Population-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration

Abstract

This paper investigates permutation flow shop scheduling (PFSS) problems under the effects of position-dependent learning and linear deterioration. In a PFSS problem, there are n jobs and m machines in series. Jobs are separated into operations on m different machines in series, and jobs have to follow the same machine order with the same sequence. The PFSS problem under the effects of learning and deterioration is introduced with a mixed-integer nonlinear programming model. The time requirement for solving large-scale problems type of PFSS problem is exceedingly high. Therefore, wellknown metaheuristic methods for the PFSS problem without learning and deterioration effects such as iterated greedy algorithms and discrete differential evolution algorithm are adapted for the problem with learning and deterioration effects in order to find a faster and near-optimal or optimal solution for the problem. Furthermore, this paper proposes a hybrid solution algorithm that is called population-based Tabu search algorithm (TS POP) with evolutionary strategies such as crossover and mutation. The search algorithm is built on the basic structure of Tabu search and it searches for the best candidate from a solution population instead of improving the current best candidate at each iteration. Furthermore, the performances of these methods in view of solution quality are discussed in this paper by using test problems for 20, 50, and 100 jobs with 5, 10, 20 machines. Experimental results show that the proposed TS POP algorithm outperforms the other existing algorithms in view of solution quality.

Genetic Algorithm Application for Permutation Flow Shop Scheduling Problems

“…Arık [50] Artificial bee colony The best component of IG combined with an artificial bee colony algorithm. Gyms et al [51] A new node decomposition scheme that combines dynamic branching and lower bound refinement strategies Arık [52] Population-based TS Hybrid solution method with crossover and mutation strategies for the problem under effects of learning and deterioration.…”

Intelligent optimization under blocking constraints: a variant iterated greedy algorithm for hybrid flow shop scheduling problem

“…In literature, FSSP has been proven to be NP-hard (Fernandez-Viagas et al 2017), and has been widely studied. For interested readers, please refer to (Wang et al 2020;Arık 2021;Kaya et al 2021). To further enhance production efficiency, some identical parallel machines are considered in the flow shop scheduling.…”

An Improved Evolution Strategy Hybridization With Simulated Annealing for Permutation Flow Shop Scheduling Problems

“…In literature, Meta-heuristic algorithms have been developed to solve PFSSP by several researchers. Most significant meta-heuristics used in literature are Artificial Bee Colony Algorithm (ABC) (Deng, Xu [35]; Han, Gong [36]; Li and Pan [37]; [38]), Differential Evolution Algorithm (DE) (Liu,Yin [39]), Evolutionary Algorithm (EA)(Qian, Wang [40], Yeh and Chiang [41]), Genetic Algorithms (GA) (Caraffa, Ianes [42]; Ruiz, Maroto [43]; Vallada and Ruiz [44]; Akhshabi, Haddadnia [45]; Andrade, Silva [46]), Hybrid Discrete Differential Evolution (HDDE)(Wang, Pan [47]), Hybrid Differential Evolution Algorithm (HDEA) (Liu,Yin [39]), Simulated Annealing (SA) (Laha and Chakraborty [48]; Lin and Ying [49]; Moslehi and Khorasanian [50]) Lin, Cheng [51], Tabu Search (TS)(Taillard [52]; Grabowski and Wodecki [53]; Grabowski and Pempera [54]; Arık [55]), TS and ABC (Li and Pan [37]), Hybrid Whale optimization algorithm(HWO) (Abdel-Basset, Manogaran [56]), Particle swarm optimization (PSO) (Zhao, Qin [57]) Evolution Strategy (ES) (de Siqueira, Souza [58]; Khurshid, Maqsood [59]), among others. These algorithms have found competitive results for different PFSSP's compared to heuristics; however, they require more computational time, as they initiate from a sequence that is constructed by heuristics and is iterated until termination criteria are achieved.…”