Efficient production planning is a critical and challenging task in Make-To-Order (MTO) Automated Manufacturing Systems (AMSs), requiring a flexible production process capable of managing large volumes of highly-customized orders while preventing resource contention. Considering the timing of customers’ needs and the availability of production resources, it becomes important to find an efficient order-dispatching sequence to optimize the coordination across multiple production units. To achieve this, a simulation model is essential to evaluate and validate the proposed algorithm’s performance prior to real-world implementation. In this study, a heuristic algorithm based on a Threshold- and Priority-Based Dispatching Rule (TPDR) is presented aimed at minimizing flow time while avoiding potential deadlocks and meeting key performance indicators (KPIs). The proposed heuristic is integrated into a discrete-event simulation (DES) framework, allowing for dynamic adjustments to the dispatching sequence of high-volume and highly-customized orders based on real-time system/machine performance. To assess its effectiveness, a case study of a Mail Order Pharmacy Automation (MOPA) system is conducted within three DES models, comparing the proposed TPDR-based heuristic with three widely used dispatching rules. The simulation results demonstrate that the TPDR-based heuristic algorithm significantly enhances productivity and eliminates production bottlenecks while maintaining throughput levels.
LiHWomerK.Optimizing the supply chain configuration for make-to-order manufacturing. Eur J Oper Res2012; 221: 118–128.
2.
WilliamsT.Special products and uncertainty in production/inventory systems. Eur J Oper Res1984; 15: 46–54.
3.
Álvarez-GilNRosilloRde la FuenteD, et al. A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system. Cent Eur J Oper Res2021; 29: 1353–1374.
4.
GhasemiELehouxNRönnqvistM.A multi-level production-inventory-distribution system under mixed make to stock, make to order, and vendor managed inventory strategies: an application in the pulp and paper industry. Int J Prod Econ2024; 271: 109201.
5.
CostaMGouveiaRSilvaF, et al. How to solve quality problems by advanced fully-automated manufacturing systems. Int J Adv Manuf Technol2018; 94: 3041–3063.
6.
El BazHWangYYoonSW, et al. Optimization of AGV sorting systems in pharmaceutical distribution: a two-stage package assignment and simulation approach. Int J Adv Manuf Technol2024; 134: 2439–2457.
7.
HuangPLiHHanL.Order scheduling problems in make-to-order manufacturing systems. In: IEEE international conference mechatronics and automation, Niagara Falls, ON, Canada, 29 July–1 August 2005, pp. 2179–2184. New York: IEEE.
8.
WangZQiYCuiH, et al. A hybrid algorithm for order acceptance and scheduling problem in make-to-stock/make-to-order industries. Comput Ind Eng2019; 127: 841–852.
9.
Al-HinaiNPiyaS. Jobshop scheduling for skill-dependent make-to-order system. In: 2015 international conference on industrial engineering and operations management (IEOM), Dubai, United Arab Emirates, 3–5 March 2015, pp. 1–5. New York: IEEE.
10.
FarughiHYousefi YeganeBFathianM.A new critical path method and a memetic algorithm for flexible job shop scheduling with overlapping operations. Simulation2013; 89: 264–277.
11.
CaldeiraRHGnanavelbabuA.A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times. Simulation2021; 97: 215–236.
12.
SanogoKMekhalef BenhafssaASahnounM.Transitioning from AGVs to AIVs in integrated job shop scheduling with transportation tasks: a multi-agent simulator for comparative analysis. Simulation. Epub ahead of print 11 November 2024. DOI: 10.1177/00375497241288894.
13.
HuHLiuY.Supervisor synthesis and performance improvement for automated manufacturing systems by using Petri nets. IEEE Trans Industr Inform2015; 11: 450–458.
14.
ZhangRWuC.A divide-and-conquer strategy with particle swarm optimization for the job shop scheduling problem. Eng Optim2010; 42: 641–670.
15.
MitićPPetrović SavićSDjordjevicA, et al. The problem of machine part operations optimal scheduling in the production industry based on a customer’s order. Appl Sci2023; 13: 11049.
