| Issue |
RAIRO-Oper. Res.
Volume 58, Number 1, January-February 2024
Graphs, Combinatorics, Algorithms and Optimization
|
|
|---|---|---|
| Page(s) | 535 - 555 | |
| DOI | https://doi.org/10.1051/ro/2023156 | |
| Published online | 19 February 2024 | |
- A.R. Ahmadi Keshavarz, D. Jaafari, M. Khalaj and P. Dokouhaki, A survey of the literature on order-picking systems by combining planning problems. Appl. Sci. 11 (2021) 10641. [CrossRef] [Google Scholar]
- A. Amirteimoori, I. Mahdavi, M. Solimanpur, S.S. Ali and E.B. Tirkolaee, A parallel hybrid pso-ga algorithm for the flexible flow-shop scheduling with transportation. Comput. Ind. Eng. 173 (2022) 108672. [CrossRef] [Google Scholar]
- D. Anghinolfi and M. Paolucci, A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times. Eur. J. Oper. Res. 193 (2009) 73–85. [CrossRef] [Google Scholar]
- K. Azadeh, R. De Koster and D. Roy, Robotized and automated warehouse systems: review and recent developments. Transp. Sci. 53 (2019) 917–945. [CrossRef] [Google Scholar]
- F. Baki and R. Vickson, One-operator, two-machine open shop and flow shop scheduling with setup times for machines and maximum lateness objective. INFOR: Inf. Syst. Oper. Res. 41 301–319. [Google Scholar]
- M.F. Baki and R.G. Vickson, One-operator, two-machine open shop and flow shop problems with setup times for machines and weighted number of tardy jobs objective. Optim. Methods Softw. 19 (2004) 165–178. [CrossRef] [MathSciNet] [Google Scholar]
- D. Biskup, Single-machine scheduling with learning considerations. Eur. J. Oper. Res. 115 (1999) 173–178. [Google Scholar]
- D. Biskup, A state-of-the-art review on scheduling with learning effects. Eur. J. Oper. Res. 188 (2008) 315–329. [Google Scholar]
- C. Blum and A. Roli, Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35 (2003) 268–308. [CrossRef] [Google Scholar]
- N. Boysen, R. De Koster and F. Weidinger, Warehousing in the e-commerce era: a survey. Eur. J. Oper. Res. 277 (2019) 396–411. [CrossRef] [Google Scholar]
- A. Branda, D. Castellano, G. Guizzi and V. Popolo, Metaheuristics for the flow shop scheduling problem with maintenance activities integrated. Comput. Ind. Eng. 151 (2021) 106989. [CrossRef] [Google Scholar]
- Ç. Cergibozan and A.S. Tasan, Order batching operations: an overview of classification, solution techniques, and future research. J. Intell. Manuf. 30 (2019) 335–349. [CrossRef] [Google Scholar]
- T.E. Cheng, G. Wang and C. Sriskandarajah, One-operator–two-machine flowshop scheduling with setup and dismounting times. Comput. Oper. Res. 26 (1999) 715–730. [CrossRef] [Google Scholar]
- X. Dong, H. Huang and P. Chen, An improved neh-based heuristic for the permutation flowshop problem. Comput. Oper. Res. 35 (2008) 3962–3968. [CrossRef] [Google Scholar]
- V. Fernandez-Viagas, R. Ruiz and J.M. Framinan, A new vision of approximate methods for the permutation flowshop to minimise makespan: state-of-the-art and computational evaluation. Eur. J. Oper. Res. 257 (2017) 707–721. [CrossRef] [Google Scholar]
- T. Franzke, E.H. Grosse, C.H. Glock and R. Elbert, An investigation of the effects of storage assignment and picker routing on the occurrence of picker blocking in manual picker-to-parts warehouses. Int. J. Logistics Manage. 28 (2017) 841–863. [CrossRef] [Google Scholar]
- Y. Jaghbeer, R. Hanson and M.I. Johansson, Automated order picking systems and the links between design and performance: a systematic literature review. Int. J. Prod. Res. 58 (2020) 4489–4505. [CrossRef] [Google Scholar]
- M. Ji, D. Yao, Q. Yang and T. Cheng, Machine scheduling with DeJong’s learning effect. Comput. Ind. Eng. 80 (2015) 195–200. [Google Scholar]
- M. Ji, X. Tang, X. Zhang and T.E. Cheng, Machine scheduling with deteriorating jobs and DeJong’s learning effect. Comput. Ind. Eng. 91 (2016) 42–47. [Google Scholar]
- P.J. Kalczynski and J. Kamburowski, On the NEH heuristic for minimizing the makespan in permutation flow shops. Omega 35 (2007) 53–60. [CrossRef] [Google Scholar]
- C. Koulamas and G.J. Kyparisis, Single-machine scheduling problems with past-sequence-dependent setup times. Eur. J. Oper. Res. 187 (2008) 1045–1049. [CrossRef] [Google Scholar]
- W.-Y. Ku and J.C. Beck, Mixed integer programming models for job shop scheduling: a computational analysis. Comput. Oper. Res. 73 (2016) 165–173. [CrossRef] [MathSciNet] [Google Scholar]
- W.-H. Kuo, Single-machine group scheduling with time-dependent learning effect and position-based setup time learning effect. Ann. Oper. Res. 196 (2012) 349–359. [CrossRef] [MathSciNet] [Google Scholar]
