Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are a...Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are actually in a complex, interdependent relationship. To provide this, an index set of multi-target tracking decision characteristics and an analytic network process （ANP） model of the UMTLD method was -established. This method brings the index set of multi-target tracking decision into the ANP model, and the optimization multitarket tracking decision is achieved via computation of the resulting supermatrix. The rationality and robustness of decision results increase in simulations by 13% and 47% respectively with analytic hierarchy process （AHP）. These results indicate that the ANP method should be the preferred method when UMTLD factors are interdependent.展开更多
In order to improve nitrogen removal in anoxic/oxic(A/O) process effectively for treating domestic wastewaters, the influence factors, DO(dissolved oxygen), nitrate recirculation, sludge recycle, SRT(solids residence ...In order to improve nitrogen removal in anoxic/oxic(A/O) process effectively for treating domestic wastewaters, the influence factors, DO(dissolved oxygen), nitrate recirculation, sludge recycle, SRT(solids residence time), influent COD/TN and HRT(hydraulic retention time) were studied. Results indicated that it was possible to increase nitrogen removal by using corresponding control strategies, such as, adjusting the DO set point according to effluent ammonia concentration; manipulating nitrate recirculation flow according to nitrate concentration at the end of anoxic zone. Based on the experiments results, a knowledge-based approach for supervision of the nitrogen removal problems was considered, and decision trees for diagnosing nitrification and denitrification problems were built and successfully applied to A/O process.展开更多
For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life(RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system re...For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life(RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system reliability and preventing disaster. RUL is affected not only by a system's intrinsic deterioration, but also by the operational conditions under which the system is operating. This paper proposes an RUL prediction approach to estimate the mean RUL of a continuously degrading system under dynamic operational conditions and subjected to condition monitoring at short equi-distant intervals. The dynamic nature of the operational conditions is described by a discrete-time Markov chain, and their influences on the degradation signal are quantified by degradation rates and signal jumps in the degradation model. The uniqueness of our proposed approach is formulating the RUL prediction problem in a semi-Markov decision process framework, by which the system mean RUL can be obtained through the solution to a limited number of equations. To extend the use of our proposed approach in real applications, different failure standards according to different operational conditions are also considered. The application and effectiveness of this approach are illustrated by a turbofan engine dataset and a comparison with existing results for the same dataset.展开更多
A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load cur...A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.展开更多
Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are random.In particular,it serves as a mathematical framework for reinforcement learning.This paper intro...Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are random.In particular,it serves as a mathematical framework for reinforcement learning.This paper introduces an extension of MDP,namely quantum MDP(q MDP),that can serve as a mathematical model of decision making about quantum systems.We develop dynamic programming algorithms for policy evaluation and finding optimal policies for q MDPs in the case of finite-horizon.The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.展开更多
Optimal policies in Markov decision problems may be quite sensitive with regard to transition probabilities.In practice,some transition probabilities may be uncertain.The goals of the present study are to find the rob...Optimal policies in Markov decision problems may be quite sensitive with regard to transition probabilities.In practice,some transition probabilities may be uncertain.The goals of the present study are to find the robust range for a certain optimal policy and to obtain value intervals of exact transition probabilities.Our research yields powerful contributions for Markov decision processes(MDPs)with uncertain transition probabilities.We first propose a method for estimating unknown transition probabilities based on maximum likelihood.Since the estimation may be far from accurate,and the highest expected total reward of the MDP may be sensitive to these transition probabilities,we analyze the robustness of an optimal policy and propose an approach for robust analysis.After giving the definition of a robust optimal policy with uncertain transition probabilities represented as sets of numbers,we formulate a model to obtain the optimal policy.Finally,we define the value intervals of the exact transition probabilities and construct models to determine the lower and upper bounds.Numerical examples are given to show the practicability of our methods.展开更多
