The parallel replacement problem with demand and capital budgeting constraints

Hartman, Joseph C.

doi:10.1002/(sici)1520-6750(200002)47:1<40::aid-nav3>3.0.co;2-t

Cited by 49 publications

(28 citation statements)

References 23 publications

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“…For both data sets, (10,10) and (15,10), the serial fractions are on average greater than 1/2, meaning that we cannot obtain a speedup greater than 2. On the other hand, looking at Table 3.8, we see some speedups close to 5 for data set (15,10). Intra-node parallelization gives better speedups with smaller number of nodes.…”

Section: Intra-node Parallelizationmentioning

confidence: 96%

“…We use the two collections of instances, labeled "mknap1" and "mknap2." We use problem instances (10,10), (15,10), (20,10), (28,10), (39,5) and (50,5) from "mknap1" and instances (60,5), (70,5), (80,5), (90,5) from "mknap2" where the first number represents the number of items and the second represents the number of knapsack dimensions.…”

Section: Prioritization With Total Order Restrictionmentioning

confidence: 99%

“…For the first setting, we use the problem instances (6,10), (10,10) and (15,10). For the second setting, we use instances (10,10), (15,10), and (20,10).…”

Section: The Primal Heuristicmentioning

confidence: 99%

“…This can be observed better by comparing the average speedups of the data sets (10,10) and (15,10). In some problem instances, there is no improvement in speedup, after some number of processors.…”

Section: Inter-node Parallelizationmentioning

confidence: 99%

“…Inspecting Table 3.4 for the data set (15,10) leads to the question of whether we can obtain increasing speedup as we increase the number of processors. This can be estimated, to some extent, by looking at the observed serial fractions in Table 3.5.…”

Section: Inter-node Parallelizationmentioning

confidence: 99%

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Prioritization via Stochastic Optimization

Koç

Morton

2015

Management Science

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We take a novel approach to decision problems involving binary activity-selection decisions competing for scarce resources. The literature approaches such problems by forming an optimal portfolio of activities. However, often practitioners instead form a rank-ordered list of activities and select those with the highest priority. We account for both viewpoints. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework, and we apply our approach to a facility location problem and a multidimensional knapsack problem. We develop two sets of cutting planes to improve computation. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1865 . This paper was accepted by Dimitris Bertsimas, optimization.

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Section: Intra-node Parallelizationmentioning

confidence: 96%

Section: Prioritization With Total Order Restrictionmentioning

confidence: 99%

“…For the first setting, we use the problem instances (6,10), (10,10) and (15,10). For the second setting, we use instances (10,10), (15,10), and (20,10).…”

Section: The Primal Heuristicmentioning

confidence: 99%

Section: Inter-node Parallelizationmentioning

confidence: 99%

Section: Inter-node Parallelizationmentioning

confidence: 99%

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Prioritization via Stochastic Optimization

Koç

Morton

2015

Management Science

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Optimal machine capacity expansions with nested limitations under stochastic demand

Çakanyıldırım

Roundy

Wood

2003

Naval Research Logistics

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This paper studies capacity expansions for a production facility that faces uncertain customer demand for a single product family. The capacity of the facility is modeled in three tiers, as follows. The first tier consists of a set of upper bounds on production that correspond to different resource types (e.g., machine types, categories of manpower, etc.). These upper bounds are augmented in increments of fixed size (e.g., by purchasing machines of standard types). There is a second-tier resource that constrains the first-tier bounds (e.g., clean room floor space). The third-tier resource bounds the availability of the second-tier resource (e.g., the total floor space enclosed by the building, land, etc.). The second and third-tier resources are expanded at various times in various amounts. The cost of capacity expansion at each tier has both fixed and proportional elements. The lost sales cost is used as a measure for the level of customer service. The paper presents a polynomial time algorithm (FIFEX) to minimize the total cost by computing optimal expansion times and amounts for all three types of capacity jointly. It accommodates positive lead times for each type. Demand is assumed to be nondecreasing in a "weak" sense.

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Holistic fleet optimization incorporating system design considerations

Henry

Hoffman

Waddell

et al. 2023

Naval Research Logistics

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The methodology described in this article enables a type of holistic fleet optimization that simultaneously considers the composition and activity of a fleet through time as well as the design of individual systems within the fleet. Often, real‐world system design optimization and fleet‐level acquisition optimization are treated separately due to the prohibitive scale and complexity of each problem. This means that fleet‐level schedules are typically limited to the inclusion of predefined system configurations and are blind to a rich spectrum of system design alternatives. Similarly, system design optimization often considers a system in isolation from the fleet and is blind to numerous, complex portfolio‐level considerations. In reality, these two problems are highly interconnected. To properly address this system‐fleet design interdependence, we present a general method for efficiently incorporating multi‐objective system design trade‐off information into a mixed‐integer linear programming (MILP) fleet‐level optimization. This work is motivated by the authors' experience with large‐scale DOD acquisition portfolios. However, the methodology is general to any application where the fleet‐level problem is a MILP and there exists at least one system having a design trade space in which two or more design objectives are parameters in the fleet‐level MILP.

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The parallel replacement problem with demand and capital budgeting constraints

Cited by 49 publications

References 23 publications

Prioritization via Stochastic Optimization

Prioritization via Stochastic Optimization

Optimal machine capacity expansions with nested limitations under stochastic demand

Holistic fleet optimization incorporating system design considerations

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