In this article, we study the multiroute job shop scheduling problem with continuous-limited output buffers (MRJSP-CLOBs). In contrast to the standard job shop scheduling problem (JSP), continuous-limited output buffers render the commonly used graph-based approaches inapplicable, and the multiroute issue further increases computational complexity. To this end, we formulate MRJSP-CLOB as a mixed-integer linear program (MILP), which is typically NP-hard. Then, we extend the critical block in the JSP by utilizing the no-time-gap relationship and design a new neighborhood structure. Furthermore, we propose a hybrid artificial immune-simulated annealing algorithm (AIA-SA) by sharing iterations and integrating a random infeasible solution repairing algorithm with a new SA acceptance rule, which enables individuals to share information and increases the robustness of the corresponding SA parameters. Finally, the AIA-SA is compared with CPLEX and state-of-the-art algorithms on MRJSP-CLOB with different sizes. Experiments for large-sized instances demonstrate that our algorithm requires less than 3% computing time of the CPLEX, while being faster and more accurate than the other algorithms.
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http://dx.doi.org/10.1109/TCYB.2021.3081805 | DOI Listing |
Sci Rep
January 2025
Laboratoire d'Ingenierie des Systemes Physiques et Numeriques, 59046, Lille, France.
The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations.
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January 2025
School of Management, Shenyang University of Technology, Shenyang, 110870, China.
The production stage of an automated job shop is closely linked to the automated guided vehicle (AGV), which needs to be planned in an integrated manner to achieve overall optimization. In order to improve the collaboration between the production stages and the AGV operation system, a two-layer scheduling optimization model is proposed for simultaneous decision making of batching problems, job sequences and AGV obstacle avoidance. Under the AGV automatic path seeking mode, this paper adopts a data-driven Bayesian network method to portray the transportation time of AGVs based on the historical operation data to control the uncertainty of the transportation time of AGVs.
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December 2024
Information Construction and Management Center, Nanyang Institute of Technology, Nanyang, 473004, Henan, China.
Given the increasingly severe environmental challenges, distributed green manufacturing has garnered significant academic and industrial interest. This paper addresses the distributed two-stage flexible job shop scheduling problem (DTFJSP) under time-of-use (TOU) electricity pricing, with the objective of minimizing both makespan and total energy consumption costs (TEC). To tackle the problem, a hybrid memetic algorithm (HMA) is proposed.
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November 2024
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, 100124, China.
This paper investigates the Dynamic Flexible Job Shop Scheduling Problem (DFJSP), which is based on new job insertion, machine breakdowns, changes in processing time, and considering the state of Automated Guided Vehicles (AGVs). The objective is to minimize the maximum completion time and improve on-time completion rates. To address the continuous production status and learn the most suitable actions (scheduling rules) at each rescheduling point, a Dueling Double Deep Q Network (D3QN) is developed to solve this problem.
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September 2024
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland.
Motivated by recent efforts to develop quantum computing for practical, industrial-scale challenges, we demonstrate the effectiveness of state-of-the-art hybrid (not necessarily quantum) solvers in addressing the business-centric optimization problem of scheduling Automatic Guided Vehicles (AGVs). Some solvers can already leverage noisy intermediate-scale quantum (NISQ) devices. In our study, we utilize D-Wave hybrid solvers that implement classical heuristics with potential assistance from a quantum processing unit.
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