A survey of dynamic programming computational procedures

Larson, Robert E.

doi:10.1109/tac.1967.1098755

Cited by 74 publications

(25 citation statements)

References 17 publications

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“…2). Dynamic programming is frequently used to solve multistage graph problems [57,58], while for problems with a huge solution space, it has extremely large computational requirements-especially storage requirements [59]. Meanwhile, due to the absence of heuristic information, dynamic programming would also be very slow in the optimization process.…”

Section: Algorithm Designmentioning

confidence: 99%

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Cao

Wang

Kang

et al. 2015

Int J Adv Manuf Technol

147

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Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

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Section: Algorithm Designmentioning

confidence: 99%

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Cao

Wang

Kang

et al. 2015

Int J Adv Manuf Technol

147

View full text Add to dashboard Cite

show abstract

“…One is to approximate G * k using a parametric family of surfaces, such as polynomials or nonlinear basis functions derived from neural networks [12]. The other is to store G * k only over a finite set of sample points and use interpolation to obtain its value at all other points [61,62].…”

Section: Using Interpolation For Continuous State Spacesmentioning

confidence: 99%

“…Value iteration with interpolation, the subject of Section 8.5.2, is sometimes forgotten in motion planning because computers were not powerful enough at the time it was developed [8,9,61,62]. Presently, however, solutions can be computed for challenging problems with several dimensions (e.g., 3 or 4).…”

Section: Further Readingmentioning

confidence: 99%

Feedback Motion Planning

LaValle

2006

Planning Algorithms

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“…This approach has been used in dynamic programming for some time to compute the value of successors that are not in the set of samples [7][8][9]. However, as LaValle points out [9], when the action space is also continuous, this becomes difficult, as solving for the value of a state now requires minimizing over an uncountably infinite set of successor states.…”

Section: Pioneersmentioning

confidence: 99%

Field D*: An Interpolation-Based Path Planner and Replanner

Ferguson

Stentz

Springer Tracts in Advanced Robotics

183

138

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Abstract. We present an interpolation-based planning and replanning algorithm for generating smooth paths through non-uniform cost grids. Most grid-based path planners use discrete state transitions that artificially constrain an agent's motion to a small set of possible headings (e.g. 0,, etc). As a result, even the 'optimal' grid planners produce unnatural, suboptimal paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and to produce paths with a continuous range of headings. Consequently, it is particularly well suited to planning smooth, least-cost trajectories for mobile robots. In this paper, we present a number of applications and results, a comparison to related algorithms, and several implementations on real robotic systems.

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A survey of dynamic programming computational procedures

Cited by 74 publications

References 17 publications

A TQCS-based service selection and scheduling strategy in cloud manufacturing

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Feedback Motion Planning

Field D*: An Interpolation-Based Path Planner and Replanner

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