Markov decision processes （MDPs） and their variants are widely studied in the theory of controls for stochastic discrete- event systems driven by Markov chains. Much of the literature focusses on the risk-neutral cr...Markov decision processes （MDPs） and their variants are widely studied in the theory of controls for stochastic discrete- event systems driven by Markov chains. Much of the literature focusses on the risk-neutral criterion in which the expected rewards, either average or discounted, are maximized. There exists some literature on MDPs that takes risks into account. Much of this addresses the exponential utility （EU） function and mechanisms to penalize different forms of variance of the rewards. EU functions have some numerical deficiencies, while variance measures variability both above and below the mean rewards; the variability above mean rewards is usually beneficial and should not be penalized/avoided. As such, risk metrics that account for pre-specified targets （thresholds） for rewards have been considered in the literature, where the goal is to penalize the risks of revenues falling below those targets. Existing work on MDPs that takes targets into account seeks to minimize risks of this nature. Minimizing risks can lead to poor solutions where the risk is zero or near zero, but the average rewards are also rather low. In this paper, hence, we study a risk-averse criterion, in particular the so-called downside risk, which equals the probability of the revenues falling below a given target, where, in contrast to minimizing such risks, we only reduce this risk at the cost of slightly lowered average rewards. A solution where the risk is low and the average reward is quite high, although not at its maximum attainable value, is very attractive in practice. To be more specific, in our formulation, the objective function is the expected value of the rewards minus a scalar times the downside risk. In this setting, we analyze the infinite horizon MDP, the finite horizon MDP, and the infinite horizon semi-MDP （SMDP）. We develop dynamic programming and reinforcement learning algorithms for the finite and infinite horizon. The algorithms are tested in numerical studies and show encouraging performance.展开更多
This paper studies the limit average variance criterion for continuous-time Markov decision processes in Polish spaces. Based on two approaches, this paper proves not only the existence of solutions to the variance mi...This paper studies the limit average variance criterion for continuous-time Markov decision processes in Polish spaces. Based on two approaches, this paper proves not only the existence of solutions to the variance minimization optimality equation and the existence of a variance minimal policy that is canonical, but also the existence of solutions to the two variance minimization optimality inequalities and the existence of a variance minimal policy which may not be canonical. An example is given to illustrate all of our conditions.展开更多
This paper is a sequel to Kageyama et al. [1], in which a Markov-type hybrid process has been constructed and the corresponding discounted total reward has been characterized by the recursive equation. The objective o...This paper is a sequel to Kageyama et al. [1], in which a Markov-type hybrid process has been constructed and the corresponding discounted total reward has been characterized by the recursive equation. The objective of this paper is to formulate a hybrid decision process and to give the existence and characterization of optimal policies.展开更多
Various process parameters exert different effects in stamping process. In order to study the relationships among the process parameters of box stamping process, including the blank holder force, friction coefficient,...Various process parameters exert different effects in stamping process. In order to study the relationships among the process parameters of box stamping process, including the blank holder force, friction coefficient, depth of drawbead, offset and length of drawbead, the decision tree algorithm C4.5 was performed to generate the decision tree using the result data of the box stamping simulation. The design and improvement methods of the decision tree were presented. Potential and valuable rules were generated by traversing the decision tree, which plays an instructive role on the practical design. The rules show that the correct combination of blank holder force and setting of drawbead are the dominant contribution for controlling the cracking and wrinkling in box stamping process. In order to validate the rules, the stamping process for box was also performed. The experiment results show good agreement with the generated rules.展开更多