16.
HendryLCKingsmanBGCheungP.The effect of workload control (WLC) on performance in make-to-order companies. J Oper Manag1998; 16: 63–75.
17.
SwarnakarVSinghATiwariAK.Evaluation of key performance indicators for sustainability assessment in automotive component manufacturing organization. Mater Today Proc2021; 47: 5755–5759.
18.
HegerJGrundsteinSFreitagM.Online-scheduling using past and real-time data. An assessment by discrete event simulation using exponential smoothing. CIRP J Manuf Sci Technol2017; 19: 158–163.
19.
GoharehMMMansouriE.A simulation-optimization framework for generating dynamic dispatching rules for stochastic job shop with earliness and tardiness penalties. Comput Oper Res2022; 140: 105650.
20.
SalamaSKaiharaTFujiiN, et al. Multi-objective approach with a distance metric in genetic programming for job shop scheduling. Int J Autom Technol2022; 16: 296–308.
21.
KulkarniKVenkateswaranJ.Hybrid approach using simulation-based optimisation for job shop scheduling problems. J Simul2015; 9: 312–324.
22.
BarbosaCMalarranhaCAzevedoA, et al. A hybrid simulation approach applied in sustainability performance assessment in make-to-order supply chains: the case of a commercial aircraft manufacturer. J Simul2023; 17: 32–57.
23.
LiLPeteringME.Discrete event simulation analysis of a reservation-based, one-way car-sharing system. J Simul2018; 12: 1–22.
24.
MoonYB.Simulation modelling for sustainability: a review of the literature. Int J Sustain Eng2017; 10: 2–19.
JerbiA.Using discrete-event simulation for planning and managing mass vaccination centers: a comprehensive examination. Simulation. Epub ahead of print 4 January 2025. DOI: 10.1177/00375497241308563.
27.
CaoNMarcusAAltarawnehL, et al. Priority-based replenishment policy for robotic dispensing in central fill pharmacy systems: a simulation-based study. Health Care Manag Sci2023; 26: 344–362.
28.
MeiKLiDYoonSW, et al. Multi-objective optimization of collation delay and makespan in mail-order pharmacy automated distribution system. Int J Adv Manuf Technol2016; 83: 475–488.
29.
LiYZhangQYoonSW.Discrete event simulation-based collation system analysis in mail-order pharmacy automation system. In: Proceedings of the 2019 IISE annual conference, Norcross, GA, 29 March 2020, pp. 828–833. Peachtree Corners, GA: Institute of Industrial and Systems Engineers (IISE).
30.
DominicPDKaliyamoorthySKumarMS.Efficient dispatching rules for dynamic job shop scheduling. Int J Adv Manuf Technol2004; 24: 70–75.
31.
SelsVGheysenNVanhouckeM.A comparison of priority rules for the job shop scheduling problem under different flow time-and tardiness-related objective functions. Int J Prod Res2012; 50: 4255–4270.
32.
JensenMT.Generating robust and flexible job shop schedules using genetic algorithms. IEEE Trans Evol Comput2003; 7: 275–288.
33.
BruckerPSchlieR.Job-shop scheduling with multi-purpose machines. Computing1990; 45: 369–375.
34.
JeongKCKimYD.A real-time scheduling mechanism for a flexible manufacturing system: using simulation and dispatching rules. Int J Prod Res1998; 36: 2609–2626.
35.
WoegingerGJ.Exact algorithms for NP-hard problems: a survey. In: JüngerMReineltGRinaldiG (eds) Combinatorial optimization—Eureka, you Shrink!Berlin: Springer, 2003, pp. 185–207.
36.
WangHSerhanDMYoonSW, et al. Collation delay optimization using discrete event simulation in mail-order pharmacy automation systems. In: Proceedings of the 2016 industrial and systems engineering research conference, https://www.scribd.com/document/385978574/CollationDelayOptimization2-2
37.
