Multi-mode Resource-constrained Project Research Articles

Model for Estimating the Impact of Interdependencies on System Recovery

Infrastructure interdependencies have been widely recognized, especially in the postdisaster restoration process. It is essential to develop models to simulate interdependencies and quantify their impact on the functionality recovery of infrastructures. This study presents a generalized simulator to investigate the impact of different types of interdependency on functionality recovery. The proposed simulator considers that there are multiple possible modes to execute a restoration task by framing the restoration process of interconnected systems as a multimode resource-constrained project scheduling problem (MRCPSP). In addition, it considers three sets of uncertainties: restoration duration and resource demand to execute a task, as well as intersystem functionality dependency. By solving the MRCPSP with the objective of minimal restoration completion time, the optimal restoration schedules for different systems are calculated to predict functionality recovery. This simulator implements three types of interdependencies at both the component level and the system level: resource-sharing interdependency, restoration precedence dependency, and functionality dependency. Through a simple example, it is demonstrated how the proposed approach can quantitatively evaluate the impact on system recovery due to different types of interdependency. Research findings from this study can help to identify the interdependencies with the strongest impact and then develop preventive mitigation actions and effective plans of emergency response and disaster recovery for interconnected systems.

Integration of Multi-mode Resource-Constrained Project Scheduling under bonus-penalty policies with Material Ordering under Quantity Discount scheme for minimizing project cost

In this paper a mixed binary integer mathematical programming model is developed for integration of Multimode Resource-Constraint Project Scheduling Problem (MRCPSP) under bonus–penalty policies and Quantity Discount Problem in Material Ordering (QDPMO) with the objective of minimizing the total project cost. By proving a theorem, an important property of the optimum solution of the problem is found which reduces the search space significantly compared to previous studies. Since the RCPSP belongs to the class of problems that are NP-hard, four hybrid meta-heuristic algorithms called COA-GA, GWO-GA, PSO-GA and GA-GA is developed and tuned to solve the problem. Each of the proposed algorithms consists of outside and inside search components, which determine the best schedule, and materials procurement plan respectively. Finally a set of standard PROGEN test problems is solved by the proposed hybrid algorithms under fixed CPU time. The results show that the COA-GA algorithm outperforms others.

Hybrid genetic algorithm for bi-objective resource-constrained project scheduling

In this study, we considered a bi-objective, multi-project, multi-mode resource-constrained project scheduling problem. We adopted three objective pairs as combinations of the net present value (NPV) as a financial performance measure with one of the time-based performance measures, namely, makespan (Cmax), mean completion time (MCT), and mean flow time (MFT) (i.e., minCmax/maxNPV, minMCT/maxNPV, and minMFT/maxNPV). We developed a hybrid non-dominated sorting genetic algorithm II (hybrid-NSGA-II) as a solution method by introducing a backward-forward pass (BFP) procedure and an injection procedure into NSGA-II. The BFP was proposed for new population generation and post-processing. Then, an injection procedure was introduced to increase diversity. The BFP and injection procedures led to improved objective functional values. The injection procedure generated a significantly high number of non-dominated solutions, thereby resulting in great diversity. An extensive computational study was performed. Results showed that hybrid-NSGA-II surpassed NSGA-II in terms of the performance metrics hypervolume, maximum spread, and the number of non-dominated solutions. Solutions were obtained for the objective pairs using hybrid-NSGA-II and three different test problem sets with specific properties. Further analysis was performed by employing cash balance, which was another financial performance measure of practical importance. Several managerial insights and extensions for further research were presented.

Mapping operations research in project management: A bibliometric analysis

Successful project management can help organisations in reducing costs and increasing its efficiency. Likewise, operations research has contributed significantly to improve project management performance with phase-project-planning methodologies. However, as far as we know, there is no literature review that compiles operations research in project management (OR+PM). Thus, this paper aims to present a bibliometric analysis of OR+PM using 1,254 research papers retrieved from ISI Web of Science. This final dataset is composed of articles from scientific journals published between 2001-2018. The results indicate that project scheduling, project management and resource-constrained project scheduling have been the backbone concepts of OR+PM; while terms, such as sustainability, earned value management, multi-objective optimisation, and multi-mode resource-constrained project scheduling problem have been gaining relevance in recent years. Results also show who have been the most influential authors, institutions and countries in the development of this field. Finally, future research opportunities for OR+PM are identified.

Research on Construction Schedule Optimization of Assembly Building Based on NSGA-II

Considering multiple possible scenarios in the process of project construction, the prefabricated project scheduling problem is studied in combination with the theory of multi-mode resource-constrained project scheduling problem. Multi-objective multi-mode resource-constrained project scheduling model with time/robustness trad-offs was constructed. Next, the adjusted non-dominated genetic algorithm (NSGA-II) was designed to solve the model. Finally, the proposed model and algorithm were implicated to a real project, in order that research achievement can guide managers to make decisions and invest resources scientifically and reasonably.