There exists a growing demand for potable water resources to fill the abysmally insufficient water needs for domestic and industrial especially in the Basement Complex terrains of Nigeria. This situation is attributab...There exists a growing demand for potable water resources to fill the abysmally insufficient water needs for domestic and industrial especially in the Basement Complex terrains of Nigeria. This situation is attributable to its complex hydrogeologic character. The present challenge has worsened due to the non-incorporation of integrated methods in groundwater exploration campaigns. To effectively combat the challenge of unacceptable failure rates in drilled water well development, there is a need for innovative scientific principles and quantitative assessment of groundwater resources to enhance sustainable and proper utilisation of these resources. Hence, it is the objective of this research to exploit the potential application of remote sensing, Geographic Information System (GIS), and Multi-Criteria Decision Analysis (MCDA) techniques and freely open datasets in mapping groundwater potential zones. Seven thematic maps have been produced based on factors that are deemed to influence and deemed to have significant control on the occurrence and movement of groundwater. These factors are geology, lineament density, slope, drainage density, rainfall, land-use/land cover, and soil class. Analytic Hierarchy Process (AHP) was used to assign normalised weights to the thematic maps based on the various relative contributions to groundwater occurrence and movement. These thematic maps were then processed in a GIS environment using the Weighted Overlay tool which implements the MCDA. The resulting Groundwater Potential Zones (GPZ) of the area gave rise to Five classes viz: Very good, Good, Moderate, Poor and Very Poor </span><span style="font-family:Verdana;">representing 19%, 8%, 14%, 47% and 13% respectively. It is recommended that the GPZ map should be used as a reconnaissance tool for selecting prospective sites for detailed groundwater resource exploitation.展开更多
Aiming to share the information,knowledge and optimizing resource via collaborating with multiple external partners across their supply chains,the concept model and system framework of multi-enterprises collaborative ...Aiming to share the information,knowledge and optimizing resource via collaborating with multiple external partners across their supply chains,the concept model and system framework of multi-enterprises collaborative resource planning (MECORP) are put forward.While there is Considerable pressure to improve the operation of MECORP system,their inherent complexity can make modelling a MECORP system a difficult task.Yet there could be considerable benefits in designing MECORP taking into account the operation of the system.In order to address the central research issue of developing of a methodology that can assist a manager in making decisions by modelling the operation of MECORP system.The methodology called process-oriented deci- sion model (PODM) is presented in the paper.This uses an abstracted network to model MECORP system.The MECORP system supported by PODM,can effective optimize the manifold resource,coordinate the relationship of multiple partners and assist deci- sion.Finally,an industry excample of MECORP system is described to illustrate the application of PODM.展开更多
Most studies on investment evaluation mainly focus on enterprise economic benefits only, without process operability and sustainability considered. In this paper, we suggest that investment evaluation in process indus...Most studies on investment evaluation mainly focus on enterprise economic benefits only, without process operability and sustainability considered. In this paper, we suggest that investment evaluation in process industries should be executed under three strategic objectives--enterprise benefits, social benefits and customer benefits. A systematic investment evaluation and decision-making method with a four-step procedure based on the analytic hierarchy process (AHP) is proposed to evaluate various qualitative and quantitative elements with various criteria. At the first step, the decision hierarchy is constructed under the three strategic objectives. Second, pair-wise comparison is utilized to evaluate the weights of elements and criteria. Third, qualitative elements are quantified by pair-wise comparison and quantitative elements are re-scaled by a uniform criterion. At the last, the best choice is made through synthesizing values upward in the hierarchy. An investment decision support system (DSS) is developed based on Microsoft Excel, and applied to a retrofit investment of united fluid catalytic cracking(FCC) and liquefied gas separation process in a refinery plant.展开更多
Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making i...Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.展开更多
This paper considers the variance optimization problem of average reward in continuous-time Markov decision process (MDP). It is assumed that the state space is countable and the action space is Borel measurable space...This paper considers the variance optimization problem of average reward in continuous-time Markov decision process (MDP). It is assumed that the state space is countable and the action space is Borel measurable space. The main purpose of this paper is to find the policy with the minimal variance in the deterministic stationary policy space. Unlike the traditional Markov decision process, the cost function in the variance criterion will be affected by future actions. To this end, we convert the variance minimization problem into a standard (MDP) by introducing a concept called pseudo-variance. Further, by giving the policy iterative algorithm of pseudo-variance optimization problem, the optimal policy of the original variance optimization problem is derived, and a sufficient condition for the variance optimal policy is given. Finally, we use an example to illustrate the conclusion of this paper.展开更多
