Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS)