Project Scheduling Model Research Articles

Developing a Robust Multi-Skill, Multi-Mode Resource-Constrained Project Scheduling Model with Partial Preemption, Resource Leveling, and Time Windows

Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim’s robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity.

Multi-skilled resource-constrained multi-project scheduling problem with dexterity improvement of workforce

In this study, a mixed-integer mathematical model has been presented for the Multi-Skilled Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where the dexterity of workers can be improved via two ways: (1) Cooperating with more proficient co-workers, and (2) Practicing the possessed skills. The model aims to determine the start and finish times of activities considering the dexterity improvement of workforces. This research presents a Modified discrete variant of the Biogeography-Based Optimization (MBBO) algorithm to solve the model and to minimize the total required duration to complete all projects. The MBBO embraces two novel migration procedures, a new mutation process, and a new habitat selection operator. On several test instances and on a case study, the efficacy of the MBBO has been put to examination in comparison to four other meta-heuristics. The MBBO has overpowered other methodologies in terms of most of the assessment criteria. The obtained results have revealed that the dexterity improvement of workers significantly improves the total required duration to complete all projects. This research and its outputs can be helpful for the researchers who work on project scheduling models. Moreover, this study can inspire future project scheduling formulations, where the dexterity of human resources is affected by the learning phenomenon.

An MINLP Model for Project Scheduling with Feeding Buffer

An MINLP Model for Project Scheduling with Feeding Buffer

A trapezoidal intuitionistic fuzzy optimization approach for crashing a budget constrained project

While the classical fuzzy applications have gained popularity in many different areas, intuitionistic fuzzy set theory emerged as an advancement to better identify various complex circumstances such as the hesitancy of a decision maker under uncertainty. In this study, we propose a project scheduling model in which the allocated budget for crashing is treated as an intuitionistic fuzzy number. With the intuitionistic fuzzy approach, the uncertainty inherent in the project completion time as well as the hesitancy of a decision maker in using the budget amount are integrated into decision making process. The proposed model numerically provides more information than that of the classical ones and better represents the complexity of decision making related to time and cost in project management. Decision makers are provided not only with an optimal schedule but also with a determined degree of satisfaction/dissatisfaction related to the budget and project completion time. Our experimental results show that the decision makers can substantially save money with some delays in the project completion time. Thus, a broader perspective encompassing the satisfaction/dissatisfaction level of a decision maker is presented to schedule a project under uncertain conditions.

Project portfolio selection and scheduling incorporating dynamic synergy

PurposeSelecting and scheduling optimal project portfolio simultaneously is a complex decision-making problem faced by organizations to realize the strategy. However, dynamic synergy relationships among projects complicate this problem. This study aims at constructing a project portfolio selection and scheduling (PPSS) model while quantifying the dynamic synergetic effects to provide decision support for managing PPSS problems.Design/methodology/approachThis study develops a mathematical model for PPSS with the objective of maximal project portfolio benefits (PPBs). To make the results align with the strategy, comprehensive PPBs are divided into financial and non-financial aspects based on the balanced scorecard. Then, synergy benefits evolve dynamically in the time horizon, and system dynamics is employed to quantify them. Lastly, a case example is conducted to verify the applicability of the proposed model.FindingsThe proposed model is an applicable model for PPSS while incorporating dynamic synergy. It can help project managers obtain the results that which project should be selected and when it should start while achieving optimal PPBs.Originality/valueThis study complements prior PPSS research in two aspects. First, financial and non-financial PPBs are designed as new criteria for PPSS, making the results follow the strategy. Second, this study illuminates the dynamic characteristic of synergy and quantifies the synergetic effect. The proposed model provides insights into managing a PPSS effectively.

