The hoist scheduling problem is a critical issue in the design and control of Automated Manufacturing Systems. To deal with the major complexities appearing in such problem, this work introduces an advanced simulation model to represent the short-term scheduling of complex hoist lines. The aim is to find the best jobs schedule that minimizing the makespan while maximizing throughput with no defective outputs. Several hard constraints are considered in the model: single shared hoist, heterogeneous recipes, eventual recycles flows, and no buffers between workstations. Different heuristic-based strategies are incorporated into the computer model in order to improve the solutions generated over time. The alternative solutions can be quickly evaluated by using a graphical user interface developed together with the simulation model.Fil: Basán, Natalia Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Pulido, Raul. Universidad Politécnica de Madrid; EspañaFil: Coccola, Mariana Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Basán, Natalia Paola

Coccola, Mariana Evangelina

Mendez, Carlos Alberto

Pulido, Raul

Iberoamerican Journal of Industrial Engineering

English

CONICET Digital

Aerospace Manufacturing Industry: A Simulation-Based Decision Support Framework for the Scheduling of Complex Hoist Lines

This paper presents an advanced simulation model for short-term scheduling of complex hoist lines. This problem, generally found in aerospace and electroplating industries, includes several hard constraints that should be considered: single shared hoist, heterogeneous recipes, eventual recycle flows, and no buffers between workstations. Different heuristic-based strategies are incorporated into the computer model in order to improve the solutions generated over time. The aim is to reduce the number of products that must be discarded while minimizing the makespan. In addition, a graphical user interface was developed for quickly evaluating simulated schedules.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Basán, Natalia P.

Pulido, Raúl

Cóccola, Mariana

Méndez, Carlos A.

