Ihsan Sabuncuoglu

Simulation optimization: A comprehensive review on theory and applications

For several decades, simulation has been used as a descriptive tool by the operations research community in the modeling and analysis of a wide variety of complex real systems. With recent developments in simulation optimization and advances in computing technology, it now becomes feasible to use simulation as a prescriptive tool in decision support systems. In this paper, we present a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments. We classify the existing techniques according to problem characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). We discuss the major advantages and possible drawbacks of the different techniques. A comprehensive bibliography and future research directions are also provided in the paper.

Analysis of reactive scheduling problems in a job shop environment

Abstract In this paper, we study the reactive scheduling problems in a stochastic manufacturing environment. Specifically, we test the several scheduling policies under machine breakdowns in a classical job shop system. In addition, we measure the effect of system size and type of work allocation (uniform and bottleneck) on the system performance. The performance of the system is measured for the mean tardiness and makespan criteria. We also investigate a partial scheduling scheme under both deterministic and stochastic environments for several system configurations.

Assembly line balancing using genetic algorithms

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.

Job shop scheduling with beam search

Beam Search is a heuristic method for solving optimization problems. It is an adaptation of the branch and bound method in which only some nodes are evaluated in the search tree. At any level, only the promising nodes are kept for further branching and remaining nodes are pruned off permanently. In this paper, we develop a beam search based scheduling algorithm for the job shop problem. Both the makespan and mean tardiness are used as the performance measures. The proposed algorithm is also compared with other well known search methods and dispatching rules for a wide variety of problems. The results indicate that the beam search technique is a very competitive and promising tool which deserves further research in the scheduling literature.

Dynamic dispatching algorithm for scheduling machines and automated guided vehicles in a flexible manufacturing system

In this paper, an on-line dispatching algorithm is proposed for the FMS scheduling problem. The algorithm uses various priority schemes and relevant information concerning the load of the system and the status of jobs in the scheduling process. This information is organized into hierarchical levels. The scheduling decision process is hierarchical in the sense that different decision criteria are applied sequentially to identify the most appropriate part and the machine to be served. The algorithm schedules the jobs on a machine or an automated guided vehicle (AGV) one at a time as the scheduling decision is needed (or as the status of the system changes). Performance of the proposed algorithm is compared with several machine and AGV scheduling rules by using the mean flow-time and the mean tardiness criteria. Simulation results indicate that the proposed algorithm produces significant mean flow-time and mean tardiness improvements over existing scheduling rules for a variety of experimental conditions.

A STUDY OF SCHEDULING RULES OF FLEXIBLE MANUFACTURING SYSTEMS : A SIMULATION APPROACH

This study examines the effects of scheduling rules on the performance of flexible manufacturing systems (FMSs). Several machine and AGV scheduling rules are tested against the mean flowtime criterion. In general, scheduling rules are widely used in practice ranging from direct applications as a stand-alone scheduling scheme to indirect application as a part of complicated scheduling systems. In this paper, we compare the rules under various experimental conditions by using an FMS simulation model. Our objective is to measure sensitivity of the rules to changes in processing time distributions, various levels of breakdown rates, and types of AGV priority schemes. A comprehensive bibliography is also presented in the paper.

Balancing of U-type assembly systems using simulated annealing

The paper presents a new simulated annealing (SA)-based algorithm for the assembly line-balancing problem with a U-type configuration. The proposed algorithm employs an intelligent mechanism to search a large solution space. U-type assembly systems are becoming increasingly popular in todays modern production environments since they are more general than the traditional assembly systems. In these systems, tasks are to be allocated into stations by moving forward and backward through the precedence diagram in contrast to a typical forward move in the traditional assembly systems. The performance of the algorithm is measured by solving a large number of benchmark problems available in the literature. The results of the computational experiments indicate that the proposed SA-based algorithm performs quite effectively. It also yields the optimal solution for most problem instances. Future research directions and a comprehensive bibliography are also provided here.

A neural network model for scheduling problems

Abstract Artificial neural networks (ANNs) have been successfully applied to solve a variety of problems. This paper proposes a new neural network approach to solve the single machine mean tardiness scheduling problem and the minimum makespan job shop scheduling problem. The proposed network combines the characteristics of neural networks and algorithmic approaches. The performance of the network is compared with the existing scheduling algorithms under various experimental conditions. A comprehensive bibliography is also provided in the paper.

Reactive scheduling in a dynamic and stochastic FMS environment

In this paper, we study the reactive scheduling problems in a dynamic and stochastic manufacturing environment. Specifically, we develop a simulation-based scheduling system for flexible manufacturing systems. We also propose several reactive scheduling policies (i.e. when-to-schedule and how-to-schedule policies) and test their performances under various experimental conditions, processing time variations, and machine breakdowns. Moreover, we compare offline and online scheduling schemes in a dynamic manufacturing environment. The results of extensive simulation experiments indicate that the variable-time-response is better than the fixed-time-response. The full scheduling scheme generally performs better than the partial scheduling. Finally, the online scheduling is more robust to uncertainty and variations in processing times than the optimum-seeking offline scheduling. A comprehensive bibliography is also provided in the paper.

Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph

In this paper, the disassembly line balancing problem, which involves determining a line design in which used products are completely disassembled to obtain useable components in a cost-effective manner, is studied. Because of the growing demand for a cleaner environment, this problem has become an important issue in reverse manufacturing. In this study, two exact formulations are developed that utilize an AND/OR Graph (AOG) as the main input to ensure the feasibility of the precedence relations among the tasks. It is also shown that traditional task precedence diagrams can be derived from the AOG of a given product structure. This procedure leads to considerably better solutions of the traditional assembly line balancing problems; it may alter the approach taken by previous researchers in this area.