- X. Li, Z. Yang, R. Ruiz, T. Chen and S. Sui, An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects. Inf. Sci. 453 (2018) 408–425. [Google Scholar]
- C.-J. Liao, C.-T. Tseng and P. Luarn, A discrete version of particle swarm optimization for flowshop scheduling problems. Comput. Oper. Res. 34 (2007) 3099–3111. [CrossRef] [Google Scholar]
- S.-W. Lin, C.-Y. Cheng, P. Pourhejazy and K.-C. Ying, Multi-temperature simulated annealing for optimizing mixed-blocking permutation flowshop scheduling problems. Expert Syst. App. 165 (2021) 113837. [CrossRef] [Google Scholar]
- C. Low, Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines. Comput. Oper. Res. 32 (2005) 2013–2025. [CrossRef] [Google Scholar]
- A.S. Manne, On the job-shop scheduling problem. Oper. Res. 8 (1960) 219–223. [CrossRef] [Google Scholar]
- M. Masae, C.H. Glock and E.H. Grosse, Order picker routing in warehouses: a systematic literature review. Int. J. Prod. Econ. 224 (2020) 107564. [CrossRef] [Google Scholar]
- G. Mosheiov and J.B. Sidney, Scheduling with general job-dependent learning curves. Eur. J. Oper. Res. 147 (2003) 665–670. [Google Scholar]
- M. Nawaz, E.E. Enscore Jr. and I. Ham, A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11 (1983) 91–95. [CrossRef] [Google Scholar]
- D. Oko lowski and S. Gawiejnowicz, Exact and heuristic algorithms for parallel-machine scheduling with DeJong’s learning effect. Comput. Ind. Eng. 59 (2010) 272–279. [CrossRef] [Google Scholar]
- Q.-K. Pan and R. Ruiz, A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime. Comput. Oper. Res. 40 (2013) 117–128. [CrossRef] [MathSciNet] [Google Scholar]
- J. Pei, X. Liu, P.M. Pardalos, K. Li, W. Fan and A. Migdalas, Single-machine serial-batching scheduling with a machine availability constraint, position-dependent processing time, and time-dependent set-up time. Optim. Lett. 11 (2017) 1257–1271. [CrossRef] [MathSciNet] [Google Scholar]
- J. Pei, X. Liu, P.M. Pardalos, A. Migdalas and S. Yang, Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine. J. Global Optim. 67 (2017) 251–262. [CrossRef] [MathSciNet] [Google Scholar]
- J. Pei, B. Cheng, X. Liu, P.M. Pardalos and M. Kong, Single-machine and parallel-machine serial-batching scheduling problems with position-based learning effect and linear setup time. Ann. Oper. Res. 272 (2019) 217–241. [CrossRef] [MathSciNet] [Google Scholar]
- I. Ribas, R. Companys and X. Tort-Martorell, Comparing three-step heuristics for the permutation flow shop problem. Comput. Oper. Res. 37 (2010) 2062–2070. [CrossRef] [Google Scholar]
- R. Ruiz and T. Stützle, A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177 (2007) 2033–2049. [CrossRef] [Google Scholar]
- R. Ruiz, C. Maroto and J. Alcaraz, Two new robust genetic algorithms for the flowshop scheduling problem. Omega 34 (2006) 461–476. [CrossRef] [Google Scholar]
- E. Vallada and R. Ruiz, A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. Eur. J. Oper. Res. 211 (2011) 612–622. [CrossRef] [Google Scholar]
- T. Van Gils, K. Ramaekers, A. Caris and R.B. de Koster, Designing efficient order picking systems by combining planning problems: state-of-the-art classification and review. Eur. J. Oper. Res. 267 (2018) 1–15. [CrossRef] [Google Scholar]
- H.M. Wagner, An integer linear-programming model for machine scheduling. Naval Res. Logistics Q. 6 (1959) 131–140. [CrossRef] [Google Scholar]
- J.-B. Wang, Single-machine scheduling with past-sequence-dependent setup times and time-dependent learning effect. Comput. Ind. Eng. 55 (2008) 584–591. [CrossRef] [Google Scholar]
- T.P. Wright, Factors affecting the cost of airplanes. J. Aeronautical Sci. 3 (1936) 122–128. [CrossRef] [Google Scholar]
- C.-C. Wu, Y. Yin, W.-H. Wu, H.-M. Chen and S.-R. Cheng, Using a branch-and-bound and a genetic algorithm for a single-machine total late work scheduling problem. Soft Comput. 20 (2016) 1329–1339. [Google Scholar]
- D. Yang, Research on Intelligent Logistics Warehousing System Design and Operation Strategy Optimizaion. Economy & Management Publishing House (2023). [Google Scholar]
- Y. Yin, C.-C. Wu, W.-H. Wu and S.-R. Cheng, The single-machine total weighted tardiness scheduling problem with position-based learning effects. Comput. Oper. Res. 39 (2012) 1109–1116. [CrossRef] [MathSciNet] [Google Scholar]
- Y. Yin, W.-H. Wu, W.-H. Wu and C.-C. Wu, A branch-and-bound algorithm for a single machine sequencing to minimize the total tardiness with arbitrary release dates and position-dependent learning effects. Inf. Sci. 256 (2014) 91–108. [CrossRef] [Google Scholar]
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