Proceedings of th
th
6 International & 27 All India Manufacturing Technology, Design and Research Conference (AIMTDR-2016) College of Engineering, Pune, Maharashtra, INDIA December 16-18, 2016
Master Production Scheduling for the Production Planning in a Dairy Industry using Teaching Learning based Optimization Method Radhika S.1, Srinivasa Rao Ch.2, Neha Krishna D.3 and Swapna D.4 1,4Dept.
of Mechanical Engineering, RVR&JC College of Engineering (A), Chowdavaram, Guntur, A.P., India of Mechanical Engineering, Andhra University College of Engineering (A), Visakhapatnam, A.P., India 3Dept. of Computer Science and Engineering, International Institute of Information Technology, Bangalore, K.A., India E-mail:
[email protected],
[email protected],
[email protected] 2Dept.
ARTICLE INFO
ABSTRACT
Keywords: Process Industries Master Production Scheduling Multi-objective Optimization Evolutionary Algorithms Teaching-Learning-Based Optimization
Dairy products are indispensable for human life. The high perishability of dairy products creates specific inventory management challenges for dairy manufacturers. Given the agreements between the farmers and the dairy, there is a continuous flow of raw milk supply coming into the dairy value chain. However, the demand side is more uncertain due to seasonal fluctuations, competitive activity and retailer price pressures. Cold storage constraints make it necessary to carefully plan for the receipt and quick usage of raw milk and finished products. Accordingly, there is constant pressure to keep inventory levels to a minimum. Improper sales forecasting can cause products kept in stock to over-run their shelf-lives and, therefore, to lose value. There is another important factor of the time window that the products can be kept after production. As most of the dairy products have a shelf life of 2 to 3 days with the exception of few, the time window plays a crucial role in determining the level of inventory. The demand is satisfied by adequate inventory levels and shortages are avoided as it directly influences the reputation of the firm. Product mix, packaging materials with longer lead times, and even product content must be balanced in order to optimize the utilization of available resources, model the cost effects of changes, and, ultimately, to maximize profitability. More competitive and optimal solutions can be obtained by the nature inspired population based algorithms. Teaching-Learning-Based Optimization (TLBO) is one such recently proposed population based algorithm which does not require any algorithm-specific control parameters. This research presents the business process redesign of the MPS for a dairy industry. The objective of this study is to plan an efficient MPS using TLBO, which is not yet found in the literature so far. The methodology is then applied to sample data set, results of which demonstrate that use of TLBO yields more optimal solutions for MPS problems.
1.
Introduction
Contrary to the production scheduling, optimization of master production scheduling is not vastly present in the literature, especially with artificial intelligent techniques. Some of the works carried out by researchers for optimizing an MPS are discussed below. There aren’t many works available in the area of production planning related to MPS. For a chemical manufacturing industry, a modified design of MPS and usage of sequence dependent scheduling heuristic is proposed by Hill et al. [1]. Sonklin et al. [2] developed an MPS for the planning and scheduling of plastic forming products. The objective was to to plan a time schedule that enables/reduces split orders and transportation surcharges. For the development of a rolling horizon MPS for a paint industry, a weighted integer goal programming model is proposed by Venkataraman and Nathan [3]. Many researchers have started applying the TLBO algorithm to their research problems. Hosseinpour et al. [4] presented a multi-objective placement of automatic voltage regulators in distribution systems in the presence of distributed generators. Satapathy and Naik [5] used TLBO algorithm for ISBN: 978-93-86256-27-0
data clustering. In another work, Satapathy et al. [6] have applied TLBO for unit maintenance scheduling problem in power systems. The application of TLBO for solving complex constrained optimization was presented by Rao and Patel [7]. Results showed that TLBO techniques have much potential. Krishnanand et al. [8] applied a multi-objective TLBO algorithm to solve the economic dispatch problem, Togan [9] presented a design procedure employing TLBO algorithm for discrete optimization of planar steel frames. Few applications of the TLBO in the manufacturing field include the works of Pawar and Rao [10]; Rao and Kalyankar [11]. Scheduling related works using TLBO were also reported in the literature, which include short-term Hydro Thermal Scheduling (HTS) problem by Roy [12], Kumar et al. [13] and the Job Shop Scheduling Problem (JSSP) by Keesari and Rao [14]. The comparison with other well established techniques in the said works demonstrated the superiority of the TLBO algorithm. Review of the literature reveals that much work has not been reported in the application of metaheuristic techniques for solving MPS problems. Continuous research is being conducted in this field and nature-inspired metaheuristic optimization 860
Master Production Scheduling for the Production Planning in a Dairy Industry using Teaching Learning based Optimization Method
the different production rates. In compliance with the industry norms, the data presented in the study are not actual figures but normalised to avoid anomalies in the outcome of the analysis.
techniques proved to be better than the traditional techniques and are widely used. Although evolutionary computation methods offer solutions that combine computational efficiency and good performance, evolutionary computational research has been criticized for the consideration of artificial test problems that are much simpler than real-life manufacturing cases. The present study makes an attempt to implement evolutionary computation to real-life production scheduling problems. 2.
