Consolidating Facilities for the Connecticut Department of Transportation: A Case-Study (original) (raw)
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Transportation Infrastructure Maintenance Management: Case Study of a Small Urban City
2009
A transportation infrastructure maintenance management system ͑TIMMS͒ for a small urban city is developed. Uintah, Utah, year 2005 population 2,000, is studied. As of Fall 2001, this suburb of the Salt Lake City-Ogden urban area featured 12 km of paved roads, 133 traffic signs, 18 street lights, pavement markings, and other transportation infrastructure. A part-time city engineer was responsible for maintaining the transportation and other infrastructure. A formal TIMMS was not in place. A TIMMS emphasizing preventive maintenance on all infrastructure, along with corrective maintenance on all pavements, was estimated to require about 79% of the city engineer's time, at an annual cost of about 88,000,or88,000, or 88,000,or7,330/ km of road ͑2002$͒. About 75% of the expenses would be devoted to pavement preservation. The cost would exceed the city's estimated transport maintenance budget ͑$55,775͒ by about 58%, while the time required of the engineer might exceed that available. A simple linear programming TIMMS formulation is developed. One heuristic solution would be to implement a scheduled maintenance program on all transport infrastructure, and corrective pavement maintenance on one major collector street. These two programs would consume the entire $55,775 budget. The budget shortfall may be indicative of a general lack of adequate funding for infrastructure maintenance in the United States. The time shortfall indicates a need for additional manpower. Further study is needed to determine how best to prioritize maintenance actions, optimize maintenance resources, and identify supplemental funding sources.
Multi-facility Maintenance and Rehabilitation Model with Coordinated Intervention
2007
We present a quadratic programming formulation for the problem of obtaining optimal maintenance and repair policies for multi-facility transportation infrastructure systems. The proposed model provides a computationally-tractable framework to support decision-making, while accounting for economic interdependencies that link the facilities that comprise these systems. To demonstrate the advantages of the proposed model, we present numerical examples that capture economic interdependencies reflecting both costs associated with disruptions/loss of throughput, as well as the benefits associated with coordinating intervention schedules of adjacent facilities to reduce costs associated with resource and personnel delivery. The results illustrate situations where it is optimal to coordinate (synchronize or alternate) interventions for clusters of facilities in transportation systems.
Reconfiguring a Multi-period Facility Model – An Empirical Test in a Dynamic Setting
BOHR Publishers, 2022
Facility location is an important problem faced by companies in many industries. Finding an optimal location for facilities and determining their size involves the consideration of many factors, including proximity to customers and suppliers, availability of skilled employees and support services, and cost-related factors, for example, construction or leasing costs, utility costs, taxes, availability of support services, and others. The demand of the surrounding region plays an important role in location decisions. A high population density may not necessarily cause a proportional demand for products or services. The demography of a region could dictate the demand for products, and this, in turn, affects a facility’s size and location. The location of a company’s competitors also affects the location of that company’s facilities. Another important aspect in facility location modeling is that many models focus on current demand and do not adequately consider future demand. However, while making location decisions in an industry in decline, carefully and accurately considering future demand is especially important, and the question in focus is whether to shrink or close down certain facilities with the objective of keeping a certain market share or maximizing profit, especially in a competitive environment.
This paper develops a model which enables companies to select sites for their businesses according to their strategy. The model analyzes the strategic position of the company and forms a guideline for the decision. It investigates which facilities should be closed, (re)opened, shrunk, or expanded. If facilities are to shrink or expand, the model also determines their new capacities. It depicts the impact on market share and accounts for the costs of closure and reopening. A number of papers deal with location theory and its applications, but few have been written for modeling a competitive environment in the case of declining demand. Existing papers in this area of research are mostly static in nature, do not offer multi-period approaches, nor do they incorporate the behavior of competitors in the market. To demonstrate the validity of the model, it is first solved using a small problem set – three facilities, three demand locations, and three periods – in LINGO solver. To get a better understanding of the model’s behavior, several additional scenarios were constructed. First, the number of demand locations was extended to 10. Our findings show that the model presented provides an extension of existing facility location models that can be applied to a variety of location problems in commercial and industry sectors that need to make their decisions considering future periods and competitors. The developed heuristic shows multiple options for solving the problem, including their advantages and disadvantages, respectively. The Java code and LINGO fragments thus developed can be used to provide easy access to related problems.
The scheduling of maintenance. A resource-constraints mixed integer linear programming model
Computers & Industrial Engineering, 2015
The scheduling of preventive maintenance is crucial in reliability and maintenance engineering. Hundreds of parts compose complex machines that require replacement and/or repairing. Maintenance involves the machine vendor (1), the machine user (2) and the service maintenance provider (3). The vendor and the maintenance service provider have to guarantee a high level of availability and productivity of the machines and maintain their down-time at a minimum even though they are installed worldwide and usually far from the vendor's headquarters and/or the locations of the provider's regional service offices. Moreover, many companies have great profits from maintenance and spare parts management. This study aims to illustrate an original mixed integer linear programming (MILP) model for the cost-based, reliability-based and resource-constraints scheduling of preventive maintenance actions. The model minimizes the total cost function made of spare parts contributions, the cost of the execution of the preventive actions and the cost of the additional repair activity in case of unplanned failure. The cost of the personnel of the producer and/or the maintenance service provider is also included. Finally, the paper presents a case study in a what-if environment demonstrating the effectiveness and the novelty of this study in real and complex applications.
