Design and optimization of Supply Chain Network (SCN), Reverse Logistics and Sustainable Logistics is very important issue, which plans, implements and controls the
efficient and effective forward/reverse flows and storage of goods, services and related information between the point of origin and the consumption to meet customers’ requirements. In the real world of such logistics systems, many combinatorial optimization problems (COPs) impose on more complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously and make the problem intractable to the traditional approaches because of NP‐hard COP. In order to develop an efficient algorithm that is in a sense “best solution”, i.e., whose reasonable computational time for NP‐hard COPs met in practice, we have to consider the following very important issues: Quality of solution, Computational time and Effectiveness for multi‐objective COPs. Metaheuristics including Evolutionary Algorithm (EA) is a generic population‐based Bio‐inspired Computation such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Teaching‐Leaning Based Optimization (TLBO) algorithms. EA is based on principles from evolution theory, and it is very powerful and broadly applicable stochastic search and heuristic optimization which is effective for solving various NP hard COP models. This tutorial talk will be summarized recent advances of several metaheuristics such as Hybrid GA (HGA) with Fuzzy Logic Controller and Local search, Hybrid GA with PSO and Hybrid GA, PSO with TLBO for applying to various scheduling and routing problems. Secondly, based on hybrid metaheuristics, real applications such as intelligent manufacturing, semiconductor manufacturing, integrated SCN, and sustainable logistics models will be summarized.