Servicio de Difusión de la Creación Intelectual

SII 2015, 4º Simposio Argentino de Informática Industrial.Aerospace Manufacturing Industry: A Simulation-based Decision Support Framework for the Scheduling of Complex Hoist Lines Natalia P. Basán1, Raúl Pulido2, Mariana Cóccola1, Carlos A. Méndez 1  1 INTEC (UNL –CONICET) Güemes 3450 Santa Fe, 3000, ARGENTINA 2Technical University of Madrid (UPM) José Gutiérrez Abascal, 2. Madrid, 28006, SPAIN ABSTRACT. This paper presents an advanced simulation model for short-term scheduling of complex hoist lines. This problem, generally found in aerospace and electroplating industries, includes several hard constraints that should be considered: single shared hoist, heterogeneous recipes, eventual recycle flows, and no buffers between workstations. Different heuristic-based strategies are incorporated into the computer model in order to improve the solutions generat-ed over time. The aim is to reduce the number of products that must be discard-ed while minimizing the makespan. In addition, a graphical user interface was developed for quickly evaluating simulated schedules.  Keywords: Simulation, Hoist Scheduling Problem, Chemical Tanks, Job-shop Scheduling Problem 1 Introduction The research into scheduling problems has drawn a great attention in the last decades with the aim to increase the effectiveness of industrial production. Particularly, the Hoist Scheduling Problem becomes much harder to solve for practitioners and re-searchers in Automated Manufacturing Systems [1] [2]. This problem deals with the processing of a set of jobs that has to be transferred through several operation stages by using a shared automated transfer device (hoist). Hoist transportation devices are used commonly in the manufacture of printed circuit boards (PCBs) in electroplating plants and also in the automated wet-etch station (AWS) in semiconductor manufac-turing systems [3] [4]. The hoist scheduling problem is usually very complex. Many exact solution ap-proaches and heuristic procedures have been proposed in literature to solve this prob-lem[5] [6]. However, such techniques are not able to efficiently represent the major complexities appearing in real-world industrial problems. The control and schedule of the hoist are critical for the system performance, especially when chemical processes are involved. This is due to the hoist transports the products, one at a time, between 44 JAIIO - SII 2015 - ISSN: 2451-7542 148SII 2015, 4º Simposio Argentino de Informática Industrial.chemical tanks. The processing time of each product in each tank is restricted to a minimum and a maximum duration. Besides, a zero-wait police is followed between stages. Not reaching the minimum processing time, or exceeding the maximum al-lowed time may cause not only waste of materials but also loosing the critical re-source of production time. In this way, the main goal of the hoist scheduling problem is to minimize the makespan while maximizing throughput with no defective product (waste).  This paper aims at developing a modern discrete-event simulation model to evalu-ate, analyze and design the operation of electroplating for the aerospace industry based on the hoist scheduling problem. The main advantage of simulation technique, with regards to the solution approaches referenced above, is that it permits to system-atically reproduce the complex company process in an abstract and integrated form, visualizing the dynamic behavior of its constitutive elements over time [7]. The com-puter model allows exploring different sequences of jobs entered to the hoist line. The results given by the simulation model are then presented through a user graphical interface, which is particularly useful for the decision-making process. 2 Problem Definition 2.1 Problem characteristic  The hoist problem consists in a set of jobs that must be processed by a sequence of chemical tanks from the input buffer to the output buffer [8]. Jobs are transported from one tank to another by a single automated hoist. The line can process several types of products, which generally follow different recipes. A recipe is defined by both the sequence of stages (or tanks) that an item must follow and the minimum and maximum processing time in each stage. For example, the recipe for the titanium sulfuric anodized is given in Table 1. In practice, jobs vary in size or other properties and require different sequences or processing times. Each produced item type has its own sequence of visiting workstations, processing intervals, etc.  The hoist is capable of transferring only one item at a time from one chemical tank to another. The transferring time of the hoist comprises the traveling time from the actual position to the tank, the loading time, the traveling time to the destination tank, and the unloading time. The loading and unloading times are constant and known in advance. The traveling time depends on the distance between the tanks. The pro-cessing time starts when the hoist unloads the item in the tank and finishes when the hoist picks up the item. If the duration of the processing time is below or above the predefined time window, the item becomes defective and must be discarded.  Each tank operates independently and has a unary capacity. In addition, there is no buffer between adjacent workstations. That is to say, once the item has been pro-cessed, it has to be moved directly to next stage without intermediate storage. Some critical tanks have an exact processing, which implies that as soon as the processing time is finished, the item should be moved immediately. A picture representing the hoist line is shown in Fig. 1. 44 JAIIO - SII 2015 - ISSN: 2451-7542 149SII 2015, 4º Simposio Argentino de Informática Industrial.Table 1. The recipe for titanium sulfuric anodized Stage Tank Minimum Time (min) Maximum Time (min) 1 5 10 15 2 6 5 6 3 13 1 2 4 12 5 5.5 5 16 3 10 6 21 10 15 7 22 10 15 8 16 3 10 9 20 5 20 10 3 20 20   Fig.1. Automated job-shop system with heterogeneous recipes. 