A destructive lower bound for the multi-mode resource-con strained projectschedulingproblem with minimal and maximal time-lags is presented. Given are n activities which may be processed in different modes without p...
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A destructive lower bound for the multi-mode resource-con strained projectschedulingproblem with minimal and maximal time-lags is presented. Given are n activities which may be processed in different modes without preemptions. During processing certain amounts of renewable and non-renewable resources are needed where the available capacity of each resource type is limited. Furthermore, minimal and maximal time-lags between the activities are given. The objective is to determine a schedule with minimal makespan. The lower bound calculations are based on two methods for proving infeasibility of a given threshold value T for the makespan. The first uses constraint propagation techniques, while the second is based on a linear programming formulation which is solved by a column generation procedure. Computational results are reported for several test instances of the multi-mode problem with and without time-lags and the single-mode version with time-lags. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-hGA) to solve the resource-constrained multiple projectschedulingproblem (rc-mPSP) which is well known NP-hard problem. Objective...
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In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-hGA) to solve the resource-constrained multiple projectschedulingproblem (rc-mPSP) which is well known NP-hard problem. Objectives described in this paper are to minimize total project time and to minimize total tardiness penalty. However, it is difficult to treat the rc-mPSP problems with traditional optimization techniques. The proposed new approach is based on the design of genetic operators with fuzzy logic controller (FLC) through initializing the revised serial method which outperforms the non-preemptive scheduling with precedence and resources constraints. For these rc-mPSP problems, we demonstrate that the proposed flc-hGA yields better results than conventional genetic algorithms and adaptive genetic algorithm. (C) 2004 Elsevier B.V. All rights reserved.
Different from traditional project management, the prefabricated building (PB) construction project has a complex distributed supply chain model, and the overall project is completed by multi-stage cooperation. Theref...
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Different from traditional project management, the prefabricated building (PB) construction project has a complex distributed supply chain model, and the overall project is completed by multi-stage cooperation. Therefore, the implementation process will be restricted by many constraints, and various uncertain factors will also interfere with the smooth implementation of the project. In order to improve the stability and reliability of the PB construction project implementation process, it is very important to study an effective robust projectscheduling method considering resource constraints in an uncertain environment. In this paper, we formulate a PB construction resource-constrainedprojectscheduling with multi-objective multi-mode, focus on the uncertainty of the execution time of the execution activity, and constructs the interval value of the execution time to express it through fuzzy theory;also considers the multiple objectives of PB construction project, including time-based profit, and cost-based profit. Secondly, we propose a hybrid cooperative co-evolution algorithm (HCOEA) to obtain the highly robust projectscheduling, reduce the impact of the uncertainty of the execution time of the activity on the overall project. resource-constrained project scheduling problem (RCPSP) is an NP-hard combinatorial optimization problem. This paper also needs to consider the complex combination of time-resource and/or time-cost constraints. At the same time, it is necessary to consider the impact of time changes in different mode combinations. Therefore, how to design an effective multi-objective optimization algorithm is very difficult. This paper design a Hybrid Cooperative Co-evolution Algorithm (HCOEA) with multi-stage representation for the activity sequencing and the resource allocation, further improve the search efficiency. We improve the cooperative co-evolution framework with a self-adaptive mechanism and a self-adaptive selection process. Finally, benchmark
resource-constrained project scheduling problem (RCPSP) is an important, but computationally hard problem. Particle swarm optimization (PSO) is a well-known and highly used meta-heuristics to solve such problems. In t...
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resource-constrained project scheduling problem (RCPSP) is an important, but computationally hard problem. Particle swarm optimization (PSO) is a well-known and highly used meta-heuristics to solve such problems. In this work, a simple, effective and improved version of PSO i.e. adaptive-PSO (A-PSO) is proposed to solve the RCPSP. Conventional canonical PSO is improved at two points;during the particle's position and velocity updation, due to dependent activities in RCPSP, a high possibility arises for the particle to become invalid. To overcome this, an important operator named valid particle generator (VPG) is proposed and embedded into the PSO which converts an invalid particle into a valid particle effectively with the knowledge of the in-degree and out-degree of the activities depicted by the directed acyclic graph. Second, inertia weight that plays a significant role in the quick convergence of the PSO is adaptively tuned by considering the effects of fitness value, previous value of and iteration counter. Performance of the model is evaluated on the standard benchmark data of the RCPSP problem. Results show the effectiveness of the proposed model in comparison to other existing state of the art model that uses heuristics/meta-heuristics. The proposed model has the potential to be applied to other similar problems.
Recently, Bianco and Caramia (Flex Serv Manuf J 25(1-2), 6-24, 2013) proposed a new model for the resource-constrained project scheduling problem. Despite its potential, the presentation of the mixed-integer programmi...
