INTELLIGENT PRODUCTION SCHEDULING A CASE STUDY

International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 6, June 2017, pp. 283 298, Article ID: IJMET_08_06_029 Available online at http://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=8&itype=6 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 IAEME Publication Scopus Indexed INTELLIGENT PRODUCTION SCHEDULING A CASE STUDY N.K. Sethy Mechanical Engineering Department IGIT, Sarang, Odisha, India Dr. D.K. Behera Mechanical Engineering Department IGIT, Sarang, Odisha, India ABSTRACT Scheduling is a significant decision making process widely used in manufacturing production, management, computer science, etc. Operation research scheduling can reduce material handling costs and time by optimising the procedure. Finding good schedules for given sets of jobs so can help factory supervisors to control job flows effectively and provide solutions for job sequencing. Scheduling problems are usually modelled and solved in a mathematically feasible way. As a result the solutions generated from these greatly simplified problems are infeasible for real-life cases. The complexity and instability of production systems are still underestimated in many scheduling techniques in academic literature and the flexible production concept in mould shop has been rarely studied. It is necessary to develop an appropriate scheduling procedure algorithm to meet the industry s need. The study presents an approach to solve NP-Hard Flexible Flow Shop problems of more than two machine center to obtain optimum makespan. This approach uses First in First out (FIFO) dispatching rule and second approach integrates Random key method with Genetic Algorithm (RKGA) these approach has been implemented with MATLAB R2014a the approach has been tested on a Flexible Flow Shop Problem which was solved previously by the random keys representation can avoid the existence of duplicated positions value in sequencing. The result shows that Random Key Genetic Algorithm approach obtains the best minimum optimum makespan for different set of jobs with different processing time. Though the computation time is more comparatively to the FIFO approach. The RKGA can be applied in different scheduling problems in future study. Key words: Scheduling, Flexible Flow Shop, FIFO, RKGA, Makespan. Cite this Article: N.K. Sethy and Dr. D.K. Behera. Intelligent Production Scheduling - A Case Study. International Journal of Mechanical Engineering and Technology, 8(6), 2017, pp. 283 298. http://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=8&itype=6 http://www.iaeme.com/ijmet/index.asp 283 editor@iaeme.com

Intelligent Production Scheduling - A Case Study 1. INTRODUCTION Scheduling is a significant process widely used in manufacturing technology, production management, computer science, etc. In simple flow shop problems, each machine center has more than one machine if at least one machine [1, 3, 4-7] the problem is called Flexible Flow Shop problem. Flexible Flow Shops are thus the generalization of simple flow shops [2]. Scheduling jobs in Flexible Flow Shop is considered as NP-Hard problem. [8, 9]. The rest of the paper is organized as follows. Introduction and Problems in production Planning are explained in section I. Previous works done on this field are given in the section II. The techniques and problem that has been used is presented in section III. Implementation of the approach and experimental results are presented in section IV. Concluding remarks are given in section V. 1.1. Problems in Production Planning 1.1.1. Inadequacy of present product planning approach The project schedule is discussed with customers before the launch of any project tooling department is responsible for giving an estimated mould making time for reference. However, production plan of each project is constructed on an ad hoc basis. The wide variation in production routes complicates the planning process. The total processing time can only be estimated roughly before the completion of detailed product design. When multiple projects are running simultaneously, it is highly possible that the projects schedulesconflict with one another s. Currently, there are two solutions to deal with the overload problems: first outsource some of the jobs; second, request for postpone of delivery date. The latter solution, which can lead to dissatisfaction of customers, is less preferable. Outsourcing is usually decided at the last minute which decreases the flexibility of the project schedule. [10] 1.1.2. Frequent happenings of unpredictable incidents Unpredictable incidents often alter projects progress and induce rescheduling of the tasks. These incidents include delays in order releases of urgent orders, cancelations of orders and machine breakdowns. Delays in order releases happen when the precedent operations cannot finish on time or the required resources are not available. Urgent orders are usually the reworking operations. A work piece is reworked when it cannot pass the quality check or its specification is amended. These urgent orders add extra workloads to the shop. Cancellations of orders occur when project managers ask for a pause. Machine breakdowns can be caused by operation errors or malfunctions of machines. It takes a period of time to fix the problems so the shop capacity drops until the machines are repaired. Frequent happenings of unpredictable incidents bring chaos to the mould shop. [10] The objective of this project is to study a scheduling algorithm which can generate feasible schedules quickly to production planner for decision making. The problem studied in the paper is a Flexible Flow Shop Problem where we assume each machine center has the same number of parallel machine. This study precisely focusses on the minimization of the makespan of the FFSP. The algorithms have been evolved to workout Flexible Flow Shop scheduling problem with more than two machine centers. The first one extends FIFO Rule approach to get a nearly optimal makespan. FIFO approach is first used to assign jobs to each flow shop and a schedule is obtained after simulation of FIFO dispatching rule. The second one is an optimal algorithm entirely using Random Key Genetic Algorithm technique. Experimental results show that Random Key Genetic Algorithm generates minimum optimal makespan. http://www.iaeme.com/ijmet/index.asp 284 editor@iaeme.com

