations
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