Multi-objective biogeography-based optimization for supply chain network design under uncertainty

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

“…3) Even if a multi-objective algorithm can find solutions on a Pareto front, there is no guarantee that distribution of multiple Pareto points becomes uniformly and this may reduce the applicability of the results [3,14,17].…”

“…In single-objective optimization, the fitness of solutions is reachable easily due to existing just one objective function while for multi-objective optimization, no single unique solution can be determined as the best; instead, a set of non-dominated solutions should be found in order to get a good approximation of the true Pareto fronts [3,[12][13][14], which leads to trade-off among the objectives [15,16] .…”

“…Multi-objective optimization (also known as vector optimization, multi criteria/attribute optimization, multiobjective programming, or Pareto optimization) is defined as the process of finding a decision vector to optimize a set of objective functions that satisfies some certain constraints [8,9] while the aim of a single objective optimization is to optimize just one objective function. In contrast 3 with a single objective optimization, multi-objective problems are more difficult and complex [10,11]. Some reasons are:…”

“…Many realistic problems contain simultaneous optimization of several objectives in which these objectives may conflict with each other and with other nonlinear constraints, if exist [1][2][3][4][5][6][7]. These types of problems are known as Multi-Objective Problems (MOPs).…”

Multi-objective optimization for supply chain management problem: A literature review

“…They developed a supply chain network design (SCND) involving suppliers, factories, distribution centers (DCs), and retailers in multi-periodic structure. Yang et al (2015) used MOBBO to solve supply chain network design (SCND) with uncertain transportation cost and uncertain customer demand.…”