N.K. Sethy and Dr. D.K. Behera 2. LITERATURE REVIEW Over the last decade the competition among over last decade the competition among mould makers is highly fierce many mould makers has adopted different approach in order to remain competitive. They are aiming at making moulds with better quality, lower cost and shorter delivery time. An intelligent mould shop has been developed by Korean researchers Choi, B. K., KO, K., & Kim, B. H. [2005] to enhance production efficiency and lower dependency on human skills. This intelligent mould shop comprises of three main location: Technical Data Processing Station, Loading Schedule Station, and Real-time Monitoring Station. The research points out that while most of the required technologies are available, the collaboration from the end-users is critical. Human factors should be considered when developing a system to replace manual works. [11] Production scheduling is a decision-making process to handle the allocation of machines to operations over a specific period of time in order to achieve one or multiple objectives [Pinedo M. L., 2008]. Study of scheduling problems has been considerably increased since 1950s. The scheduling problems. A great variety of the problems in practice causes the difficulty to formulate a common model for scheduling problems. A workable algorithm for one problem may not be effective on another slightly different problem. Many algorithms have been developed to deal with the variants of the problems. [12] 2.1. Intelligent Production Planning & Scheduling for Mould Making A studied problem of the mould shop [Liu, Liao, Yang, Wang, & Zhao, 2010] is classified as job shop scheduling problem. Genetic algorithm (GA) is employed to find the schedule with minimum makespan. The problem consists of seven machines and nine different production routes with 24 operations. Operation dependent transportation times and machine setup times are considered. The chromosomes are encoded in integers. Each gene represents a job, so the number of occurrences of a number is equal to the number of operations it undergoes. The operations of GA include roulette wheel method for chromosome selection, position based crossover, and two point exchange mutation. The population size is 5000, crossover probability is 0.8; and mutation probability is 0.1. The computation time used is 49.36 seconds. [13] Choy in the year 2011 proposed a hybrid scheduling decision support model (SDSM) to solve the mould making scheduling problem. The studied problem of the mould shop is classified as job shop scheduling problem with identical parallel machines. The model is comprised of two modules: scheduling module and optimization module. The scheduling module generates the schedules with GA with the objective to minimize the makespan. The optimization module finds the most economic option to handle tardiness problem. It is proved that this model is more effective than manual scheduling. [14] http://www.iaeme.com/ijmet/index.asp 285 editor@iaeme.com

