Quality and production control with opportunities and exogenous random shocks

Cataloged from PDF version of article.In a production process, opportunities arise due to exogenous or indigenous factors, for cost reduction. In this dissertation, we consider such opportunities in quality control chart design and production planning for the lot sizing problem. In the first part of the dissertation, we study the economic design of X control charts for a single machine facing exogenous random shocks, which create opportunities for inspection and repair at reduced cost. We develop the expected cycle cost and expected operating time functions, and invoking the renewal reward theorem, we derive the objective function to obtain the optimum values for the control chart design parameters. In the second part, we consider the quality control chart design for the multiple machine environment operating under jidoka (autonomation) policy, in which the opportunities are due to individual machine stoppages. We provide the exact model derivation and an approximate model employing the single machine model developed in the first part. For both models, we conduct extensive numerical studies and observe that modeling the inspection and repair opportunities provide considerable cost savings. We also show that partitioning of the machines as opportunity takers and opportunity non-takers yields further cost savings. In the third part, we consider the dynamic lot sizing problem with finite capacity and where there are opportunities to keep the process warm at a unit variable cost for the next period if more than a threshold value has been produced. For this warm/cold process, we develop a dynamic programming formulation of the problem and establish theoretical results on the optimal policy structure. For a special case, we show that forward solution algorithms are available, and provide rules for identifying planning horizons. Our numerical study indicates that utilizing the undertime option results in significant cost savings, and it has managerial implications for capacity planning and selection.Toy, Ayhan ÖzgürPh.D

Route, aircraft prioritization and selection for airlift mobility optimization

The Throughput II mobility optimization model was developed at the Naval Postgraduate School for the Air Force Studies and Analysis Agency (AFSAA). The purpose of Throughput II is to help answer questions about the ability of the USAF to conduct airlift of soldiers and equipment in support of major military operations. Repeated runs of this model have helped AFSAA generate insights and recommendations concerning the selection of aircraft assets. Although Throughput II has earned the confidence of AFSAA, repeated applications are hampered by the fact that it can take over three hours to run on a fast workstation. This is due to the model's size; it is a linear program whose dimensions can exceed 100,000 variables, 100,000 constraints, and 1 million nonzero coefficients, even alter extensive model reduction techniques are used. The purpose of this thesis is to develop heuristics that can be performed prior to running Throughput II in order to reduce the model's size. Specifically, this thesis addresses the fact that the Throughput II formulation has many variables and constraints that depend on the number of available routes for each aircraft. The goal is to carefully eliminate routes so as to make the problem smaller without sacrificing much solution qualityhttp://archive.org/details/routeaircraftpri00toyaLieutenant Junior Grade, Turkish NavyApproved for public release; distribution is unlimited

PERFORMANCE COMPARISON OF META-HEURISTICS FOR THE MULTIBLOCK WAREHOUSE ORDER PICKING PROBLEM

This study focuses on streamlining the order-picking process in a warehouse. We consider determining the picking sequence of items in a pick-list to minimize the total traveled distance in a multiblock warehouse, where a low-level picker-to-parts manual picking system is employed. We assume that the items are stored randomly in the warehouse. First, we construct a distance matrix of the shortest path between any pair of items. Next, using the distance matrix, we implement two meta-heuristics—the tabu search algorithm and the iterated greedy algorithm—to determine the picking sequence with the minimum total traveled distance. Through a numerical study, the performances of the meta-heuristic algorithms are compared with those of popular rule-based heuristics (S-shape, largest gap, and Combined+) and the best-known solutions. We conducted the numerical study in two stages. In the first stage, we considered a two-block rectangular warehouse, and in the second stage, we considered a three-block rectangular warehouse. The performance of the heuristics was calculated based on the optimal solution when available or the best calculated bound when the optimal solution is not available. We observed that the iterated greedy algorithm significantly outperforms the other heuristics for both stages.

An efficient procedure for optimal maintenance intervention in partially observable multi-component systems

With rapid advances in technology, many systems are becoming more complex, including ever-increasing numbers of components that are prone to failure. In most cases, it may not be feasible from a technical or economic standpoint to dedicate a sensor for each individual component to gauge its wear and tear. To make sure that these systems that may require large capitals are economically maintained, one should provide maintenance in a way that responds to captured sensor observations. This gives rise to condition-based maintenance in partially observable multi-component systems. In this study, we propose a novel methodology to manage maintenance interventions as well as spare part quantity decisions for such systems. Our methodology is based on reducing the state space of the multi-component system and optimizing the resulting reduced-state Markov decision process via a linear programming approach. This methodology is highly scalable and capable of solving large problems that cannot be approached with the previously existing solution procedures.</p

An efficient procedure for optimal maintenance intervention in partially observable multi-component systems

With rapid advances in technology, many systems are becoming more complex, including ever-increasing numbers of components that are prone to failure. In most cases, it may not be feasible from a technical or economic standpoint to dedicate a sensor for each individual component to gauge its wear and tear. To make sure that these systems that may require large capitals are economically maintained, one should provide maintenance in a way that responds to captured sensor observations. This gives rise to condition-based maintenance in partially observable multi-component systems. In this study, we propose a novel methodology to manage maintenance interventions as well as spare part quantity decisions for such systems. Our methodology is based on reducing the state space of the multi-component system and optimizing the resulting reduced-state Markov decision process via a linear programming approach. This methodology is highly scalable and capable of solving large problems that cannot be approached with the previously existing solution procedures.</p