A Learning-Based Hybrid Artificial Bee Colony Algorithm for Energy-Efficient Distributed Heterogeneous Type-2 Fuzzy Welding Shop Scheduling Problem With Factory Eligibility

Abstract

In the era of economic globalization, the distributed heterogeneous welding shop scheduling problem (DHWSP) has been considered. Meanwhile, in some actual production scenarios, some jobs can only be processed in certain designated factories (i.e., factory eligibility), and the inevitable uncontrollable system disturbances (e.g., machine maintenance and human factors) lead to uncertain processing time for jobs. However, the research considering uncertain processing time in DHWSP with factory eligibility remains unexplored. Considering the advantages of interval type-2 fuzzy number (IT2FN) in representing the high level of uncertainty, the concept of IT2FN is introduced to tackle uncertain processing time. Then, under the context of green manufacturing, this paper investigates an energy-efficient distributed heterogeneous type-2 fuzzy welding shop scheduling problem with factory eligibility (EDHFWSP-FE). A learning-based hybrid artificial bee colony algorithm (LHABC) is designed to minimize both total energy consumption and makespan in EDHFWSP-FE. Within LHABC, a cooperative initialization is presented to create excellent initial solutions. In employed bee phase, a Q-learning based method is developed to help solutions select a superior neighborhood structure. In onlooker bee phase, a variable neighborhood search (VNS) is proposed to excavate promising neighborhood solutions. In scout bee phase, an estimation of distribution algorithm (EDA) based method is devised to generate new excellent solutions. Finally, experimental results on 27 test instances demonstrate that LHABC outperforms five other multi-objective optimization algorithms. Note to Practitioners—This paper aims to extend DHWSP to EDHFWSP-FE according to practical situations. In the actual production procedure, DHWSP poses more significant challenges. Not only operation sequencing, factory allocation and welding machine allocation matters, but also uncertain processing time and production constraint (e.g., factory eligibility) in the environment are important. Thus, this paper formulates an EDHFWSP-FE with the goals of minimizing total energy consumption and makespan. This problem model is applicable across many welding manufacturing enterprises with complex production environments. To solve this problem, we design a LHABC that hybridizes ABC with other effective methods to enhance its performance. Each component of LHABC is meticulously designed based on the specific characteristics of the problem domain, offering efficient schedules that are both energy-efficient and high-performing for practical implementation. Experimental results verify that all improved components of LHABC contribute to its performance, and LHABC outperforms other optimization algorithms in solving the problem.

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