An adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems

Abstract

The proposed new technologies in the context of industry 4.0 challenge the current practices of scheduling in industry and their associated research in academia. The conventional optimization techniques that are employed for solving scheduling problems are either computationally expensive or lack the required quality. Therefore, in this paper, we propose an adaptive scheduling framework to address scheduling problems taking into account multi-objective optimality measures. The framework is motivated by a hybrid design to combine the use of heuristic and metaheuristic approaches. The main idea behind the presented concept is to achieve an acceptable tradeoff between the quality of the suggested solutions for a problem and the required computational effort to obtain them. The perused narrative in such implementation is combining some advantages of heuristic and metaheuristic approaches such as: the light execution time of heuristics and the robustness as well as the quality of metaheuristic approaches. The framework is evaluated for solving hybrid flow shop scheduling problems that are derived from a real use case.

Deep Reinforcement Learning Techniques For Solving Hybrid Flow Shop Scheduling Problems: Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C)

“…In this section, we will shortly present the preliminaries of the considered problem, which is based on the work presented in [20]. A job j∈ J is characterized by the following attributes:…”

“…Based on the selection of the agent, families and their associated jobs are reallocated between available machines in the first processing stage over time. The agent has access to six different allocation algorithms that are based on the work presented in [20]. The sequencing part of the problem is solved using a sequencing algorithm presented in [11].…”

“…To address these concerns, many research efforts on the design and use of hybrid techniques are presented for instance in [15,[18][19][20]. However, we found limited research on the use of machine learning techniques for addressing scheduling problems of such complexity as we will discuss in the related works section.…”