Metaheuristics for multi-mode capital-constrained project payment scheduling

This paper involves the multi-mode capital-constrained project payment scheduling problem, where the objective is to assign activity modes and payments so as to maximize the net present value (NPV) of the contractor under the constraint of capital availability. In the light of different payment patterns adopted, four optimization models are constructed using the event-based method. For the NP-hardness of the problem, metaheuristics, including tabu search and simulated annealing, are developed and compared with multi-start iterative improvement and random sampling based on a computational experiment performed on a data set generated randomly. The results indicate that the loop nested tabu search is the most promising procedure for the problem studied. Moreover, the effects of several key parameters on the contractor’s NPV are investigated and the following conclusions are drawn: The contractor’s NPV rises with the increase of the contractor’s initial capital availability, the payment number, the payment proportion, or the project deadline; the contractor has a decreasing marginal return as the contractor’s initial capital availability goes up; the contractor’s NPVs under the milestone event based payment pattern are not less than those under the other three payment patterns.     

An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances

In this paper, an overview is presented of the existing metaheuristic solution procedures to solve the multi-mode resource-constrained-project scheduling problem, in which multiple execution modes are available for each of the activities of the project. A fair comparison is made between the different metaheuristic algorithms on the existing benchmark datasets and on a newly generated dataset. Computational results are provided and recommendations for future research are formulated.     

Proactive policies for the stochastic resource-constrained project scheduling problem

The resource-constrained project scheduling problem involves the determination of a schedule of the project activities, satisfying the precedence and resource constraints while minimizing the project duration. In practice, activity durations may be subject to variability. We propose a stochastic methodology for the determination of a project execution policy and a vector of predictive activity starting times with the objective of minimizing a cost function that consists of the weighted expected activity starting time deviations and the penalties or bonuses associated with late or early project completion. In a computational experiment, we show that our procedure greatly outperforms existing algorithms described in the literature.     

Tabu search for multi-mode resource-constrained project scheduling with schedule-dependent setup times

In this paper, a multi-mode resource-constrained project scheduling problem with schedule-dependent setup times is considered. A schedule-dependent setup time is defined as a setup time dependent on the assignment of resources to activities over time, when resources are, e.g., placed in different locations. In such a case, the time necessary to prepare the required resource for processing an activity depends not only on the sequence of activities but, more generally, on the locations in which successive activities are executed. Activities are non-preemptable, resources are renewable, and the objective is to minimize the project duration. A local search metaheuristic—tabu search is proposed to solve this strongly NP-hard problem, and it is compared with the multi-start iterative improvement method as well as with random sampling. A computational experiment is described, performed on a set of instances based on standard test problems constructed by the ProGen project generator. The algorithms are computationally compared, the results are analyzed and discussed, and some conclusions are given.     

Simulated annealing and tabu search for multi-mode project payment scheduling

This paper involves the multi-mode project payment scheduling problem where the activities can be performed with one of several discrete modes and the objective is to assign activities’ modes and progress payments so as to maximize the net present value of the contractor under the constraint of project deadline. Using the event-based method the basic model of the problem is constructed and in terms of the different payment rules it is extended as the progress based, expense based, and time based models further. For the strong NP-hardness of the problem which is proven by simplifying it to the deadline subproblem of the discrete time–cost tradeoff problem, we develop two heuristic algorithms, namely simulated annealing and tabu search, to solve the problem. The two heuristic algorithms are compared with the multi-start iterative improvement method as well as random sampling on the basis of a computational experiment performed on a data set constructed by ProGen project generator. The results show that the proposed simulated annealing heuristic algorithm seems to be the most promising algorithm for solving the defined problem especially when the instances become larger. In addition, the effects of several key parameters on the net present value of the contractor are analyzed and some conclusions are given based on the results of the computational experiment.     

Competence-driven project portfolio selection,scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.     

A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem

In this paper we present a genetic algorithm for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. We also introduce the preemptive extension of the problem which allows activity splitting (P-MRCPSP). To solve the problem, we apply a bi-population genetic algorithm, which makes use of two separate populations and extend the serial schedule generation scheme by introducing a mode improvement procedure. We evaluate the impact of preemption on the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that our procedure is amongst the most competitive algorithms.     

A faster exact method for solving the robust multi-mode resource-constrained project scheduling problem

This paper presents a mixed-integer linear programming formulation for the multi-mode resource-constrained project scheduling problem with uncertain activity durations. We consider a two-stage robust optimisation approach and find solutions that minimise the worst-case project makespan, whilst assuming that activity durations lie in a budgeted uncertainty set. Computational experiments show that this easy-to-implement formulation is many times faster than the current state-of-the-art solution approach for this problem, whilst solving over 40% more instances to optimality over the same benchmarking set.     

A scatter search algorithm for project scheduling under partially renewable resources

In this paper we develop a heuristic algorithm, based on Scatter Search, for project scheduling problems under partially renewable resources. This new type of resource can be viewed as a generalization of renewable and non-renewable resources, and is very helpful in modelling conditions that do no fit into classical models, but which appear in real timetabling and labor scheduling problems. The Scatter Search algorithm is tested on existing test instances and obtains the best results known so far.     

Priority rule-based heuristic for multi-mode resource-constrained project scheduling problems with resource vacations and activity splitting

This paper presents a priority rule-based heuristic for the multi-mode resource-constrained project scheduling problem with the splitting of activities around unavailable resources allowed. All resources considered are renewable and each resource unit may not be available at all times due to resource vacations, which are known in advance. A new concept called moving resource strength is developed to help identify project situations where activity splitting is likely to be beneficial during scheduling. The moving resource strength concept is implemented in priority rule-based heuristics to control activity splitting when scheduling. Multiple comparisons of the performance of combination of activity–mode priority rules used in the heuristics are provided. Computational experiments demonstrate the effectiveness of the heuristic in reducing project makespan, and minimizing activity splitting.     

