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A Mixed-Integer Linear Programming (Milp) Model For Short-Range Production Scheduling Of Cement Quarry Operations

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  • SAFI UR REHMAN

    (Mining Engineering, NWFP University of Engineering & Technology, Pakistan)

  • MOHAMMAD WAQAR ALI ASAD

    (Mining Engineering, NWFP University of Engineering & Technology, Pakistan)

Abstract

A limestone quarry is the major source for supplying raw materials for cement manufacturing operations. Depending upon the available reserves, a quarry is divided into thousands of mineable blocks. Hence, raw materials inventory is identified in terms of a block model projecting the quantity and quality of critical chemical constituents desired in the cement manufacturing process. An individual block never satisfies the process quality constraints; therefore, the blending of various quarry blocks with few additives purchased from the market becomes a prerequisite. As each block is represented as an integer (0-1) variable, the objective of an optimal quarry production scheduling model issequential miningof these blocks such that the plant quantity and quality requirements are satisfied at the lowest possible cost. This paper presents a new mixed-integer linear programming (MILP) based blending optimization model accomplishing the defined objective as a short-range production planning tool. The benefits of the model are established through a case study of an existing cement manufacturing operation in the northern part of Pakistan, ensuring significant cost savings compared to schedules produced manually.

Suggested Citation

  • Safi Ur Rehman & Mohammad Waqar Ali Asad, 2010. "A Mixed-Integer Linear Programming (Milp) Model For Short-Range Production Scheduling Of Cement Quarry Operations," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 27(03), pages 315-333.
  • Handle: RePEc:wsi:apjorx:v:27:y:2010:i:03:n:s0217595910002727
    DOI: 10.1142/S0217595910002727
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    1. Simon van Norden & Huntley Schaller & ), 1995. "Regime Switching in Stock Market Returns," Econometrics 9502002, University Library of Munich, Germany.
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