Table 1 period-wise gross requirements for dairy industry Products Initial Periods Inventory 1 2 J1 75 810 260 J2 40 690 170 J3 10 550 760 For conciseness, gross requirements for periods 3 thru 22 and for products J4 thru J9 are not shown J10 20 20 700 J11 15 570 210 J12 10 810 930
Problem description
The Dairy industry, established in 1976 is the one of the largest co-operative milk dairies in South India, with an annual turnover of around 600 crore rupees. The procurement of milk is approximately 4.5 lakh litres per day and is one of the leading dairy industries nationwide. Dairy industry considered is a manufacturer, wholesaler and retailer of milk and milk products, manufacturing a variety of 21 products. The major products of their production line are Table butter, skim milk powder, Ghee, Basundhi, Doodh peda, Sterilized Flavoured Milk, Curd, Sweet Lassi, Kalakand, Malai laddu etc for which the major ingredient is milk. The products are available in standard size packing, thus the orders are taken in multiples of the standard size. The high perishability of dairy products creates specific inventory management challenges for dairy manufacturers. Given the agreements between the farmers and the dairy, there is a continuous flow of raw milk supply coming into the dairy value chain. However, the demand side is more uncertain due to seasonal fluctuations, competitive activity and retailer price pressures. Cold storage constraints make it necessary to carefully plan for the receipt and quick usage of raw milk and finished products. Accordingly, there is constant pressure to keep inventory levels to a minimum. Improper sales forecasting can cause products kept in stock to over-run their shelf-lives and, therefore, to lose value. Since the quality of incoming raw materials may vary considerably with respect to fat content and solids, dairies must be able to adjust their manufacturing ratios in real-time to account for these fluctuations. There is another important factor of the time window that the products can be kept after production. As most of the dairy products have a shelf life of 2 to 3 days with the exception of few, the time window plays a crucial role in determining the level of inventory. The demand is satisfied by adequate inventory levels and shortages are avoided as it directly influences the reputation of the firm. Thus trade-offs are necessary so that inventory is optimally allocated as well as no backlogs are allowed. Product mix, packaging materials with longer lead times, and even product content must be balanced in order to optimize the utilization of available resources, model the cost effects of changes, and, ultimately, to maximize profitability. The mathematical model and notations employed in the current paper are taken from the work of same author, Radhika et al [15]. The subsequent sections give the details of the problem considered and the solution obtained using and TLBO. 3.
Table 2 production rates for dairy industry Products Product Code Skimmer 1 (Sk1) White Butter J1 120 Kalakand J2 120 Panner J3 100 Doodpeda J4 120 Basundi J5 40 MalaiLaddu J6 120 Lasssy J7 50 Butter Milk J8 50 SF Milk J9 90 B Ghee J10 60 Milk Cake J11 70 Curd J12 120
4.
Resources Skimmer 2 (Sk2) 120 50 100 90 100 100 70 90 70 90 50 90
23 490 370 390
24 80 590 10
960 210 340
470 600 240
Skimmer 3 (Sk3) 90 70 90 70 130 130 110 130 70 100 100 120
Results
Comparing the values obtained with the quantities being produced currently, it can be concluded that the percentage of unfilled demands is very low and at the same time maintaining very less inventory levels. The values in Table 3 infer that the TLBO methodology could successfully reduce the Ending Inventory (EI) levels which is of major concern to the dairy industry. Table 3 performance measures EI (units/hr) 41.61
RNM (units/hr) 91.78
BSS (units/hr) 16.51
Table 4 suggests that the master schedules generated by the TLBO method are in close approximation with the input data ie, gross requirements. Fig. 1 depicts the period wise production for the product J5. Fig. 2 describes the period wise utilization of resources for product J5. Table 3 comparison of total quantities produced for each product Product Total Obtained from MPS TLBO Total Gross Requirement (units) (units) J1 10805 11150 J2 12575 12430 J3 10935 11050 J4 11615 11860 J5 13790 14020 J6 9180 9050 J7 13425 13380 J8 13550 13680 J9 12390 12270 J10 12725 12800 J11 11435 11540 J12 11525 11420
Inputs of the problem considered
The scenario of dairy industry considered for the present work is with a planning horizon of twenty four periods, twelve products and three production resources (which can be work centres, production lines or cells, machines for instance). It considered 100 units to be kept as safety inventory (safety stock) at the end of each period. Table 1 describes the different initial inventory quantity for each product and also presents the period wise gross requirements for each product. Table 2 gives 861
ISBN: 978-93-86256-27-0
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6 International & 27 All India Manufacturing Technology, Design and Research Conference (AIMTDR–2016) College of Engineering, Pune, Maharashtra, INDIA
optimizing the raw material inputs for the production of different products under study.
