Multi-objective optimization of production scheduling with evolutionary computation: A review

Abstract

Multi-Objective (MO) optimization is a well-known research field with respect to the complexity of production planning and scheduling. In recent years, many different Evolutionary Computation (EC) methods have been applied successfully to MO production planning and scheduling. This paper is focused on making a review of MO production scheduling methods, starting from production scheduling presentation, notation and classification. The research field of EC methods is presented, then EC algorithms` classification is introduced for the purpose of production scheduling optimization. As a main goal, MO optimization is focused on hybrid EC methods, and presenting their advantages and limitations. Finally, a survey of five scientific databases is presented, with the analysis of the scientific publications the terminology development of the scientific field is presented. Using the citation analysis of the scientific publications, the application for the MO optimization in manufacturing scheduling is discussed.

Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules

“…The machine can only perform one operation at a time. The assignment of operation to the machine is given to the occupancy of the machine and the relevance of the machine to carry out the operation [7,26], so only some number of routings are possible for individual product.…”

“…In this context, heuristic procedures, nature-inspired methods, neural networks, and utilizing priority dispatching rules are the most applied methods to solve the optimization problem in scheduling [1]. The most common metaheuristic procedures, such as Simulating Annealing [6], Tabu search [12], Random Search, or many types of nature-inspired optimization methods, such as evolutionary [7,28,29] and genetic algorithms [18], ant colony optimization [30], or particle swarm optimization [16,19,31], can provide acceptable solutions near the optimum. Although metaheuristics are advantageous in the sense of the solution quality and robustness, they do not guarantee solution optimality [1,27], they depend on the choice of method's parameters [11], and they are often too complex to implement computationally in a real-time system [2].…”

“…Exact optimization methods are mainly used only for the systems which have a specific topology where very strong simplifying assumptions must be used [6], so they are not too applicable in a real-world scenario for more complex systems. In that case, approximate optimization methods and metaheuristics [7] based on stochastic local search approach, machine learning techniques, especially artificial neural networks (ANN), fuzzy logic methods, and expert systems, are at the center of research interest to find optimal or near-optimal solutions [8,9] instead of exact mathematical optimization models.…”

Chaotic Multi-Objective Simulated Annealing and Threshold Accepting for Job Shop Scheduling Problem

“…Finding the optimal solution for this problem is too complex, and so it is classified in the NP-hard class [1,2]. On the other hand, the JSSP foundations provide a theoretical background for developing efficient algorithms for other significant sequencing problems, which have many production systems applications [3]. Furthermore, designing and evaluating new algorithms for JSSP is relevant not only because it represents a big challenge but also for its high industrial applicability [4].…”

Flow Shop Scheduling Based on Palmer-Neh, Gupta-Neh and Dannenbring-Neh Algorithms to Minimize the Energy Cost

“…Production scheduling is defined as allocating limited resources to do several jobs [1,2,3]. Scheduling is a decision-making process related to job sequence determination used in many manufacturing and services industries.…”