Abstract
Hybrid flowshop scheduling problem with lot-streaming (HLFS) has played an important role in modern industrial systems. In this paper, we preset an improved migrating birds optimization (IMBO) algorithm for HLFS to minimize makespan. To ensure the diversity of initial population, a Nawaz-Enscore-Ham (NEH) heuristic algorithm is used to generate the leader, and the remaining solutions are randomly generated. According to the characteristics of the HLFS problem, we propose a combined neighborhood search structure that consists of four different neighborhood operators. We design effective local search procedure to explore potential promising domains. In addition, a reset mechanism is added to avoid falling into local optimum. Extensive experiments and comparison demonstrate the feasibility and effectiveness of the proposed algorithm.
Highlights
In the process of production and processing, how to allocate resources reasonably, make full use of resources, and reduce enterprise resource consumption is an important part of enterprise resource allocation [1]
We focus on hybrid flowshop scheduling problem with lotstreaming (HLFS)
As the successful applications of MBO algorithm, this paper proposes an improved migrating birds optimization (IMBO) algorithm for the HLFS problem to minimise the makespan
Summary
In the process of production and processing, how to allocate resources reasonably, make full use of resources, and reduce enterprise resource consumption is an important part of enterprise resource allocation [1]. Ping et al.: IMBO Algorithm to Solve HFS Problem With Lot-Streaming a real production environment [13] It has important practical applications in textile, chemical, steel [14] and many others industries. Dios et al [9] considered the hybrid flow shop scheduling problem of integrated batching and lot-streaming with variable sublots to reduce the total weighted completion time. Nejati et al [17] studied a two-stage assembly lot-streaming hybrid flow shop scheduling problem with a work shift constraint to minimize the sum of weighted completion times of products in each shift. Zhang et al [23] proposed a multi-objective MBO algorithm to solve the multi-objective hybrid flowshop scheduling problem related to production efficiency and production environment instability in a dynamic shop environment.
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