Variable neighborhood search for multi-objective resource allocation problems

On the configuration of supply chains for assemble-to-order products: Case studies from the automotive and the CNC machine building sectors

“…Metaheuristics have been applied to the majority of design and planning problems, aiming at multi or single-objective optimisation [16]. Commonly used metaheuristics are: Simulated Annealing [17], Tabu Search [18], cross-entropy [19], variable neighbourhood search [20], as well as nature-inspired methods like genetic algorithms [21], evolution strategies [22], ant colony optimization [23], particle swarm optimization [24], greedy randomized adaptive search procedures (Grasp) [25], approximate and non-deterministic tree search procedures (ANTS) [26], and hybrid methods [27], such as the probabilistic beam search derivate Beam-ACO [28], that combine two or more of the previous methods. Metaheuristics are preferred to simple heuristics [29], mainly due to the fact that the latter have inherent biases and provide suboptimal results.…”

A comparative study on multiobjective metaheuristics for solving constrained in-core fuel management optimisation problems

“…This procedure is repeated until all solutions in the nondominated set have been explored using all the neighbourhood move operators. Three variants of MOVNS have been proposed by Liang & Chuang [43], namely basic, per-430 turbation and perturbation + base solution. The variants involve different strategies for selecting a solution to be explored, as well as the marking of selected solutions in the nondominated set.…”

“…Due to the limited number of function evaluations available for MICFMO and the large neighbourhood sizes associated with the operators, we only generate a fixed number of neighbouring solutions during each iteration of MOVNS instead of considering the entire neighbourhood. A pilot study was performed between the three variants 440 proposed by Liang & Chuang [43] and it was found that the perturbation variant yielded the most promising results in the context of MICFMO. We therefore employed this variant of MOVNS within our comparative study.…”

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

“…() compared three multiobjective algorithms based on MO‐VNS applied to the single machine scheduling problem with sequence‐dependent setup times and distinct due windows. Liang and Chuang () addressed a biobjective resource allocation problem by MO‐VNS. Rego et al.…”