Intelligent Production Scheduling - A Case Study Figure 2.1 Architecture of hybrid scheduling decision support model Choy [2011pp. 1931-1941] [14] Tang LapYing in the year 2013 proposed the paper Intelligent Production Scheduling for Mould Making to tackle the Asah is mould shop scheduling problem with new heuristic and meta-heuristic algorithms they are Random Keys Harmony Search (RKHS), hybrid Nawaz- Enscore-Ham (NEH), and Random Keys Genetic Algorithm (RKGA). Where he had done comparison between both heuristic and meta-heuristic algorithms and thus suggested that RKGA method performs best and suggests that the method gives best makespan with the job size bellow 20 RKHS is suggested to be implemented for large jobs. [15] Gwoboa Horng, Tzung-Pei Hong, Pei-Ying Huang and Chan-Lon Wang in the year 2007 proposed three algorithms based on they are Sriskandarajah and Sethi s method by combining both the LPT and the Search-and-prune approaches to get a nearly optimal makespan. It is suitable for a medium sized number of jobs. The second one is optimal algorithm, using the Search-and-prune technique. It can work only when the job number is small. The third one is similar to the first one, except that it uses petrov s approach (PT) to deal with job sequencing instead of Search-and-prune. The experimental result shows that the computational time for the proposed algorithm is in the relation as follows: Algorithm 3 < Algorithm 1 < Algorithm 2, and the makespan has the following relation: Algorithm 3 >Algorithm 1 > Algorithm 2. A trade-off can thus be achieved between accuracy and time complexity. The choice among the three approaches to solve a Flexible Flow Shop Problem thus depends on the problem size, the allowed execution time and the allowed error. [16] 2.2. Theory of Constraints Fredendall & Lea in the year 1997 studied the application of TOC in master production scheduling. [17] Graham [in the year 2000 pointed out that the PERT/CPM approach can cause project overrun as people misuse the safety buffer time. They tend to start the activity as http://www.iaeme.com/ijmet/index.asp 286 editor@iaeme.com

N.K. Sethy and Dr. D.K. Behera late as possible. TOC applies the buffer at the end of the whole project, so no feeding buffer in between activities. [18] Figure 2.2 Comparison between PERT/CPM and TOC with regard to safety time.graham [2000] 3. METHODOLOGIES The FFSSP is proved to be NP-hard, which has not been solved optimality in a reasonable time. The problem taken in this paper is the non-permutation FFSSP. Previously the problem has been solved by using three algorithms they are Sriskandarajah and Sethi s method by combining both the LPT and the Search-and-prune approaches to get a nearly optimal makespan. It is suitable for a medium sized number of jobs. The second one is optimal algorithm, using the Search-and-prune technique. It can work only when the job number is small. The third one is similar to the first one, except that it uses petrov s approach (PT) to deal with job sequencing instead of Search-and-prune. An integrated approach of heuristic and random keys representation is proposed to minimize the makespan which is the completion time of the last job. The problem has been solved by using FIFO rule then by the application of (RKGA) Random Key Genetic Algorithm and comparison has been done between two algorithms and result has been given. The studied problem has been formulated as bellow: Assume five jobs, J1 to J5, each having three tasks (t1j, t2j, t3j), are to be scheduled via three operations. Each operation is executed by a machine at the corresponding machine center. Each machine center includes two parallel machines. Assume the execution times of these jobs are listed. [16] Assumptions Jobs are not pre-emptive. Each job has m (m > 2) tasks with processing times, executed respectively on each of m machine centers. All machine centers have the same number of parallel machines. http://www.iaeme.com/ijmet/index.asp 287 editor@iaeme.com

Intelligent Production Scheduling - A Case Study Table 3.1 Processing Time for5 Jobs Jobj t1j t2j t3j J1 4 7 3 J2 1 5 2 J3 5 2 4 J4 2 5 3 J5 5 5 6 Table 3.2 Processing Time for 12 jobs Job j m1 m2 m3 j1 1.75 1.25 4.25 j2 0.75 0 5.5 j3 2.5 1.25 6 j4 2.5 0 2.25 j5 3.25 0 5 j6 1.5 2 4 j7 0.75 0.25 3.5 j8 0.25 3.25 2 j9 2.75 4 0.75 j10 2 0.25 1.75 j11 3.25 1.75 2 j12 4 0 5.5 Table 3.3 Processing Time for 20 jobs Job j m1 m2 m3 j1 0.75 0.5 5.5 j2 3.5 0 0.5 j3 2 0.25 3 j4 0.25 0 4 j5 1 0 5.25 j6 4 0 1.25 j7 1.5 0.5 2.25 j8 2 0 3.5 j9 0.25 0 4.5 j10 1.75 3.25 0.25 j11 2.75 0.5 0.5 j12 0.25 0 5.5 j13 3.5 0 3.5 j14 3 0 3.5 j15 2.25 0 3.5 j16 2.75 0 0.75 j17 0.75 2.5 2.75 j18 1.25 0.75 2 j19 1 0 2.75 j20 0.25 0.75 2.5 FIFO is the dispatching rule that does not follow either SPT (Shortest Processing Time) or EDD (Earliest Due Date). A schedule can be obtained after simulation of FIFO, below is the FIFO dispatching flow chart: http://www.iaeme.com/ijmet/index.asp 288 editor@iaeme.com

