This paper addresses the multi-stage hybrid flowshop scheduling problem with identical
parallel machines at each stage by considering the effect of human factors. The various …

Abstract Machine Learning (ML) can help solve combinatorial optimization (CO) problems
better. A popular approach is to use a neural net to compute on the parameters of a given …

Hybrid flowshop scheduling problems include the generalization of flowshops with parallel
machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard …

Although significant research has gone into the field of electrical discharge machining
(EDM), analysis related to the machining efficiency of the process with different electrodes …

This paper proposes an experimental investigation and optimization of various machining
parameters for the die-sinking electrical discharge machining (EDM) process using a multi …

We address the no-wait k-stage flexible flowshop scheduling problem where there are m
identical machines at each stage. The objectives are to schedule the available n jobs so that …

Among the potential road maps to sustainable production, efficient manufacturing
scheduling is a promising direction. This paper addresses the lack of knowledge in the …

Due to intense competition in the market place, effective scheduling has now become an
important issue for the growth and survival of manufacturing firms. To sustain in the current …

Solar clean energy can be harnessed by several methods using technologies like solar
heating, photovoltaic cell, solar architecture, photosynthesis. Solar energy is converted …

Abstract Deep-reinforcement-learning (DRL) based neural combinatorial optimization (NCO)
methods have demonstrated efficiency without relying on the guidance of optimal solutions …