Quantity
J5 1000 920 910 940 880 900 800 800810 725 800 690 680 660 640 620 700 600 450 425 500 370 360 400 310 270 260 220 300 210 200 100 40 100 0
Acknowledgment The authors sincerely thank M/s Sangam Dairy Industries Limited, Vadlamudi for permitting us to visit their plants, to have an insight into practical production scheduling systems and for the data provided. References [1]
1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Periods [2]
Fig. 1. Periodwise production for product J5
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[6] Fig. 2. Periodwise utilization of resources for product J5
5.
Conclusion
[7]
Manufacturing industry is always a challenging area because of the unpredictable uncertainties and obstacles that might occur anytime throughout the operation. To maintain a profitable growth, a company must develop different approaches to reduce its operational cost. These approaches raise the demand for new and evolved management systems. Production scheduling system is one of the management tools that is widely used in manufacturing industries proving its capabilities and effectiveness through many success stories. In the present work, an evolutionary based meta-heuristic approach namely, Teaching Learning Based Optimization algorithm is used in optimization of parameters of a Master Production Schedule. The implementation of the proposed production scheduling system shows positive and encouraged improvement based on the data collected and is compared with the data before the implementation, where significant amount of time is saved by using the scheduling method in managing flow of processes and orders. The results obtained were found to be in line with the currently employed master schedules in the company. The same methodology could be applied to the rest of the products too. The output from the tested evolutionary methodology, TLBO is found to be in close approximation with the gross requirements, suggesting that the proposed method could be used for
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[9] [10]
[11]
[12]
[13]
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James A. Hill, William L. Berry, G. Keong Leong and David A. Schilling (2000). Master production scheduling in capacitated sequence-dependent process industries, International Journal of Production Research, Vol.38, No.18, 4743- 47. Sonklin,A., Somboonwiwat,T.: Schedule Creation of Molded Plastic Production for Parallel Machinery Identical under Different Production Ratio. Presented at National Operations Research Conference, Thailand, pp89-96 (2009). Ray Venkataraman and Jay Nathan (1999). Master Production Scheduling for a Process Industry Environment. International Journal of Operations and Production Management 14(10), 44-53. Hosseinpour H., Niknam T., Taheri SI (2011). A modified TLBO algorithm for placement of AVRs considering DGs. 26th International Power System Conference, 31st October–2nd November, Tehran, Iran. Satapathy SC, Naik A. (2011). Data clustering based on teaching– learning-based optimization. Swarm, evolutionary, and memetic computing, lecture notes in computer science. Springer, Berlin, pp 148– 156, 7077/2011. Satapathy, S. C., Naik, A., and Parvathi, K. (2012). 0-1 integer programming for generation maintenance scheduling in power systems based on teaching learning based optimization (TLBO). In Contemporary Computing (pp. 53-63). Springer Berlin Heidelberg. Rao, R., and Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4). Krishnanand KR, Panigrahi BK, Rout PK, Mohapatra A (2011). Application of multi-objective teaching–learning-based algorithm to an econ omic load dispatch problem with incommensurable objectives. Swarm, evolutionary, and memetic computing, lecture notes in computer science. Springer, Berlin, pp 697–705. Togan, V. (2012). Design of planar steel frames using teaching–learning based optimization. Eng Struct 34:225 Pawar, P. J., and Rao, R. V. (2012). Parameter optimization of machining processes using teaching–learning-based optimization algorithm. The International Journal of Advanced Manufacturing Technology, 1-12. Rao, R.V., and Kalyankar, V.D. (2010). Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes, DOI:10.1080/10426914.2011.602792. Roy, P. K. (2013). Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint. International Journal of Electrical Power & Energy Systems, 53, 10-19. Kumar Roy, P., Sur, A., and Pradhan, D. K. (2013). Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization. Engineering Applications of Artificial Intelligence, 26(10), 2516-2524. Keesari, H. S., and Rao, R. V. (2013). Optimization of job shop scheduling problems using teaching-learning-based optimization algorithm. OPSEARCH, 1-17. Radhika, S., Rao, C.S.: (2014) A New Multi-Objective Optimization of Master Production Scheduling Problems Using Differential Evolution. International Journal of Applied Science and Engineering 12(1), 75–86.