A stochastic bi-objective project scheduling model under failure of activities

A stochastic bi-objective project scheduling model under failure of activities

Integrated resource-constrained project scheduling and material ordering problem considering storage space allocation

Limited storage space has become a tricky bottleneck for projects that are executed in downtown areas. To relieve this practical dilemma, this paper addresses the concurrent decisions on storage space allocation, activity scheduling and material ordering under limited storage space. Aiming to minimize the total cost of project execution, an integrated model for the resource-constrained project scheduling and material ordering problem with limited storage space (RCPSMOP-LSS) is constructed, and a space-saving material ordering strategy, namely OSTP (Ordering Strategy with Time Period) is utilized. Due to the NP-hardness of the RCPSMOP-LSS, a double-layer heuristic algorithm (DLHA) is designed to solve the model. In the outer layer, an improved genetic algorithm is proposed to obtain a space allocation plan and a schedule of all activities. Based on the outcome from the outer layer, an exact algorithm is developed in the inner layer to determine the material ordering scheme. Furthermore, numerical experiments are carried out to verify the efficiency of the proposed DLHA. Specifically, the Taguchi method is utilized to calibrate the parameters of DLHA. Besides, the experimental results demonstrate that the DLHA can greatly save the total cost compared to a traditional decentralized method (TDM). The efficiency of DLHA is also superior to CPLEX solver and TDM, especially for medium- and large-sized instances. Finally, a sensitivity analysis of key components shows that increasing the storage space exerts an increasing effect on cost reduction.

Modified Resource-Constrained Project Scheduling to Minimize Idle Time for Banbury Mixing Process: A Case Study in the Tire Manufacturing Industry

Abstract The Banbury mixing process (BMP) is to supplies the specific characteristic compounds for the tire manufacturing process. Idle time in the BMP was a problem caused by the aging process between mixing steps and the limited space for processing, measured in pallets. In this study, the resource-constrained project scheduling model (RCPS) is modified in case of the objective function and the input value of resource constraint to minimize idle time (SST). The complete minimization (Cmax) is changed from minimizing the starting time of the last job to the starting time of all jobs. In addition, the non-limited resource is defined as the input for the space capacity to reduce the idle time. As the results, the SST can provide the schedule that make less 5 time periods of idle time. Moreover, when considering the relationship between mixing and aging, aging process that is scheduled from SST starts immediately comparison to Cmax that some of aging process are not. Furthermore, the effect of the quantity of pallets was also examined. Although the non-limited resource does not make any delay to the schedule but the limited quantity is not. When pallets are limited, aging jobs were significantly impacted, with the last aging pallet being delayed. To reduce delays, it prepares an adequate supply of pallets that is close to or equal to its requirement that is defined by the non-limited resource. Further research combining the scheduling of the BMP with the tire manufacturing process and more techniques to modify RCPS are applied.

Simultaneous project portfolio selection and scheduling from contractor perspective

Optimum project portfolio selection is one of the critical topics in project management. Scheduling selected projects to maximize profit and making project portfolio execution feasible are essential issues in this topic. This research presents a comprehensive model for simultaneous project portfolio selection and scheduling while maximizing the project portfolio’s net present value (PPNPV) and minimizing its resource fluctuations (RF). The presented study considers resource constraints and integrates the selection and scheduling process to form the portfolio with the highest efficiency among different possible solutions from the contractor’s perspective in construction management. The optimum project portfolio with its schedule, resource usage, cash flow and profit are outputted as the results. The developed model has been utilized on six hypothetical projects with diverse task numbers and networks containing all kinds of relations, lags and leads. The results presented an efficient point between fulfiling both goal functions by increasing 8.2% PPNPV and decreasing 8.06% and 13.76% of RF parameters for both resources, respectively. Moreover, the model’s superiority is demonstrated by comparing the results with the existing literature. The model is validated by considering different scenarios and comparing the results with what is expected. Considering task level scheduling, all four types of task precedence relation types and task lags lead to more realistic project networks, therefore, modelling the projects’ situations more appropriately. In addition, contractor’s resource sharing policies are engaged in this study to model contractor’s financial situation more realistically. This model views project portfolio selection and scheduling form contractor’s perspective, therefore, it lets contractors to benefit from using detailed scheduling and modelling contractor’s resource sharing policies. It is demonstrated that the presented model allows contractors to form an optimum project portfolio while considering financial and resource constraints.

