Browsing by Author "Uzsoy, R."

Now showing 1 - 5 of 5

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A chance constraint based multi-item production planning model using simulation optimization
(IEEE, 2016) Albey, Erinç; Uzsoy, R.; Kempf, K. G.; Industrial Engineering; ALBEY, Erinç
We consider a single stage multi-item production-inventory system under stochastic demand. We had previously proposed a production planning model integrating ideas from forecast evolution and inventory theory to plan work releases into a production facility in the face of stochastic demand. However, this model is tractable only if the capacity allocations are exogenous. This paper determines the capacity allocated to each product in each period using a genetic algorithm. Computational experiments reveal that the proposed algorithm outperforms the previous approach in both total cost and service level.
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Load dependent lead time modelling: a robust optimization approach
(IEEE, 2018-01-04) Albey, Erinç; Yanıkoğlu, İhsan; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç; YANIKOĞLU, Ihsan
Although production planning models using nonlinear CFs have shown promising results for semiconductor wafer fabrication facilities, the lack of an effective methodology for estimating the CFs is a significant obstacle to their implementation. Current practice focuses on developing point estimates using least-squares regression approaches. This paper compares the performance of a production planning model using a multi-dimensional CF and its robust counterpart under several experimental settings. As expected, as the level of uncertainty is increased, the resulting production plan deviates from the optimal solution of the deterministic model. On the other hand, production plans found using the robust counterpart are less vulnerable to parameter estimation errors.
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Multi-dimensional clearing functions for aggregate capacity modelling in multi-stage production systems
(Informa, 2017) Albey, Erinç; Bilge, Ü.; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç
Nonlinear clearing functions have been proposed in the literature as metamodels to represent the behaviour of production resources that can be embedded in optimisation models for production planning. However, most clearing functions tested to date use a single-state variable to represent aggregate system workload over all products, which performs poorly when product mix affects system throughput. Clearing functions using multiple-state variables have shown promise, but require significant computational effort to fit the functions and to solve the resulting optimisation models. This paper examines the impact of aggregation in state variables on solution time and quality in multi-item multi-stage production systems with differing degrees of manufacturing flexibility. We propose multi-dimensional clearing functions using alternative aggregations of state variables, and evaluate their performance in computational experiments. We find that at low utilisation, aggregation of state variables has little effect on system performance; multi-dimensional clearing functions outperform single-dimensional ones in general; and increasing manufacturing flexibility allows the use of aggregate clearing functions with little loss of solution quality.
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A robust optimization approach for production planning under exogenous planned lead times
(IEEE, 2019) Albey, Erinç; Yanıkoğlu, İhsan; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç; YANIKOĞLU, Ihsan
Many production planning models applied in semiconductor manufacturing represent lead times as fixed exogenous parameters. However, in reality, lead times must be treated as realizations of released lots' cycle times, which are in fact random variables. In this paper, we present a distributionally robust release planning model that allows planned lead time probability estimates to vary over a specified ambiguity set. We evaluate the performance of non-robust and robust approaches using a simulation model of a scaled-down wafer fabrication facility. We examine the effect of increasing uncertainty in the estimated lead time parameters on the objective function value and compare the worst-case, average optimality, and feasibility of the two approaches. The numerical results show that the average objective function value of the robust solutions are better than that of the nominal solution by a margin of almost 20% in the scenario with the highest uncertainty level.
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Rounding heuristics for multiple product dynamic lot-sizing in the presence of queueing behavior
(Elsevier, 2018-12) Kang, Y.; Albey, Erinç; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç
We present heuristics for solving a difficult nonlinear integer programming (NIP) model arising from a multi-item single machine dynamic lot-sizing problem. The heuristic obtains a local optimum for the continuous relaxation of the NIP model and rounds the resulting fractional solution to a feasible integer solution by solving a series of shortest path problems. We also implement two benchmarks: a version of the well-known Feasibility Pump heuristic and the Surrogate Method developed for stochastic discrete optimization problems. Computational experiments reveal that our shortest path based rounding procedure finds better production plans than the previously developed myopic heuristic and the benchmarks.