Daniel A. Finke

Multiple machine JIT scheduling: a tabu search approach

This paper presents an approach to solving the multiple machine, non-preemptive, earliness-tardiness scheduling problem with unequal due dates in a flow shop with machine tiers (FMT). In this variant of the flow shop problem, machines are arranged in tiers or groups, and the jobs must visit one machine in each tier. The processing times, machine assignments, and due dates are deterministic and known in advance. The objective is to find a permutation schedule that minimizes the total deviation of each job from its due date. A tabu search (TS) meta-heuristic combined with an LP evaluation function is applied to solve this problem and results are compared to optimal permutation solutions for small problems and the earliest due date schedule for large problems. Several neighborhood generation methods and two diversification strategies are examined to determine their effect on solution quality. Results show that the TS method works well for this problem. TS found the optimal solution in all but one of the small problem instances and improved the earliest due date solutions for larger instances where no optimal solutions could be found.

Shop scheduling using Tabu search and simulation

An important goal in scheduling products through a manufacturing facility is to assure that the work is completed as close as possible to its due date. Work that is late creates downstream delays, while early completion can be detrimental if storage space is limited. This paper reports initial results in developing a scheduling procedure for an automated steel plate fabrication facility. The approach uses Tabu search combined with simulation to schedule product through a set of machines. Performance of the procedure is evaluated by comparison to the optimal solution for small problem instances, and to a good heuristic for larger problems. Results show that the Tabu search method works well for this problem. Combining Tabu search with simulation allows the incorporation of more realistic constraints on system operation.

Discrete simulation development for a proposed shipyard steel processing facility

This paper describes the efforts required to convert conceptual designs and undefined processes for a proposed advanced steel processing shipyard facility into a discrete event simulation. Modeling of a completely non-existent entity poses many difficulties, yet the results can still be beneficial. The lack of actual production data and corresponding business rules, causes an in-depth review of all available information combined with that which can be extrapolated from vendor specification sheets or human experience. Most of the equipment required for this advanced processing facility will be custom built to suit the needs of this highly technical complex. This facility which will ultimately support construction of vessels, was driven by high expectations of improved production efficiencies. The model is expected to support not only the preconstruction design phases of the building, but also to serve as a post-construction production planning tool.

On the estimation of operations and maintenance costs for defense systems

The estimation of operations and maintenance (O&M) costs for weapon systems has been termed ‘infeasible’ due to: 1) a lack of detailed prior (O&M) costs, 2) a large amount of uncertainty in the operational tempo for the system, and 3) uncertainty in the predicted reliability of system components. This research proposes the creation of a flexible discrete event simulation model to estimate O&M costs by predicting events that occur during a systems life cycle. Such a model takes as inputs a given concept of operations, maintenance strategy, and system reliabilities to determine lifecycle events such as: consumables used and maintenance operations performed on the entire system throughout its life cycle. The uncertain cost of each event can be used to estimate a distribution of total O&M costs. The results can finally be analyzed to determine the attribution of the uncertainty of those costs to all of the different possible sources.

Automating the development of shipyard manufacturing models

Simulation results are often needed within a short time frame, while the development of simulation models can be time consuming. We develop a methodology to facilitate rapid generation of simulation models from an enterprise database. Data is communicated between product lifecycle management (PLM) software and Flexsim using a standard Microsoft Excel format. We have developed a custom Flexsim interface and software-specific model generator that creates a discrete event simulation model from the PLM input data. Preliminary results show that the methodology can reduce the cost of simulation model generation while simultaneously improving the accuracy of generated models. This work highlights the benefits of automatic model generation techniques, describes a ship-building implementation of the methodology, and provides direction for future work.

What I wish they would have taught me (or that I would have better remembered!) in school

This panel reflects upon their experiences as simulation professionals and shares their thoughts regarding elements of their simulation education that they have found most helpful in their work as well as things they wish they would have learned. With diverse backgrounds and simulation application areas, their perspectives may provide food for thought to simulation course developers and to those in the midst of their educational process.

Graphical methods for robust design of a semiconductor burn-in process

Discrete-event simulation is a common tool for the analysis of semiconductor manufacturing systems. With the aid of a simulation model, and in conjunction with sensitivity analysis and metamodeling techniques, robust design can be performed to optimize a system. Robust design problems often include integer decision variables. This paper shows a graphical approach to robust design that is effective in the presence of discrete or qualitative variables. The graphical robust design methodology was applied to a backend semiconductor manufacturing process. Changes in specific resource capacities and product mix were examined to determine their effect on the level and variance of cycle time and work in process.