Library PSPLIB Research Articles

Evolve ensemble rules automatically for the block spatial scheduling under dynamic environments via surrogate-assisted cooperative evolution genetic programming

ABSTRACT The spatial scheduling problem is a crucial investigated problem in operations research and is widely used in shipbuilding, assembly line production and engineering projects. In this paper, we introduce a new block spatial scheduling problem (BSSP) by considering regular resources (manpower and equipment) and dynamic environments. Then, a surrogate-assisted cooperative evolution genetic programming (SCE-GP) is designed to address the BSSP. For the developed algorithm, we firstly propose a new surrogate model by considering the problem surrogate and fitness function surrogate simultaneously, and compare it with the existing models that consider only the fitness function surrogate or problem surrogate under different uncertain environments. Secondly, the cooperative evolution mechanism and random forest technique are embedded in the algorithm to improve its performance. More importantly, we compare different methods for selecting promising individuals. In addition, the design-of-experiment (DOE) approach is utilised to explore the effect of parameter settings. Finally, the performance of SCE-GP with different surrogate models is investigated on our configured data sets based on the benchmark instances of the PSPLIB library. At the same time, we verify the effectiveness of the SCE-GP under different surrogate models and uncertain environments, the performance of the cooperative evolution mechanism, random forest technique and selected method for promising individuals through extensive numerical experiments is also investigated. The results show that the SCE-GP is more excellent than traditional heuristic priority rules (PRs), but different surrogate models yield different results in different uncertain environments.

A project scheduling problem with spatial resource constraints and a corresponding guided local search algorithm

Spatial area is a nonlinear resource encountered in many industries. Using the block assembly process in a shipbuilding company as a case study, we propose a project scheduling problem with spatial resource constraints (2D-RCPSP). Due to the two-dimensional properties of spatial resources, the 2D-RCPSP is more difficult to solve than the well-known NP-hard resource constrained project scheduling problem (RCPSP). For the 2D-RCPSP, we develop a sophisticated guided local search algorithm (GLS). The GLS incorporates penalty terms into the cost function and uses it, instead of the original objective function, to guide the search to the promising regions in search space. Furthermore, rapid improvement strategies are investigated in order to increase the efficiency of the local search. To test our GLS for the 2D-RCPSP, we have designed three classes of problem instances, two of which are adapted from the commonly referenced benchmark instance library PSPLIB for RCPSP, whereas the other is derived from real shipyard production data. Three types of tests, each for one class of instances, are implemented. The computational results on these instances as well as the tuning for key parameter in the GLS are presented.

A novel algorithm for solving resource-constrained project scheduling problems: a case study

PurposeConstruction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.Design/methodology/approachDue to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.FindingsAccording to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.Practical implicationsICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.Originality/valueIn this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

Improved discrete cuckoo search for the resource-constrained project scheduling problem

An Improved Discrete Cuckoo Search (IDCS) is proposed in this paper to solve resource-constrained project scheduling problems (RCPSPs). The original Cuckoo Search (CS) was inspired by the breeding behaviour of some cuckoo species and was designed specifically for application in continuous optimisation problems, in which the algorithm had been demonstrated to be effective. The proposed IDCS aims to improve the original CS for solving discrete scheduling problems by reinterpreting its key elements: solution representation scheme, Lévy flight and solution improvement operators. An event list solution representation scheme has been used to present projects and a novel event movement and an event recombination operator has been developed to ensure better quality of received results and improve the efficiency of the algorithm. Numerical results have demonstrated that the proposed IDCS can achieve a competitive level of performance compared to other state-of-the-art metaheuristics in solving a set of benchmark instances from a well-known PSPLIB library, especially in solving complex benchmark instances.

Integrated optimisation of the flexible resource workload balancing and investment project scheduling problem

In this paper, we investigate the extension of the resource investment project scheduling problem: the flexible resource workload balancing and investment project scheduling problem (FRWBIP). The integrated optimisation of FRWBIP is established with the objective of minimising the weighted resource availability cost and workload balancing penalty. A heuristic algorithm is designed to obtain the upper and lower bounds of resource requirements. The skills of the flexible resources are effectively allocated by the minimum cost-maximum flow flexible resource allocation model. A genetic algorithm with an improved serial schedule generation scheme and the resource usage adjustment module is proposed to solve FRWBIP. Every individual of the population is constructed from two lists: the activity priority list and the resource capacity list. Finally, a series of computational experiments are carried out by using the modified standard example library PSPLIB, and the effectiveness of the algorithm is verified by comparison with other literature algorithm.

