Modeling and Dynamic Assignment of the Adaptive Buffer Spaces in Serial Production Lines

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Jing Huang ◽  
Qing Chang ◽  
Jorge Arinez
Keyword(s):  
Real Time ◽  
Production Systems ◽  
Dynamic Programming Algorithm ◽  
Computation Time ◽  
Programming Algorithm ◽  
System Throughput ◽  
Novel Concept ◽  
Serial Production Lines ◽  
Buffer Capacities ◽  
The Impact

Abstract In production systems, the buffer capacities have usually been assumed to be fixed during normal operations. Inspired by the observations from the real industrial operations, a novel concept of Adaptive Buffer Space (ABS) is proposed in this paper. The ABS is a type of equipment, such as movable racks or mobile robots with racks, which can be used to provide extra storage space for a production line to temporarily increase certain buffers’ capacities in a real-time fashion. A good strategy to assign and reassign the ABS can significantly improve real-time production throughput. In order to model the production systems with changing buffer capacities, a data-driven model is developed to incorporate the impact of buffer capacity variation in system dynamics. Based on the model, a real-time ABS assignment strategy is developed by analyzing real-time buffer levels and machine status. The strategy is demonstrated to be effective in improving the system throughput. An approximate dynamic programming algorithm, referred to as ABS-ADP, is developed to obtain the optimal ABS assignment policy based on the strategy. Traditional ADP algorithms often initialize the state values with zeros or random numbers. In this paper, a knowledge-guided value function initialization method is proposed in ABS-ADP algorithm to expedite the convergence, which saves up to 80% computation time in the case study.

scholarly journals Using Computer Simulation Method to Improve Throughput of Production Systems by Buffers and Workers Allocation

2015 ◽  
Vol 6 (4) ◽  
pp. 60-69 ◽  
Author(s):  
Sławomir Kłos ◽  
Peter Trebuna
Keyword(s):  
Computer Simulation ◽  
Production System ◽  
Production Systems ◽  
Simulation Method ◽  
System Throughput ◽  
Buffer Allocation ◽  
Simulation Experiments ◽  
Buffer Capacities ◽  
The Relationship ◽  
Support Decision Making

Abstract This paper proposes the application of computer simulation methods to support decision making regarding intermediate buffer allocations in a series-parallel production line. The simulation model of the production system is based on a real example of a manufacturing company working in the automotive industry. Simulation experiments were conducted for different allocations of buffer capacities and different numbers of employees. The production system consists of three technological operations with intermediate buffers between each operation. The technological operations are carried out using machines and every machine can be operated by one worker. Multi-work in the production system is available (one operator operates several machines). On the basis of the simulation experiments, the relationship between system throughput, buffer allocation and the number of employees is analyzed. Increasing the buffer capacity results in an increase in the average product lifespan. Therefore, in the article a new index is proposed that includes the throughput of the manufacturing system and product life span. Simulation experiments were performed for different configurations of technological operations.

Optimal scheduling algorithms of system chip power density based on network on chip

2021 ◽  
pp. 120-127
Author(s):  
Jiashen Li ◽  
◽  
Yun Pan ◽  
Keyword(s):  
Dynamic Programming ◽  
Power Density ◽  
Simulated Annealing Algorithm ◽  
Dynamic Programming Algorithm ◽  
Network On Chip ◽  
Thermal Design ◽  
Optimal Scheduling ◽  
Programming Algorithm ◽  
System Throughput ◽  
On Chip

The improvement of chip integration leads to the increase of power density of system chips, which leads to the overheating of system chips. When dispatching the power density of system chips, some working modules are selectively closed to avoid all modules on the chip being turned on at the same time and to solve the problem of overheating. Taking 2D grid-on-chip network as the research object, an optimal scheduling algorithm of system-on-chip power density based on network-on-chip (NoC) is proposed. Under the constraints of thermal design power (TDP) and system, dynamic programming algorithm is used to solve the optimal application set throughput allocation from bottom to top by dynamic programming for the number and frequency level of each application configuration processor under the given application set of network-on-chip. On this basis, the simulated annealing algorithm is used to complete the application mapping aiming at heat dissipation effect and communication delay. The open and closed processor layout is determined. After obtaining the layout results, the TDP is adjusted. The maximum TDP constraint is iteratively searched according to the feedback loop of the system over-hot spots, and the power density scheduling performance of the system chip is maximized under this constraint, so as to ensure the system core. At the same time, chip throughput can effectively solve the problem of chip overheating. The experimental results show that the proposed algorithm increases the system chip throughput by about 11%, improves the system throughput loss, and achieves a balance between the system chip power consumption and scheduling time.

