Some of my earlier published papers

Some of my earlier published papers

Below is a list of my papers which have been published in referred journals or as technical paper.  All of the work was done by me during my post graduate studies at Western Michigan University, Kalamazoo, Michigan, USA.  I was there since 1987 to 1991.

My research projects included:

  • Paper Recycling
  • Paper Color modeling and prediction
  • Simulation Modeling and Analysis of a Just In Time production system

Based on my research, I was awarded All University Graduate Creative and Research Scholar award by the University and was given a Award Citation by the University President in a Award Ceremony.

 

Effect of Recycling on the Physical Properties of Specific Fibers and Their Networks,”

by John F. Bobalek and Mayank Chaturvedi. In Proceedings, 1988 TAPPI Pulping Conference, p. 183-187.

 

 

Bobalek, John F., and Mayank Chaturvedi. 1989.

“The Effects of Recycling on the Physical Properties of Handsheets with Respect to Specific Wood Species.”

Tappi Journal June: 123- 125.

 

http://imisrise.tappi.org/TAPPI/Products/89/JUN/89JUN123.aspx

 

 

PREDICTION OF PAPER COLOR: A PROCESS SIMULATION APPROACH

WMU Masters Thesis 890

http://scholarworks.wmich.edu/masters_theses/890/

 

 

PREDICTION OF PAPER COLOR: A PROCESS SIMULATION APPROACH

IPST Technical Paper 469

https://smartech.gatech.edu/bitstream/handle/1853/2105/tps-469.pdf

 

 

PREDICTION OF PAPER COLOR: A PROCESS SIMULATION APPROACH

IPST Annual Research Review

 

https://smartech.gatech.edu/bitstream/handle/1853/179/a31667.pdf

 

 

Simulation modelling and analysis of a JIT production system

Mayank Chaturvedi, Damodar Y Golhar
1992/1/1
Production Planning & Control
Volume 3 Issue 1 Pages 81-92
Taylor & Francis Group
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Hierarchical Planning: Integration of Strategy, Planning, Scheduling, and Execution

Hierarchical Planning: Integration of Strategy, Planning, Scheduling, and Execution

In Manufacturing environments, there are hierarchical levels of planning and analysis.

  • Strategic – Tactical – Operational
  • Long Term – Medium Term – Short Term
  • Corporate – Business – Functional
  • Aggregate Plans – Detailed Schedules – Execution Results
  • Time Buckets – Gantt Charts – Plan Vs Actuals
  • Forecasts – Plans – Schedules – Results
  • Strategy – Planning – Execution
  • Time Series – Optimization-Simulation-Statistics
  • Structural – Cyclical – Sequential

 

slide_18

Beyond this, there are other analytical approaches:

  • Industry Analysis,
  • Scenario Planning,
  • Environmental Scanning,
  • Sectorial Analysis
  • Macro-Economics

 

From Integration of multi-scale planning and scheduling problems

A supply chain may be defined as an integrated process wherein various entities work together in an effort to meet the objectives of each entity as well as the common objectives of the overall supply chain. It is theoretically possible and preferable to build mathematical models for entire supply chains including all interacting strategic and operational decisions throughout the supply chain. Such monolithic models will not be consistent with the nature of the managerial decision process or practical due to computational complexity of models, data and solution techniques. Mathematical programming is most commonly used to formulate planning and scheduling problems within the process industry. The problems are combinatorial in nature which makes them very difficult to solve and it is vital to develop efficient modelling strategies, mathematical formulations and solutions methods. One of the major difficulties in building mathematical programming models is to keep the size within reasonable limits without sacrificing accuracy. To solve full-scale real-world planning and scheduling problems efficiently, simplification, approximation or aggregation strategies are most often necessary (Grunow et al., 2002, Engell et al., 2001).

It is widely recognized that the complex problem of what to produce and where and how to produce it is best considered through an integrated, hierarchical approach which also acknowledges typical corporate structures and business processes (Shah, 1999). Production planning and scheduling in a typical enterprise involves managers at various echelons within the organization and the decisions that need to be made differ by scope and time horizon and the underlying input information differs by its degree of certainty and aggregation. The decisions also need to be made with different timing and frequency and according to the correct sequence which even further makes the case for an integrated hierarchical approach.

The literature often describes problems solved individually but less often the integration of different problems or the integration of different detail levels of the same problems. An example of an integrated strategic and operational planning problem is described by Kallrath (2002) and an investigation on the integration of long-term, mid-term and short-term planning operations through a common data model is reported by Das et al. (2000). Some typical economical benefits of integrated decision making are listed by Shobrys and White (2002) who conclude that the major challenges in integrating planning, scheduling and control systems are involved in issues like changing human and organizational behavior rather than technical issues. The general conclusion made in the literature is that the integration of decisions with synchronized models is desirable but at the same time it is very difficult to solve such models efficiently.

 

Key Sources of Research:

Bodington, Charles E., and Thomas E. Baker.