Evolutionary Algorithm for Software Project Scheduling Considering Team Relationships

During software development, human relationships between team members have a significiant influence on development efficiency. Meanwhile, finishing tasks might require new skills that employees have not yet grasped due to changes of customer requirements or other reasons. However, such factors have not been taken seriously in existing software project scheduling work. To address this, a software project scheduling model considering the team relationships is established, which minimizes the duration and cost of software projects by finding the best employee-task allocation. The model introduces the communication cost factor of each team member, and its quantitative connections with changes in human relationships and the growth of skill proficiency are analyzed, respectively. In addition, a selection mechanism for external employees is designed to meet new skill requirements. To solve the model, a bi-population discrete evolutionary algorithm based on information feedback is proposed. Some initial individuals are generated by utilizing heuristic information of "employee suitability". Adaptive tuning of the subpopulation size is realized by the feedback of "evolutionary quality". The selection probabilities of different crossover operators are adjusted according to the "improvement rate". Additionally, a local search strategy is developed based on the "degree of team cooperation" and the ‘improvement rate’. The experimental results show that by considering human relationships under different communication cost factors, the scheduling performance is significantly improved. Compared with the state-of-the-art algorithms, the proposed algorithm can obtain a schedule with a higher quality in problems of different scales.

Scenario‐oriented repetitive project scheduling optimization

AbstractResearch on project scheduling optimization is trending toward many‐objective optimization, in which the number of objectives exceeds three. However, existing studies usually only consider the necessary logical constraints, ignoring descriptions of practical scenarios and corresponding complex constraints, which limit the designed algorithms in problems with such scenarios and constraints. This study focuses on the many‐objective repetitive project scheduling problem considering practical scenarios with complex constraints (MRPSP‐PSCC). Various constraints are described under flexible matching/mapping between multi‐crew, multi‐mode, and multi‐section scenarios. A many‐objective project scheduling model is proposed for synchronous optimization of time, cost, quality, resource usage, and interruption time. The multiphase balance of diversity and convergence nondominated sorting genetic algorithm III (B‐NSGA‐III) with unique advantages for continuous many‐objective optimization problems is transformed for discrete many‐objective optimization. A series of unique designs is employed in the algorithm, including three‐layer coding rules, constraint handling, and local search, to improve the problem‐solving efficiency of the algorithm. The effectiveness and superiority of the model and algorithm for MRPSP‐PSCC were verified through a case study.

An optimization model for construction project scheduling by considering CO2 emissions with multi-mode resource constraints under interval-valued fuzzy uncertainty

An optimization model for construction project scheduling by considering CO2 emissions with multi-mode resource constraints under interval-valued fuzzy uncertainty

Scientific and Technological Innovation Rapid Emergency Resource Constraint-Improved Particle Swarm Optimization Project Scheduling Method

In the environment of scientific and technological innovation, the human and material resources that can reach the project threshold are often limited. For enterprise projects that require high R&D and production level to produce rapid emergency resources, they often face the problem of difficult effective allocation of emergency resources, resulting in project delay. Therefore, in the process of high-tech projects, it is necessary to deal with the lack of emergency scientific and technological resources. Therefore, based on the particle swarm optimization algorithm which is easy to be realized by scientific and technological innovation enterprises, this study improves its two shortcomings of easy local optimization and low performance, constructs the CD-PSO project scheduling model, verifies the performance of the improvement points, and tests the performance of the model in the actual project case of enterprise rapid emergency resource scheduling. The results show that the optimal solution of CD-PSO model in the three sample sets is higher than that of D-PSO model in the same group, and the average scheduling duration is the shortest, which is 350 days, and the proportion of reaching the optimal duration is 100%. The operation effect is obviously better than other scheduling models. The CD-PSO project scheduling model designed in this study is easier to implement and can calculate the optimal solution of the first project more efficiently. It not only has excellent performance, but also has a strong popularization foundation.

A new interval-valued fuzzy multi-objective approach for project time–cost–quality trade-off problem with activity crashing and overlapping under uncertainty

PurposeMost projects are facing delays, and accelerating the pace of project progress is a necessity. Project managers are responsible for completing the project on time with minimum cost and with maximum quality. This study provides a trade-off between time, cost, and quality objectives to optimize project scheduling.Design/methodology/approachThe current paper presents a new resource-constrained multi-mode time–cost–quality trade-off project scheduling model with lags under finish-to-start relations. To be more realistic, crashing and overlapping techniques are utilized. To handle uncertainty, which is a source of project complexity, interval-valued fuzzy sets are adopted on several parameters. In addition, a new hybrid solution approach is developed to cope with interval-valued fuzzy mathematical model that is based on different alpha-levels and compensatory methods. To find the compatible solution among conflicting objectives, an arithmetical average method is provided as a compensatory approach.FindingsThe interval-valued fuzzy sets approach proposed in this paper is denoted to be scalable, efficient, generalizable and practical in project environments. The results demonstrated that the crashing and overlapping techniques improve time–cost–quality trade-off project scheduling model. Also, interval-valued fuzzy sets can properly manage expressions of the uncertainty of projects which are realistic and practical. The proposed mathematical model is validated by solving a medium-sized dataset an adopted case study. In addition, with a sensitivity analysis approach, the solutions are compared and the model performance is confirmed.Originality/valueThis paper introduces a new continuous-based, resource-constrained, and multi-mode model with crashing and overlapping techniques simultaneously. In addition, a new hybrid compensatory solution approach is extended based on different alpha-levels to handle interval-valued fuzzy multi-objective mathematical model of project scheduling with influential uncertain parameters.

