Multi-mode Resource-Constrained Project Scheduling Research Articles

Optimizing time, cost and quality in multi-mode resource-constrained project scheduling

PurposeThe purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.Design/methodology/approachA case study approach identified the activity execution modes in building construction projects in India to support multi-mode resource-constrained project scheduling. The data required to compute time, cost and quality of each activity are compiled from real construction projects. A binary integer-programming model has been developed to perform multi-objective optimization and identify Pareto optimal solutions. The RR-PARETO3 algorithm was used to identify the best compromise trade-off solutions. The effectiveness of the proposed framework is demonstrated through sample case study projects.FindingsResults show that good compromise solutions are obtained through multi-objective optimization of time, cost and quality.Research limitations/implicationsCase study data sets were collected only from eight building construction projects in India.Practical implicationsIt is feasible to adopt multi-objective optimization in practical construction projects using time, cost and quality as the objectives; Pareto surfaces help to quantify relationships among time, cost and quality. It is shown that cost can be reduced by increasing the duration, and quality can be improved only by increasing the cost.Originality/valueThe use of different activity execution modes compiled from multiple projects in optimization is illustrated, and good compromise solutions for the multi-mode resource-constrained project scheduling problems using multi-objective optimization are identified.

Estimation of Distribution Algorithm for solving the Multi-mode Resource Constrained Project Scheduling Problem

The Multi-mode Resource Constrained Project Scheduling Problem is characterized by a set of tasks, resources and an objective function. All tasks of a project must be organized carefully taking into account precedence relations, the mode in which they are performed and availability of resources at a

Maximising the weighted number of activity execution modes in project planning

In multimode resource-constrained project scheduling, activity modes are selected and activity start times are determined to minimise the project makespan subject to resource constraints. When disruptions occur during project execution delays to project activities may ensue. Therefore, the a priori selected modes restrict the options to adapt the project schedule given the deadline. During the project scheduling phase, information on the best execution mode to include in the baseline schedule for each activity is usually not available. Scheduling these projects requires decisions on the modes to incorporate in the solution to maximise the flexibility during project execution and to postpone the decision on how to implement the activity until more information is available. In this paper, we study a project scheduling problem with multiple execution alternatives. Our objective is to maximise the weighted number of alternative activity execution modes in the project solution under three different assumptions. The research is motivated by real-life project scheduling applications, where the activities to be planned are known in advance, but the execution of these activities is subject to uncertainty. We present a problem description and three mathematical formulations. Additionally, computational results on the efficiency of the formulations and the increased flexibility are reported.

Experimental Evaluation of Overlapping Strategy for the Multimode Resource-Constrained Project Scheduling Problem

Reduction in project completion time is important in today’s competitive environment. This paper presents a solution to the realistic problem of scheduling activities to minimize the project duration, in which the activities (a) require units of multiple renewable and non-renewable resources for which a limited availability is imposed, (b) are performed in one of several modes, and (c) can be executed in parallel by allowing the downstream activity to start before the end of the upstream activity based on preliminary information. To solve the above-mentioned problem, a mixed-integer nonlinear programming model is built and an efficient genetic algorithm combined with design structure matrix is proposed. A revised serial schedule generation scheme, which considers resource constraints, precedence relation constraints and overlapping relation constraints, is designed to decode the chromosome. An example illustrates that the implementation of overlapping strategy can benefit from multimode and thus accelerate the project process. Results based on a large set of generated instances demonstrate the effectiveness of the proposed algorithm and reveal the impacts of significant factors on the time reduction in project makespan.

Experimental Evaluation of Agent-based Approaches to Solving Multi-mode Resource-Constrained Project Scheduling Problem

ABSTRACTIn this paper, a rank-based nonparametric statistical test for measuring the effect of cooperation between optimization agents solving the multi-mode resource-constrained project scheduling problem is presented. To solve this NP-hard optimization problem, different methods are applied including population- and agent-based approaches. One of them is a team of asynchronous agents composed of multiple optimization agents, management agents, and common memories, which through interactions produce solutions of hard optimization problems. Optimization agents represent different methods including local search, path relinking, or tabu search. Interactions are managed through various cooperation strategies based on applying heuristics, reinforcement learning, or population learning.

