Integrated optimization of storage location assignment and crane scheduling in an unmanned slab yard. (November 2021)

• Record Type: Journal Article
• Authors: Peng, Gongzhuang; Wu, Youqi; Zhang, Chunjiang; Shen, Weiming
• Is Part Of: Computers & industrial engineering. Volume 161(2021)
• Publication Date: 2021-11
• Subjects: Storage location assignment -- Crane scheduling -- Unmanned slab yard -- Multi-objective optimization -- Modified NSGA-III; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107623
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Establish an efficient production and operation mechanism for an unmanned slab yard. Propose an integrated optimization model of storage location assignment and crane scheduling. Modify the NSGA-III and compare it with MOEA/D. Case studies with actual inventory and production data show the effectiveness of the proposed algorithm. Abstract: In order to respond quickly to diverse needs of customers, iron and steelmaking industries have to improve the efficiency of their logistics operations. Storage location assignment and crane scheduling are both important problems in slab yards, which connect the continuous casting area and the hot rolling area and play a vital buffer role in maintaining the balance of the hot rolling production process. This paper presents a mixed integrated programming (MIP) model with two objectives – relocation rate and feed length, which indicate the performance of storage location assignment and crane scheduling in an unmanned slab yard, respectively. To solve this multi-objective optimization problem with large-scale parameters and complicated constraints, this paper proposes to modify the standard NSGA-III (Non-dominated Sorting Genetic Algorithm III) by employing dispatching rules-based initial population generation, adaptive crossover and mutation operations, and improved reference point generation. A case study with actual inventory and production data shows that the proposed algorithm reduces the relocation rate from 30% to 10%. The well-known MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) is applied to compare the performance of the modified NSGA-III. The quality of the Pareto front and the objective values indicate that the proposed algorithm can achieve the integrated optimization of storage location assignment and crane scheduling and performs better than MOEA/D.