dc.contributor.advisor | Yaman, Hande | |
dc.contributor.author | Koca, Esra | |
dc.date.accessioned | 2016-05-02T13:53:59Z | |
dc.date.available | 2016-05-02T13:53:59Z | |
dc.date.copyright | 2015-07 | |
dc.date.issued | 2015-07 | |
dc.date.submitted | 11-07-2015 | |
dc.identifier.uri | http://hdl.handle.net/11693/29035 | |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (leaves 100-110). | en_US |
dc.description | Thesis (Ph. D.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2015. | en_US |
dc.description.abstract | In this study, we consider di erent variations of the lot sizing problem encountered
in many real life production, procurement and transportation systems.
First, we consider the deterministic lot sizing problem with piecewise concave
production cost functions. A piecewise concave function can represent quantity
discounts, subcontracting, overloading, minimum order quantities, and capacities.
Computational complexity of this problem was an open question in the literature.
We develop a dynamic programming (DP) algorithm to solve the problem and
show that the problem is polynomially solvable when number of breakpoints of
the production cost function is xed and the breakpoints are time-invariant. We
observe that the time complexity of our algorithm is as good as the complexity
of existing algorithms in the literature for the special cases with capacities, minimum
order quantities, and subcontracting. Our algorithm performs quite well for
small and medium sized instances. For larger instances, we propose a DP based
heuristic to nd a good quality solution in reasonable time.
Next, we consider the stochastic lot sizing problem with controllable processing
times where processing times can be reduced in return for extra compression
cost. We assume that the compression cost function is a convex function in
order to re
ect the increasing marginal cost of larger reductions in processing
times. We formulate the problem as a second-order cone mixed integer program,
strengthen the formulation and solve it by a commercial solver. Moreover, we
obtain some convex hull and computational complexity results. We conduct an
extensive computational study to see the e ect of controllable processing times in
solution quality and observe that even with small reductions in processing times,
it is possible to obtain a less costly production plan.
As a nal problem, we study the multistage stochastic lot sizing problem with nervousness considerations and controllable processing times. System nervousness
is one of the main problems of dynamic solution strategies developed for
stochastic lot sizing problems. We formulate the problem so that the nervousness
of the system is restricted by some additional constraints and parameters. Mixing
and continuous mixing set structures are observed as relaxations of our formulation.
We develop valid inequalities for the problem based on these structures and
computationally test these inequalities. | en_US |
dc.description.statementofresponsibility | by Esra Koca. | en_US |
dc.format.extent | xii, 111 leaves : charts. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Lot sizing | en_US |
dc.subject | Piecewise concave cost function | en_US |
dc.subject | Convex cost function | en_US |
dc.subject | Controllable processing times | en_US |
dc.subject | Nervousness | en_US |
dc.title | Lot sizing with nonlinear production cost functions | en_US |
dc.title.alternative | Doğrusal olmayan üretim maliyeti fonksiyonları olan kafile büyüklüğü problemi | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Industrial Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | Ph.D. | en_US |
dc.identifier.itemid | B150948 | |
dc.embargo.release | 2017-07-07 | |