A theory on information prediction process proposed by Weng Wenpo(1991)is applied to the earthquake prediction decision process.Four cycles represent the theory(conception),earthquake prediction decision result,anomal...A theory on information prediction process proposed by Weng Wenpo(1991)is applied to the earthquake prediction decision process.Four cycles represent the theory(conception),earthquake prediction decision result,anomalies,and earthquake assemblage,respectively.The interception and overlapping of the four cycles indicate different combinations,resulting in formation of 13 regions.In the case of decision conclusion on earthquake to occur,seven decision results of different characters are distinguished.The six other results were obtained in the case of absence of decision.Results of four characters show correct decision on earthquake to occur and those of three characters show the erroneous decision on earthquake to occur.Until now,theories of earthquake prediction have been incomplete,and the coincidence ratio of decision on earthquake to occur is also considerably low.Systematic analysis of the decision process is beneficial to understanding the causes for missing,virtual,pseudo,false,and correct展开更多
This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is p...This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is proposed that updates an increasingly accurate approximate version of the original problem with a view to saving computations at the early iterations, when one is typically far from the optimal solution. The proposed algorithm is compared to classical value iteration for a broad set of adaptive parameters and the results suggest that significant computational savings can be obtained, while also ensuring a robust performance with respect to the parameters.展开更多
We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional va...We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.展开更多
In this study, we use the respective advantages of the tabu search (TS) and the Web-based technologies to develop a Web-based decision support system (DSS) for cell formation (CF) problems considering alternative proc...In this study, we use the respective advantages of the tabu search (TS) and the Web-based technologies to develop a Web-based decision support system (DSS) for cell formation (CF) problems considering alternative process routings and machine sequences simultaneously. With the assistance of our developed Web-based system, the CF practitioners in the production departments can interact with the systems without knowing the details of algorithms and can get the best machine cells and part families with minimize the total intercellular movement wherever and whenever they may need it. To further verify the feasibility and effectiveness of the system developed, an example taken from the literature is ado- pted for illustrational purpose. Moreover, a set of test problems with various sizes drawn from the literature is used to test the performance of the proposed system. Corresponding results are compared to several well-known algorithms previously published. The results indicate that the proposed system improves the best results found in the literature for 67% of the test problems. These show that the proposed system should thus be useful to both practitioners and researchers.展开更多
基金Supported by the State Key Laboratory Foundation under Grant No.9140C2304080607the Aviation Science Foundation under Grant No.05F53027
文摘Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are actually in a complex, interdependent relationship. To provide this, an index set of multi-target tracking decision characteristics and an analytic network process （ANP） model of the UMTLD method was -established. This method brings the index set of multi-target tracking decision into the ANP model, and the optimization multitarket tracking decision is achieved via computation of the resulting supermatrix. The rationality and robustness of decision results increase in simulations by 13% and 47% respectively with analytic hierarchy process （AHP）. These results indicate that the ANP method should be the preferred method when UMTLD factors are interdependent.
文摘In order to improve nitrogen removal in anoxic/oxic(A/O) process effectively for treating domestic wastewaters, the influence factors, DO(dissolved oxygen), nitrate recirculation, sludge recycle, SRT(solids residence time), influent COD/TN and HRT(hydraulic retention time) were studied. Results indicated that it was possible to increase nitrogen removal by using corresponding control strategies, such as, adjusting the DO set point according to effluent ammonia concentration; manipulating nitrate recirculation flow according to nitrate concentration at the end of anoxic zone. Based on the experiments results, a knowledge-based approach for supervision of the nitrogen removal problems was considered, and decision trees for diagnosing nitrification and denitrification problems were built and successfully applied to A/O process.