ThürerMStevensonM.Order release, dispatching and resource assignment in multiple resource-constrained job shops: an assessment by simulation. Int J Prod Res2022; 60: 3669–3681.
38.
CostaFKunduKRossiniM, et al. Comparative study of bottleneck-based release models and load-based ones in a hybrid MTO-MTS flow shop: an assessment by simulation. Oper Manag Res2023; 16: 33–48.
39.
BlocherJDChhajedD.The customer order lead-time problem on parallel machines. Nav Res Logist1996; 43: 629–654.
40.
SchwiegelshohnUYahyapourR. Analysis of first-come-first-serve parallel job scheduling. In: SODA ‘98: Proceedings of the ninth annual ACM-SIAM symposium on discrete algorithms, San Francisco, CA, 25–27 January, pp. 629–638. New York: ACM.
41.
RoychowdhurySAllenTTAllenNB.A genetic algorithm with an earliest due date encoding for scheduling automotive stamping operations. Comput Ind Eng2017; 105: 201–209.
42.
WengWChenJZhengM, et al. Realtime scheduling heuristics for just-in-time production in large-scale flexible job shops. J Manuf Syst2022; 63: 64–77.
KundakcıNKulakO.Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Comput Ind Eng2016; 96: 31–51.
45.
XiongHFanHJiangG, et al. A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints. Eur J Oper Res2017; 257: 13–24.
46.
ShadySKaiharaTFujiiN, et al. Evolving dispatching rules using genetic programming for multi-objective dynamic job shop scheduling with machine breakdowns. Procedia CIRP2021; 104: 411–416.
47.
BrauneRBendaFDoernerKF, et al. A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems. Int J Prod Econ2022; 243: 108342.
48.
ZeiträgYFigueiraJRHortaN, et al. Surrogate-assisted automatic evolving of dispatching rules for multi-objective dynamic job shop scheduling using genetic programming. Expert Syst Appl2022; 209: 118194.
49.
GhalebMTaghipourSZolfaghariniaH.Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance. J Manuf Sys2021; 61: 423–449.
50.
Al-TurkiUAndijaniAArifulsalamS.A new dispatching rule for the stochastic single-machine scheduling problem. Simulation2004; 80: 165–170.
51.
JiaSMorriceDJBardJF.A performance analysis of dispatch rules for semiconductor assembly & test operations. J Simul2019; 13: 163–180.
52.
ThürerMStevensonMSilvaC, et al. Drum-buffer-rope and workload control in high-variety flow and job shops with bottlenecks: an assessment by simulation. Int J Prod Econ2017; 188: 116–127.
53.
ÇağrıSHamzadayiA.A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem. Pamukkale Üniv Müh Bilim Derg2018; 24: 665–674.
54.
TurgutYBozdagCE. Deep Q-network model for dynamic job shop scheduling problem based on discrete event simulation. In: 2020 winter simulation conference (WSC), Orlando, FL, 14–18 December, pp. 1551–1559. New York: IEEE.
55.
LiZWuNZhouM.Deadlock control of automated manufacturing systems based on Petri nets—a literature review. IEEE Trans Syst Man Cybern C Appl Rev2011; 42: 437–462.
56.
FantiMPZhouM.Deadlock control methods in automated manufacturing systems. IEEE Trans Syst Man Cybern A Syst Hum2004; 34: 5–22.
57.
ChenHWuNZhouM.A novel method for deadlock prevention of AMS by using resource-oriented Petri nets. Inf Sci2016; 363: 178–189.
58.
GuanXDaiX.Deadlock-free multi-attribute dispatching method for AGV systems. Int J Adv Manuf Technol2009; 45: 603–615.
59.
WuNZhouM.Avoiding deadlock and reducing starvation and blocking in automated manufacturing systems. IEEE Trans Robot Autom2001; 17: 658–669.
60.
LevineDLTaylorRN.Metric-driven reengineering for static concurrency analysis. ACM SIGSOFT Softw Eng Notes1993; 18: 40–50.
