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An Improved Heuristic for Multilevel Lot Sizing in Material Requirements Planning
Author(s) -
Coleman B. Jay,
McKnew Mark A.
Publication year - 1991
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1991.tb01267.x
Subject(s) - sizing , heuristics , computer science , heuristic , consistency (knowledge bases) , mathematical optimization , material requirements planning , operations research , industrial engineering , mathematics , artificial intelligence , production (economics) , engineering , art , economics , visual arts , macroeconomics , operating system
Improvement on existing techniques for multilevel lot sizing in material requirements planning was sought by developing a theoretical and mathematical basis for the problem. Such a foundation was employed to design a heuristic which was superior in both cost performance and computational efficiency, and provided consistent results regardless of designated environmental factors. This represents a significant advantage over other algorithms tested here and within the literature, in that their performances are dependent upon various lot‐sizing environmental factors. The heuristic is based on the technique for order placement and sizing (TOPS), a single‐item technique, and utilizes a mechanism for linking interdependent lot‐sizing decisions. The inputs are applied in a sequential manner to constitute sequential TOPS with incremental look‐down (STIL). A two‐phase experimental design was employed to evaluate the performance of STIL against optimality and other heuristics. Results from the initial phase of simplified problems revealed practical indifference from optimality for the new algorithm. Phase 2 emphasized the relative strength and consistency of the proposed heuristic versus all other rules tested over more difficult examples. The final stage also indentified environments in which STIL performed particularly well. These were more descriptive of common industrial settings than were cases in which existing methods have been shown to be effective.