Multi-project Scheduling Research Articles

A multi-agent based cooperative approach to decentralized multi-project scheduling and resource allocation

While project crashing is a prominent practice in project management and can be approached by the well-known time–cost tradeoff problem (TCTP), the existing methodologies assume a centralized decision paradigm. In this paper, we study a new decentralized multi-project time-cost tradeoff problem, called MPTCTP-D, where project scheduling and resource allocation decisions are made in a distributed way by autonomous project managers to optimize their local objective, subject to a global available budget for all the projects. We show and prove the existence of price of anarchy as the difference between the optimal objective value of MPTCTP-D and its centralized counterpart. A multi-agent based cooperative approach with negotiation protocol is proposed to mitigate resource competition of autonomous local decisions and achieve a reasonable allocation of resource for global decision-maker, with both exact and local-search based heuristic algorithms developed. Our exact algorithm is able to find optimal solutions and prove optimality for instances with up to five projects and 120 activities per project. The hybrid randomized and 2-opt improvement algorithm is able to efficiently find optimal solutions to small instances and significantly better solutions than other heuristics alone for instances with up to 20 projects and 120 activities per project. Managerial insights on the optimal solutions and the price of anarchy are obtained and discussed.

Resource-constrained multi-project scheduling with activity and time flexibility

Project scheduling in manufacturing environments often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new resource-constrained project scheduling problem (RCPSP) is introduced where both decisions (activity flexibility and time flexibility) are integrated. Besides the minimization of makespan, two new alternative objectives are presented: maximization of balanced length of selected activities (time balance) and maximization of balanced resource utilization (resource balance). New mixed integer and constraint programming (CP) models are proposed for the developed integrated flexible project scheduling problem. Benchmark instances on an already existing flexible RCPSP and the newly developed problem are solved to optimality. The real-world applicability of the suggested CP models is shown by additionally solving a large industry case.

An efficient genetic algorithm for decentralized multi-project scheduling with resource transfers

<p style='text-indent:20px;'>This paper investigates the decentralized resource-constrained multi-project scheduling problem with transfer times (DRCMPSPTT) in which the transfer times of the global resources among different projects are assumed to be sequence-independent, while transfers of local resources take no time within a project. First, two decision variables (<inline-formula><tex-math id="M1">\begin{document}$ {y_{ijg}} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M2">\begin{document}$ {w_{ijg}} $\end{document}</tex-math></inline-formula>) are adopted to express the transition state of global resources between projects. <inline-formula><tex-math id="M3">\begin{document}$ {y_{ijg}} $\end{document}</tex-math></inline-formula> (takes a value of 0 or 1) represents whether activity <i>i</i> transfers global resource <i>g</i> to activity <i>j</i>; accordingly, the transferred quantity is denoted as <inline-formula><tex-math id="M4">\begin{document}$ {w_{ijg}} $\end{document}</tex-math></inline-formula>. Then, we construct an integer linear model with the goal of minimizing the average project delay for the DRCMPSPTT. Second, an adaptive genetic algorithm (GA) is developed to solve the DRCMPSPTT. To gain the schedules for the DRCMPSPTT, the traditional serial and parallel scheduling generation schemes (SGSs) are modified to combine with different resource transfer rules and to design multiple decoding schemes. Third, the numerical experiments are implemented to analyse the effects of eight decoding schemes, and we found that the scheme comprising the parallel SGS and maxRS rule can make the GA work the best; furthermore, the effectiveness of the GA_maxRS (GA embedded with the best scheme) is demonstrated by solving some instances with different sizes.</p>

Editorial: The Innovation and Project Management Department in the Journal of Operations Management

Editorial: The Innovation and Project Management Department in the <i>Journal of Operations Management</i>

A multi-objective optimization model for multiple project scheduling and multi-skill human resource assignment problem based on learning and forgetting effect and activities' quality level

