With increasing product customization, just-in-time part replenishment has become a significant scheduling problem in the automobile assembly system. This paper investigates a new unrelated parallel machine scheduling problem of an assembly line, where machines are employed to deliver material boxes from an in-house warehouse to workstations. The schedule is to appropriately specify the assignment and sequence of material boxes on each machine for minimizing line-side inventories under no stock-out constraints. By taking advantages of domain properties, an exact algorithm is developed to cope up with small-scale instances. In terms of real-world scale instances, a hybrid teaching–learning-based optimization metaheuristic is established by integrating teaching–learning-based optimization with a beam search technique. Experimental results indicate that the scheduling algorithms are effective and efficient in solving the proposed unrelated parallel machine scheduling.
BattiniDBoysenNEmdeS. Just-in-Time supermarkets for part supply in the automobile industry. J Manage Control2013; 24: 209–217.
4.
BoysenNEmdeSHoeckMet al.Part logistics in the automotive industry: decision problems, literature review and research agenda. Eur J Oper Res2015; 242: 107–120.
5.
PottsCN. Analysis of a linear programming heuristic for scheduling unrelated parallel machines. Discrete Appl Math1985; 10: 155–164.
6.
TorabiSASahebjamniaNMansouriSAet al.A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Appl Soft Comput2013; 13: 4750–4762.
7.
BoysenNFliednerMSchollA. A classification of assembly line balancing problems. Eur J Oper Res2007; 183: 674–693.
8.
BoysenNFliednerMSchollA. Sequencing mixed-model assembly lines: Survey, classification and model critique. Eur J Oper Res2009; 192: 349–373.
9.
BoysenNBockS. Scheduling just-in-time part supply for mixed-model assembly lines. Eur J Oper Res2011; 211: 15–25.
10.
RaoYQWangMCWangKPet al.Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line. Comput Oper Res2013; 40: 2599–2610.
11.
ZhouBPengT. Scheduling the in-house logistics distribution for automotive assembly lines with just-in-time principles. Assembly Autom2017; 37: 51–63.
12.
EmdeSFliednerMBoysenN. Optimally loading tow trains for just-in-time supply of mixed-model assembly lines. IIE Trans2012; 44: 121–135.
13.
FathiMAlvarezMJHassani MehrabanFet al.A multiobjective optimization algorithm to solve the part feeding problem in mixed-model assembly lines. Math Probl Eng2014; 2014: 1–12.
14.
FathiMRodríguezVAlvarezMJ. A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem. Int J Adv Manuf Technol2014; 75: 629–643.
15.
FathiMRodríguezVFontesDBet al.A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines. Int J Prod Res2016; 54: 878–893.
EmdeSGendreauM. Scheduling in-house transport vehicles to feed parts to automotive assembly lines. Eur J Oper Res2017; 260: 255–267.
18.
ZhouBLiM. Scheduling method of robotic cells with machine–robot process and time window constraints. Proc IMechE, Part E: J Process Mechanical Engineering. Epub ahead of print 27 September 2017. DOI: 10.1177/0954408917733850.
19.
ZhouBXuJ. A novel optimized cyclic part feeding system with line-integrated supermarkets. Proc IMechE, Part B: J Engineering Manufacture. Epub ahead of print 23 January 2018. DOI: 0954405417752510.
20.
ErolRSahinCBaykasogluAet al.A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems. Appl Soft Comput2012; 12: 1720–1732.
21.
BoysenNBockSFliednerM. Scheduling of inventory releasing jobs to satisfy time-varying demand: an analysis of complexity. J Schedule2013; 16: 185–198.
22.
Alvarado-IniestaAGarcia-AlcarazJLRodriguez-BorbonMIet al.Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm. Expert Syst Appl2013; 40: 4785–4790.
23.
RaoRVSavsaniVJVakhariaDP. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des2011; 43: 303–315.
24.
RaoRVSavsaniVJVakhariaDP. Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inform Sci2012; 183: 1–15.
25.
LinWYTsaiYHHsiaoKM. Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231: 1871–1883.
26.
GarcíaJAMMenaAJG. Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm. Int J Electr Power Energy Syst2013; 50: 65–75.
27.
ZouFWangLHeiXet al.Multi-objective optimization using teaching-learning-based optimization algorithm. Eng Appl Artif Intell2013; 26: 1291–1300.
28.
LiDZhangCShaoXet al.A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. J Intell Manuf2016; 27: 725–739.
29.
Rao RV. Teaching-learning-based optimization (TLBO) algorithm and its engineering applications. Switzerland: Springer International Publishing, 2016.
30.
SabuncuoğluİGocgunYErelE. Backtracking and exchange of information: methods to enhance a beam search algorithm for assembly line scheduling. Eur J Oper Res2008; 186: 915–930.
31.
BaykasoğluABaykasoğluC. Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231: 2005–2016.
32.
LinWYHsiaoKM. A new differential evolution algorithm with a combined mutation strategy for optimum synthesis of path-generating four-bar mechanisms. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231: 2690–2705.
33.
BlumC. Beam-ACO—hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput Oper Res2005; 32: 1565–1591.
34.
KayvanfarVKomakiGMAalaeiAet al.Minimizing total tardiness and earliness on unrelated parallel machines with controllable processing times. Comput Oper Res2014; 41: 31–43.
35.
AkayBKarabogaD. A modified artificial bee colony algorithm for real-parameter optimization. Inform Sci2012; 192: 120–142.
36.
HamzadayiAGokalpY. A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines. Comput Ind Eng2013; 66: 1070–1084.
37.
DerracJGarcíaSMolinaDet al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput2011; 1: 3–18.
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