Project scheduling based on resource leveling using fuzzy ranking and genetic algorithm

Project planning and scheduling is often addressed in the paradigm of combined and fuzzy optimization, which is largely owing to the inherent features of the combination and the ensuing uncertainty in determining the variables, none more significant than the timetables and deadlines of activities. As such, the purpose of the current study is to present a novel metaheuristic model for the aforementioned problem by applying fuzzy ranking and genetic algorithm on the resource leveling indicator. This method will be case-studied on fuzzy numbers needed to express uncertain variables in the real world. Project planning and scheduling using resource leveling and the fuzzy approach is of paramount significance to the industry, as it has shown to greatly ensure the proper and effective use of resources. This research seeks to propose a new model for project scheduling in which the uncertainty of the timetable of the activities and resource levelling are examined at the same time. To generate the initial population in the genetic algorithm, parallel fuzzy prioritization method is used to optimally level the resources, while fuzzy theory is further employed to model the uncertainty of the duration of activities.

Environment, resources, and surroundings based dynamic project schedule model for the road construction industry in New Zealand

PurposeThe purpose of this paper is to provide insights into the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in New Zealand (NZ).Design/methodology/approachThis study adopted narrative inquiry methodology that involved semi-structured interviews (SSI)/expert opinion and systematic literature review (SLR) data to determine the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in NZ. The data were analysed by using descriptive analysis, Likert scale and thematic analysis techniques to understand the relationship of these factors to propose a system dynamic model.FindingsThis study concludes that weather, pandemic, material, geotechnical and disaster factors highly influence while other factors such as equipment shortage, breakdown, design error, labour and event had mixed impact on the dynamic scheduling (DS) that aids on-time project delivery. The proposed system dynamic model can enhance the understanding of factors affecting DS.Research limitations/implicationsSLR is limited to English literature. The limitations of an SSI and a small sample size are acknowledged.Practical implicationsThe proposed model can reduce the uncertainty and scheduling errors during the planning phase and aid in the lesser scheduling modification during the execution phase. In practice, this study will be helpful for road contractors to understand environment, surroundings and resource in-control and out-of-control factors, overcome road construction delays, reduce cost, aid in stakeholder management and sustainable development.Social implicationsThe inclusion of environment, resource and surroundings factors in force majeure clauses will bring an understanding between contracting parties and in turn reduce disputes and delays and help social causes such as on-time infrastructure delivery.Originality/valueFor the first time in a road construction, dynamic project scheduling model that collectively included and linked environment, resource, and surroundings factors to determine the in-control and out-of-control factors for an organisation is proposed. The novelty in the paper is provided by the inclusion of the events, disasters, and pandemics influence on DS in the NZ road construction industry for the first time.

A Robust Bi-objective Optimization Model for Resource Levelling Project Scheduling Problem with Discounted Cash Flows

Due to limited financial resources and high costs of infrastructure projects, project stakeholders seek to gain maximum profits with optimal resource utilization as well as cost and time minimization. Therefore, this study presents a multi-mode resource-constrained project scheduling model considering the uncertain parameters of cost and time together with the goals of maximizing the net present value and minimizing resource usage fluctuation. Also, the assumptions related to the real-world projects regarding multi-mode activities, limitation of renewable resources, and the deadline of project are incorporated into the proposed model. Moreover, a robust scheduling method is presented to better deal with the inherent uncertainties of projects regarding cost and time. The model is solved with the exact method named lexicographic goal programming (LGP). Due to the NP-hardness of the problem, two metaheuristic algorithms named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are applied to solve various medium and large size problems. The obtained results indicate the high efficiency of the two metaheuristic algorithms in solving the problem and the better performance of the MOPSO algorithm compared with NSGA-II in terms of five indices. Furthermore, the model is implemented in an offshore equipment installation phase of a wellhead platform project. Finally, the sensitivity analysis of the proposed robust model is performed considering different conservation levels, and the results are evaluated by Monte Carlo simulation with three normal, uniform and triangular distributions. The findings demonstrate that the robustness of the model against the variations of uncertain parameters.

