WSC 2005

WSC 2005 Final Abstracts

Simulation-Based Scheduling Track

Tuesday 8:30:00 AM 10:00:00 AM
Scheduling and Control

Chair: Kristin Thoney (NC State University)

Simulation Optimization Decision Support System for Ship Panel Shop Operations
Allen G. Greenwood, Travis W. Hill, Jeffery W. Miller, Clayton T. Walden, Sucharith Vanguri, Burak Eksioglu, and Pramod Jain (Mississippi State University)

Simulation is a powerful tool that is used to understand and analyze the effect of changes on real systems. However, developing and using simulation models requires high-level engineering skills. The objective of this research is to put state-of-the-art problem-solving technologies into the hands of decision makers, e.g. planners and supervisors. This paper presents a Decision Support System (DSS) that utilizes discrete-event simulation models and heuristic optimization, yet permits effective use without detailed knowledge of the methodologies. The system is developed for the Panel Shop at Northrop Grumman Ship Systems’ (NGSS) Pascagoula Operations; the shop is considered the bottleneck for the shipyard. This system focuses on two key opportunities for improvement: sequencing panel production and resource allocation among steps in the production processes. The DSS is designed to be reused in similar operations at other shipyards and portions of the DSS may be used to apply simulation optimization to most industries.

Simulation-Based Scheduling for Parcel Consolidation Terminals: A Comparison of Iterative Improvement and Simulated Annealing
Douglas L. McWilliams (Purdue University)

This research explores the application of a simulation-based scheduling algorithm to generate unload schedules for processing feeder trailers in a parcel consolidation terminal. The study compares the performance of iterative improvement and simulated annealing to produce quality schedules. The paper reports the results from a number of experimental test problems.

Performance Analysis of a Configured to Order Business with a Variable Product Configuration Recipe
Soumyadip Ghosh, Tom Ervolina, and Young M. Lee (IBM T.J. Watson Research Center) and Barun Gupta (IBM Integrated Supply Chain Group)

We study a complex Configured To Order (CTO) business operating under variable product configuration recipes and high component commonality within product Bills of Materials. In particular we conduct a series of simulation experiments to compare certain supply chain designs and investigate how their performance changes with changing variability of product configurations. The results indicate that under higher variability and component commonality conditions a supply chain design that lays more emphasis on tracking the evolution of component demands and inventory positions performs better.

Tuesday 10:30:00 AM 12:00:00 PM

Chair: Jon Marvel (Gettysburg College)

Planning and Control for a Warranty Service Facility
Amir Messih (Eaton Corporation) and Silvanus T. Enns (University of Calgary)

A warranty service facility for industrial products that also provides internal support by reworking production defects is considered. An important concern is the evaluation of policies for how technicians with flexible skills should be moved between the service facility and adjacent production facility. Relevant measures include warranty cycle times and the overall technician utilization levels. An approach is developed that allows incoming warranty work loads to be monitored using control charts. Workload information is then used in simulating behavior using different “flexing” policies. As well, warranty cycle times are monitored using control charts. Results indicate the approach is of practical interest and can be effectively implemented.

Decision Support System for Fisheries Management
Tu H. Truong, Brian J. Rothschild, and Farhad Azadivar (University of Massachusetts, Dartmouth)

This paper presents a decision support system that is oriented toward fisheries policy and management decisions. The important current issues involve the development of an optimal harvesting plan for the fishing industry. A simulation optimization has been built to assist authorities in scheduling for a fleet of hundreds of vessels in terms of time and location of fishing, as well as amount and target species to be fished. Marine fisheries are highly complex and stochastic. A simulation model, therefore, is required. Simulation-based optimization utilizes the simulation model in obtaining the objective function values of a particular fishing schedule. A Genetic Algorithm is used as the optimization routine to determine the optimal fishing schedule, subject to fleet capacity and conservation requirements. The decision support system is then applied to the real situation in the Northeastern U.S.

Validating the Capacity Planning Process and Flowline Product Sequencing Through Simulation Analysis
Jon H. Marvel (Gettysburg College), Mark A. Schaub (Mark Schaub Consulting) and Gary Weckman (Ohio University)

This article illustrates the integration of discrete event simulation into the capacity planning process of a tier two automobile supplier. In this application, the capacity planning process is able to generate a feasible schedule for the 30% of the product line which generates 80% of the business. The schedule is “feasible” based on the ability to produce sufficient inventory to cover customer demand. The capacity planning process was unable to develop a schedule for the production of the remaining 70% of the product line or take into account shortages in customer supplied materials used in the production process. Simulation is used to validate the capacity planning process as well as generate a feasible schedule for the remaining products during the planning period as well as: evaluating the plan for customer supplied materials; identifying potential areas for improvement in the production process and determining material storage requirements for the facilities planner.