N.K. Sethy and Dr. D.K. Behera Table 3.4 Input Parameters for FIFO N L Mi Vi k Ui,j Si L A0,k F0,j Ki,j Pi,j Ri,j Fi,j Ai,k MS Number of jobs Number of stages Number of machines at Stage i Speed of Machine k at Stage i Standard processing time of Job j at Stage i Setup time of a job at Stage i Entry point sequence of job set Available time of Machine k at Stage 0, i.e. current machine available time Finish time of Job j at Stage 0, i.e. job release time Table 3.5 Variables FIFO Selected machine of Job j at Stage i Processing time of Job j at Stage i Release time of Job j at Stage i Finish time of Job j at Stage i Available time of Machine k at Stage i Makespan of the schedule Figure 3.1 FIFO Algorithm Flow Chart http://www.iaeme.com/ijmet/index.asp 289 editor@iaeme.com

Random Keys Genetic Algorithm. Intelligent Production Scheduling - A Case Study Figure 3.2 Flow Chart of Random Key Genetic Algorithm 4. IMPLIMENTATION The proposed algorithm is coded in MATLAB(2014) and executed on a laptop with 2.41GHz Intel i3 processor, 4GB RAM and windows 8 operating system. MATLAB is chosen over the other programming languages because it has matrix manipulation ability. Several mathematical operations that work on arrays or matrices are built-in to the MATLAB environment. The graphical output is optimized for interaction. Plotting is easy using the graphical interactive tools. MATLAB s functionality can be greatly expanded by the addition of toolboxes. Excel link allows data to be written in a format recognized by Excel. There are numeric resources about coding in MATLAB on internet. Problem instances are generated for testing the effectiveness of the proposed scheduling algorithms the static data such as machine speed, setup time, job initial release time, machine initial release time are kept constant. The experiment has conducted 10 iteration for 3 different sets of job for FIFO dispatching approach and the computation time is taken. Then RKGA approach is implemented with 100 iterations for 3 different sets of jobs and the optimum makespan is obtained the data has been taken from literature. Comparison between the makespan and computation times of both algorithms are done. The detailed about FIFO has been caried by[tang LapYing in the year 2013](15). http://www.iaeme.com/ijmet/index.asp 290 editor@iaeme.com

5. EXPERIMENT AND RESULT N.K. Sethy and Dr. D.K. Behera 5.1. First In First Out (FIFO) The result analysis is shown in this part. For FIFO dispatching rule the algorithm has been computed which obtains a Machine available time, Makespan & computation time in seconds for 3 different sets of job and different processing time with 10 iteration given below: Table 5.1 Machine available time for 5 jobs JOB j M1 M2 M3 J1 14 21 28 J2 11 22 31 J3 15 26 30 J4 12 22 34 J5 14 26 34 Table 5.2 Machine available time for 12 jobs. JOB j M1 M2 M3 J1 25.75 36.75 54.25 J2 24.75 39 52.75 J3 26.5 39.75 55.25 J4 26.5 38.5 57.75 J5 25.75 39 55.25 J6 24.75 36.75 54.25 J7 26.5 38.5 52.75 J8 26.5 39.75 57.75 J9 24.75 36.75 54.25 J10 26.5 39.75 57.75 J11 26.5 38.5 52.75 J12 25.75 39 55.25 Table 5.3 Machine available time for 20 jobs. JOB j M1 M2 M3 J1 40.75 60.25 84.25 J2 43.5 61.25 86.75 J3 42 62.25 85.25 J4 40.25 63.5 84 J5 40.75 61.25 86.75 J6 43.5 63.5 84 J7 40.25 60.25 84.25 J8 42 62.25 85.25 J9 43.5 63.5 84 J10 42 62.25 85.25 J11 40.25 60.25 84.25 J12 40.75 61.25 86.75 J13 42 62.25 85.25 J14 40.25 60.25 84.25 J15 40.75 61.25 86.75 J16 43.5 63.5 84 J17 40.25 60.25 84.25 J18 40.75 61.25 86.75 J19 43.5 63.5 84 J20 42 62.25 85.25 http://www.iaeme.com/ijmet/index.asp 291 editor@iaeme.com