A multiproject scheduling and resource management model in projects construction

PurposeThis research presents a multiproject scheduling and resource management (MPSRM) model that includes an M/M/c/n queue system, a p-hub median model, a parallel machine scheduling and a hub location problem solution method. This research aims to design a project network and then sequence raw materials delivery to hub factories.Design/methodology/approachThis research is implemented as a case study in construction and industrial company. It considers several mines to supply raw materials for production in hub factories. In the sequencing phase, a parallel mine scheduling problem specifies the delivery of raw materials to hub factories. Furthermore, a multisource project scheduling model is studied and designed for building project-oriented companies.FindingsThe results suggest that the proposed MPSRM model in this study significantly reduces project transportation costs. Therefore, creating accurate planning in projects, especially in construction projects where transportation plays an important role, can help reduce time and costs. Researchers, project managers and those dealing with projects can use this model to exploit their projects.Research limitations/implicationsWhen the number of construction projects in a construction company increases in a region, choosing an appropriate strategy to supply resources and raw materials becomes very important in terms of profit and loss and project completion as scheduled. An increase in transportation demands alongside the economic development on the one hand and providing competitive transportation services, on the other hand, have increasingly spotlighted the significance of hub networks in transportation systems. In addition, in this research, there is no suitable access road from the mines to the project sites, and considering the workload, if any of the projects become a hub, a proper road should be built.Originality/valueTo the best of the knowledge, there is no outstanding research in which a p-hub median location problem by considering queuing model and different transportation modes and a parallel machine scheduling problem are studied simultaneously.

Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

Project scheduling is a key objective of many models and is the proposed method for project planning and management. Project scheduling problems depend on precedence relationships and resource constraints, in addition to some other limitations for achieving a subset of goals. Project scheduling problems are dependent on many limitations, including limitations of precedence relationships, resource constraints, and some other limitations for achieving a subset of goals. Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required, which are known and stable during the implementation process. The concept of deterministic project scheduling conflicts with real situations, in which in many cases, some data on the activity' s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations. Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us. We use the multi-mode resource allocation and scheduling model that takes into account the dynamicity features of all parameters, that is, the scheduling process must be flexible to dynamic environment features. In this paper, we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations, in which we apply the proposed heuristic rules (PHR) for scheduling multi-mode resource-constrained projects. Five projects are considered test problems for the PHR. The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems. The results in many cases of the proposed priority rules are very promising, where they achieve better scheduling dates in many test case problems and the same results for the others. The proposed model is based on the dynamic features for project topography.

Non-Sequential Linear Construction Project Scheduling Model for Minimizing Idle Equipment Using Constraint Programming (CP)

Over the last several decades, the scheduling of linear construction projects (LCPs) has been explored extensively by experts. The linear scheduling method (LSM), which focuses on work rate and work continuity, has the advantage of tackling LCPs’ scheduling problems. The traditional LSM uses work continuity to monitor resource allocation continuity on the premise that activities with the same type of work use the same crew. However, some LCPs require a combination of different types of equipment to comprise the crew. Sometimes, parts of different crews require the same types of equipment, and sometimes, the same crew requires different equipment configurations. This causes the pattern of work continuity to be different from the pattern of resource allocation continuity. Therefore, we propose an optimization model of the LSM to minimize idle equipment on a non-sequential linear construction project—i.e., a road network maintenance project. This model is intended to minimize the number of idle equipment and their idle time to achieve more efficient scheduling for linear construction projects. This model offers novel details of resource allocation continuity assessment by taking into account equipment combination and configuration (ECC). Therefore, the scheduling concept used by the proposed model is named the linear scheduling model with ECC (LSM–ECC). The model was developed using constraint programming (CP), as CP has good performance and robustness in the optimization field. The model was implemented to a representation of a road network maintenance project and has satisfactory results.