A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment

A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment

Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem

PurposeConstruction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).Design/methodology/approachA direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.FindingsComputational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.Research limitations/implicationsIt is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.Originality/valueAn integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

Work package-based information modeling for resource-constrained scheduling of construction projects

Abstract As an essential problem in construction management, the resource-constrained project scheduling problem (RCPSP) has been studied for decades; however, an integrated information model that fully supports the RCPSP solving process is still lacking. Though building information modeling (BIM) was proposed to meet the data requirements in the building life cycle, some scheduling and resource information are not considered in information transfers between the information model and the RCPSP mathematical model. This paper presents an integrated approach that enables fluent data flow from the information model to the RCPSP model for construction scheduling. Within this approach, a work package-based information model is proposed to capture all the required data of the RCPSP. Then, a semiautomatic method that integrates multisource data is introduced to form the proposed information model, and an adaptive data transmission method is used to support a designed multimode resource-constrained project scheduling problem (MRCPSP) model. The models and approaches are validated using the data of an actual project, demonstrating the feasibility and efficiency. This study contributes a novel integrated approach to formalizing a construction information model using a semiautomatic data integration approach, covering the information requirement and enables fluent data flow in the RCPSP solving process. Meanwhile, the work package-based information model is a successful attempt to introduce previously-gained knowledge into automatic schedule generation processes. Future work, such as extending the information model, creating new methods for RCPSP model generation, and data analytics, can bring new opportunities to apply more complex and intelligent methods in project scheduling and construction management.

Multimode resource-constrained project scheduling in flexible projects

Flexible agile and extreme project management methods have become increasingly popular among practitioners, particularly in the IT and R&D sectors. In contrast to the theoretically and algorithmically well-established and developed trade-off and multimode methods applied in traditional project management methods, flexible project scheduling methods, which are applied in agile, hybrid, and especially extreme project management, lack a principled foundation and algorithmic handling. The aim of this paper is to fill this gap. We propose a matrix-based method that provides scores for alternative project plans that host flexible task dependencies and undecided, supplementary task completion while also handling the new but unplanned tasks. In addition, traditional multimode resource-constrained project scheduling problems are also covered. The proposed method can bridge the flexible and traditional approaches.

A robust optimization approach for the multi-mode resource-constrained project scheduling problem

This paper suggests a robust optimization approach for the multi-mode resource-constrained project scheduling problem with uncertain activity durations. The objective is to minimize the worst-case project duration by deciding on activity modes, resource allocations and a schedule baseline. The problem is solved by a Benders decomposition approach with specialized cuts. We consider polyhedral uncertainty sets in which the level of conservatism can be adjusted. Using a computational study in which various problem instances are explored under varying levels of uncertainty, conservatism and several types of duration distributions, we provide insights about the price of robustness and the performance of the approach. The hope is that these insights can guide future multi-mode project scheduling implementations when there is partial information about the distribution of activity durations.

A Genetic Algorithm with a New Local Search Method for Solving the Multimode Resource-constrained Project Scheduling Problem

The existence of limited resources and precedence relations between activities makes project scheduling a difficult task. In this paper, the multimode resource-constrained project scheduling problem (MRCPSP) as one of the important project scheduling problems is focused. In this regard, a genetic algorithm (GA) is proposed for solving the multimode resource-constrained project scheduling problem (MRCPSP) improved by a new local search method. The Activity Mode List (AML) is used as encoding and the multimode serial schedule generation scheme (MSSGS) is employed as the decoding procedure. The aim of the suggested local search is maximum use of existed nonrenewable resources of the project such that its makespan is decreased. For this purpose, the local search procedure allocates the unused nonrenewable resources of each feasible solution to the activities with lower total float. Comparing results of the proposed method with other ones using the set J20 in Project Scheduling Problem Library (PSPLIB) validates the effectiveness of the proposed algorithm to solve the MRCPSP.

A bi-level fuzzy random model for multi-mode resource-constrained project scheduling problem of photovoltaic power plant

As the largest energy consumption country, China now pays more and more attention to photovoltaic power generation because solar energy is the largest renewable and sustainable energy reserve in the world. In this case, the photovoltaic power generation plant project plays a crucial role in sustainable development, especially in the underdeveloped northwest region of China. This paper focuses on applying bi-level programing to the multimode resource-constrained project scheduling problem (MRCPSP) in photovoltaic power generation plant construction, which simultaneously considers the practical hierarchical organization structure and an uncertain decision-making environment. A bi-level fuzzy random multiple objective model is developed, wherein the government agency is in the upper decision level, while the contractor is in the lower decision level. Then, motivated by the particular mathematical nature of the proposed bi-level MRCPSP model, a hybrid intelligent algorithm is designed. Finally, a practical case from the Shenneng Futa Kashi-Tashi-Kuergan photovoltaic power generation plant project in a Chinese energy company is applied, and the results validate the practicability of the proposed model and solution algorithm for solving practical photovoltaic power plant project scheduling problems.