A Path-Relinking algorithm for the multi-mode resource-constrained project scheduling problem

This paper proposes a Path-Relinking (PR) algorithm for the well-known and NP-hard Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP). This problem generalizes the Resource-Constrained Project Scheduling Problem (RCPSP) where the project activities have a set of execution modes. For each execution mode, the processing time, the renewable and nonrenewable resource demands are given. The MRCPSP goal is to minimize the total makespan of the project. The PR algorithm works by travelling through the solution space between two solutions, it performs local search around the intermediate solutions. This work also presents computational tests using benchmark instances to compare our implementation with the most competitive methods from the literature. The PR’s computational results improve the earlier results reported for the benchmark instance sets.

Solving a Multi-Objective Mathematical Model for a Multi-Skilled Project Scheduling Problem by Particle Swarm Optimization and Differential Evolution Algorithms

A Multi-Skilled Project Scheduling Problem (MSPSP) that is an extension of a Multi-Mode Resource-Constrained Project Scheduling Problem (MM-RCPSP) has been generally addressed to schedule a project with staff members as resources. In MSPSP, each activity requires different specialties and each staff member has a known skill level in performing an activity. This causes to encounter a huge number of modes while performing activities of a project. This research focuses on a special type of MSPSP known as Multi-Objective Multi-Skilled Project Scheduling Problem (MOMSPSP) which incorporates some new objectives in the MSPSP and develops a multi-objective mixed-integer nonlinear programming (MINLP) model. The model is exactly solved for small-sized instances using CPLEX solver. To solve such a NP-hard problem for medium and large-sized instances, two efficient meta-heuristic algorithms based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) are proposed. To evaluate the efficiency of the proposed algorithms, the results are compared with each other as well as to the optimal ones obtained by the CPLEX solver for small instances. Finally, the designed DE algorithm is identified as the superior proposed algorithm for solving the propounded MOMSPSP in terms of some performance metrics.

An Integrated Simulation-Genetic Algorithm for the Stochastic Multi-Mode Resource Constrained Project Scheduling Problem

This paper presents a simulation-genetic algorithm for the stochastic multi-mode resource-constrained project scheduling problem (SMRCPSP), in which multiple execution modes are available for each of the activities with random durations. A genetic algorithm combined with nonrenewable resource feasibility checking and topological sort procedure to find a near optimal sequence of tasks to be performed. Simulation is employed to evaluate the performance (makespan) of the sequence under stochastic duration conditions. Computational results for the SMRCPSP reveal that the proposed algorithm is effective in solving the problem.

Multi-objective algorithms to multi-mode resource-constrained projects under mode change disruption

The present paper studies the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) wherein tasks have different execution modes, each implying a duration and different resource requirements. During execution, the project may encounter some problems that may cause schedule deterioration or even unfeasibility. Its utmost goals are firstly the proposition of a set of new disruption measures that can be used in new objective functions, and then the resolution of a bi-objective problem minimizing the project makespan and a disruption measure. The latter is solved by an Evolutionary algorithm and a new reactive multi-objective heuristic. Considering that the project is in progress, the paper focuses mainly on problem feasibility, Pareto Front performance, and algorithm CPU time. Given that the feasibility problem of static MRCPSP is NP-Complete (Kolisch & Drexl, 1997), we transform the bi-objective MRCPSP problem to a multi-objective problem by including a penalty to cope with the potential violation of non-renewable resource constraints, and enlarge the search space. We show that schedule adjustment is necessary for the problem solving process. A diverse set of non-time-consuming operators is also included in the algorithm.

On the generalization of constraint programming and boolean satisfiability solving techniques to schedule a resource-constrained project consisting of multi-mode jobs

In our paper, we analyze new exact approaches for the multi-mode resource-constrained project scheduling (MRCPSP) problem with the aim of makespan minimization. For the single-mode RCPSP (SRCPSP) recent exact algorithms combine a Branch and Bound algorithm with principles from Constraint Programming (CP) and Boolean Satisfiability Solving (SAT). We extend the above principles for the solution of MRCPSP instances. This generalization is on the one hand achieved on the modeling level. We propose three CP-based formulations of the MRCPSP for the G12 CP platform and the optimization framework SCIP which both provide solution techniques combining CP and SAT principles. For one of the latter we implemented a new global constraint for SCIP, which generalizes the domain propagation and explanation generation principles for renewable resources in the context of multi-mode jobs. Our constraint applies the above principles in a more general way than the existing global constraint in SCIP. We compare our approaches with the state-of-the-art exact algorithm from the literature on MRCPSP instances with 20 and 30 jobs. Our computational experiments show that we can outperform the latter approach on these instances. Furthermore, we are the first to close (find the optimal solution and prove its optimality for) 628 open instances with 50 and 100 jobs from the literature. In addition, we improve the best known lower bound of 2815 instances and the best known upper bound of 151 instances.

Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm (GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.

An Adaptive Large Neighborhood Search as a Matheuristic for the Multi-Mode Resource-Constrained Project Scheduling Problem

The multi-mode resource-constrained project scheduling problem is a well-known problem in the field of scheduling. The objective of the problem is to find the minimum makespan for the project. Hence, each activity has to be assigned to a mode and a starting time. At the same time, precedence and resource constraints must not be violated. We present a hybrid approach which combines an adaptive large neighbourhood search with mixed integer programming. We applied the procedure to datasets from the MMLIB library with up to 100 activities and nine modes. The computational results show that the approach is competitive with other state-of-the-art heuristics. Moreover, it found 294 new best known solutions and outperformed all other published methods on the MMLIB+ dataset. [Received 30 September 2016; Revised 6 April 2017; Accepted 26 April 2017]

A new fuzzy multi-objective multi-mode resource-constrained project scheduling model

In this paper, a new model is developed to address the so-called multi-objective multi-mode resource-constrained project scheduling problem under uncertainty conditions in the problem parameters. The two objective functions are minimising the project NPV and the project makespan, which is the first contribution of the current research. The proposed model is much more realistic one in comparison to previously developed model. This approach explicitly considers the risk acceptance level and the optimism of the project managers in the final decision, which are the main contributions of the current research. To show the efficiency of the proposed model in real applications, an efficient metaheuristic method based on memetic algorithm and cuckoo optimisation algorithm is developed to solve this problem, which is in fact, the second contribution of the paper. The algorithm embeds the cuckoo optimisation algorithm as a powerful local search method inside the genetic algorithm to improve its performance. To test the algorithm performance, a number of test problems from PSPLIB library were taken and then the proposed algorithm and the famous NSGA-II were examined over these problems, separately. The results were compared according to three different criteria. Computational results show that the proposed memetic-cuckoo algorithm is more efficient than the NSGA-II according to three different comparison criteria.

A new fuzzy multi-objective multi-mode resource-constrained project scheduling model

In this paper, a new model is developed to address the so-called multi-objective multi-mode resource-constrained project scheduling problem under uncertainty conditions in the problem parameters. The two objective functions are minimising the project NPV and the project makespan, which is the first contribution of the current research. The proposed model is much more realistic one in comparison to previously developed model. This approach explicitly considers the risk acceptance level and the optimism of the project managers in the final decision, which are the main contributions of the current research. To show the efficiency of the proposed model in real applications, an efficient metaheuristic method based on memetic algorithm and cuckoo optimisation algorithm is developed to solve this problem, which is in fact, the second contribution of the paper. The algorithm embeds the cuckoo optimisation algorithm as a powerful local search method inside the genetic algorithm to improve its performance. To test the algorithm performance, a number of test problems from PSPLIB library were taken and then the proposed algorithm and the famous NSGA-II were examined over these problems, separately. The results were compared according to three different criteria. Computational results show that the proposed memetic-cuckoo algorithm is more efficient than the NSGA-II according to three different comparison criteria.

An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources

The issue of resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating is frequently seen after initial scheduling of a project in practice and causes significant amount of efforts to modify the initial schedules. In this research, a new method is developed for modifying over-allocated schedules in a multi-mode resource constrained project scheduling problems (MRCPSPs) with positive cash flows (MRCPSP-PCF). The aim is to maximize profit of the MRCPSPs or logically minimizing costs. The proposed method can be used as a macro in Microsoft Office Project® Software to modify resource over-allocated days after scheduling a project. This research considers progress payment method and preemptive resources. The proposed approach maximizes profit by scheduling activities through the resource calendar respecting to the available level of preemptive resources and activity numbers. To examine the performance of the proposed method a number of experiments derived from the literature are solved. The results are then compared with the circumstances where resource constraints are relaxed. The outcomes show that in all studied cases, the proposed algorithm can provide modified schedules with no over-allocated days. Afterward the method is applied to modify a manufacturing project in practice.

A Practical and Robust Execution Time-Frame Procedure for the Multi-Mode Resource-Constrained Project Scheduling Problem with Minimal and Maximal Time Lags

Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP), improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future growth. Nonetheless, despite all of the research dedicated to solving the RCPSP and its multi-mode variations, there is no standardized procedure that can guide project management practitioners in their scheduling tasks. This is mainly because many of the proposed approaches are either based on unrealistic/oversimplified scenarios or they propose solution procedures not easily applicable or even feasible in real-life situations. In this study, we solve a more true-to-life and complex model, Multimode RCPSP with minimal and maximal time lags (MRCPSP/max). The complexity of the model solved is presented, and the practicality of the proposed approach is justified depending on only information that is available for every project regardless of its industrial context. The results confirm that it is possible to determine a robust makespan and to calculate an execution time-frame with gaps lower than 11% between their lower and upper bounds. In addition, in many instances, the solved lower bound obtained was equal to the best-known optimum.

Hybrid Optimization Method for Large-Scale Multimode Resource-Constrained Project Scheduling Problem

AbstractDespite many research efforts that have focused on the multimode resource-constrained project scheduling problem (MRCPSP), very little success has been achieved in solving the problem for large-scale projects. In this paper a new hybrid optimization method is presented to achieve an advancement in optimal planning and scheduling of large-scale construction projects with multiple duration/resource execution modes and resource constraints. The proposed method consists of a novel heuristic and unique genetic optimization algorithm. The heuristic is designed to achieve schedules with efficient resource utilizations through dynamic selection of modes. A genetic optimization algorithm is integrated to the proposed optimization method to further improve the quality of the solutions obtained in the heuristic phase. The computational experiment results reveal that the new hybrid method outperforms the state-of-the-art methods and achieves high-quality solutions in significantly less computation time, espec...

Extending the Multimode Resource-Constrained Project Scheduling Problem by Including Value Considerations

The current trend in modern project management methodologies is to maximize project values. We suggest a quantitative model that balances time, cost, and value. It generates detailed project plans, which are feasible with respect to time, cost, and resource-constraints, and considers interactions between technical performance, time, and cost. Such interactions, which existing project management models typically ignore or project managers deal with intuitively, are important for increasing projects' values. The model, which extends the multimode resource-constrained project scheduling problem by including value, generates an efficient frontier that includes the best project plans for varying project costs. We provide illustrative examples to demonstrate the suggested approach.

Multi-mode resource constrained project scheduling under resource disruptions

Over the last few decades, research on resource constrained project scheduling has focused on the development of mathematical programming based approaches for the generation of a nominal schedule under a deterministic environment. During the implementation phase, however, the nominal schedule may need to be revised when one or more resources are disrupted for a length of time. In this paper, we formulate two discrete time based models to deal with two different disruption scenarios for multi-mode resource constrained problems. We propose a reactive re-scheduling procedure for a single, as well as a series of disruptions, without having any disruption information in advance. To test the proposed approaches, sets of ten, twenty and thirty-activity multi-mode test instances from Project Scheduling Library (PSLIB) were used after introducing randomly generated disruption events. The experimental studies were also carried out to determine the effect of different factors related to the disruption recovery process.

A modified modeling approach and a heuristic procedure for the multi-mode resource constrained project scheduling problem with activity splitting

This paper presents a new heuristic method for solving multi-mode resource constrained project scheduling problems with renewable resources. Assumptions such as resource vacation and activity splitting are also considered. The proposed heuristic determines one mode for the execution of each activity first, so that the multi-mode problem is reduced to a single-mode one. Our method is compared to two of the existing methods in terms of computational time and solution quality. The results show that while it can outperform one of them (especially as the size of the problem grows), it is outperformed by the other for tested instances with low complexity, but can yield good results for tested instances with higher values of this parameters. This quality may be useful in real-world scheduling problems. We also validate the first phase of our method, i.e. , mode selection, with numerical experiments. Our results indicate that better mode vectors selected in the first phase lead to better makespans for the MRCPSP. Moreover, we correct some erroneous constraints in the mathematical model of the problem. We implement the mathematical programming model of the problem in GAMS, and show that solving it to optimality requires much more computational effort compared to our method.