2.2 Different conflicts  When the hoist problem is solved, it is needed to assure that feasible schedules are generated. When the work-in-progress (WIP) of the system is higher than 1, three types of conflicts can be presented [9]: 1. Conflict by tank availability: a conflict may occur when a job finishes its pro-cessing in a stage and the next tank in the recipe is busy. In this case, the hoist must first serve the job that is in the destination tank before moving the first job. Unfor-tunately this is not always possible because when the second tank is released the job in the first tank may be defective. The worst version of this conflict is when the destination tank of job A is the current location of job B, and destination tank of job B is the current location of job A (see Fig. 2). 2. Conflict by hoist availability: a conflict may occur when a job is ready to be trans-ported and the hoist is being utilized by other job. The job should wait until the hoist is idle, but sometimes is too late. This becomes more critical when the mini-mum and maximum processing times are equal, because there is no extra time to wait for the hoist. In this way, it was needed to develop an algorithm (see Fig. 3) to verify the status of both the robot and the jobs waiting for it. 44 JAIIO - SII 2015 - ISSN: 2451-7542 150SII 2015, 4º Simposio Argentino de Informática Industrial.3. Conflibut thecomm  3 PrGenerallyusing purguages, ttechniquelems [10than solvSimulaan organithe time systems oct by hoist loc hoist is too on when the hoposed Sim, real-world se mathematiche increase os have made]. In contrast e, allowing thtion allows ezation. The cand cost requperations canation: a conffar and when oist is unloadFig. 2. CoFig. 3. Hulation Mystems are hial approachef computatiosimulation anto optimizatie model to bexperimentingompanies canirements asso be assessed blict may occuthe hoist arriing in one exnflict by tank aoist decision dodel ghly complexs. The increanal power,  appropriateon methods,  observed.   and analyzin model their ciated with py developingr when a job ves is too latetreme of the tvailability. iagram.  and virtuallysing availabiand the deve tool to deal simulation mg different oprocess in vihysical testin a discrete evneeds to be tr. This conflicransportation   impossible tolity of simullopment of swith this kindodels are “ruperation procrtual settings,g. Thereforeent simulationansported t is more line.   solve by ation lan-imulation  of prob-n” rather edures of  reducing , complex  model. 44 JAIIO - SII 2015 - ISSN: 2451-7542 151SII 2015, 4º Simposio Argentino de Informática Industrial.Moreover, some simulation packages provide an user-friendly 3D graphical inter-face which allows obtaining a better visual experience to the world of simulation models. It provides rich 3D objects to make the simulation looks more realistic. In addition, simulation models can easily be tweaked and adjusted, providing rapid re-sponses to even the most abstract situations. In this paper, a simulation model, developed with SIMIO software, was construct-ed to represent the operation of the electroplating line. SIMIO is a simulation model-ing framework based on intelligent objects [11]. An object can be a machine, robot, airplane, customer, doctor, tank, bus, ship, etc. A model is built by combining objects that represent the physical components of the system. It is worth to remark that SIMIO allows to build 3D animated model which provides a moving picture of the system in operation. The following subsections describe the major components of the computer model. 3.1 Model assumptions  The major assumptions for constructing the simulation model are:  There are N types of jobs following a given production sequence (recipe); they have to be processed by a sequence of chemical tanks from the input buffer to the output buffer (some tanks may be skipped in the process); there are re-entrant and possible recycle flows to the same unit; each stage has specific time windows of processing time in each tank; products will become spoiled if the processing time falls outside of the time window.  There are M workstations (chemical tanks). Each tank has specific functionality; has a single production unit per stage; never breaks down; no intermediate storage between stages.  There is a single automated material-handling device (hoist), which transports jobs between the tanks. Its loading / unloading speeds are constants. Its capacity is one item at a time. The travelling speed is constant. The hoist can experience break-downs. 3.2 Input variables The major input variables used in the simulation model, among others, are:  Max_WIP: Maximum number of jobs that could simultaneously be in the system.  Input_Order: It is the sequence in which the jobs enter the system; it is defined by different proposed heuristics.  Interarrival_Time: Minimum period of time between the inputs of two orders.  Priority: Three different methods were used to assign the priority to request the hoist. The first takes into consideration the time to become defective, assigning highest priority to the jobs next to expire. Similarly to the first method, the second one assigns highest priority to the jobs that are more advanced. The third method 44 JAIIO - SII 2015 - ISSN: 2451-7542 152SII 2015, 4º Simposio Argentino de Informática Industrial.takes into consideration the time that the job has exceeded the minimum processing time.  3.3 Output variables The performance indicators are:  Makespan: The time in which the last job is completed. This variable aims to be minimized.  Job Finished / Defective: The number of non-defective jobs that are completed and the defective jobs. The latter should be minimized.  Cost: It represents the total cost to manufacture all the orders. It is computed as the sum of the operation cost of the line plus the cost of the defective units. This varia-ble should be minimized. 