Intelligent Production Scheduling - A Case Study As we know the highest value is taken as the makespan in case of FIFO approach.the above machine available time has been demonstrated in the form of Bar Chart: Figure 5.1 Machine available time for 5 jobs Figure 5.2 Machine available time for 12 jobs. Figure 5.3 Machine available time for 20 jobs. http://www.iaeme.com/ijmet/index.asp 292 editor@iaeme.com

N.K. Sethy and Dr. D.K. Behera Table 5.4 Computation time taken for 3 different sets of jobs by FIFO approach Number of jobs Makespan Iterations Computation time in (sec)avg 5 34 10 0.00044173 12 57.75 10 0.00044170 20 86.75 10 0.00050487 5.2. Random Key Genetic Algorithm (RKGA) Here In the genetic algorithm the population size is taken as 3. As Genetic Algorithm is a heuristic approach we have taken Randomkey aproach as a result the method becomes metaheuristic. For the experiment we have taken 3 different job sets with different processing time and performed 100 iteration.bellow the result is discused: For 5 jobs: Figure 5.4 Makespan obtained for 5 jobs. http://www.iaeme.com/ijmet/index.asp 293 editor@iaeme.com

Intelligent Production Scheduling - A Case Study For 12 jobs: For 20 jobs: Figure 5.5 Makespan obtained for 12 jobs. http://www.iaeme.com/ijmet/index.asp 294 editor@iaeme.com

N.K. Sethy and Dr. D.K. Behera Figure 5.6 Makespan obtained for 20 jobs. Table 5.5 makespan number of iterations and computation time obtained by RKGA. Number of jobs Makespan Iterations Computation time in (sec) 5 28 100 0.5977 12 43.25 100 0.5024 20 59.50 100 0.7274 Table 5.6 Comparing the computation time of FIFO with RKGA approach. Number of jobs Computation time in (sec) for FIFO Computation time in (sec) for RKGA 5 0.00044173 0.5977 12 0.00044170 0.5024 20 0.00050487 0.7274 http://www.iaeme.com/ijmet/index.asp 295 editor@iaeme.com

Intelligent Production Scheduling - A Case Study Figure 5.7 The average CPU times for processing 5,12,20 jobs by FIFO and RKGA approach. Makespan of FIFO And RKGA 100 80 86.75 Makespan 60 40 20 34 28 57.75 43.25 59.5 FIFO RKGA 0 0 5 10 15 20 25 JOBj Figure 5.8 The average Makespan of processing 5,12,20 jobs by FIFO and RKGA approach. 6. CONCLUSIONS Applicable inteligent scheduling can not only reduce manufacturing costs but also reduces the material handling cost and time. Finding good schedules not only help company supervisors but also helps in controling the job flows and provide god job sequencing. Scheduling jobs in flexible flow shop has been known as NP-hard problem. In this study we have taken a NPhard problem which has been previously solved using sriskandrajah and sethi s method by combining both the LPT and the search- and prune approaches second one by search-and prune technique third one by petrovs approach. Thus in this study two approach has been presented based on First In First Out(FIFO)dispatching rule and the second one is integrated approach of Random Search and Genetic Algorithm. From the result as the time complexity in case of RKGA is higher at the cost of better makespan therefore it is economical from above work that RKGA is recommended for small jobs preferably below 20 jobs and 3 stages. http://www.iaeme.com/ijmet/index.asp 296 editor@iaeme.com

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