Tuesday 1:30:00 PM 3:00:00 PM
Construction Industry Scheduling

Chair: Haiyan Xie (University of Arkansas at Little Rock)

A Framework for Integration Model of Resource-Constrained Scheduling Using Genetic Algorithms
Jin-Lee Kim and Ralph D. Ellis (University of Florida)

The objective of this paper is to present an optimal algorithm for a resource allocation model, which would be implemented into a framework for the development of an integration model. Unlike present heuristic-based resource allocation models, the model does not depend solely on a set of heuristic rules, but adopts the concept of future float to set the order of priority when activities compete for resources. The model determines the shortest duration by allocating available resources to a set of activities simultaneously. Genetic algorithms (GAs) are adopted to search optimal solutions. The results obtained from a case example indicate that the model is capable of producing optimal scheduling alternatives, compared to a single solution that is produced by either the total float model or the least impact model.

Resource Allocation and Planning for Program Management
Kabeh Vaziri, Linda K. Nozick, and Mark A. Turnquist (Cornell University)

Much of the project scheduling literature treats task durations as deterministic. In reality, however, task durations are subject to considerable uncertainty and that uncertainty can be influenced by the resources assigned. The purpose of this paper is to provide the means for program managers (who may have responsibility for multiple projects) to optimally allocate resources from common resource pools to individual tasks on several competing projects. Instead of the traditional use of schedules, we develop control policies in the form of planned resource allocation to tasks that capture the uncertainty associated with task durations and the impact of resource allocation on those durations. We develop a solution procedure for the model and illustrate the ideas in an example.

Tuesday 3:30:00 PM 5:00:00 PM
Factory Scheduling

Chair: Kristin Thoney (NC State University)

Assessing Risk in a Job Schedule: Integrating a Scheduling Heuristic and a Simulation Model to a Spreadsheet
Kusuma Rojanapibul and Juta Pichitlamken (Kasetsart University)

Deterministic scheduling algorithms are often applied to problems in stochastic settings perhaps because they are already hard to solve even without considering stochastic characteristics. We are interested in assessing the measure of risk in performance measures (e.g., makespan) when these algorithms are used in probabilistic environment. We design an easy-to-use Microsoft Excel program that integrates a Visual Basic Application (VBA) subroutine which performs scheduling procedures, with an Arena simulation model that imitates the stochastic production environment. Our program suggests a job schedule, its associated performance measures and the corresponding prediction intervals. At the moment, we only consider the m-machine permutation flowshop problem with the makespan (or completion time) objective.

Minimizing the Total Weighted Completion Time on Unrelated Parallel Machines with Stochastic Times
Jean-Paul M. Arnaout and Ghaith Rabadi (Old Dominion University)

This paper addresses the problem of batch scheduling in an unrelated parallel machine environment with sequence dependent setup times and an objective of minimizing the weighted mean completion time. Identical jobs are batched together and are available at time zero. Processing time of each job of a batch is determined according to both the machine it will be assigned to and the batch group to which the job belongs. The jobs’ processing times and setup times are stochastic for better depiction of the real world. This is a NP-hard problem and in this paper, a solution heuristic is developed and compared to existing ones using simulation. The results and analysis obtained from the computational experiments proved the superiority of the proposed algorithm PMWP over the other algorithms presented.

A Simulation Based Learning Meachanism for Scheduling Systems
Ihsan Sabuncuoglu (Bilkent university) and Gokhan Metan (Lehigh University)

A simulation based learning mechanism is proposed in this study. The system learns in the manufacturing environment by constructing a learning tree and selects a dispatching rule from the tree for each scheduling period. The system utilizes the process control charts to monitor the performance of the learning tree which is automatically updated whenever necessary. Therefore, the system adapts itself for the changes in the manufacturing environment and works well over time. Extensive simulation experiments are conducted for the system parameters such as monitoring (MPL) and scheduling period lengths (SPL) on a job shop problem with objective of minimizing average tardiness. Simulation results show that the performance of the proposed system is considerably better than the simulation-based single-pass and multi-pass scheduling algorithms available in the literature.