基金the National Natural science Foundation of China (No. 71701008) for supporting this research
文摘For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life(RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system reliability and preventing disaster. RUL is affected not only by a system's intrinsic deterioration, but also by the operational conditions under which the system is operating. This paper proposes an RUL prediction approach to estimate the mean RUL of a continuously degrading system under dynamic operational conditions and subjected to condition monitoring at short equi-distant intervals. The dynamic nature of the operational conditions is described by a discrete-time Markov chain, and their influences on the degradation signal are quantified by degradation rates and signal jumps in the degradation model. The uniqueness of our proposed approach is formulating the RUL prediction problem in a semi-Markov decision process framework, by which the system mean RUL can be obtained through the solution to a limited number of equations. To extend the use of our proposed approach in real applications, different failure standards according to different operational conditions are also considered. The application and effectiveness of this approach are illustrated by a turbofan engine dataset and a comparison with existing results for the same dataset.
文摘A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.
基金partly supported by National Key R&D Program of China(No.2018YFA0306701)the Australian Research Council(Nos.DP160101652 and DP180100691)+1 种基金National Natural Science Foundation of China(No.61832015)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences。
文摘Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are random.In particular,it serves as a mathematical framework for reinforcement learning.This paper introduces an extension of MDP,namely quantum MDP(q MDP),that can serve as a mathematical model of decision making about quantum systems.We develop dynamic programming algorithms for policy evaluation and finding optimal policies for q MDPs in the case of finite-horizon.The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.
基金Supported by the National Natural Science Foundation of China(71571019).
文摘Optimal policies in Markov decision problems may be quite sensitive with regard to transition probabilities.In practice,some transition probabilities may be uncertain.The goals of the present study are to find the robust range for a certain optimal policy and to obtain value intervals of exact transition probabilities.Our research yields powerful contributions for Markov decision processes(MDPs)with uncertain transition probabilities.We first propose a method for estimating unknown transition probabilities based on maximum likelihood.Since the estimation may be far from accurate,and the highest expected total reward of the MDP may be sensitive to these transition probabilities,we analyze the robustness of an optimal policy and propose an approach for robust analysis.After giving the definition of a robust optimal policy with uncertain transition probabilities represented as sets of numbers,we formulate a model to obtain the optimal policy.Finally,we define the value intervals of the exact transition probabilities and construct models to determine the lower and upper bounds.Numerical examples are given to show the practicability of our methods.
文摘Markov decision processes （MDPs） and their variants are widely studied in the theory of controls for stochastic discrete- event systems driven by Markov chains. Much of the literature focusses on the risk-neutral criterion in which the expected rewards, either average or discounted, are maximized. There exists some literature on MDPs that takes risks into account. Much of this addresses the exponential utility （EU） function and mechanisms to penalize different forms of variance of the rewards. EU functions have some numerical deficiencies, while variance measures variability both above and below the mean rewards; the variability above mean rewards is usually beneficial and should not be penalized/avoided. As such, risk metrics that account for pre-specified targets （thresholds） for rewards have been considered in the literature, where the goal is to penalize the risks of revenues falling below those targets. Existing work on MDPs that takes targets into account seeks to minimize risks of this nature. Minimizing risks can lead to poor solutions where the risk is zero or near zero, but the average rewards are also rather low. In this paper, hence, we study a risk-averse criterion, in particular the so-called downside risk, which equals the probability of the revenues falling below a given target, where, in contrast to minimizing such risks, we only reduce this risk at the cost of slightly lowered average rewards. A solution where the risk is low and the average reward is quite high, although not at its maximum attainable value, is very attractive in practice. To be more specific, in our formulation, the objective function is the expected value of the rewards minus a scalar times the downside risk. In this setting, we analyze the infinite horizon MDP, the finite horizon MDP, and the infinite horizon semi-MDP （SMDP）. We develop dynamic programming and reinforcement learning algorithms for the finite and infinite horizon. The algorithms are tested in numerical studies and show encouraging performance.