“A history of mathematical programming in the petroleum industry.”

Interfaces 20.4 (1990): 117-127.

 

Baker, Thomas E., and Leon S. Lasdon.

“Successive linear programming at Exxon.”

Management science 31.3 (1985): 264-274.

 

Baker, Thomas E.

“Petro-chemical industry.”

Encyclopedia of Operations Research and Management Science. Springer US, 2001. 612-614.

 

Baker, Thomas E., and Donald E. Shobrys.

“The integration of planning, scheduling and control.”

Natl. Pet. Refiners Assoc.,(Tech. Pap.);(United States) 200.CONF-8510288- (1985).

 

Baker, Thomas E.

“A hierarchical/relational approach to modeling.”

Computer Science in Economics and Management 3.1 (1990): 63-80.

 

Jones, Chris, and Thomas E. Baker.

“MIMI/G: A graphical environment for mathematical programming and modeling.”

Interfaces 26.3 (1996): 90-106.

 

 

Cleaves, Gerard W., and Thomas E. Baker.

“Chesapeake R&D sponsor groups.”

Interfaces 20.6 (1990): 83-87.

 

A RELATIONAL MODELING SYSTEM FOR LINEAR AND INTEGER PROGRAMMING

A.ATAMTU RK,E.L.JOHNSON,J.T.LINDEROTH,andM.W.P.SAVELSBERGH

http://www.ieor.berkeley.edu/~atamturk/pubs/_published/or48-2000.pdf

 

A bibliography for the development of an intelligent mathematical programming system

Harvey J. Greenberg

http://www.math.ucdenver.edu/~hgreenbe/papers/Greenberg96impsBib.pdf

 

Supply Chain Planning Optimization 

http://www74.homepage.villanova.edu/sohail.chaudhry/MBA8503/Readings/Supply%20Chain/AMR%20Supply%20Chain.pdf

 

MIMI Brings OR Tools Together

http://www.eudoxus.com/mp-in-action/software/mpac9712

 

INTEGRATION OF PRODUCTION PLANNING AND SCHEDULING: OVERVIEW, CHALLENGES AND OPPORTUNITIES

Christos T. Maravelias and Charles Sung

https://www.researchgate.net/profile/Christos_Maravelias2/publication/220341953_Integration_of_production_planning_and_scheduling_Overview_Challenges_and_Opportunities/links/004635251c8f0cd8fd000000.pdf

 

HIERARCHICAL INTEGRATION OF PRODUCTION PLANNING AND SCHEDULING

Arnoldo C. Hax and Harlan C. Meal

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.2470&rep=rep1&type=pdf

 

Discrete Optimization Methods and their Role in the Integration of Planning and Scheduling

Ignacio E. Grossmann , Susara A. van den Heever and Iiro Harjunkoski

March 1, 2001

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.1826&rep=rep1&type=pdf

 

Planning and scheduling models for refinery operations

J.M. Pinto , M. Joly , L.F.L. Moro

https://www.researchgate.net/profile/Marcel_Joly2/publication/242817901_Planning_and_Scheduling_Models_for_Refinery_Operations/links/5686be3508ae1e63f1f5aa45.pdf

 

MATHEMATICAL PROGRAMMING MODELS AND METHODS FOR PRODUCTION PLANNING AND SCHEDULING

by Jeremy F. Shapiro

January, 1989

http://dspace.mit.edu/bitstream/handle/1721.1/5082/OR-191-89-24512977.pdf?sequence

 

Supporting supply chain planning and scheduling decisions in the oil and chemical industry

Winston Lasschuit, Nort Thijssen

https://www.researchgate.net/profile/Nort_Thijssen/publication/222406596_Supporting_Supply_Chain_Planning_and_Scheduling_Decisions_in_the_Oil_and_Chemical_Industry/links/5516aa990cf2f7d80a383c39.pdf

 

Planning and Scheduling in Supply Chains: An Overview of Issues in Practice

Stephan Kreipl • Michael Pinedo

https://www.researchgate.net/profile/Michael_Pinedo/publication/240271728_Planning_and_Scheduling_in_Supply_Chains_An_Overview_of_Issues_in_Practice/links/0c96052de86f847e9b000000.pdf

 

Hierarchical approach for production planning and scheduling under uncertainty

Dan Wu, Marianthi Ierapetritou

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.169.9977&rep=rep1&type=pdf

 

Supply chain management and advanced planning––basics, overview and challenges

Hartmut Stadtler

https://www.researchgate.net/profile/Hartmut_Stadtler/publication/223817098_Supply_chain_management_and_advanced_planning_-_Basics_overview_and_challenges/links/00b7d53327c5cd6d44000000.pdf

 

Integration of multi-scale planning and scheduling problems

Hlynur Stefanssona, Pall Jenssonb, Nilay Shah

 

https://www.researchgate.net/profile/Hlynur_Stefansson/publication/251469193_Integration_of_multi-scale_planning_and_scheduling_problems/links/00b7d53aa958d6f7ba000000.pdf

 

Bitran, Gabriel R., and Arnold C. Hax.