Facilities management: the strategic selection of a maintenance system
Journal of Facilities Management, 2014
Purpose -A major role of facilities management is ensuring the useability, reliability, and safety of the asset being managed. To achieve this management must use a system to control the maintenance function. The purpose of the paper is to identify and describe the various maintenance management models and systems available for facilities managers to consider. Design/methodology/approach -Two comprehensive reviews of the literature were undertaken to categorise the various maintenance management models and identify popular models in practice. Findings -The review identified 37 maintenance management models. From these, four were found to be popular: total productive maintenance (TPM), condition-based maintenance (CBM), reliability-centred maintenance (RCM), and condition monitoring (CM). While many thousands of papers can be found of these four models, the support in the literature for the remaining 33 models is very limited. Research limitations/implications -While providing a sound foundation for future research, the papers findings are based solely on reviewing literature. Practical implications -For facilities managers seeking to expand their knowledge of a particular model or maintenance management systems in general, the paper provides a practical understanding. Originality/value -Papers focused solely on identifying and describing maintenance management models are scarce and this paper makes a concerted attempt to link academic research with management practitioners.
Optimal design of a multi-item, multi-location, multi-repair type repair and supply system
Naval Research Logistics Quarterly, 1974
The design of a system with many locations, each with many items which may fail while in use, is considered. When itemr fail, they require repair; the particular type of repair being governed by a probability distribution. As repairs may be lengthy, spares are kept on hand to replace failed items. System ineffectiveness is measured by expected weighted shortages over all items and locations, in steady state. This can be reduced by either having more spares or shorter expected repair times. Design consists of a provisioning of the number of spares for each item, by location; and specifying the expected repair times for each type of repair, by item and location. The optimal design minimizes expected shortages within a budget constraint, which covers both (i) procurement of spares and (ii) procurement of equipment and manning levels for the repair facilities. All costs are assumed to be separable so that a Lagrangian approach is fruitful, yielding an implementable algorithm with outputs useful for sensitivity analysis. A numerical example is presented. *This research was sponsored by the Office of Naval Research under contract N00014-724-0266 (NR-347-022).
An Integrated Resource Allocation Model for Infrastructure Maintenance
A key decision in infrastructure management is the allocation of resources to maintenance activities which consist of periodic rehabilitation actions and routine day-to-day operations. This decision involves investments into several products that improve the service quality of different infrastructure assets, contribute to different objectives, and differ from each other in other ways as well; yet all these products impact the same infrastructure system and compete for resources from the same budget. There are also important temporal tradeoffs: for example, although increased funding of routine operations may improve customer satisfaction in the shorter term, it may result in a lower funding of rehabilitation actions and erode the quality of assets in the longer term. In response to these challenges, we present a generic resource allocation model for which we developed for the Finnish Road Administration (Finnra) by building and interlinking (i) a preference model which yields the aggregate value of maintenance products by applying multi-attribute value functions to their quality distributions, (ii) a life-cycle model which captures the deterioration-improvement dynamics of rehabilitation actions, (iii) an optimization model which determines funding recommendations that maximize the aggregate long-term value of maintenance investments. The results were explored in facilitated workshops where 'on-the-fly' computations gave senior managers insights into how the recommended allocations depended on preferences and budget levels. The case study was awarded in Finnra's research program, and it was also recognized as a Finalist for the Decision Analysis Society Practice Award in 2007.
2007
We present a quadratic programming formulation for the problem of obtaining optimal maintenance and repair policies for multifacility transportation infrastructure systems. The proposed model provides a computationally tractable framework to support decision making, while accounting for economic interdependencies that link the facilities that comprise these systems. To demonstrate the advantages of the proposed model, we present numerical examples that capture economic interdependencies reflecting both costs associated with disruptions/loss of throughput, as well as the benefits associated with coordinating intervention schedules of adjacent facilities to reduce costs associated with resource and personnel delivery. The results illustrate situations where it is optimal to coordinate ͑synchronize or alternate͒ interventions for clusters of facilities in transportation systems.
The Concept of a Regional Maintenance Center
Journal of Public Transportation, 2009
Key contributors to address maintenance concerns for rural transit systems are an aging fleet, poor maintenance practices, and a lack of technical expertise. This lack of local maintenance expertise is especially severe in rural areas. This paper examines the general requirements and maintenance service approach for a Regional Maintenance Center model in rural transportation systems. Among other findings of this study, it was determined that Regional Maintenance Centers, with a training center for mechanics and drivers, could improve vehicle reliability, increase vehicle longevity, and improve service to transit clientele. Also determined was that a generalized "onesize-fits-all" regional maintenance program could actually be counter-productive. Regional Maintenance Centers must be designed and located so potential use by rural transit service providers is maximized in order to provide superior serviceability and quality customer service.