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Modeling Dense Storage Systems With Location Uncertainty
Title
Modeling Dense Storage Systems With Location Uncertainty.
Creator
Awwad, Mohamed, Pazour, Jennifer, Elshennawy, Ahmad, Thompson, William, Leon, Steven, University of Central Florida
Abstract / Description
This dissertation focuses on developing models to study the problem of searching and retrieving items in a dense storage environment. We consider a special storage configuration called an inverted T configuration, which has one horizontal and one vertical aisle. Inverted T configurations have fewer aisles than a traditional aisle-based storage environment. This increases the storage density; however, requires that some items to be moved out of the way to gain access to other more deeply...
Show moreThis dissertation focuses on developing models to study the problem of searching and retrieving items in a dense storage environment. We consider a special storage configuration called an inverted T configuration, which has one horizontal and one vertical aisle. Inverted T configurations have fewer aisles than a traditional aisle-based storage environment. This increases the storage density; however, requires that some items to be moved out of the way to gain access to other more deeply stored items. Such movement can result in item location uncertainty. When items are requested for retrieval in a dense storage environment with item location uncertainty, searching is required. Dense storage has a practical importance as it allows for the use of available space efficiently, which is especially important with the scarce and expensive space onboard of US Navy's ships that form a sea base. A sea base acts as a floating distribution center that provides ready issue material to forces ashore participating in various types of missions. The sea basing concept and the importance of a sea base's responsiveness is our main motivation to conduct this research.In chapter 2, we review three major bodies of literature: 1) sea based logistics, 2) dense storage and 3) search theory. Sea based logistics literature mostly focuses on the concept and the architecture of a sea base, with few papers developing mathematical models to solve operational problems of a sea base, including papers handling the logistical and sustainment aspects. Literature related to dense storage can be broken down into work dealing with a dense storage environment with an inverted T configuration and other papers dealing with other dense storage configurations. It was found that some of the dense storage literature was motivated by the same application, i.e. sea based logistics. Finally, we surveyed the vast search theory literature and classification of search environments. This research contributes to the intersection of these three bodies of literature. Specifically, this research, motivated by the application of sea basing, develops search heuristics for dense storage environments that require moving items out of the way during searching. In chapter 3, we present the problem statements. We study two single-searcher search problems. The first problem is searching for a single item in an inverted T dense storage environment. The second one is searching for one or more items in an inverted T storage environment with items stacked over each other in the vertical direction.In chapter 4, we present our first contribution. In this contribution we propose a search plan heuristic to search for a single item in an inverted T, k-deep dense storage system with the objective of decreasing the expected search time in such an environment. In this contribution, we define each storage environment entirely by the accessibility constant and the storeroom length. In addition, equations are derived to calculate each component of the search time equation that we propose: travel, put-back and repositioning. Two repositioning policies are studied. We find that a repositioning policy that uses the open aisle locations as temporary storage locations and requires put-back of these items while searching is recommended. This recommendation is because such a policy results in lower expected search time and lower variability than a policy that uses available space outside the storage area and handles put-back independently of the search process. Statistical analysis is used to analyze the numerical results of the first contribution and to analyze the performances of both repositioning polices. We derive the probability distribution of search times in a storeroom with small configurations in terms of the accessibility constant and length. It was found that this distribution can be approximated using a lognormal probability distribution with a certain mean and standard deviation. Knowing the probability distribution provides the decision makers with the full range of all possible probabilities of search times, which is useful for downstream planning operations.In chapter 5, we present the second contribution, in which we propose a search plan heuristic but for multiple items in an inverted T, k-deep storage system. Additionally, we consider stacking multiple items over each other. Stacking items over each other, increases the number of stored items and allows for the utilization of the vertical space. In this second contribution, we are using the repositioning policy that proved its superiority in the first contribution. This contribution investigates a more general and a much more challenging environment than the one studied in the first contribution. In the second environment, to gain access to some items, not only may other items need to be moved out of the way, but also the overall number of movements for items within the system will be highly affected by the number of items stacked over each other. In addition, the searcher is given a task that includes searching and retrieving a set of items, rather than just one item.For the second contribution, the performance of the search heuristic is analyzed through a Statistical Design of Experiments, and it was found that searching and retrieving multiple items instead of just a single item, would decrease the variability in search times for each storeroom configuration. Finally, in chapter 6, conclusions of this research and suggestions for future research directions are presented.
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Date Issued
2015
Identifier
CFE0006256, ucf:51045
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006256
IMPROVING AIRLINE SCHEDULE RELIABILITY USING A STRATEGIC MULTI-OBJECTIVE RUNWAY SLOT ASSIGNMENT SEARCH HEURISTIC
Title
IMPROVING AIRLINE SCHEDULE RELIABILITY USING A STRATEGIC MULTI-OBJECTIVE RUNWAY SLOT ASSIGNMENT SEARCH HEURISTIC.
Creator
Hafner, Florian, Sepulveda, Alejandro, University of Central Florida
Abstract / Description
Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the...
Show moreImproving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.
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Date Issued
2008
Identifier
CFE0002067, ucf:47572
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002067