One of the most important aspects of human resource management is the allocation of the workforce to activities. Human resource assignment to project activities for its scheduling is one of the most real and common issues in project management and scheduling. This becomes even more significant when human resource assignment to multiple projects simultaneously is considered. On the one hand, workforces can have multi skills due to technological and scientific development so that they can be assigned to project activities based on their skill level. On the other hand, the learning effect is also taken into account to make the model more realistic. These factors can affect completion time, total cost and execution quality of projects. In this study, a multi-objective optimization model for multi-project scheduling and multi-skilled human resource assignment problem based on the learning effect and activities' quality is presented. A mixed-integer linear programming model (MILP) is developed for the proposed problem and solved by the e-constraint method in GAMS software. Managers can select a solution based on their priority. Finally, a sensitivity analysis is done on the learning and forgetting effect to investigate their impacts on each objective function.

Reactive Strategies in the Multiproject Scheduling with Multifactor Disruptions

Multiproject scheduling aims at the generation of the baseline schedules, which has been studied for several years with the goal of minimizing the total cost of the project. In this paper, we analyzed the impact of network structure disruption, activity disruption, and resource disruption on scheduling scheme, respectively; this problem is related to the disruptions in scheduling process, which leads to a deviation between actual scheduling and baseline scheduling. The mode of the related activities is changed and the start time is reset. Because it is a NP-hard problem and involves a large number of activities, the dual population genetic algorithm is designed to solve this problem. From the results of case analysis, we find that single disruption on scheduling was local, when there was no resource conflict. While multifactor disruptions have greater effect on duration and total cost, multifactor disruptions would affect each other, so it was more complicated.

Tabu search and simulated annealing for resource-constrained multi-project scheduling to minimize maximal cash flow gap

In reality, a contractor may implement multiple projects simultaneously and in such an environment, how to achieve a positive balance between cash outflow and inflow by scheduling is an important problem for the contractor has to tackle. For this fact, this paper investigates a resource-constrained multi-project scheduling problem with the objective of minimizing the contractor's maximal cash flow gap under the constraint of a project deadline and renewable resource. In the paper, we construct a non-linear integer programming optimization model for the studied problem at first. Then, for the NP-hardness of the problem, we design three metaheuristic algorithms to solve the model: tabu search (TS), simulated annealing (SA), and an algorithm comprising both TS and SA (SA-TS). Finally, we conduct a computational experiment on a data set coming from existing literature to evaluate the performance of the developed algorithms and analyze the effects of key parameters on the objective function. Based on the computational results, the following conclusions are drawn: Among the designed algorithms, the SA-TS with an improvement measure is the most promising for solving the problem under study. Some parameters may exert an important effect on the contractor's maximal cash flow gap.

Resource-constrained multi-project scheduling: benchmark datasets and decoupled scheduling

In this paper, we propose a new dataset for the resource-constrained multi-project scheduling problem and evaluate the performance of multi-project extensions of the single-project schedule generation schemes. This manuscript contributes to the existing research in three ways. First, we provide an overview of existing benchmark datasets and classify the multi-project literature based on the type of datasets that are used in these studies. Furthermore, we evaluate the existing summary measures that are used to classify instances and provide adaptations to the data generation procedure of Browning and Yassine (J Scheduling 13(2):143-161, 2010a). With this adapted generator we propose a new dataset that is complimentary to the existing ones. Second, we propose decoupled versions of the single-project scheduling schemes, building on insights from the existing literature. A computational experiment shows that the decoupled variants outperform the existing priority rule heuristics and that the best priority rules differ for the two objective functions under study. Furthermore, we analyse the effect of the different parameters on the performance of the heuristics. Third, we implement a genetic algorithm that incorporates specific multi-project operators and test it on all datasets. The experiment shows that the new datasets are challenging and provide opportunities for future research.