基金supported by the National Natural Science Foundation of China(10801056)the Natural Science Foundation of Ningbo(2010A610094)
文摘This paper studies the limit average variance criterion for continuous-time Markov decision processes in Polish spaces. Based on two approaches, this paper proves not only the existence of solutions to the variance minimization optimality equation and the existence of a variance minimal policy that is canonical, but also the existence of solutions to the two variance minimization optimality inequalities and the existence of a variance minimal policy which may not be canonical. An example is given to illustrate all of our conditions.
文摘This paper is a sequel to Kageyama et al. [1], in which a Markov-type hybrid process has been constructed and the corresponding discounted total reward has been characterized by the recursive equation. The objective of this paper is to formulate a hybrid decision process and to give the existence and characterization of optimal policies.
文摘Various process parameters exert different effects in stamping process. In order to study the relationships among the process parameters of box stamping process, including the blank holder force, friction coefficient, depth of drawbead, offset and length of drawbead, the decision tree algorithm C4.5 was performed to generate the decision tree using the result data of the box stamping simulation. The design and improvement methods of the decision tree were presented. Potential and valuable rules were generated by traversing the decision tree, which plays an instructive role on the practical design. The rules show that the correct combination of blank holder force and setting of drawbead are the dominant contribution for controlling the cracking and wrinkling in box stamping process. In order to validate the rules, the stamping process for box was also performed. The experiment results show good agreement with the generated rules.
文摘There exists a growing demand for potable water resources to fill the abysmally insufficient water needs for domestic and industrial especially in the Basement Complex terrains of Nigeria. This situation is attributable to its complex hydrogeologic character. The present challenge has worsened due to the non-incorporation of integrated methods in groundwater exploration campaigns. To effectively combat the challenge of unacceptable failure rates in drilled water well development, there is a need for innovative scientific principles and quantitative assessment of groundwater resources to enhance sustainable and proper utilisation of these resources. Hence, it is the objective of this research to exploit the potential application of remote sensing, Geographic Information System (GIS), and Multi-Criteria Decision Analysis (MCDA) techniques and freely open datasets in mapping groundwater potential zones. Seven thematic maps have been produced based on factors that are deemed to influence and deemed to have significant control on the occurrence and movement of groundwater. These factors are geology, lineament density, slope, drainage density, rainfall, land-use/land cover, and soil class. Analytic Hierarchy Process (AHP) was used to assign normalised weights to the thematic maps based on the various relative contributions to groundwater occurrence and movement. These thematic maps were then processed in a GIS environment using the Weighted Overlay tool which implements the MCDA. The resulting Groundwater Potential Zones (GPZ) of the area gave rise to Five classes viz: Very good, Good, Moderate, Poor and Very Poor </span><span style="font-family:Verdana;">representing 19%, 8%, 14%, 47% and 13% respectively. It is recommended that the GPZ map should be used as a reconnaissance tool for selecting prospective sites for detailed groundwater resource exploitation.
文摘Aiming to share the information,knowledge and optimizing resource via collaborating with multiple external partners across their supply chains,the concept model and system framework of multi-enterprises collaborative resource planning (MECORP) are put forward.While there is Considerable pressure to improve the operation of MECORP system,their inherent complexity can make modelling a MECORP system a difficult task.Yet there could be considerable benefits in designing MECORP taking into account the operation of the system.In order to address the central research issue of developing of a methodology that can assist a manager in making decisions by modelling the operation of MECORP system.The methodology called process-oriented deci- sion model (PODM) is presented in the paper.This uses an abstracted network to model MECORP system.The MECORP system supported by PODM,can effective optimize the manifold resource,coordinate the relationship of multiple partners and assist deci- sion.Finally,an industry excample of MECORP system is described to illustrate the application of PODM.