A path relinking-based scatter search for the resource-constrained project scheduling problem

Project scheduling has received growing attention from researchers in recent decades in order to recommend models and methods to tackle problems for real-size projects. In this paper, we consider the resource-constrained project scheduling problem (RCPSP), which consists of scheduling activities in order to minimise the project duration in presence of precedence and resource constraints. We propose a hybrid metaheuristic based on scatter search that involves forward-backward improvement and reversing the project network at each iteration of the search. A bidirectional path relinking method with a new move is used as a solution combination method and a new improvement procedure is proposed in the reference set update method. The proposed method is applied to the standard benchmark projects from the PSPLIB library. The computational results show that the proposed scatter search produces high-quality solutions in a reasonable computational time and is among the best performing metaheuristics.

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature.

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

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

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

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

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

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

Linear solution schemes for Mean-SemiVariance Project portfolio selection problems: An application in the oil and gas industry

We study the Mean-SemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal risk-reward portfolio of non-divisible projects when the risk is measured by the semivariance of the portfolio׳s Net-Present Value (NPV) and the reward is measured by the portfolio׳s expected NPV. Similar to the well-known Mean-Variance portfolio selection problem, when integer variables are present (e.g., due to transaction costs, cardinality constraints, or asset illiquidity), the MSVP problem can be solved using Mixed-Integer Quadratic Programming (MIQP) techniques. However, conventional MIQP solvers may be unable to solve large-scale MSVP problem instances in a reasonable amount of time. In this paper, we propose two linear solution schemes to solve the MSVP problem; that is, the proposed schemes avoid the use of MIQP solvers and only require the use of Mixed-Integer Linear Programming (MILP) techniques. In particular, we show that the solution of a class of real-world MSVP problems, in which project returns are positively correlated, can be accurately approximated by solving a single MILP problem. In general, we show that the MSVP problem can be effectively solved by a sequence of MILP problems, which allow us to solve large-scale MSVP problem instances faster than using MIQP solvers. We illustrate our solution schemes by solving a real MSVP problem arising in a Latin American oil and gas company. Also, we solve instances of the MSVP problem that are constructed using data from the PSPLIB library of project scheduling problems.

Insertion Algorithms with Justification for Solving the Resource-Constrained Project Scheduling Problem

The paper presents the resource-constrained project scheduling problem with the makespan minimisation criterion. To solve the problem, the authors propose insertion algorithms which generate schedules with use of forward serial and parallel decoding procedures. Schedules are improved with the use of the double justification by extremes technique (first right and then left justification). The efficiency of the procedures proposed is tested on standard test problems from the PSPLIB library.

PSPLIB-ENERGY: a extension of PSPLIB library to assess the energy optimization in the RCPSP

Scheduling problems is one of the core areas in the planning and development of any project, with a wide applicability to real-world situations. Due to the high complexity of these problems, the solving process is often based on metaheuristics techniques, so that the evaluation of these methods is empirical. Therefore benchmarks, which provide a set of test cases to assess the behavior of algorithms, are generated. This paper extends the PSPLIB library. This extension incorporates to each instance of RCPSP (Resource Constrained Project Scheduling Problem), a realistic mathematical model of energy consumption. This proposal provides an alternative to the current trend in the eld of optimization and manufacturing that requires the inclusion of components and methods that reduce the environmental impact in the process of decision making. Finally a new optimality criterion is proposed to compare dierent search techniques. The PSPLIB-ENERGY is available at http://gps. webs.upv.es/psplib-energy/.

Memetic algorithm for multi-mode resource-constrained project scheduling problems

A memetic algorithm (MA) for a multi-mode resource-constrained project scheduling problem (MRCPSP) is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called “strategic oscillation” to make the search process have a higher probability to visit solutions around a �sible boundary”. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.

