ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING
Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 06, Issue 06, June 2021 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 25 A STUDY ON MULTI TASK AND MULTISTAGE PRODUCTION PLANNING
AND SCHEDULING FOR PROCESS INDUSTRIES
1Brajesh Kumar, 2Ram Kumar Vishwakarma
1Research Scholar, Dept of Mechanical Engineering, SVN University, Sagar (M.P.)
2Assistant Professor, Dept of Mechanical Engineering, SVN University, Sagar (M.P.)
Abstract:- In this research, we consider the production environment that produces intermediate products, by-products and finished goods at a production stage. By-products are recycled to recover reusable raw materials. Inputs to a production stage are raw materials, intermediate products and reusable raw materials. Complexities in the production process arise due to the desired coordination of various production stages and the recycling process.
We consider flexible production resources where equipments are shared amongst products.
This often leads to conflict in the capacity requirements at an aggregate level and at the detailed scheduling level. The environment is characterized by dynamic and deterministic demands of finished goods over a finite planning horizon, high set-up times, transfer lot sizes and perish ability of products. The decisions in the problem are to determine the production quantities and inventory levels of products, aggregate capacity of the resources required and to derive detailed schedules at minimum cost.
1. INTRODUCTION
Today’s business environment has become highly competitive. Manufacturing firms have started recognizing the importance of manufacturing strategy in their businesses.
Firms are increasingly facing external pressures to improve customer response time, increase product offerings, manage demand variability and be price competitive. In order to meet these challenges, firms often find themselves in situations with critical shortages of some products and excess inventories of other products.
This raises the issue of finding the right balance between cutting costs and maintaining customer responsiveness. Firms are facing internal pressures to increase profitability through improvements in manufacturing efficiency and reductions in operational costs. There are several instances in industry where the above-mentioned changes in business environment have affected the profitability of firms. Harris Corporation, an electronics company based in U.S.A., increased its product range considerably and invested in flexible manufacturing resources in the early 1990s.
They had to provide competitive on- time delivery performance over a much greater product mix. Their inefficient handling of a large product variety resulted in late deliveries, lost sales and average losses of $75 million annually (Leachman et al., 1996). IBM faced record losses in 1993 in the manufacturing and distribution operations of its computer business due to high operational costs. They
could not handle the high demand variability of their products and reported high inventory costs and stock outs (Feigin et al., 1996).
Fuel inventory costs have risen considerably in electric utility industries in U.S.A. in the last two decades as a result of electricity demand fluctuations (Chao et al., 1989). H & R Johnson, the largest tile manufacturer in India, had to increase its product variety in terms of size and design, in order to meet the demand of expanding construction market. This resulted in high inventory costs. Their customer response time increased considerably, resulting in loss of sales (Gupta, 1993).
Synpack, an Indian chemical manufacturing firm, increased its product portfolio in the mid 1990s. However, it could not handle the delivery commitments. The company’s market share reduced considerably and they incurred high inventory costs (Akthar, 2004). Indian manufacturing firms are facing stiff global competition, especially from China. Today, China has become the world’s largest manufacturing base.
China’s capability to offer a large variety of products at low prices, and its fast responsiveness to the market has severely affected the sales of many Indian manufacturing firms. Indian companies are now forced to be competitive on prices, increase product offerings, and have shorter lead times in production.
ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING
Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 06, Issue 06, June 2021 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 26 Figure 1: Multi-level Product Structure and Concept of Stage
Figure 2: Inputs and Outputs ofa Production Process 2. EXPERIMENT DESIGN OF SUB
PROBLEM
The procedures described in the heuristic solution of sub-problem 2 are applied to
benchmark problems in the literature on flow shop scheduling (Taillard, 1993). The parameters used in the experiments are shown in the table 1 below.
Table 1: Parameters in Experiment Design of Sub-Problem 2 Number of jobsn n = 5, 10, 20, 50, 80, 100
Number of machinesm m= 5, 10, 15, 20
Number of instances I of test problems I = 50 Processing time of a job on a machine
in each instance.
Random number uniformly distribution
between1 and 99.
Number of tabu iterations 50, 60, 70, 80
Tabu tenure Random number between5 and 10
Figure 3: Average% Deviation of Heuristic Solution from Lower Bound: 5 Machines
ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING
Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 06, Issue 06, June 2021 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 27 Figure 4: Average of Deviation of Heuristic Solution from Lower Bound: 10
Machines In this research, we address the decisions of complex production planning and scheduling problems existing in discrete parts manufacturing industries and process industries. We consider a multi-stage, multi- product, multi-machine batch-processing environment.
3. CONCLUSION
We model the production planning and scheduling decisions through sequence of hierarchical models. First, we develop a mixed integer program (MIP) for production planning decisions. The objective of the production- planning model is to minimize inventory costs and setup costs of intermediate products and finished goods, inventory costs of by-products and reusable raw materials, and cost of fresh raw material. In the next step, we develop an MIP for finished goods scheduling decisions. Then, we develop an MIP for intermediate products scheduling problem decisions. The objective of the scheduling problem is to minimize the absolute deviation of job completion times from a common due date.
REFERENCES
1. Abdul-Razaq, T. S. and C. N. Potts,
“Dynamic Programming in State-Space Relaxation for Single Machine Scheduling”, Jour nal of Operations Research Society, 39. 1988, 141-152.
2. Akthar, P. “ACCPAC Streamlines Synpack’s Manufacturing Processes”, Express Computer, India Express Newspapers Ltd., 2004.
