Douglas D. Gemmill

Using tabu search to schedule activities of stochastic resource-constrained projects

Abstract In this paper, a higher level heuristic procedure “tabu search” is proposed to provide good solutions to resource-constrained, randomized activity duration project scheduling problems. Our adaptation of tabu search uses multiple tabu lists, randomized short-term memory, and multiple starting schedules as a means of search diversification. The proposed method proves to be an efficient way to find good solutions to both deterministic and stochastic problems. For the deterministic problems, most of the optimal schedules of the test projects investigated are found. Computational results are presented which establish the superiority of tabu search over the existing heuristic algorithms.

Scheduling a two-machine flowshop with travel times to minimize maximum lateness

As fully automated manufacturing becomes a realization, the problem of scheduling jobs to meet customer due dates come to the forefront. In this paper, possible sequencing methods are generated for an automated two-machine flowshop with non-negligible transportation times and blocking of the second machine. Two heuristics are developed to sequence a set of jobs with the objective of minimizing maximum lateness. The cases of scheduling the flowshop when it starts in a null state and a busy state are both investigated.

A genetic algorithm approach to optimization of asynchronous automatic assembly systems

This paper presents the application of genetic algorithms to the performance optimization of asynchronous automatic assembly systems (AAS). These stochastic systems are subject to blocking and starvation effects that make complete analytic performance modeling difficult. Therefore, this paper extends genetic algorithms to stochastic systems. The performance of the genetic algorithm is measured through comparison with the results of stochastic quasi-gradient (SQM) methods to the same AAS. The genetic algorithm performs reasonably well in obtaining good solutions (as compared with results of SQM) in this stochastic optimization example, even though genetic algorithms were designed for application to deterministic systems. However, the genetic algorithms performance does not appear to be superior to SQM.

Improved methods of assembly sequence determination for automatic assembly systems

Abstract The assembly sequence is determined using product unit cost as the performance measure. The global optimum for a given problem is found by partially enumerating assembly station configurations with branch and bound methods. The study shows that the proposed methods perform faster than simulated annealing for the example problems used. It is shown that the unit cost function is not necessarily convex which is assumed in previous research.

Approximate solutions for the cutting stock ‘portfolio’ problem

Abstract This study deals with the development of general engineering guidelines for the portfolio problem. The portfolio problem consists of determining the best combination of sheet or ‘bin’ sizes to keep in inventory in order to minimize material wastage. At the expense of large amounts of computer time, numerical representations of response surfaces are made and used as atool in developing some general engineering guidelines for the proper portfolio selection.

Solution to the assortment problem via the genetic algorithm

The assortment problem considers what standard sizes of material should be maintained in an inventory from which to cut required bills of material of smaller pieces. An example could come from an industry utilizing steel pipe where it is required to cut a bill of material of various pipe lengths out of standard lengths of pipe kept in inventory. This paper introduces the use of genetic algorithms in the solution of the assortment problem, and a comparison is made between the results obtained with the genetic algorithm and the results of an existing heuristic method. It is shown under what circumstances each of the two methods should be applied.

Improving Resource-Constrained Project Schedules with Look-Ahead Techniques

A method, referred to as “look-ahead,” has been developed that can provide an improved sequence in which to perform the activities of a resource-constrained project schedule. In particular, this method has been applied to the scheduling of activities required to complete depot maintenance of aircraft. In many cases, the method is able to decrease the total makespan of resource-constrained projects over that achieved when using existing heuristics.

Maintenance and recurrent event analysis of circuit breaker data

Purpose – The purpose of this paper is to review cotemporary maintenance programs and analyze factory production data for an SF6 gas filled circuit breaker population. Various maintenance techniques and studies are reviewed to understand the reliability of circuit breaker models and the impact manufacturing can have on long term maintenance considerations.Design/methodology/approach – Production and field event data were analyzed using statistical analysis tools. The population data were formatted so that a recurrent event analysis could be conducted to establish the mean cumulative function (MCF) by model and product family (class). Average Field Two‐year Recorded Event Rate (AFTRER) is introduced and compared to commonly used Field Incident Rate (FIR) and Mean‐Time between Failure (MTBF) measures.Findings – Common managerial operating questions can be answered as exhibited for the provided circuit breaker population. This includes the longevity of field issues, the anticipated life cycle of a model or c...

A comparison of solution methods for the assortment problem

The problem of economically cutting a required bill of material out of standard size stock pieces is shared by the metal, leather, glass, electronic, shipbuilding, and lumber industries. One of the problems in this area is the assortment problem; that is, determining the best combination of stock sizes to keep in inventory to cut a single known (deterministic) bill of material. This problem includes determining the dimensions or the stock pieces as well as the number of unique sizes to stock. In this research a comparison is made between three common heuristic methods for solution of the assortment problem and the introduction of the application of optimization homotopy to the assortment problem, a method developed for the solution of stochastic problems. It is found that while utilizing a specific combination of nesting and sequencing algorithms, the three heuristic methods work very well for simpler problems, and as the complexity of the problem increases optimization homotopy becomes more advantageous.

Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics

A framework for smart maintenance decision support system is provided.Applications using big data analytics and a specific case study for the electrical utility industry are detailed.An integrated expert system making use of Markov Decision Process and Analytical Hierarchy Process Models is developed. The purpose of this article is to outline the architectural design and the conceptual framework for a Smart Maintenance Decision Support System (SMDSS) based on corporate data from a Fortune 500 company. Motivated by the rapidly transforming landscape for big data analytics and predictive maintenance decision making, we have created a system capable of providing end users with recommendations to improve asset lifecycles. Methodologically, a cost minimization algorithm is used to analyze a large industry service and warranty data sets and two analytical decision models were developed and applied to a case study for an electrical circuit breaker maintenance problem. Some of these techniques can be applied to other industries, such as jet engine maintenance, and can be expanded to others with implications for robust decision analysis. The SMDSS provides a predictive analytical model that can be applied in manufacturing and service based industries. Our findings and results show that existing solution algorithms and optimization models can be applied to large data sets to lay out executable decisions for managers.