BoltonADGoosenLKritzingerE. Unified communication technologies at a global automotive organization. In: Khosrow-PourM (ed.) Encyclopedia of organizational knowledge, administration, and technology. Hershey, PA: IGI Global, 2021, pp. 2592–2608.
63.
ZającJMałopolskiW.Structural on-line control policy for collision and deadlock resolution in multi-AGV systems. J Manuf Syst2021; 60: 80–92.
64.
FantiMPManginiAMPedroncelliG, et al. Decentralized deadlock-free control for AGV systems. In: 2015 American control conference (ACC), Chicago, IL, 1–3 July, pp. 2414–2419. New York: IEEE.
65.
WuNZhouM.Modeling and deadlock control of automated guided vehicle systems. IEEE/ASME Trans Mechatron2004; 9: 50–57.
66.
YanXZhangCQiM. Multi-AGVs collision-avoidance and deadlock-control for item-to-human automated warehouse. In: 2017 international conference on industrial engineering, management science and application (ICIMSA), Seoul, South Korea, 13–15 June, pp. 1–5. New York: IEEE.
67.
WuNZhouM.Deadlock resolution in automated manufacturing systems with robots. IEEE Trans Autom Sci Eng2007; 4: 474–480.
68.
WuN.Necessary and sufficient conditions for deadlock-free operation in flexible manufacturing systems using a colored Petri net model. IEEE Trans Syst Man Cybern C Appl Rev1999; 29: 192–204.
69.
WuNZhouMLiZ.Resource-oriented Petri net for deadlock avoidance in flexible assembly systems. IEEE Trans Syst Man Cybern A Syst Hum2007; 38: 56–69.
70.
WuNZhouM.Modeling and deadlock avoidance of automated manufacturing systems with multiple automated guided vehicles. IEEE Trans Syst Man Cybern B Cybern2005; 35: 1193–1202.
71.
YooJwSimESCaoC, et al. An algorithm for deadlock avoidance in an AGV system. Int J Adv Manuf Technol2005; 26: 659–668.
72.
MihoubiBBouzouiaBGahamM.Reactive scheduling approach for solving a realistic flexible job shop scheduling problem. Int J Prod Res2021; 59: 5790–5808.
73.
BouazzaWSallezYTrentesauxD.Dynamic scheduling of manufacturing systems: a product-driven approach using hyper-heuristics. Int J Comput Integr Manuf2021; 34: 641–665.
74.
KimHLimDELeeS.Deep learning-based dynamic scheduling for semiconductor manufacturing with high uncertainty of automated material handling system capability. IEEE Trans Semicond Manuf2020; 33: 13–22.
75.
LiuRPiplaniRToroC.Deep reinforcement learning for dynamic scheduling of a flexible job shop. Int J Prod Res2022; 60: 4049–4069.
76.
DragovićBTzannatosEParkNK.Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool. Flex Serv Manuf J2017; 29: 4–34.
77.
MorrisTPWhiteIRCrowtherMJ.Using simulation studies to evaluate statistical methods. Stat Med2019; 38: 2074–2102.
78.
LawAMKeltonWDKeltonWD.Simulation modeling and analysis, vol 3. New York: Mcgraw Hill, 2007.
79.
SweetserA (1999) A comparison of system dynamics (SD) and discrete event simulation (DES). In Proceedings of the 17th international conference of the system dynamics society and the 5th Australian & New Zealand systems conference, Wellington, New Zealand, 20–23 July 1999, pp. 1–8.
80.
World Health Organization. Coronavirus disease 2019 (Covid-19): situation report. Geneva: World Health Organization, 2020.
81.
RomanoSGalanteHFigueiraD, et al. Time-trend analysis of medicine sales and shortages during Covid-19 outbreak: data from community pharmacies. Res Social Admin Pharm2021; 17: 1876–1881.
82.
LiDYoonSW.A novel fill-time window minimisation problem and adaptive parallel tabu search algorithm in mail-order pharmacy automation system. Int J Prod Res2015; 53: 4189–4205.