A Multi-PR Heuristic for Distributed Multi-Project Scheduling With Uncertain Duration

Multiple projects are often managed and run in a decentralized setting. In this paper, considering the uncertainty in project implementation, we study the distributed multi-project scheduling problem with uncertain duration. A multi-PR heuristic (MPR-H) is then proposed to dynamically coordinate the global resource conflicts while minimizing the expected total tardiness cost. Three priority rules based on current known information are also proposed and incorporated in our approach. We further consider the opportunistic behaviour of self-interested agents and design a payment negotiation process which is added to the MPR-H. In this paper, we then evaluate the performance of the MPR-H on the benchmark dataset MPSPLIB. The computational results confirm that MPR-H achieves significant improvements in comparison with several state-of-the-art distributed/centralized algorithms. The proposed algorithm also provides the senior manager with an efficient method to allocate global resources for large-size and strong conflicting instances under various activity duration distributions. Besides, we show that multi-projects with relative slack global resource constraints are more affected by the change of uncertainty. By analyzing the strategic behaviour of the agents in problems with two projects, we also show that in our MPR-H with payment negotiation approach, rational agents have to behave truthfully that is the dominant-strategy equilibrium leading to high-quality results.

A survey in the resource-constrained project and multi-project scheduling problems

A survey in the resource-constrained project and multi-project scheduling problems

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

AbstractToday, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&amp;D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&amp;D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&amp;D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.

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.

A Robust Optimization Scheduling for Carrier Aircraft Support Operation Based on Critical Chain Method

The maintenance and service support of carrier aircraft (MSSCA) is a complex process involving many types of resources and activities that require optimisation which can be considered as a multi-resource constrained multi-project scheduling problem (MRCMPSP). It is of great significance to optimize the makespan and obtain a proactive robust schedule to be adaptive to the changes in the dynamic flight deck environment. This paper develops a critical chain method (CCM) for carrier aircraft support robust scheduling in a time-critical and resource-constrained operation environment. The CCM consists of developing a desirable deterministic schedule under multi resource constraints and time-critical issues, and adding a project buffer (PB) to the end of the schedule is used to get a robust proactive scheduling and deal with uncertainty. The triangular fuzzy number (TFN) is applied to describe the durations of each activities and calculate the PB size and obtain an appropriate proportionality between the activity duration and the buffer size. In the numerical research, different size simulations are designed to calculated the robust optimization scheduling. The computational results show that the CMM for carrier aircraft support robust scheduling can make a better decision in the robust proactive optimization scheduling about the resource allocation.

Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival

The resource constrained multi-project scheduling problem (RCMPSP) is a general and classic problem, which is usually considered and solved in a deterministic environment. However, in real project management, there are always some unforeseen factors such as one or more new project arrivals that give rise to intermittent changes in the activity duration (or stochastic duration) of the current project in execution by inserting the new project. This study takes two practical factors in terms of stochastic duration of project activities and new project arrivals waiting for insertion into account of the problem space to form a stochastic resource constrained multi-project scheduling problem with new project arrivals (SRCMPSP-NPA). Based on the benchmark of the PSPLIB (Project Scheduling Problem Library), a new data set is built and 20 priority rules (PRs) are applied to solve the problem and their performances are analyzed. In addition, a heuristic hybrid method is designed for solving the problem timely by dividing the entire scheduling process into multi-state scheduling problems solved by the corresponding rules separately. This approach has been verified by experiments and its performance is better than that of a single rule in most situations.

A repeated-negotiation game approach to distributed (re)scheduling of multiple projects using decoupled learning

