Browsing by Author "Gunalay, Yavuz"

Now showing 1 - 2 of 2

Citation - WoS: 27
Citation - Scopus: 30
Customer order scheduling problem: a comparative metaheuristics study
(Springer London Ltd, 2008) Hazir, Oncue; Gunalay, Yavuz; Erel, Erdal; 56488; 3019; 1986
The customer order scheduling problem (COSP) is defined as to determine the sequence of tasks to satisfy the demand of customers who order several types of products produced on a single machine. A setup is required whenever a product type is launched. The objective of the scheduling problem is to minimize the average customer order flow time. Since the customer order scheduling problem is known to be strongly NP-hard, we solve it using four major metaheuristics and compare the performance of these heuristics, namely, simulated annealing, genetic algorithms, tabu search, and ant colony optimization. These are selected to represent various characteristics of metaheuristics: nature-inspired vs. artificially created, population-based vs. local search, etc. A set of problems is generated to compare the solution quality and computational efforts of these heuristics. Results of the experimentation show that tabu search and ant colony perform better for large problems whereas simulated annealing performs best in small-size problems. Some conclusions are also drawn on the interactions between various problem parameters and the performance of the heuristics.
Citation - WoS: 60
Citation - Scopus: 63
Robust Optimization Models for the Discrete Time/Cost Trade-Off Problem
(Elsevier Science Bv, 2011) Hazir, Oncu; Erel, Erdal; Gunalay, Yavuz
Developing models and algorithms to generate robust project schedules that are less sensitive to disturbances are essential in today's highly competitive uncertain project environments. This paper addresses robust scheduling in project environments; specifically, we address the discrete time/cost trade-off problem (DTCTP). We formulate the robust DTCTP with three alternative optimization models in which interval uncertainty is assumed for the unknown cost parameters. We develop exact and heuristic algorithms to solve these robust optimization models. Furthermore, we compare the schedules that have been generated with these models on the basis of schedule robustness. (C) 2010 Elsevier B.V. All rights reserved.