Stochastic Resource Constrained Project Scheduling Research Articles

Effective Proactive Policies for the Stochastic Resource-Constrained Project Scheduling Problem with Inventory Consideration

Effective Proactive Policies for the Stochastic Resource-Constrained Project Scheduling Problem with Inventory Consideration

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Activity requires to be performed again given the performance of project. This repetition of work is called rework. It is previously considered to occur upon completion of activity; however, due to the fact that changes in requirements in the project cause the occurrence time of rework vary from the beginning to the end. Thus, model and algorism to apply for the activity need to be developed.<BR> In this research, among SRCPSP (Stochastic Resource Constrained Project Scheduling Problem), statistical model of RCPSP (Resource Constrained Project Scheduling Problem) typical project scheduling problem, the method which when the period of activity acts as probability variable, expected value and multiplier of the unexpected value is used for estimation. Along with the method, a mathematical model, which shows rework can occur at arbitrary point, is developed and has proven its validity through simulated tests with applicable algorism. This research is significant because of extended experimental period of activity from the beginning to the end along with the distinguished means of the mathematical model which the period of rework is estimated via the expected value and multiplier of the value.

A reinforcement learning based multi-method approach for stochastic resource constrained project scheduling problems

The Resource-Constrained Project Scheduling Problem (RCPSP) has been widely accepted as a challenging research topic due to its NP-hard nature. Because of the dynamic nature of real-world problems, stochastic-RCPSPs (SRCPSPs) are also receiving greater attention among researchers. To solve these extended RCPSPs (i.e., SRCPSPs), this paper proposes an reinforcement learning based meta-heuristic switching approach that utilizes the powers of both multi-operator differential evolution (MODE) and discrete cuckoo search (DCS) algorithms in single algorithmic framework. Reinforcement learning (RL) is introduced as a technique to select either MODE or DCS based on the diversity of population and quality of solutions. To deal with uncertain durations, a chance-constrained based approach with some belief degrees is also considered and solved by this proposed RL based multi-method approach (i.e., DECSwRL-CC). Extensive experimentation with benchmark data from the project scheduling library (PSPLIB) demonstrates the efficacy of this proposed multi-method approach. Numerous state of the art chance constrained approaches are taken from the literature to compare the proposed approach and to validate the efficacy of this multi-method approach. This particular strategy is applicable to the risk-averse decision-makers who want to realize the project schedule with a high degree of certainty.

Tracking Predictive Gantt Chart for Proactive Rescheduling in Stochastic Resource Constrained Project Scheduling

Proactive-reactive scheduling is important in the situations where the project collaborators need to coordinate their efforts. The coordination is mostly achieved through the combination of the shared baseline schedule and the deviation penalties. In this paper, we present an extension of predictive Gantt chart to the proactive-reactive scheduling needs. It can be used to track the evolution of the relationship between dynamic and static elements through the time. The dynamic elements are evolving probability distributions due to the uncertainty and revealed information. The static elements are time-agreements in the baseline schedule. We demonstrate that in the state-of-the-art proactive-reactive scheduling, the baseline schedule is agnostic to the information received during the project execution. The sources of such inflexibility in the problem model and the scheduling methods are analyzed. The visualization is highlighted as a precursor to developing new methods that proactively change the baseline schedule in accordance with the gained information.

Solving the FS-RCPSP with hyper-heuristics: A policy-driven approach

In this paper, a problem in the area of scheduling, namely Fuzzy Stochastic Resource-Constrained Project Scheduling Problem (FS-RCPSP), is addressed. Like the original Resource-Constrained Project Scheduling Problem (RCPSP), the objective is to minimise the expected makespan of the project subject to precedence and resource constraints. However, due to mixed uncertainty comprising fuzziness and randomness in the estimates of activity durations, the makespan is a fuzzy stochastic number. Recognising both fuzziness and randomness in activity durations results in more robust schedules but the scheduling problem is harder to solve. A hyper-heuristic, named Self-adaptive Differential Evolution to Scheduling Policy (SADESP) is proposed to address this issue. SADESP has two key modules: (1) a module (policyEvolver) which evolves scheduling policy and (2) a dynamic scheduling procedure (dScheduler) which makes scheduling decisions using a particular scheduling policy. The performance of SADESP is benchmarked against CPLEX across an extensive set of 960 problems created with ProGen – a standardised problem generator for creating benchmark problems in scheduling. The results returned by SADESP for FS-RCPSP are very encouraging, both in terms of accuracy and computational performance.

Applying hybrid Monte Carlo Tree Search methods to Risk-Aware Project Scheduling Problem

In this paper we investigate an application of hybrid Monte Carlo Tree Search (MCTS) based algorithms to solving dynamic decision making problems.We employ UCT (the most popular MCTS approach) in combination with well-known Resource Constrained Project Scheduling Problem (RCPSP) and Stochastic Resource Constrained Project Scheduling Problem (SRCPSP) solvers to devise strategies for a generic and highly dynamic version of RCPSP, which we call Risk-Aware Project Scheduling Problem (RAPSP). We compare these strategies’ performance with results of both pure MCTS approach and non-MCTS solvers for projects of varied characteristics. We reach a conclusion that proposed hybrid simulation-heuristic methods are promising approaches to dynamic decision making problems, RAPSP in particular. Consequently, we argue that more research effort should be directed to applications of MCTS algorithm outside the domain of game-playing, with which it is commonly associated.At the same time, to the best of our knowledge, this paper is the first attempt at defining generalized SRCPSP model encompassing arbitrary risks and risk response / mitigation strategies as an optimization problem and applying Computational Intelligence methods to build fully-automated decision making systems. We strongly believe it to be a research direction worth further investigation, combining project scheduling, risk management and metaheuristic optimization techniques into a well-defined platform allowing direct comparisons of different strategies.

