Testing the Performance of Bat-Algorithm for Permutation Flow Shop Scheduling Problems with Makespan Minimization

Abstract

In this work, a BAT Algorithm is proposed to solve the permutation flow shop scheduling problem (PFSSP) with minimizing makespan criterion. In a PFSSP, there are n-jobs and m-machines with a proportional deterioration is considered in which all machines process the jobs in the same order, i.e., a permutation schedule. Every job comprises of a foreordained arrangement of assignment operations, each of which should be handled without intrusion for a given timeframe on a given machine. As of late, optimization algorithms such as ant colony optimization (ACO), simulated annealing (SA), artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO) and tabu search (TS) have assumed a significant role in solving PFSSPs. The popular NEH algorithm is considered as the parent algorithm to find the initial solution, and the makespan is minimized in two stages of simulation. The proposed algorithm is tested on 12 flow shop scheduling bench mark problems from OR Library. The proposed algorithm is validated with a well-chosen set of benchmark problems in the literature. Computational results indicate that the proposed bat algorithm is more efficient than the TLBO & HPSO algorithm.

Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

“…Once the internal comparison of HPSO with standard PSO, PSO-VNS, and validation against HGA and HGSA was completed, the last part of validation was against other notable techniques already reported in the literature. For this purpose, a more detailed comparison was carried out with WOA [16], Chaotic Whale Optimization (CWA) [17], the BAT-algorithm [18], NEHT (NEH algorithm together with the improvement presented by Taillard) [31], ACO [50], CPSO (Combinatorial PSO) [51], PSOENT (PSO with Expanding Neighborhood Topology) [40], and HAPSO (Hybrid Adaptive PSO) [52]. This comparison was solely based on ARPD values and is shown in Table 6 and graphically illustrated in Figure 8.…”

“…Therefore, the focus of research shifted towards meta-heuristics. Many such approaches, as viable solutions to PFSP, have already been reported in the literature, which includes GA (Genetic Algorithms) [10,11], PSO (Particle Swarm Optimization) [12,13] and ACO (Ant Colony Optimization) [14], Q-Learning algorithms [15], HWOA (Hybrid Whale Optimization Algorithms) [16], CWA (Enhanced Whale Optimization Algorithms) [17], and BAT-algorithms [18]. Metaheuristic-based approaches start with sequences generated randomly by a heuristic and then iterate until a stopping criterion is satisfied [19].…”

Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making

“…Nature has inspired many metaheuristic algorithms, such as the BAT algorithm (Bellabai et al, 2022), which is inspired by the echolocation system of bats to solve problems. The HMSA algorithm (Marichelvam et al, 2017) combines elements of the Monkey Search algorithm with other techniques to solve the flow shop problem.…”