Dynamic Programming Approach for Online Freeway Flow Propagation Adjustment

2002 ◽  
Vol 1802 (1) ◽  
pp. 263-270 ◽  
Author(s):  
Xuesong Zhou ◽  
Hani S. Mahmassani
Keyword(s):  
Dynamic Programming ◽  
Control Method ◽  
Dynamic Programming Algorithm ◽  
Superior Performance ◽  
Programming Algorithm ◽  
Programming Approach ◽  
Base Case ◽  
Solution Quality ◽  
Flow Propagation ◽  
The Impact

An optimization framework for online flow propagation adjustment in a freeway context was proposed. Instead of performing local adjustment for individual links separately, the proposed framework considers the interconnectivity of links in a traffic network. In particular, dynamic behavior in the mesoscopic simulation is approximated by the finite-difference method at a macroscopic level. The proposed model seeks to minimize the deviation between simulated density and anticipated density. By taking advantage of the serial structure of a freeway, an efficient dynamic programming algorithm has been developed and tested. The experiment results compared with analytic results as the base case showed the superior performance of dynamic programming methods over the classical proportion control method. The effect of varying update intervals was also examined. The simulation results suggest that a greedy method considering the impact of inconsistency propagation achieves the best trade-off in terms of computation effort and solution quality.

A Game Theoretical Real Options Framework for Investment Decisions in Mobile TV Infrastructure

2017 ◽  
Vol 34 (04) ◽  
pp. 1750014
Author(s):  
Amal Abdel Razzac ◽  
Linda Salahaldin ◽  
Salah Eddine Elayoubi ◽  
Yezekael Hayel ◽  
Tijani Chahed
Keyword(s):  
Real Options ◽  
Cellular Network ◽  
Investment Decision ◽  
Dynamic Programming Algorithm ◽  
Investment Decisions ◽  
Programming Algorithm ◽  
Maximization Problem ◽  
Market Uncertainty ◽  
Mobile Tv ◽  
The Impact

This work presents a strategic investment framework for mobile TV infrastructure. We address the question of whether an operator should enter the mobile TV market and, if yes, how and when to do so. We perform a capacity and QoS analysis that shows that Mobile TV is not a simple added-on service but requires new planning strategies that need heavy investments. We consider two possible settings: a competitive mobile TV network where a digital video broadcasting (DVB) operator and a cellular network operator build their independent networks and a cooperative setting where the TV network is mainly relying on a DVB infrastructure whose coverage can be complemented by a cellular network. Two main issues alter the investment decision of the stakeholders, namely, the market uncertainty and the competition or the willingness of cooperation between the main technological players. We define a game theoretical real options framework and propose a novel bi-level dynamic programming algorithm that solves the underlying profit maximization problem. Our numerical results illustrate the optimal investment decisions of both actors and the impact of the system parameters as well as the degree of uncertainty on the investment dates.

Optimal Flight Path Planning of Cruising Phase with No-Fly Zone Constraints Based on Dynamic Programming Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 5911-5917
Author(s):  
Su Xiao Wang ◽  
Yong Sheng Yang ◽  
Zhong Liang Jing
Keyword(s):  
Dynamic Programming ◽  
Path Planning ◽  
Real Time ◽  
Optimal Path ◽  
Dynamic Programming Algorithm ◽  
Optimization Method ◽  
Flight Path ◽  
Programming Algorithm ◽  
Effective Implementation ◽  
Arbitrary Selection

The purpose of flight path planning is to find the optimal path from the real-time and conflict-free airspace to meet the targets, according to one or several performance index. Effective avoiding the no-fly zones, such as the areas of martial movement and the areas of rain and thunderstorm, has great significance to the current flight management system (FMS) that is real-time and effective implementation of the flight plan. The dynamic optimization method of level route based on DP (Dynamic Programming) algorithm without no-fly zone constraints is discussed. Quick and effective to find out an optimal path from the waypoints of arbitrary selection and input can be realized. On this basis, the situation of adding no-fly zone constraints is focused on. In order to ensure that the aircraft is able to effectively avoid no-fly zone constraints in actual flight, Gauss Kruger projection method to convert geographic coordinates to plane coordinates is adopted. Simulation results show that the method used can not only effectively avoid no-fly zone constraints, and the path passed is still optimal.

scholarly journals An improved heuristic-dynamic programming algorithm for rectangular cutting problem