New strategies for stochastic resource-constrained project scheduling

We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a priori decisions in a preprocessing phase, while the remaining scheduling decisions are made online. A two-phase local search algorithm is proposed to optimize within the class. Our computational results show that the algorithm has been efficiently tuned toward finding high-quality solutions and that it outperforms all existing algorithms for large instances. The results also indicate that the optimality gap even within the larger class of elementary policies is very small.

The Preemptive Stochastic Resource-Constrained Project Scheduling Problem: An Efficient Globally Optimal Solution Procedure

We present a globally optimal solution procedure to tackle the preemptive stochastic resource-constrained project scheduling problem (PSRCPSP). A solution to the PSRCPSP is a policy that allows to construct a precedence- and resource-feasible schedule that minimizes the expected makespan of a project. The PSRCPSP is an extension of the stochastic resource-constrained project scheduling problem (SRCPSP) that allows activities to be interrupted. The SRCPSP and PSRCPSP both assume that activities have stochastic durations. Even though the deterministic preemptive resource-constrained project scheduling problem (PRCPSP) has received some attention in the literature, we are the first to study the PSRCPSP. We use phase-type distributions to model the stochastic activity durations, and define a new Continuous-Time Markov Chain (CTMC) that drastically reduces memory requirements when compared to the well-known CTMC of Kulkarni and Adlakha (1986). In addition, we also propose a new and efficient approach to structure the state space of the CTMC. These improvements allow us to easily outperform the current state-of-the-art in optimal project scheduling procedures, and to solve instances of the PSPLIB J90 and J120 data sets. Last but not least, if activity durations are exponentially distributed, we show that elementary policies are globally optimal for the SRCPSP and the PSRCPSP.

New Benchmark Results for the Stochastic Resource-Constrained Project Scheduling Problem

We study the stochastic resource-constrained project scheduling problem or SRCPSP, where project activities have stochastic durations. A solution is a scheduling policy, and we propose a new class of policies that is a generalization of most of the classes described in the literature. A policy in this new class makes a number of a-priori decisions in a preprocessing phase while the remaining scheduling decisions are made online. A two-phase local search algorithm is proposed to optimize within the class. Our computational results show that the algorithm has been efficiently tuned towards finding high-quality solutions, and that it outperforms all existing algorithms for large instances. The results also indicate that the optimality gap even within the larger class of elementary policies is very small.

Stochastic Resource-Constrained Project Scheduling Based on Discrete-Event Simulation and Multiple-Comparison Procedure

Stochastic Resource-Constrained Project Scheduling Based on Discrete-Event Simulation and Multiple-Comparison Procedure

A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling

Aiming to minimize the average project duration, a discrete-event simulation (DES) approach with multiple-comparison procedure is presented to solve the stochastic resource-constrained project scheduling problem (SRCPSP). The simulation model of SRCPSP is composed of a resource management model and a project process model, where the resource management model is used to administrate resources of the project, and the project process model based on an extended-directed-graph is proposed to describe the precedence constraints and resource constraints in SRCPSP. A simplified simulation strategy based on activity scanning method is used in the simulation model to generate feasible schedules of the problem. A multiple-comparison procedure based on the common random numbers is adopted to compare the multiple scheduling alternatives obtained from the stochastic simulation model and provide more information to select the optimal scheduling alternative. The cases are given to compare with other methods for the same SRCPSP from literature and show that the simulation tool by utilizing DES with a statistical method improves the efficiency of simulation in stochastic project planning.

A Novel Class of Scheduling Policies for the Stochastic Resource-Constrained Project Scheduling Problem

We study the resource-constrained project scheduling problem with stochastic activity durations. We introduce a new class of scheduling policies for this problem, which make a number of a-priori sequencing decisions in a pre-processing phase, while the remaining decisions are made dynamically during project execution. The pre-processing decisions entail the addition of precedence constraints to the scheduling instance, hereby resolving some potential resource conflicts. We compare the performance of this new class with existing scheduling policies for the stochastic resource-constrained project scheduling problem, and we observe that the new class is significantly better when the variability in the activity durations is medium to high.

Linear preselective policies for stochastic project scheduling

In the context of stochastic resource-constrained project scheduling we introduce a novel class of scheduling policies, the linear preselective policies. They combine the benefits of preselective policies and priority policies; two classes that are well known from both deterministic and stochastic scheduling. We study several properties of this new class of policies which indicate its usefulness for computational purposes. Based on a new representation of preselective policies as and/or precedence constraints we derive efficient algorithms for computing earliest job start times and state a necessary and sufficient dominance criterion for preselective policies. A computational experiment based on 480 instances empirically validates the theoretical findings.

The role of the nonanticipativity constraint in commercial software for stochastic project scheduling

Recently announced commercial project scheduling products address the stochastic project scheduling problem. Their solution methods do not consider the nonanticipativity constraint and thus provide potentially unattainable solutions to stochastic resource constrained project scheduling problems. The nonanticipativity constraint of multiperiod stochastic problems imposes the requirement that solutions be based only on information known at the time of decisions. Users should use the results from these programs with extreme care.