A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem

In this paper, we propose a particle swarm optimization (PSO) based hyper-heuristic algorithm for solving the resource constrained project scheduling problem (RCPSP). To the best of our knowledge, this is the first attempt to develop a PSO hyper-heuristic and apply to the classic RCPSP. The hyper-heuristic works as an upper-level algorithm that controls several low-level heuristics which operate to the solution space. The solution representation is based on random keys. Active schedules are constructed by the serial scheduling generation scheme using the priorities of the activities which are modified by the low-level heuristics of the algorithm. Also, the double justification operator, i.e. a forward–backward improvement procedure, is applied to all solutions. The proposed approach was tested on a set of standard problem instances of the well-known library PSPLIB and compared with other approaches from the literature. The promising computational results validate the effectiveness of the proposed approach.

English

Multi-mode resource-constrained project scheduling problems (MRCPSPs) are classified as NP-hard problems, in which a task has different execution modes characterized by different resource requirements. Estimation of distribution algorithm (EDA) has shown an effective performance for solving such real-world optimization problems but it fails to find the desired optima. This paper integrates a novel hybrid local search technique with EDA to enhance their local search ability. The new local search is based on delete-then-insert operator and a random walk (DIRW) to enhance exploitation abilities of EDA in the neighborhoods of the search space. The proposed algorithm is capable to explore and exploit the search mechanism in the search space through its outer and inner loops. The proposed algorithm is tested and evaluated using benchmark test problems of the project scheduling problem library PSPLIB. Simulation results of the proposed algorithm are compared with the classical EDA algorithm. The obtained results showed that the effectiveness of the proposed algorithm and outperformed the compared EDA algorithm.

Chaotic differential evolution algorithm for resource constrained project scheduling problem

In this paper, a new optimisation method based on chaotic differential evolution CDE algorithm for solving resource-constraint project scheduling problem RCPSP is proposed. By using improved logistic chaotic map and penalty function, the objective function can be solved more perfectly. Project scheduling problem library PSPLIB has been taken into account so as to test the effect of novel hybrid method. Simulation results and comparisons show that the chaotic differential evolution algorithm is competitive and stable in performance with other optimisation approaches presented in literatures.

The Resource Leveling Problem with multiple resources using an adaptive genetic algorithm

article i nfo Article history: Accepted 3 October 2012 Available online 29 October 2012 Resource management ensures that a project is completed on time and at cost, and that its quality is as previously defined; nevertheless, resources are scarce and their use in the activities of the project leads to conflicts in the schedule. Resource leveling problems consider how to make the resource consumption as efficient as possible. This paper presents an Adaptive Genetic Algorithm for the Resource Leveling Problem, and its novelty lies in using the Weibull distribution to establish an estimation of the global optimum as a ter- mination condition. The extension of the project deadline with a penalty is allowed, avoiding the increase in the project criticality. The algorithm is tested with the Project Scheduling Problem Library PSPLIB. The pro- posed algorithm is implemented using VBA for Excel 2010 to provide a flexible and powerful decision support system that enables practitioners to choose between different feasible solutions to a problem in realistic environments.

Artificial Bee colony for resource constrained project scheduling problem

Article history: Received 15 April 2010 Received in revised form 19 July 2010 Accepted 20 July 2010 Available online 20 July 2010 Solving resource constrained project scheduling problem (RCPSP) has important role in the context of project scheduling. Considering a single objective RCPSP, the goal is to find a schedule that minimizes the makespan. This is NP-hard problem (Blazewicz et al., 1983) and one may use meta-heuristics to obtain a global optimum solution or at least a near-optimal one. Recently, various meta-heuristics such as ACO, PSO, GA, SA etc have been applied on RCPSP. Bee algorithms are among most recently introduced meta-heuristics. This study aims at adapting artificial bee colony as an alternative and efficient optimization strategy for solving RCPSP and investigating its performance on the RCPSP. To evaluate the artificial bee colony, its performance is investigated against other meta-heuristics for solving case studies in the PSPLIB library. Simulation results show that the artificial bee colony presents an efficient way for solving resource constrained project scheduling problem. © 2010 Growing Science Ltd. All rights reserved.

A Boolean satisfiability approach to the resource-constrained project scheduling problem

We formulate the resource-constrained project scheduling problem as a satisfiability problem and adapt a satisfiability solver for the specific domain of the problem. Our solver is lightweight and shows good performance both in finding feasible solutions and in proving lower bounds. Our numerical tests allowed us to close several benchmark instances of the RCPSP that have never been closed before by proving tighter lower bounds and by finding better feasible solutions. Using our method we solve optimally more instances of medium and large size from the benchmark library PSPLIB and do it faster compared to any other existing solver.