2020 ◽  
Vol 17 (3) ◽  
pp. 717-735
Author(s):  
Aihua Yin ◽  
Chong Chen ◽  
Dongping Hu ◽  
Jianghai Huang ◽  
Fan Yang
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Programming Algorithm ◽  
Recursive Method ◽  
Two Dimensional ◽  
One Dimensional ◽  
Block Width ◽  
Cutting Problem

In this paper, the two-dimensional cutting problem with defects is discussed. The objective is to cut some rectangles in a given shape and direction without overlapping the defects from the rectangular plate and maximize some profit associated. An Improved Heuristic-Dynamic Program (IHDP) is presented to solve the problem. In this algorithm, the discrete set contains not only the solution of one-dimensional knapsack problem with small rectangular block width and height, but also the cutting positions of one unit outside four boundaries of each defect. In addition, the denormalization recursive method is used to further decompose the sub problem with defects. The algorithm computes thousands of typical instances. The computational experimental results show that IHDP obtains most of the optimal solution of these instances, and its computation time is less than that of the latest literature algorithms.

scholarly journals Efficient Solution Algorithms for Factored MDPs

2003 ◽  
Vol 19 ◽  
pp. 399-468 ◽  
Author(s):  
C. Guestrin ◽  
D. Koller ◽  
R. Parr ◽  
S. Venkataraman
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Central Element ◽  
Dynamic Bayesian Network ◽  
Computation Time ◽  
Programming Algorithm ◽  
Small Subset ◽  
Planning Under Uncertainty ◽  
State Variables ◽  
Solution Algorithms

This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model using a dynamic Bayesian network. This representation often allows an exponential reduction in the representation size of structured MDPs, but the complexity of exact solution algorithms for such MDPs can grow exponentially in the representation size. In this paper, we present two approximate solution algorithms that exploit structure in factored MDPs. Both use an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. A key contribution of this paper is that it shows how the basic operations of both algorithms can be performed efficiently in closed form, by exploiting both additive and context-specific structure in a factored MDP. A central element of our algorithms is a novel linear program decomposition technique, analogous to variable elimination in Bayesian networks, which reduces an exponentially large LP to a provably equivalent, polynomial-sized one. One algorithm uses approximate linear programming, and the second approximate dynamic programming. Our dynamic programming algorithm is novel in that it uses an approximation based on max-norm, a technique that more directly minimizes the terms that appear in error bounds for approximate MDP algorithms. We provide experimental results on problems with over 10^40 states, demonstrating a promising indication of the scalability of our approach, and compare our algorithm to an existing state-of-the-art approach, showing, in some problems, exponential gains in computation time.

scholarly journals Are all global alignment algorithms and implementations correct?

2015 ◽  
Author(s):  
Tomáš Flouri ◽  
Kassian Kobert ◽  
Torbjørn Rognes ◽  
Alexandros Stamatakis
Keyword(s):  
Dynamic Programming ◽  
Phylogenetic Analyses ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Global Alignment ◽  
Sequence Alignments ◽  
Optimal Sequence ◽  
Divergence Time Estimates ◽  
Alignment Algorithms ◽  
The Impact

While implementing the algorithm, we discovered two mathematical mistakes in Gotoh's paper that induce sub-optimal sequence alignments. First, there are minor indexing mistakes in the dynamic programming algorithm which become apparent immediately when implementing the procedure. Hence, we report on these for the sake of completeness. Second, there is a more profound problem with the dynamic programming matrix initialization. This initialization issue can easily be missed and find its way into actual implementations. This error is also present in standard text books. Namely, the widely used books by Gusfield and Waterman. To obtain an initial estimate of the extent to which this error has been propagated, we scrutinized freely available undergraduate lecture slides. We found that 8 out of 31 lecture slides contained the mistake, while 16 out of 31 simply omit parts of the initialization, thus giving an incomplete description of the algorithm. Finally, by inspecting ten source codes and running respective tests, we found that five implementations were incorrect. Note that, not all bugs we identified are due to the mistake in Gotoh's paper. Three implementations rely on additional constraints that limit generality. Thus, only two out of ten yield correct results. We show that the error introduced by Gotoh is straightforward to resolve and provide a correct open-source reference implementation. We do believe though, that raising the awareness about these errors is critical, since the impact of incorrect pairwise sequence alignments that typically represent one of the very first stages in any bioinformatics data analysis pipeline can have a detrimental impact on downstream analyses such as multiple sequence alignment, orthology assignment, phylogenetic analyses, divergence time estimates, etc.

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