“On the design of hierarchical production planning systems.”

Decision Sciences 8.1 (1977): 28-55.

 

Bitran, Gabriel R., Elizabeth A. Haas, and Arnoldo C. Hax.

“Hierarchical production planning: A single stage system.”

Operations Research 29.4 (1981): 717-743.

 

Bitran, Gabriel R., Elizabeth A. Haas, and Arnoldo C. Hax.

“Hierarchical production planning: A two-stage system.”

Operations Research 30.2 (1982): 232-251.

 

Bitran, Gabriel R., and Devanath Tirupati.

“Hierarchical production planning.”

Handbooks in operations research and management science 4 (1993): 523-568.

 

Axsäter, Sven, and Henrik Jönsson.

“Aggregation and disaggregation in hierarchical production planning.”

European Journal of Operational Research 17.3 (1984): 338-350.

 

Gfrerer, Helmut, and Günther Zäpfel.

“Hierarchical model for production planning in the case of uncertain demand.”

European Journal of Operational Research 86.1 (1995): 142-161.

 

Hax, Arnoldo C., and Gabriel R. Bitran.

“Hierarchical planning systems—a production application.”

Disaggregation. Springer Netherlands, 1979. 63-93.

 

THE CORPORATE STRATEGIC PLANNING PROCESS

Arnoldo C.:,Hax and Nicolas S. Majluft

1983

 

http://dspace.mit.edu/bitstream/handle/1721.1/2031/SWP-1396-09362356.pdf?..

 

A hierarchical decision support system for production planning (with case study)

 

Linet Ozdamar *, M. Ali Bozyel, S. Ilker Birbi

https://www.researchgate.net/profile/Linet_Ozdamar/publication/261592155_Hierarchical_planning_approach_for_a_production-distribution_system/links/55f3fdbf08ae63926cf26516.pdf

Gelders, Ludo F., and Luk N. Van Wassenhove.

 

“Hierarchical integration in production planning: Theory and practice.”

Journal of Operations Management 3.1 (1982): 27-35.

Gabbay, Henry.

 

A Hierarchical Approach to Production Planning.

No. TR-120. MASSACHUSETTS INST OF TECH CAMBRIDGE OPERATIONS RESEARCH CENTER, 1975.

 

Axsäter, Sven.

“Technical note—On the feasibility of aggregate production plans.”

Operations Research 34.5 (1986): 796-800.

 

Gabbay, Henry.

“Optimal aggregation and disaggregation in hierarchical planning.”

Disaggregation. Springer Netherlands, 1979. 95-106.

 

Fleischmann, Bernhard, and Herbert Meyr.

“Planning hierarchy, modeling and advanced planning systems.”

Handbooks in operations research and management science 11 (2003): 455-523.

 

Liberatore, Matthew J., and Tan Miller.

“A hierarchical production planning system.”

Interfaces 15.4 (1985): 1-11.

 

Nam, Sang-jin, and Rasaratnam Logendran.

“Aggregate production planning—a survey of models and methodologies.”

European Journal of Operational Research 61.3 (1992): 255-272.

 

Hierarchical mathematical programming for operational planning in a process industry 

W.G.M.M. Rutten

https://pure.tue.nl/ws/files/1423171/394701.pdf

 

Saad, Germaine H.

“Hierarchical production-planning systems: extensions and modifications.”

Journal of the Operational Research Society 41.7 (1990): 609-624.

 

Omar, Mohamed K., and S. C. Teo.

“Hierarchical production planning and scheduling in a multi-product, batch process environment.”

International Journal of Production Research 45.5 (2007): 1029-1047.

 

Kistner, Klaus-Peter, and Marion Steven.

“Applications of operations research in hierarchical production planning.”

Modern Production Concepts. Springer Berlin Heidelberg, 1991. 97-113.

 

Shobrys, Donald E., and Douglas C. White.

“Planning, scheduling and control systems: why cannot they work together.”

Computers & chemical engineering 26.2 (2002): 149-160.

 

Stadtler, Hartmut.

“Hierarchical production planning: Tuning aggregate planning with sequencing and scheduling.”

Multi-stage production planning and inventory control. Springer Berlin Heidelberg, 1986. 197-226.

 

McKay, Kenneth N., Frank R. Safayeni, and John A. Buzacott.

“A review of hierarchical production planning and its applicability for modern manufacturing.”

Production Planning & Control 6.5 (1995): 384-394.

 

Combined Strategic and Operational Planning – An MILP Success Story in Chemical Industry

Josef Kallrath

 

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.506.4194&rep=rep1&type=pdf

 

Das, B. P., et al.

“An investigation on integration of aggregate production planning, master production scheduling and short-term production scheudling of batch process operations through a common data model.”

Computers & Chemical Engineering 24.2 (2000): 1625-1631.