Gail W. DePuy

Production planning for remanufactured products

This paper presents a methodology for production planning within facilities involved in the remanufacture of products. Remanufacturing refers to the process of accepting inoperable units, salvaging good and repairable components from those units, and then re-assembling good units to be re-issued into service. These types of facilities are common, yet many suffer from the unpredictability of good and repairable component yields, as well as processing time variation. These problems combine to make it extremely difficult to predict whether overall production output will be sufficient to meet demand. Low yields of key components can lead to shortages which require the facility to purchase new components for legacy systems, often with long lead times, thus causing overall delays. The approach developed here is a probabilistic form of standard material requirements planning (MRP), which considers variable yield rates of good, bad, and repairable components that are harvested from incoming units, and probabilistic processing times and yields at each stage of the remanufacturing process. The approach provides estimates of the expected number of remanufactured units to be completed in each future period. In addition, we propose a procedure for generating a component purchase schedule to avoid shortages in periods with a low probability of meeting demand. The proposed methodology is applied to an antenna remanufacturing process at the Naval Surface Warfare Center (NSWC). In this case study the proposed methodology identifies a potential shortage of a key component and suggests a corrective action to avoid significant delay in the delivery of remanufactured units.

Computer-based monitoring and fault diagnosis: a chemical process case study

Abstract Principal component analysis (PCA) for process modeling and multivariate statistical techniques for monitoring, fault detection, and diagnosis are becoming more common in published research, but are still underutilized in practice. This paper summarizes an in-depth case study on a chemical process with 20 monitored process variables, one of which reflects product quality. Data from intervals of “good” operation times are used to determine a PCA model, and then data sets from intervals of “bad” operation times are compared to the model to detect the faulty variable and determine those variables responsible for the poor product quality. The analysis is performed using the PLS_Toolbox 2.01 with MATLAB. The methods used are reviewed summarily, and then results are shown based on typical application of the multivariate statistical techniques. An enhancement is made by using confidence limits on the residuals of each variable for fault detection rather than just confidence limits on an overall residual. Results show that the time required for fault detection is reduced. This approach was suggested in the literature, but its efficacy not demonstrated. Finally, ways to more effectively monitor processes and to more promptly detect and diagnose faults when they occur are identified.

Meta-RaPs approach for the 0-1 multidimensional Knapsack problem

A promising solution approach called Meta-RaPS is presented for the 0-1 Multidimensional Knapsack Problem (0-1 MKP). Meta-RaPS constructs feasible solutions at each iteration through the utilization of a priority rule used in a randomized fashion. Four different greedy priority rules are implemented within Meta-RaPS and compared. These rules differ in the way the corresponding pseudo-utility ratios for ranking variables are computed. In addition, two simple local search techniques within Meta-RaPS improvement stage are implemented. The Meta-RaPS approach is tested on several established test sets, and the solution values are compared to both the optimal values and the results of other 0-1 MKP solution techniques. The Meta-RaPS approach outperforms many other solution methodologies in terms of differences from the optimal value and number of optimal solutions obtained. The advantage of the Meta-RaPS approach is that it is easy to understand and easy to implement, and it achieves good results.

On the effectiveness of incorporating randomness and memory into a multi-start metaheuristic with application to the Set Covering Problem

The construction of good starting solutions for multi-start local search heuristics is an important, yet not well-studied problem. In these heuristics, randomization methods are usually applied to explore new promising areas and memory mechanisms are incorporated with the main purpose of reinforcing good solutions. Under the template of a typical multi-start metaheuristic, Meta-RaPS (Meta-heuristic for Randomized Priority Search), this paper presents several randomization methods and memory mechanisms with a focus on comparing their effectiveness and analyzing their interaction effects. With the Set Covering Problem (SCP) as the application problem, it is found that these randomization methods work well for Meta-RaPS with an improvement phase while the memory mechanisms better the solution quality of the construction phase. The quality and efficiency of Meta-RaPS can be improved through the use of both memory mechanisms and randomization methods. This paper also discovers several efficient algorithms that maintain a good balance between randomness and memory and finds the optimal or best-known solutions for the 65 SCP test instances from the OR-library.

A simple and effective heuristic for the resource constrained project scheduling problem

This paper investigates the development and application of a simple heuristic to the resource constrained project scheduling problem (RCPSP). This computer heuristic, which is based on the COMSOAL heuristic, constructs a feasible solution at each iteration and chooses the best solution of several iterations. Although COMSOAL was originally a solution approach for the assembly-line balancing problem, it can be extended to provide solutions to the resource allocation problem. The Modified COMSOAL technique presented in this paper uses priority schemes intermittently with a random selection technique. This hybrid of randomness and priority scheme allows a good solution to be found quickly while not being forced into the same solution at each iteration. Several different priority schemes are examined within this research. The COMSOAL heuristic modified with the priority schemes heuristic was tested on several established test sets and the solution values are compared with both known optimal values and the results of several other resource allocation heuristics. In the vast majority of cases, the Modified COMSOAL heuristic outperformed the other heuristics in terms of both average and maximum percentage difference from optimal. The Modified COMSOAL heuristic seems to have several advantages over other RCPSP heuristics in that it is easy to understand, easy to implement, and achieves good results.