An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem

This paper investigates the construction of an automatic algorithm selection tool for the multi-mode resource-constrained project scheduling problem (MRCPSP). The research described relies on the notion of empirical hardness models. These models map problem instance features onto the performance of an algorithm. Using such models, the performance of a set of algorithms can be predicted. Based on these predictions, one can automatically select the algorithm that is expected to perform best given the available computing resources. The idea is to combine different algorithms in a super-algorithm that performs better than any of the components individually. We apply this strategy to the classic problem of project scheduling with multiple execution modes. We show that we can indeed significantly improve on the performance of state-of-the-art algorithms when evaluated on a set of unseen instances. This becomes important when lots of instances have to be solved consecutively. Many state-of-the-art algorithms perform very well on a majority of benchmark instances, while performing worse on a smaller set of instances. The performance of one algorithm can be very different on a set of instances while another algorithm sees no difference in performance at all. Knowing in advance, without using scarce computational resources, which algorithm to run on a certain problem instance, can significantly improve the total overall performance.     

GRASP and path relinking for project scheduling under partially renewable resources

Recently, in the field of project scheduling problems the concept of partially renewable resources has been introduced. Theoretically, it is a generalization of both renewable and non-renewable resources. From an applied point of view, partially renewable resources allow us to model a large variety of situations that do not fit into classical models, but can be found in real problems in timetabling and labor scheduling. In this paper, we develop some preprocessing techniques and several heuristic algorithms for the problem. Preprocessing significantly reduces the dimension of the problems, therefore improving the efficiency of solution procedures. Heuristic algorithms based on GRASP and Path relinking are then developed and tested on existing test instances, obtaining excellent results.     

A hybrid rank-based evolutionary algorithm applied to multi-mode resource-constrained project scheduling problem

We consider the multi-mode resource-constrained project scheduling problem (MRCPSP), where a task has different execution modes characterized by different resource requirements. Due to the nonrenewable resources and the multiple modes, this problem is NP-hard; therefore, we implement an evolutionary algorithm looking for a feasible solution minimizing the makespan.     

Integrated scheduling and batch ordering for construction project

Multi-Mode Resource Constrained Project Scheduling Problem and material batch ordering for construction project are integrated to help project manager consider various trade-offs among several costs, such as renewable resources’ cost, material price, ordering cost, back-ordering cost, inventory holding cost and reward/penalty for early/late project completion. Therefore, we prove a mixed integer programming model and impel to calculate inventory holding cost and back order cost in objective function. Moreover, a hybrid algorithm combined adapted harmony search and genetic algorithm is proposed correspondingly. In order to inherit elitist solution and maintain population’s diversity simultaneously, we add a selection operator when the harmony memory is initialized and modify the replacement operator based on distance. Besides, genetic algorithm is adopted based on a ‘012’ coding scheme. Finally, algorithm and model performance is presented and several project instances are provided with different network structures and realizations to discuss the factors on total cost.     

Linear preselective policies for stochastic project scheduling

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

Maximizing the net present value of a project under uncertainty

We address the maximization of a project’s expected net present value when the activity durations and cash flows are described by a discrete set of alternative scenarios with associated occurrence probabilities. In this setting, the choice of scenario-independent activity start times frequently leads to infeasible schedules or severe losses in revenues. We suggest to determine an optimal target processing time policy for the project activities instead. Such a policy prescribes an activity to be started as early as possible in the realized scenario, but never before its (scenario-independent) target processing time. We formulate the resulting model as a global optimization problem and present a branch-and-bound algorithm for its solution. Extensive numerical results illustrate the suitability of the proposed policy class and the runtime behavior of the algorithm.     

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.     

A heuristic procedure for solving the dynamic probabilistic project expediting problem

Because of activity duration uncertainties, large-scale projects can often be modeled most realistically as probabilistic activity networks. The complex interactions among activities with uncertain durations virtually assures a low probability that these projects will be completed before predetermined due dates. As a result, it is often necessary to expedite individual activities in these projects to improve due date performance. This research introduces a dynamically applied matrix simulation approach for selecting expediting options in order to control the probability of successful project completion before predefined due dates. Experiments are conducted to demonstrate the ability of this new approach to generate quality alternatives and efficiently evaluate large-scale projects.     

Nash equilibria in competitive project scheduling

We consider the problem of scheduling activities of a project by a firm that competes with another firm that has to perform the same project. The profit that a firm gets from each activity depends on whether the firm finishes the activity before or after its competitor. It is required to find a Nash equilibrium solution or show that no such solutions exist. We present a structural characterization of Nash equilibrium solutions, and a low order polynomial algorithm for the problem.     

Multi-objective decision analysis for competence-oriented project portfolio selection

This paper develops a multi-objective optimization model for project portfolio selection taking employee competencies and their evolution into account. The objectives can include economic gains as well as gains expressed in terms of aggregated competence increments according to pre-defined profiles. In order to determine Pareto-optimal solutions, the overall problem is decomposed into a master problem addressing the portfolio selection itself, and a slave problem dealing with a suitable assignment of personnel to the work packages of the selected projects over time. We provide an asymptotic approximation of the problem by a linearized formulation, which allows an efficient and exact solution of the slave problem. For the solution of the master problem, we compare the multi-objective metaheuristics NSGA-II and P-ACO. Experimental results both for synthetically generated test instances and for real-world test instances, based on an application case from the E-Commerce Competence Center Austria, are presented.