3. Afentakis, P., B. Gavish and U. Karmarkar,
“Computationally Efficient Optimal Solutions to the Lot-Sizing Problem in Multi-Stage Systems”, Management Science, 30(2). 1984, 222-239.
4. Afentakis, P. and B. Gavish, “Optimal Lot- Sizing for Complex Product Structures”, Operations Research, 34(2). 1986, 237- 249.
5. Ahmadi, R. H. and U. Bagchi, “Improved Lower Bounds for Minimizing the Sum of Completion Times of n Jobs over m Machines in a Flow shop”, European Journal of Operational Research, 44. 1990, 331-336.
6. Bagchi, U., R. Sullivan and Y. Chang,
“Minimizing Mean Absolute Deviation of Completion Times about a Common Due Date”, Naval Research Logistics Quarterly, 33. 1986, 227-240.
7. Bagchi, U., Y. Chang and R. Sullivan,
“Minimizing Absolute and Squared Deviations of Completion Times with Different Earliness and Penalties and a Common Due Date”, Naval Research Logistics Quarterly, 34. 1987, 739-751.
8. Bagchi, U., R. Sullivan and Y. Chang,
“Minimizing Mean Squared Deviation of Completion Times About a Common Due Date”, Management Science, 33. 1987, 894-906.
9. Bahl, H. C., L. P. Ritzman and J. D. N.
Gupta, “Determination of Lot-Sizes and Resource Requirements”, Operations Research, 35. 1987, 329-345.
10. Baker, K. R. and G. D. Scudder,
“Sequencing with Earliness and Tardiness Penalties: A Review”, Operations Research, 38(1). 1990, 22-36.
11. Baker, T. E. and L. S. Lasdon, “Successive Linear Programming at Exxon”, Management Science, 31. 1985, 264-272.
12. Bector, C., Y. Gupta and M. Gupta,
“Determination of an Optimal Common Due Date and Optimal Sequence in a Single Machine Job Shop”, International Journal of Production Research, 26. 1988, 613-628.
13. Billington, P. J., J. O. McClain and L. J.
Thomas, “Mathematical Approaches to Capacity Constrained MRP Systems:
Review, Formulation and Problem Reduction”, Management Science, 29.
1983,1126-1141.
14. Bitran, G. R. and A. C. Hax, “On the Design of Hierarchical Production Planning Systems”, Decision Sciences, 8. 1977, 28- 55.
15. Bitran, G. R. and D. Tirupati, “Hierarchical Production Planning”, Chapter 10 in S. C.
Graves et al. (eds.), Handbooks in
ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING
Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 06, Issue 06, June 2021 IMPACT FACTOR: 7.98 (INTERNATIONAL JOURNAL) 28 Operations Research and Management
Science. Vol. 4, Elsevier Science, 1993, 523-566.
16. Bitran, G. R. and D. Tirupati, “Planning and Scheduling For Epitaxial Wafer Production Facilities”, Operations Research, 36(1). 1998a, 34-49.
17. Bitran, G. R. and D. Tirupati,
“Development and Implementation of a Scheduling System: An Application in a Wafer Fabrication Facility”, Operations Research, 36(3). 1998b, 377- 395.
18. Bitran, G. R., E. A Haas and A. C. Hax,
“Hierarchical Production Planning: A Single Stage System”, Operations Research, 29.
1981, 717-743.
19. Bitran, G. R., E. A Haas and A. C. Hax,
“Hierarchical Production Planning: A Two- Stage System”, Operations Research, 30.
1982, 232-251.
20. Blackburn, J. and R. Millen, “Improved Heuristics for Multi-Stage Requirements Planning”, Management Science, 28(1).
1982, 44-46.
21. Blomer, F. and H. O. Gunther, “Scheduling of a Multi-Product Batch Process in a Chemical Industry”, Computers in Industry, 36. 1998, 245-259.
22. Bowers, M. R. and J. P. Jarvis, “A Hierarchical Production Planning and Scheduling Model”, Decision Sciences, 23(1). 1992, 144-158.
23. Bradley, S.P, A. C. Hax and T. L. Magnanti,
“Integration of Strategic and Tactical Planning in the Aluminum Industry”, Chapter 6 in Applied Mathematical Programming, Edison Wesley, Reading, MA, 1977.
24. Chao, H., S. Chapel, C. Clark, P. Morris, M.
Sandling and R. Grimes, “EPRI Reduces Fuel Inventory Costs in the Electric Utility Industry”, Interfaces, 19(1). 1989, 48-67.
25. Cheng, T, “Optimal Due Date Determination and Sequencing of n Jobs on a Single Machine”, Journal of Operatio ns Research Society, 35. 1984, 433-437.
26. Cheng, T, “An Algorithm for the Due Date Determination and Sequencing Problem”, Computers and Operations Research, 14.
1987, 537-542.
27. Cheng, T.C.E. and H. G. Kahlbacher, “A Proof of the Longest-First Job Policy in One Machine Scheduling”, Naval Research Logistics Quarterly, 38. 1991, 715-720.
28. Conway, R.W., W.L. Maxwell and L.W.
Miller, Theory of Scheduling, Addison- Wesley, MA, 1967.
29. Chu, C., “A Branch and Bound Algorithm to Minimize Total Tardiness with Different Release Dates”, Naval Research Logistics Quarterly, 39. 1992, 265-283.
30. Chu, C. and M. C. Portmann, “Some New Efficient Methods to Solve n/1/ri/? Ti Scheduling Problem”, European Journal of Operation Research, 58. 1992, 404- 413.