A Meta-RaPS for the early/tardy single machine scheduling problem

This paper investigates a meta-heuristic solution approach to the early/tardy single machine scheduling problem with common due date and sequence-dependent setup times. The objective of this problem is to minimise the total amount of earliness and tardiness of jobs that are assigned to a single machine. The popularity of just-in-time (JIT) and lean manufacturing scheduling approaches makes the minimisation of earliness and tardiness important and relevant. In this research the early/tardy problem is solved by Meta-RaPS (meta-heuristic for randomised priority search). Meta-RaPS is an iterative meta-heuristic which is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element. In this case a greedy heuristic, the shortest adjusted processing time, is modified by Meta-RaPS and the good solutions are improved by a local search algorithm. A comparison with the existing ETP solution procedures using well-known test problems shows Meta-RaPS produces better solutions in terms of percent difference from optimal. The results provide high quality solutions in reasonable computation time, demonstrating the effectiveness of the simple and practical framework of Meta-RaPS.

Using Board Puzzles to Teach Operations Research

Engineering design is a difficult task. Designers work at the boundaries between physical laws, societal or governmental regulations, the need for efficiency, and the desire to develop creative or aesthetic solutions. Teaching engineering design is therefore a difficult task. How is it possible to stimulate students to think of creative solutions while adhering to established principles and necessary rules? Certainly, it is important for students to have deep-seated roots in engineering fundamentals. These fundamentals include both a strong overall engineering core and coursework specific to the particular branch of engineering study undertaken by the student. For industrial engineers, the study of operations research is fundamental in preparing them for careers in engineering design. This paper addresses one way of encouraging creativity in teaching operations research in industrial engineering design curricula. Specifically, this paper provides examples of the use of games as pedagogical tools in teaching operations research. These games cannot be used as a substitute for teaching practical applications, but they are excellent supplementary tools that encourage students to tackle highly structured problems creatively while working on interesting and enjoyable tasks. Four examples are provided within the paper.

A simulation-based software system for barge dispatching and boat assignment in inland waterways

Abstract In this paper, the authors present a simulation-based scheduling system designed to assist in barge dispatching and boat assignment problems for inland waterways. Specifically, the system assists in the assignment of barge freight to boats. The simulation platform provides the ability to explicitly consider time-based cost trade-offs between barge handling requirements and equipment dwell time. The efficacy of use for the system is demonstrated via case studies utilizing data provided by American Commercial Barge Line, LLC (ACBL) for the Ohio River in North America. The scheduling system described herein is now being implemented as ACBL’s primary dispatching tool for the Ohio River. Results indicate that the solution approach is viable for general use in large-scale dispatching and load assignment problems on major commercial rivers.

Graphical enhancement to support PCA-based process monitoring and fault diagnosis.

Principal component analysis (PCA) for process modeling and multivariate statistical techniques for monitoring, fault detection, and diagnosis are becoming more common in published research, but are still underutilized in practice. This paper summarizes an in-depth case study on a chemical process with 20 monitored process variables, one of which reflects product quality. The analysis is performed using the PLS_Toolbox 2.01 with MATLAB, augmented with software which automates the analysis and implements a statistical enhancement that uses confidence limits on the residuals of each variable for fault detection rather than just confidence limits on an overall residual. The newly developed graphical interface identifies and displays each variables contribution to the faulty behavior of the process; and it aids greatly in analyzing results. The case study analyzed within shows that using the statistical enhancement can reduce the fault detection time, and the automated graphical interface implements the enhancement easily.

Applying the COMSOAL computer heuristic to the constrained resource allocation problem

Generating a project schedule that meets all activity precedence constraints while minimizing the overall project duration is often difficult. The complex project-scheduling problem is further complicated by the real-world constraint that often a limited number of resources must be allocated to competing activities. This paper investigates the application of the computer method COMSOAL to this resource allocation problem. COMSOAL (Computer Method of Sequencing Operations for Assembly Lines), originally a solution approach for the assembly line balancing problem, is a computer heuristic that can be used to generate a feasible solution to the resource allocation problem at each iteration of the heuristic. A solution methodology of repeatedly running COMSOAL will result in many feasible solutions from which the best is chosen. This solution approach now becomes viable given the increased speed of inexpensive computers. This paper discusses the adaptation of the COMSOAL approach to the resource allocation problem as well as a designed experiment used to investigate the appropriateness of COMSOAL for a known set of resource allocation test problems. Results from this experiment show COMSOAL is a viable method to solve these resource allocation problems when compared to the results from several well-known resource allocation algorithms and to the optimal solutions.