Resource-constrained project scheduling problem（RCPSP） is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities＇ cost with the re...Resource-constrained project scheduling problem（RCPSP） is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities＇ cost with the resource constraints that is a critical sub-problem in partner selection of construction supply chain management because the capacities of the renewable resources supplied by the partners will effect on the project scheduling. Its mathematic model is presented firstly, and analysis on the characteristic of the problem shows that the objective function is non-regular and the problem is NP-complete following which the basic idea for solution is clarified. Based on a definition of preposing activity cost matrix, a heuristic algorithm is brought forward. Analyses on the complexity of the heuristics and the result of numerical studies show that the heuristic algorithm is feasible and relatively effective.展开更多
This paper presents the two-machine flowshop group scheduling problem with the optimal objective of maximum lateness. A dominance rule within group and a dominance rule between groups are established. These dominance ...This paper presents the two-machine flowshop group scheduling problem with the optimal objective of maximum lateness. A dominance rule within group and a dominance rule between groups are established. These dominance rules along with a previously established dominance rule are used to develop a heuristic algorithm. Experimental results are given and analyzed.展开更多
Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rule...Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rules are studied in this paper. Then a heuristic inference algorithm is presented according to the system.展开更多
Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is amo...Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.展开更多
To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating sched...To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating scheduling problems domain of dual-armed cluster tools,a non-integer programming model was set up with a minimizing objective function of the makespan.Combining characteristics of residency time and reentrant constraints,a scheduling algorithm of searching the optimal operation path of dual-armed transport module was presented under many kinds of robotic scheduling paths for dual-armed cluster tools.Finally,the experiments were designed to evaluate the proposed algorithm.The results show that the proposed algorithm is feasible and efficient for obtaining an optimal scheduling solution of dual-armed cluster tools with residency time and reentrant constraints.展开更多
Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the...Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the character and structure of scheduling. An optimal scheduling strategy in collision window is presented. Performance evaluation of this algorithm is given. Simulation indicates that the proposed algorithm is better than other common heuristic algorithms on both the total performance and stability.展开更多
Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-lev...Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.展开更多
Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular...Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.展开更多
Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research.Because of uncertain factors,such as the weather or OD(origin-destination)variations caused b...Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research.Because of uncertain factors,such as the weather or OD(origin-destination)variations caused by traffic accidents,travel time is a random variable.In emergency situations,it is particularly necessary to determine the optimal reliable route of rescue vehicles from the perspective of uncertainty.This paper first proposes an optimal reliable path finding(ORPF)model for rescue vehicles,which considers the uncertainties of travel time,and link correlations.On this basis,it investigates how to optimize rescue vehicle allocation to minimize rescue time,taking into account travel time reliability under uncertain conditions.Because of the non-additive property of the objective function,this paper adopts a heuristic algorithm based on the K-shortest path algorithm,and inequality techniques to tackle the proposed modified integer programming model.Finally,the numerical experiments are presented to verify the accuracy and effectiveness of the proposed model and algorithm.The results show that ignoring travel time reliability may lead to an over-or under-estimation of the effective travel time of rescue vehicles on a particular path,and thereby an incorrect allocation scheme.展开更多
The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters （which will be utilized as the primary me...The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters （which will be utilized as the primary means of transport in a mountain region struck by a devastating earthquake） at pointed temporary facilities, including helicopter-based delivery plans for commodities and evacuation plans for critical population, in which relief demands are considered as uncertain. The proposed mobilization model is a two-stage stochastic mixed integer program with two objectives： maximizing the expected fill rate and minimizing the total expenditure of the mobilization campaign. Scenario decomposition based heuristic algorithms are also developed according to the structure of the proposed model. The computational results of a numerical example, which is constructed from the scenarios of the Great Wenchuan Earthquake, indicate that the model can provide valuable decision support for the mobilization of post-earthquake relief, and the proposed algorithms also have high efficiency in computation.展开更多
With increased dependence on space assets,scheduling and tasking of the space surveillance network(SSN)are vitally important.The multi-sensor collaborative observation scheduling(MCOS)problem is a multi-constraint and...With increased dependence on space assets,scheduling and tasking of the space surveillance network(SSN)are vitally important.The multi-sensor collaborative observation scheduling(MCOS)problem is a multi-constraint and high-conflict complex combinatorial optimization problem that is nondeterministic polynomial(NP)-hard.This research establishes a sub-time window constraint satisfaction problem(STWCSP)model with the objective of maximizing observation profit.Considering the significant effect of genetic algorithms(GA)on solving the problem of resource allocation,an evolution heuristic(EH)algorithm containing three strategies that focus on the MCOS problem is proposed.For each case,a task scheduling sequence is first obtained via an improved GA with penalty(GAPE)algorithm,and then a mission planning algorithm(heuristic rule)is used to determine the specific observation time.Compared to the model without sub-time windows and some other algorithms,a series of experiments illustrate the STWCSP model has better performance in terms of total profit.Experiments about strategy and parameter sensitivity validate its excellent performance in terms of EH algorithms.展开更多
The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertain...The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.展开更多
文摘Resource-constrained project scheduling problem（RCPSP） is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities＇ cost with the resource constraints that is a critical sub-problem in partner selection of construction supply chain management because the capacities of the renewable resources supplied by the partners will effect on the project scheduling. Its mathematic model is presented firstly, and analysis on the characteristic of the problem shows that the objective function is non-regular and the problem is NP-complete following which the basic idea for solution is clarified. Based on a definition of preposing activity cost matrix, a heuristic algorithm is brought forward. Analyses on the complexity of the heuristics and the result of numerical studies show that the heuristic algorithm is feasible and relatively effective.
