Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem. (1st March 2023)

• Record Type: Journal Article
• Authors: Goli, Alireza; Ala, Ali; Hajiaghaei-Keshteli, Mostafa
• Is Part Of: Expert systems with applications. Volume 213:Part B(2023)
• Publication Date: 2023-03-01
• Subjects: Non-permutation flow-shop scheduling -- Limited time intervals -- Multi-objective Ant Lion optimization algorithm -- Multi-objective Keshtel Algorithm -- Social Engineering Optimizer; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119077
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Addressing the energy awareness in a flexible flow-shop scheduling problem. Proposing multi-objective algorithms for the proposed problem. Utilizing recent multi-objective and hybrid algorithms in this research area. Evaluating the performance of the proposed algorithms in extensive cases. Abstract: This study investigates the optimization of non-permutation flow-shop scheduling problems and lot-sizing simultaneously. Contrary to previous works, we first study the energy awareness of non-permutation flow-shop scheduling and lot-sizing using modified novel meta-heuristic algorithms. In this regard, first, a mixed-integer linear mathematical model is proposed. This model aimed to determine the size of each sub-category and determine each machine's speed within each sub-category to minimize makespan and total consumed energy simultaneously. In order to optimize this model, Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Keshtel Algorithm (MOKA), and Multi-objective Keshtel and Social Engineering Optimizer (MOKSEA) are proposed. First, the validation of the mathematical model is evaluated by implementing it in a real case of the food industry using GAMS software. Next, the Taguchi design of the experiment is applied to adjust the meta-heuristic algorithms' parameters. Then the efficiency of these meta-heuristic algorithms is evaluated by comparing with Epsilon-constraint (EPC), Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) using several test problems. The results demonstrated that the MOALO, MOKA, and MOKSEO algorithms could find optimal solutions that can be viewed as a set of Pareto solutions, which means the used algorithm has the necessary validity. Moreover, the proposed hybrid algorithm can provide Pareto solutions in a shorter time than EPC and higher quality than NSGA-II and MOPSO. Finally, the model's key parameters were the subject of sensitivity analysis; the results showed a linear relationship between the processing time and the first and second objective functions.