基金Supported by National Natural Science Foundation of China (No. 79931000) and The State Major Basic Research Development Program (G20000263).
文摘Most studies on investment evaluation mainly focus on enterprise economic benefits only, without process operability and sustainability considered. In this paper, we suggest that investment evaluation in process industries should be executed under three strategic objectives--enterprise benefits, social benefits and customer benefits. A systematic investment evaluation and decision-making method with a four-step procedure based on the analytic hierarchy process (AHP) is proposed to evaluate various qualitative and quantitative elements with various criteria. At the first step, the decision hierarchy is constructed under the three strategic objectives. Second, pair-wise comparison is utilized to evaluate the weights of elements and criteria. Third, qualitative elements are quantified by pair-wise comparison and quantitative elements are re-scaled by a uniform criterion. At the last, the best choice is made through synthesizing values upward in the hierarchy. An investment decision support system (DSS) is developed based on Microsoft Excel, and applied to a retrofit investment of united fluid catalytic cracking(FCC) and liquefied gas separation process in a refinery plant.
基金supported by National Basic Research Program (973 Program,No.2004CB719402)National Natural Science Foundation of China (No.60736019)Natural Science Foundation of Zhejiang Province, China(No.Y105430).
文摘Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.
文摘This paper considers the variance optimization problem of average reward in continuous-time Markov decision process (MDP). It is assumed that the state space is countable and the action space is Borel measurable space. The main purpose of this paper is to find the policy with the minimal variance in the deterministic stationary policy space. Unlike the traditional Markov decision process, the cost function in the variance criterion will be affected by future actions. To this end, we convert the variance minimization problem into a standard (MDP) by introducing a concept called pseudo-variance. Further, by giving the policy iterative algorithm of pseudo-variance optimization problem, the optimal policy of the original variance optimization problem is derived, and a sufficient condition for the variance optimal policy is given. Finally, we use an example to illustrate the conclusion of this paper.
文摘A theory on information prediction process proposed by Weng Wenpo(1991)is applied to the earthquake prediction decision process.Four cycles represent the theory(conception),earthquake prediction decision result,anomalies,and earthquake assemblage,respectively.The interception and overlapping of the four cycles indicate different combinations,resulting in formation of 13 regions.In the case of decision conclusion on earthquake to occur,seven decision results of different characters are distinguished.The six other results were obtained in the case of absence of decision.Results of four characters show correct decision on earthquake to occur and those of three characters show the erroneous decision on earthquake to occur.Until now,theories of earthquake prediction have been incomplete,and the coincidence ratio of decision on earthquake to occur is also considerably low.Systematic analysis of the decision process is beneficial to understanding the causes for missing,virtual,pseudo,false,and correct
文摘This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is proposed that updates an increasingly accurate approximate version of the original problem with a view to saving computations at the early iterations, when one is typically far from the optimal solution. The proposed algorithm is compared to classical value iteration for a broad set of adaptive parameters and the results suggest that significant computational savings can be obtained, while also ensuring a robust performance with respect to the parameters.
文摘We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.
文摘In this study, we use the respective advantages of the tabu search (TS) and the Web-based technologies to develop a Web-based decision support system (DSS) for cell formation (CF) problems considering alternative process routings and machine sequences simultaneously. With the assistance of our developed Web-based system, the CF practitioners in the production departments can interact with the systems without knowing the details of algorithms and can get the best machine cells and part families with minimize the total intercellular movement wherever and whenever they may need it. To further verify the feasibility and effectiveness of the system developed, an example taken from the literature is ado- pted for illustrational purpose. Moreover, a set of test problems with various sizes drawn from the literature is used to test the performance of the proposed system. Corresponding results are compared to several well-known algorithms previously published. The results indicate that the proposed system improves the best results found in the literature for 67% of the test problems. These show that the proposed system should thus be useful to both practitioners and researchers.