3.4 Computer model The simulation model is integrated by the following components:  Tanks / workstation: where different chemicals processes are performed, e.g. sulfu-ric aluminum anodized, chromic anodized, passivation, chroming by immersion, cleaning and so on. Each tank is represented by a Server object. In SIMIO, a Server object is used for representing a capacitated process such as a machine or service operation.  Jobs: they are represented as Entities. An entity is a dynamic object that can be generated / destructed during the simulation run.  Hoist: device materials transferring the jobs between tanks. It is represented by a Resource object. A 2D view of the simulation model is given in Fig. 4 while the 3D animation view is given in Fig. 5.   Fig. 4. 2D view of the simulation model. 44 JAIIO - SII 2015 - ISSN: 2451-7542 153SII 2015, 4º Simposio Argentino de Informática Industrial.3.5 HAfter sevsystem, tform explowing he Heurissmalle Heuriscase, th Heuriscessing Heuris Heurislation-ables tThe sidevelop afound the euristics eral experimhe following erimental desuristics: tic 1: Jobs arr total processtic 2: Jobs are jobs sequentic 3: Jobs ar the major qutic 4: Jobs aretic 5: OptQuebased optimizo optimize thmulation resun iterative op best values fFig.6. OFig. 5. 3D vientation and heuristics weigns. The jobe sequenced ing times aree sequenced ce is generate ordered accantity of jobs sequenced bst is used to iation package response varlts obtained timization proor the responsptimization byew of the simusuggestions fre implemen sequences waccording to  placed first iaccording to ed by alternatording to the is defined asased on their dentify the bee that evaluatiables. by evaluatingcess. Such pe variables de Simulation forlation model. rom the opeted in the simere created bthe total prodn the sequencthe total proding short jobs use of critic the “critical tlast processinst job sequenes different v alternative hrocess, descrifined.  the electroplat rator of the rulation mody considerinuction time. e.  uction time b and long jobal tank. The ank”. g tank.  ce. OptQuest alues of the ieuristics werbed in Fig. 6,  ing line. eal-world el to per-g the fol-Jobs with ut in this s. tank pro-is a simu-nput vari-e used to is used to 44 JAIIO - SII 2015 - ISSN: 2451-7542 154SII 2015, 4º Simposio Argentino de Informática Industrial.4 SIMULATION RESULTS The simulation model was used to analyze the performance of an electroplating line working in a real-world aeronautical manufacturing system. This problem comprises 30 chemical tanks and one single hoist. There are 24 types of jobs, each one with its specific processing sequence. Some jobs can visit the same tank more than once. After constructing the simulation model, several alternative scenarios were defined in order to run experimental designs. A multifactorial experimental design was exe-cuted for determining the factors that affect the response variables. Each scenario was run 5 times. The configuration and results for the top 10 scenarios are given in Table 2.  Note that the type of heuristic used to define the initial sequence of jobs is one of the control variables. Here it is worth to remark that Heuristic 5 was not listed in Ta-ble 2 because the computational time required for this strategy was higher than the other proposed heuristics.  Table 2. Best results obtained for different scenarios solved by the simulation model.  Control Variables  Results Scenario Heuristic Max WIP Inter. Time Priority Cost MK Def. Jobs 1 4 4 12 2 189.176 18.9176 0 2 4 3 13 2 195.042 19.5042 0 3 1 3 13 2 195.209 19.5209 0 4 2 3 13 2 195.209 19.5209 0 5 3 3 13 2 195.209 19.5209 0 6 1 3 12 2 195.237 19.5237 0 7 2 3 12 2 195.237 19.5237 0 8 3 3 12 2 195.237 19.5237 0 9 4 3 12 2 195.309 19.5309 0 10 4 3 14 2 195.376 19.5376 0  From experimental results, it follows that there are no significant differences in simulation results when the Max WIP is equal to 3, but when this value is increased to 4, the only sequence (heuristic) that does not generate defective jobs is the Heuristic 4.A maximum WIP below 3 jobs increases the cost since the system has idle capacity. Maximum WIP above 5 increases the cost since the number of defective units is high-er.  After evaluating all results, the best configuration that minimizes both the makespan and the number of defective products is shown in Fig.7 and 8. The jobs schedule is given in Fig. 7 while the hoist schedule is depicted in Fig. 8.  Note that the results reported by simulation runs are represented graphically by us-ing a user-graphical interface. This interface is integrated with the simulation model for quickly evaluating simulation results and helping the decision-making process.   44 JAIIO - SII 2015 - ISSN: 2451-7542 155SII 2015, 4º Simposio Argentino de Informática Industrial.5 CThis papeshort-termmonly in also in thtems. SimThe propscenariosminimizinDifferentjob sequeReferen1. Cramufactu2. Rierauling 3. AguirMILPmanuonclusionsr has present scheduling the manufacte automated ulation is a posed  strategy in a short timg the total m heuristics wences to be proces a, Y.: Conditioring systems. E, D. and YorkeProblem. Annare, A. M., M/heuristic algofacturing. IberoFigFiged an innovatof a complexure of printedwet-etch statroper approa was capable period. Thakespan whre embedded cessed in thenal optimizatiouropean Journ-Smith, N.: Anls of Operationéndez, C. A.,rithms to autoamerican Journ.7. Jobs Sched . 8. 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Aerospace Manufacturing Industry: A Simulation-based Decision Support Framework for the Scheduling of Complex Hoist Lines

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Aerospace Manufacturing Industry: A Simulation-Based Decision Support Framework for the Scheduling of Complex Hoist Lines

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