With peer-to-peer software technologies based on Blockchain and Smart Contracts, automated negotiation of client-server relationships for enterprise networking and project-oriented fractal organizations can now be readily implemented. To this aim, the distributed (emergent) schedule of client-server contracts must be a Nash equilibrium from which any agent finds no incentive to deviate. Also, to respond effectively to unplanned events and disturbances, the renegotiating process of all concerned client-server contracts must pursue a new Nash equilibrium solution in the face of incomplete information by each individual agent. In this work, distributed multi-project (re)scheduling is formulated as a repeated-negotiation game in a multi-agent setting where each agent resorts to decoupled learning rules for deciding the terms and conditions in each contract settlement. After a finite number of stages of the negotiation game, a new emergent schedule close to a Nash equilibrium is found. An agent-based simulation framework is proposed to implement the repeated negotiation game based on bilateral client-server contracts. The effectiveness of the proposed approach is demonstrated using a case study of a project-oriented fractal company in the pharmaceutical industry. Simulation results obtained highlight that repeated negotiations and decoupled learning are key for approaching a Nash equilibrium and welfare solutions to a (re)scheduling problem.

Solving the resource-constrained multi-project scheduling problem with an improved critical chain method

In view of the fact that the critical chain method (CCM) is mainly used in single project scheduling, this article improves the method and applies it to multi-project scheduling. Taking into consideration the flow of the drum resource within and among sub-projects, this study proposes methods that can be used to identify the critical chain and buffer settings, based on the concepts of the task chain, sub-project drum resource flow, and multi-project drum resource flow. The improvement of the CCM includes two aspects. First, by analysing the impact of the drum buffer and capacity constrained buffer on the project buffer and feeding buffer, the calculation methods of these buffers are optimised. Second, a risk contribution index is designed for the modification of these buffers, in order to further improve their anti-interference ability. Then, combined with the improved CCM and the different hierarchical scheduling objectives, a critical chain resource-constrained multi-project scheduling model with a hierarchical strategy is proposed as a way of solving multi-project scheduling plans. Eight theoretical test cases with different scales, and a practical example under different risk levels of uncertainty are used to test this model. The results show that the stability of the scheduling plans clearly improved, and the project duration and tardiness costs were significantly reduced, thus proving the effectiveness of the model.

Symmetry breaking of identical projects in the high-multiplicity RCPSP/max

This article considers the high-multiplicity resource-constrained project scheduling problem with generalised precedence constraints (RCPSP/max). Projects, which can be partitioned into relatively few classes, are to be scheduled subject to resource and generalised precedence constraints. We show that there exists symmetry between projects of the same class and propose two approaches of symmetry breaking: (1) adding additional constraints to the model in the form of precedence constraints, (2) remodelling the problem to reduce the number of variables. To test the usefulness of the symmetry breaking approaches a computational study is completed considering two families of discrete-time based MIP models and a number of state-of-the-art CP-based scheduling approaches. The study shows that both symmetry breaking approaches allow all solving methods to find and prove more optimal solutions. The best CP approach is then used to find a number of new best solutions to relevant problems from the MPSPlib, a multi-project scheduling problem library, for both the total makespan and average project delay objective, whereas the best MIP approach is used to determine a number of tighter lower bounds.

Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process

The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.

Integrated Multi-project Scheduling and Worker Allocation for the Assembly of Steam Turbines

The steam turbine is typically an engineer-to-order product, the assembly of steam turbine is mainly performed by a group of workers. In this paper, we investigate the integrated multi-project scheduling and worker allocation for the assembly of steam turbine. These two problems are interrelated as the tasks durations are not predefined, but depend on the number of workers assigned to that task as well as their skill levels. We develop a mathematical model to represent the problem. In order to solve with the minimization of makespan as the objective, we propose the hybrid algorithm combining particle swarm optimization (PSO) and simulated annealing (SA). The proposed algorithm is tested on the case that uses industrial data from a steam turbine company. The experimental results show that the solution quality of the hybrid algorithm outperform the other three traditional metaheuristic algorithms.

A hybrid algorithm of product-service framework for the multi-project scheduling in ETO assembly process

The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies the multi-project scheduling in assembly process of ETO products. The multi-project scheduling problem is to schedule tasks of different projects under many constraints and assign hierarchical workers to each task. We develop a mathematical model to represent the problem. In order to solve with the minimization of makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scale benchmark instances and the case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager.