A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Typically, the total number of required workstations are minimised for a given cycle time (this problem is referred to as type-1), or cycle time is minimised for a given number of workstations (this problem is referred to as type-2) in traditional balancing of assembly lines. However, variation in workload distributions of workstations is an important indicator of the quality of the obtained line balance. This needs to be taken into account to improve the reliability of an assembly line against unforeseeable circumstances, such as breakdowns or other failures. For this aim, a new problem, called lexicographic bottleneck mixed-model assembly line balancing problem (LB-MALBP), is presented and formalised. The lexicographic bottleneck objective, which was recently proposed for the simple single-model assembly line system in the literature, is considered for a mixed-model assembly line system. The mathematical model of the LB-MALBP is developed for the first time in the literature and coded in GAMS solver, and optimal solutions are presented for some small scale test problems available in the literature. As it is not possible to get optimal solutions for the large-scale instances, an artificial bee colony algorithm is also implemented for the solution of the LB-MALBP. The solution procedures of the algorithm are explored illustratively. The performance of the algorithm is also assessed using derived well-known test problems in this domain and promising results are observed in reasonable CPU times

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets

Lot Streaming in Different Types of Production Processes: A PRISMA Systematic Review

At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry.This work was supported by the Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”

BASA: An improved hybrid bees algorithm for the single machine scheduling with early/tardy jobs

[EN] In this paper, we present a novel hybrid meta-heuristic by enhancing the Basic Bees Algorithm through the integration of a local search method namely Simulated Annealing and Variable Neighbourhood Search like algorithm. The resulted hybrid bees algorithm (BASA) is used to solve the Single Machine Scheduling Problem with Early/Tardy jobs, where the generated outcomes are compared against the Basic Bees Algorithm (BA), and against some stat-of-the-art meta-heuristics. Computational results reveal that our proposed framework outperforms the Basic Bees Algorithm, and demonstrates a competitive performance compared with some algorithms extracted from the literature.Abdessemed, AA.; Mouss, LH.; Benaggoune, K. (2023). BASA: An improved hybrid bees algorithm for the single machine scheduling with early/tardy jobs. International Journal of Production Management and Engineering. 11(2):167-177. https://doi.org/10.4995/ijpme.2023.18077167177112Abdul-Razaq, T. S., & Potts, C. N. (1988). Dynamic programming state-space relaxation for single-machine scheduling. Journal of the Operational Research Society, 39(2), 141-152. https://doi.org/10.1057/jors.1988.26Abdullah, S., & Alzaqebah, M. (2013). A hybrid self-adaptive bees algorithm for examination timetabling problems. Applied Soft Computing, 13(8), 3608-3620. https://doi.org/10.1016/j.asoc.2013.04.010Baker, K. R., & Scudder, G. D. (1990). Sequencing with earliness and tardiness penalties: a review. Operations research, 38(1), 22-36. https://doi.org/10.1287/opre.38.1.22Castellani, M., Pham, Q. T., & Pham, D. T. (2012). Dynamic optimisation by a modified bees algorithm. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 226(7), 956-971. https://doi.org/10.1177/0959651812443462Dereli, T., & Das, G. S. (2011). A hybrid 'bee (s) algorithm'for solving container loading problems. Applied Soft Computing, 11(2), 2854-2862. https://doi.org/10.1016/j.asoc.2010.11.017Dowsland, K. A., & Thompson, J. (2012). Simulated annealing. Handbook of natural computing, 1623-1655. https://doi.org/10.1007/978-3-540-92910-9_49Hansen, P., Mladenović, N., & Moreno Pérez, J. A. (2010). Variable neighbourhood search: methods and applications. Annals of Operations Research, 175(1), 367-407. https://doi.org/10.1007/s10479-009-0657-6Ho, Y. C., & Pepyne, D. L. (2001). Simple explanation of the no free lunch theorem of optimization. In Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No. 01CH37228) (Vol. 5, pp. 4409-4414). IEEE.Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671Lara, C., Flores, J. J., & Calderón, F. (2008). Solving a school timetabling problem using a bee algorithm. In Mexican International Conference on Artificial Intelligence (pp. 664-674). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_63Lim, C. H., Lim, S., How, B. S., Ng, W. P. Q., Ngan, S. L., Leong, W. D., & Lam, H. L. (2021). A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach. Renewable and Sustainable Energy Reviews, 135, 110223. https://doi.org/10.33932/rir.44.3.2Mei, C. A., Pham, D. T., Anthony, J. S., & Kok, W. N. (2010, November). PCB assembly optimisation using the Bees Algorithm enhanced with TRIZ operators. In IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society (pp. 2708-2713). IEEE. https://doi.org/10.1109/IECON.2010.5675114M'Hallah, R., & Alhajraf, A. (2016). Ant colony systems for the single-machine total weighted earliness tardiness scheduling problem. Journal of Scheduling, 19(2), 191-205. https://doi.org/10.1007/s10951-015-0429-xNguyen, K., Nguyen, P., & Tran, N. (2012). A hybrid algorithm of harmony search and bees algorithm for a university course timetabling problem. International Journal of Computer Science Issues (IJCSI), 9(1), 12.Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm-a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd. https://doi.org/10.1177/0959651811422759Pham, D. T., Koc, E., Lee, J. Y., & Phrueksanant, J. (2007a). Using the bees algorithm to schedule jobs for a machine. In Proceedings Eighth International Conference on Laser Metrology, CMM and Machine Tool Performance, LAMDAMAP, Euspen, UK, Cardiff (pp. 430-439).Pham, D. T., Otri, S., & Darwish, A. H. (2007b). Application of the Bees Algorithm to PCB assembly optimisation. In Proceedings of the 3rd virtual international conference on intelligent production machines and systems (IPROMS 2007) (pp. 511-516).Pham, D. T., Castellani, M., & Fahmy, A. A. (2008). Learning the inverse kinematics of a robot manipulator using the bees algorithm. In 2008 6th IEEE International Conference on Industrial Informatics (pp. 493-498). IEEE. https://doi.org/10.1109/INDIN.2008.4618151Pham, Q. T., Pham, D. T., & Castellani, M. (2012). A modified bees algorithm and a statistics-based method for tuning its parameters. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 226(3), 287-301. https://doi.org/10.1177/0959651811422759Seeley, T. D. (2009). The wisdom of the hive: the social physiology of honey bee colonies. Harvard University Press. https://doi.org/10.2307/j.ctv1kz4h15Sourd, F. (2009). New exact algorithms for one-machine earliness-tardiness scheduling. INFORMS Journal on Computing, 21(1), 167-175. https://doi.org/10.1287/ijoc.1080.0287Sourd, F., & Kedad-Sidhoum, S. (2008). A faster branch-and-bound algorithm for the earliness-tardiness scheduling problem. Journal of Scheduling, 11(1), 49-58. https://doi.org/10.1007/s10951-007-0048-2Tanaka, S., Fujikuma, S., & Araki, M. (2009). An exact algorithm for single-machine scheduling without machine idle time. Journal of Scheduling, 12(6), 575-593. https://doi.org/10.1007/s10951-008-0093-5Von Frisch, K. (2014). Bees: their vision, chemical senses, and language. Cornell University Press.Wan, L., & Yuan, J. (2013). Single-machine scheduling to minimize the total earliness and tardiness is strongly NP-hard. Operations Research Letters, 41(4), 363-365. https://doi.org/10.1016/j.orl.2013.04.007Yau, H., Pan, Y., & Shi, L. (2008). New solution approaches to the general single-machine earliness-tardiness problem. IEEE Transactions on Automation Science and Engineering, 5(2), 349-360. https://doi.org/10.1109/TASE.2007.895219Yuce, B., Packianather, M. S., Mastrocinque, E., Pham, D. T., & Lambiase, A. (2013). Honey bees inspired optimization method: the bees algorithm. Insects, 4(4), 646-662. https://doi.org/10.3390/insects4040646Yuce, B., Fruggiero, F., Packianather, M. S., Pham, D. T., Mastrocinque, E., Lambiase, A., & Fera, M. (2017). Hybrid Genetic Bees Algorithm applied to single machine scheduling with earliness and tardiness penalties. Computers & Industrial Engineering, 113, 842-858. https://doi.org/10.1016/j.cie.2017.07.018Yurtkuran, A., & Emel, E. (2016). A discrete artificial bee colony algorithm for single machine scheduling problems. International Journal of Production Research, 54(22), 6860-6878. https://doi.org/10.1080/00207543.2016.118555

Modeling and Solving Flow Shop Scheduling Problem Considering Worker Resource

In this paper, an uninterrupted hybrid flow scheduling problem is modeled under uncertainty conditions. Due to the uncertainty of processing time in workshops, fuzzy programming method has been used to control the parameters of processing time and preparation time. In the proposed model, there are several jobs that must be processed by machines and workers, respectively. The main purpose of the proposed model is to determine the correct sequence of operations and assign operations to each machine and each worker at each stage, so that the total completion time (Cmax) is minimized. Also this paper, fuzzy programming method is used for control unspecified parameter has been used from GAMS software to solve sample problems. The results of problem solving in small and medium dimensions show that with increasing uncertainty, the amount of processing time and consequently the completion time increases. Increases from the whole work. On the other hand, with the increase in the number of machines and workers in each stage due to the high efficiency of the machines, the completion time of all works has decreased. Innovations in this paper include uninterrupted hybrid flow storage scheduling with respect to fuzzy processing time and preparation time in addition to payment time. The allocation of workers and machines to jobs is another innovation of this article