Casting production scheduling problem has attracted increasing research attention in recent years to facilitate the profits, efficiency, and environment issues of casting industry. is often characterized by properties intensive energy consumption complex process routes, which motivate in-depth investigation on construction practical multi-objective models development effective algorithms. In th...

Optimum controller placement in the presence of several conflicting objectives has received significant attention Software-Defined Wide Area Network (SD-WAN) deployment. Multi-objective evolutionary algorithms, like Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swamp Optimization (MOPSO), have proved helpful solving Controller Placement Problem (CPP) SD-WAN. However, these a...

Distribution centers (DCs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.This paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. An evolutionary algorithm named non-dominated sorting ant colony optimization (NSACO) is used as the optimi...

We consider an open shop scheduling problem with setup and processing times separately such that not only the setup times are dependent on the machines, but also they are dependent on the sequence of jobs that should be processed on a machine. A novel bi-objective mathematical programming is designed in order to minimize the total tardiness and the makespan. Among several mult...

time-dependent vehicle routing problem is one of the most applicable but least-studied variants of routing and scheduling problems. in this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and multiple depots, is proposed. to deal with the traffic congestions, we also considered that the vehicles are not forced to come...

This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed uses non-dominated sorting genetic algorithm (NSGA-II) to enable traditional stochastic gradient algorithms (e.g., ADAM) escape local minima effectively. Additionally, NSGA-II enables satisfying initial and boundary conditions encoded into ...