Solving the Flexible Job Shop Scheduling Problem Using a Hybrid Artificial Bee Colony Algorithm

Abstract

In this work, a hybrid artificial bee colony algorithm is proposed for solving the flexible job shop scheduling problem (FJSP) which is a classification of the classical job shop scheduling problem (JSP) considered to NP-hard in nature. In FJSP, an operation can be processed on a set of capable machines with different processing times, thereby dealing with a routing and sequencing problem. The objective considered is to minimize the makespan. The basic artificial bee colony (ABC) algorithm stresses on the balance between global exploration and local exploitation. However, the drawback of the basic ABC algorithm is that it converges prematurely and may get trapped in the local optima. Hence to improve its exploration capability in local space, it is hybridized using a Tabu search (TS) algorithm. At first, initial solutions are generated with certain quality and diversity as food sources using multiple strategies in combination. Crossover and mutation operations are carried out for machine assignment and operation sequencing separately generating new neighboring solutions. Lastly, a local search strategy based on TS is proposed to enhance the local search capability. Kacem's and Brandimarte's benchmark instances are used to compare the performance of the proposed approach to five other well-known algorithms in the literature. Experimental results revealed the superiority of the proposed approach in solving FJSP.

Application of Tabu Search for Job Shop Scheduling Based on Manufacturing Order Swapping

“…These worse but acceptable moves help the optimization process escape from local optima by exploring the solution space more thoroughly. The Tabu list is a form of short-term memory that records the most recently visited moves and discourages the Tabu search from revisiting them to prevent cycles [5]. To generate more neighborhoods, a method to resolve the distributed permutation flow-shop scheduling problem (DPFSP) is proposed, which involves swapping the sub-sequences of jobs [6].…”

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

“…In COPs' literature, many approximation algorithms are used for solving the MOCOPs, such as, multi-objective discrete artificial bee colony (ABC) [22], [23], [24], [25], [26], [27]; multi-objective ant colony optimization (MOACO) [28]; improved artificial immune algorithm [29]; MOEA/D [30]; multi-objective memetic algorithm [31], [32], [33], [34]; water wave optimization [35]; modified particle swarm optimization (PSO) [36], [37]; multi-objective hybrid immune algorithm [38]; GA [39]; grey wolf optimization [40]; cooperative swarm intelligence algorithm for MODOP [41]; multi-objective fruit fly optimization algorithm [42]; multi-objective discrete virus optimization algorithm [43]; NSGA-II & SPEA-II [44] and subpopulation based multiobjective evolutionary algorithm [45]. As this study is focussed on reviewing NSGA-II for MOCOPs, a detailed view of NSGA-II implementations for selected MOCOPs is given in next sub-sections.…”

Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)

“…Experiments are conducted with 50 particles and 100 iterations (5000 visited points). Table 15 presents the comparison against PSO-based metaheuristics: PSO [35], artificial bee colony (ABC) [36], quantum annealing based optimization (QAO) [37], genetic algorithm (GA) [38], human learning optimization algorithm and PSO (HLO-PSO) [39] and hybrid brain storm optimization algorithm and late acceptance hill climbing (hybrid PSO) [40] (-denotes the data's unavailability). A bold value indicates that the E2L-PSO result is either optimal or the best.…”