Since multi-objective flow shop scheduling problem (MFSP) plays a key role in practical
scheduling, there has been an increasing interest in MFSP according to the literature …

Many-objective optimization has posed a great challenge to the classical Pareto dominance-
based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary …

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted
tremendous attention and achieved great success in the fields of optimization and decision …

Solving optimization problems with parallel algorithms has a long tradition in OR. Its future
relevance for solving hard optimization problems in many fields, including finance, logistics …

Many-objective optimization problems are common in real-world applications, few
evolutionary optimization methods, however, are suitable for solving them up to date due to …

When solving multiobjective optimization problems, preference-based evolutionary
multiobjective optimization (EMO) algorithms introduce preference information into an …

The paper focuses on investors whose strength of interest in sustainability issues (such as
environmental, social, and governance) causes ESG to become a third criterion alongside …

The use of Pareto dominance for fitness evaluation has been the mainstream in evolutionary
multiobjective optimization for the last two decades. Recently, it has been pointed out in …

This book arose from a realization that modeling using mathematical programming should
be tightly linked with algorithms and their software implementation to solve optimization …

A case study of the wine industry in southern Portugal was conducted to provide a balanced
approach to sustainable supply chain network design. A multi-objective model was …