Model and Algorithm for Human Resource-Constrained R&D Program Scheduling Optimization

In resource-constrained project scheduling problems, renewable resources can be expanded into human resources with competency differences. A flexible resource-constrained project scheduling problem with competency differences is proposed, which is a practical extension close to Research and Development (R&D) program management, from the traditional multimode resource-constrained project scheduling problem. A parameter and estimation formula to measure staff competency is presented, and a mixed-integer programming model is established for the problem. The single-objective optimization problems of optimal duration and optimal cost are solved sequentially according to the biobjective importance. To solve the model, according to the assumptions and constraints of the model, the initial network diagram of multiple projects is determined, the enumeration algorithm satisfying constraint conditions provides the feasible solution sets, and the algorithm based on dynamic programming is designed for phased optimization. Experimental results show that the proposed optimization model considering competence differences can solve the problem effectively.

A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction

Abstract Practical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.

Two continuous-time assignment-based models for the multi-mode resource-constrained project scheduling problem

In the multi-mode resource-constrained project scheduling problem, a set of precedence-related project activities and, for each activity, a set of alternative execution modes are given. Each activity requires some time and some scarce resources during execution; these requirements depend on the selected execution mode. Sought is a project schedule, i.e, a start time and an execution mode for each activity, such that the project makespan is minimized. In the literature, besides a large variety of specific solution approaches, several Mixed-Integer Linear Programming (MILP) models have been proposed for this problem. We present two novel MILP models that are based on mode-selection, resource-assignment and sequencing variables; we enhance the performance of the models by eliminating some symmetric solutions from the search space and by adding some redundant sequencing constraints for pairs and for triples of activities that cannot be processed in parallel. In a comparison with reference models from the literature, it turned out that the advantages of the novel models are a simple structure, an enhanced flexibility, and a superior performance when the range of the activities’ durations is relatively large.

Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms

This study addresses the multi-objective multi-mode resource-constrained project scheduling problem with payment planning where the activities can be done through one of the possible modes and the objectives are to maximize the net present value and minimize the completion time concurrently. Moreover, renewable resources including manpower, machinery, and equipment as well as non-renewable ones such as consumable resources and budget are considered to make the model closer to the real-world. To this end, a non-linear programming model is proposed to formulate the problem based on the suggested assumptions. To validate the model, several random instances are designed and solved by GAMS-BARON solver applying the e-constraint method. For the high NP-hardness of the problem, we develop two metaheuristics of non-dominated sorting genetic algorithm II and multi-objective simulated annealing algorithm to solve the problem. Finally, the performances of the proposed solution techniques are evaluated using some well-known efficient criteria.

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.

Coordination-based reactive resource-constrained project scheduling

Due to the increasing use of prefabricated building blocks in construction projects, on-site storages with limited space need to be utilized more efficiently to start the construction activities relating to the blocks on time. Especially when the construction schedule is disrupted by an unexpected event, it needs to be revised by coordinating the delivery times of prefabricated blocks from suppliers, subject to the limited storage space, for minimizing the impact on the project duration. In this paper, a coordination-based reactive scheduling procedure is proposed for a reactive multimode resource-constrained project scheduling problem with limited on-site storage space and its effectiveness is validated through computational experiments. The authors expect that the proposed procedure is also applicable to engineer-to-order (ETO) and make-to-order (MTO) manufacturing systems with limited on-site storage space for raw materials.

A genetic algorithm for scheduling multimode resource-constrained project problem in the presence of preemptive resources

A genetic algorithm for scheduling multimode resource-constrained project problem in the presence of preemptive resources

Multi-mode resource-constrained project scheduling problem with alternative project structures

Abstract This paper proposes to consider alternative project structures when dealing with the multi-mode resource-constrained project scheduling problem. An AND-OR network is used to present this alternative project structures innovatively. The problem is formulated as a bi-objective linear integer program, which minimizes the makespan and total cost. A hybrid metaheuristic is developed based on AND-OR network to solve this NP-hard problem efficiently, which nests adapted Tabu Search (outer layer) and NSGA-II algorithm (inner layer). A large amount of computational experiments of different sizes are conducted, and compared with solutions generated by CPLEX, traditional Tabu Search, enumeration-based method and single-layer NSGA-II. The experimental results show the advantages of the proposed model and method, especially with large-size problems.