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Recently, Bianco and Caramia (Flex Serv Manuf J 25(1-2), 6-24, 2013) proposed a new model for the resource-constrained project scheduling problem. Despite its potential, the presentation of the mixed-integer programming model contains some ambiguity which may create misunderstanding in the implementation phase. Here, we clarify the definitions of the decision variables and illustrate their corresponding values using a numerical example. Furthermore, we propose a different interpretation of two decision variables which gives rise to an alternative model formulation also presented using the same numerical example.
作者：
Zhu, JieLi, XiaopingShen, WeimingSoutheast Univ
Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ
Key Lab Comp Network & Informat Integrat Minist Educ Nanjing Jiangsu Peoples R China CNR
Ctr Computer Assisted Construct Technol London ON Canada
In this paper, a specific preemptive resource-constrained project scheduling problem (PRCPSP) with makespan minimization is considered of which each activity could be interrupted at most M times. According to activity...
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In this paper, a specific preemptive resource-constrained project scheduling problem (PRCPSP) with makespan minimization is considered of which each activity could be interrupted at most M times. According to activity requirements and resource availability, resources are allocated to activities in different intervals. A resource-fragment chain is constructed to keep resource states dynamically. The resource allocation problem is transferred to the well-known 0-1 knapsack problem and solved by dynamic programming in pseudo-polynomial time complexity. The schedule enhancement method is developed to further improve the quality of obtained schedules by removing and rescheduling each activity in the activity list. By integrating the resource allocation and the schedule enhancement method, a genetic algorithm is proposed for the considered problem with the objective to minimize makespan. Computational experiments on the standard J30 and J120 sets show that the proposed algorithm is amongst the most competitive algorithms in literature for the pre-emptive cases.
In this paper, the Pre-emptive resource-constrainedprojectschedulingproject (PRCPSP) is considered. The paper mainly focuses on the problem 1_PRCPSP, where a maximum of one interruption per activity is allowed. A t...
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ISBN:
(纸本)9781424465866
In this paper, the Pre-emptive resource-constrainedprojectschedulingproject (PRCPSP) is considered. The paper mainly focuses on the problem 1_PRCPSP, where a maximum of one interruption per activity is allowed. A time-fragment linked-list method (TFLLM) is proposed to generate an effective solution for a given precedence-feasible activity list. Based on the TFLLM, an evolutionary algorithm is developed with the objective of makespan minimization. Computational experiments on the standard J30 and J60 sets show that the proposed algorithm can perform better than the compared approach in literature for the pre-emptive cases.
We study the problem of determining both the structure and the schedule of projects subject to capacity constraints. We assume that those projects are flexible in the sense that the activities to be implemented are no...
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We study the problem of determining both the structure and the schedule of projects subject to capacity constraints. We assume that those projects are flexible in the sense that the activities to be implemented are not entirely known in advance. In such a setting, decisions must be made with respect to the implementation of the optional activities. Such decisions affect the duration, cost, quality and eventual revenue of the project. Examples of this type of problem can often be found when complex capital goods such as aircraft engines are overhauled, or when buildings are renovated to meet higher environmental and efficiency standards. We describe the problem, develop a mixed-integer optimisation model, explain specific features of a genetic algorithm to solve the problem and report the results of a numerical study.
We study the complex combinatorial optimization problem to schedule the activities of a single project with the objective to complete the project within the shortest-possible amount of time such that the limited resou...
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ISBN:
(纸本)9781728138046
We study the complex combinatorial optimization problem to schedule the activities of a single project with the objective to complete the project within the shortest-possible amount of time such that the limited resource capacities as well as the prescribed precedence relations between pairs of the activities are taken into account. In addition to various specific solution algorithms, the related literature proposes several Mixed-Integer Linear Programming (MILP) models, but these models remain complex to solve even for small-sized instances. We present a novel approach based on an MILP model in which the resource-capacity constraints are formulated for all inclusion-minimal sets of activities which, due to the limited capacities of the resources, cannot be processed simultaneously. We propose to remove these constraints from the model and iteratively add those constraints back which are violated in the solutions obtained. For a set of test instances from the literature, our computational results indicate that with respect to both, the deviation of the project duration obtained from the lower bound devised from the critical-path length and the number of instances solved to optimality, the novel lazy-constraints approach outperforms ten state-of-the-art MILP models.
In this paper, we propose an Activity-List based Nested Partitions algorithm for solving the resource-constrained project scheduling problem(RCPSP). This algorithm is based on traditional Serial scheduling scheme (SSS...
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ISBN:
(纸本)9781479958252
In this paper, we propose an Activity-List based Nested Partitions algorithm for solving the resource-constrained project scheduling problem(RCPSP). This algorithm is based on traditional Serial scheduling scheme (SSS) and partitions the feasible solution space which is formulated by activity-lists into subregions by the nested partitions approach. We also utilize Double Justification as local search to improve the solutions. The algorithm is tested on J120 in PSPLIB with the result that the algorithm is relatively effective for solving large-scale, complex RCPSPs.
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