文摘This paper presents the two-machine flowshop group scheduling problem with the optimal objective of maximum lateness. A dominance rule within group and a dominance rule between groups are established. These dominance rules along with a previously established dominance rule are used to develop a heuristic algorithm. Experimental results are given and analyzed.
文摘Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rules are studied in this paper. Then a heuristic inference algorithm is presented according to the system.
基金supported by the National Natural Science Foundation of China(60573159)
文摘Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.
基金Projects(7107111561273035)supported by the National Natural Science Foundation of China
文摘To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating scheduling problems domain of dual-armed cluster tools,a non-integer programming model was set up with a minimizing objective function of the makespan.Combining characteristics of residency time and reentrant constraints,a scheduling algorithm of searching the optimal operation path of dual-armed transport module was presented under many kinds of robotic scheduling paths for dual-armed cluster tools.Finally,the experiments were designed to evaluate the proposed algorithm.The results show that the proposed algorithm is feasible and efficient for obtaining an optimal scheduling solution of dual-armed cluster tools with residency time and reentrant constraints.
文摘Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the character and structure of scheduling. An optimal scheduling strategy in collision window is presented. Performance evaluation of this algorithm is given. Simulation indicates that the proposed algorithm is better than other common heuristic algorithms on both the total performance and stability.
基金the National Basic Research Program (973 Program) (2002CB312200)
文摘Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.
基金Project(61174140)supported by the National Natural Science Foundation of ChinaProject(13JJA002)supported by Hunan Provincial Natural Science Foundation,ChinaProject(20110161110035)supported by the Doctoral Fund of Ministry of Education of China
文摘Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.
基金Projects(72071202,71671184)supported by the National Natural Science Foundation of ChinaProject(22YJCZH144)supported by Humanities and Social Sciences Youth Foundation,Ministry of Education of China+3 种基金Project(2022M712680)supported by Postdoctoral Research Foundation of ChinaProject(22KJB110027)supported by Natural Science Foundation of Colleges and Universities in Jiangsu Province,ChinaProject(D2019046)supported by Initiation Foundation of Xuzhou Medical University,ChinaProject(2021SJA1079)supported by General Project of Philosophy and Social Science Research in Jiangsu Universities,China。
文摘Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research.Because of uncertain factors,such as the weather or OD(origin-destination)variations caused by traffic accidents,travel time is a random variable.In emergency situations,it is particularly necessary to determine the optimal reliable route of rescue vehicles from the perspective of uncertainty.This paper first proposes an optimal reliable path finding(ORPF)model for rescue vehicles,which considers the uncertainties of travel time,and link correlations.On this basis,it investigates how to optimize rescue vehicle allocation to minimize rescue time,taking into account travel time reliability under uncertain conditions.Because of the non-additive property of the objective function,this paper adopts a heuristic algorithm based on the K-shortest path algorithm,and inequality techniques to tackle the proposed modified integer programming model.Finally,the numerical experiments are presented to verify the accuracy and effectiveness of the proposed model and algorithm.The results show that ignoring travel time reliability may lead to an over-or under-estimation of the effective travel time of rescue vehicles on a particular path,and thereby an incorrect allocation scheme.
基金supported by the National Natural Science Foundation of China 71371181 91024006China Postdoctoral Science Foundation (2012M521918)
文摘The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters （which will be utilized as the primary means of transport in a mountain region struck by a devastating earthquake） at pointed temporary facilities, including helicopter-based delivery plans for commodities and evacuation plans for critical population, in which relief demands are considered as uncertain. The proposed mobilization model is a two-stage stochastic mixed integer program with two objectives： maximizing the expected fill rate and minimizing the total expenditure of the mobilization campaign. Scenario decomposition based heuristic algorithms are also developed according to the structure of the proposed model. The computational results of a numerical example, which is constructed from the scenarios of the Great Wenchuan Earthquake, indicate that the model can provide valuable decision support for the mobilization of post-earthquake relief, and the proposed algorithms also have high efficiency in computation.
基金supported by the National Natural Science Foundation of China(11802333)the Scientific Research Program of the National University of Defense Technology(ZK19-31)。
文摘With increased dependence on space assets,scheduling and tasking of the space surveillance network(SSN)are vitally important.The multi-sensor collaborative observation scheduling(MCOS)problem is a multi-constraint and high-conflict complex combinatorial optimization problem that is nondeterministic polynomial(NP)-hard.This research establishes a sub-time window constraint satisfaction problem(STWCSP)model with the objective of maximizing observation profit.Considering the significant effect of genetic algorithms(GA)on solving the problem of resource allocation,an evolution heuristic(EH)algorithm containing three strategies that focus on the MCOS problem is proposed.For each case,a task scheduling sequence is first obtained via an improved GA with penalty(GAPE)algorithm,and then a mission planning algorithm(heuristic rule)is used to determine the specific observation time.Compared to the model without sub-time windows and some other algorithms,a series of experiments illustrate the STWCSP model has better performance in terms of total profit.Experiments about strategy and parameter sensitivity validate its excellent performance in terms of EH algorithms.
基金Project(BX20180268)supported by National Postdoctoral Program for Innovative Talent,ChinaProject(300102228101)supported by Fundamental Research Funds for the Central Universities of China+1 种基金Project(51578150)supported by the National Natural Science Foundation of ChinaProject(18YJCZH130)supported by the Humanities and Social Science Project of Chinese Ministry of Education
文摘The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.
