A Practical Algorithmic Approach Towards Multi-Modal Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP)

2015 ◽  
Author(s):  
Michael Völker ◽  
Taiba Zahid ◽  
Thorsten Schmidt
Keyword(s):  
Project Scheduling ◽  
Decision Support Tool ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Decision Strategy ◽  
Resource Constrained ◽  
Support Tool ◽  
Decision Variables ◽  
Np Hard Problem ◽  
Project Scheduling Problem

The literature concerning resource constrained project scheduling problems (RCPSP) are mainly based on series or parallel schedule generation schemes with priority sequencing rules to resolve conflicts. Recently, these models have been extended for scheduling multi-modal RCPSP (MMRCPSP) where each activity has multiple possibilities to be performed thus providing decision managers a useful tool for manipulating resources and activities. Nonetheless, this further complicates the scheduling problem inflicted by increase of decision variables. Multiple heuristics have been proposed for this NP-hard problem. The main solution strategy adopted by such heuristics is a two loops decision strategy. Basically the problem is split between two parts where first part is conversion of MMRCPSP to RCPSP (mode fix) while second is finding feasible solution for a resource constrained project and is restricted to single project environments. This research aims on the development of scheduling heuristics, exploring the possibilities of scheduling MMRCPSP with parallel assignment of modes while sequencing the activities. The work addresses Multi-Mode Resource Constrained Multi-Project Scheduling Problem, (MMRCMPSP) by formulating a mathematical model that regards practical requirements of working systems. The algorithm is made intelligent and flexible in order to adopt and shift among various defined heuristic rules under different objectives to function as a decision support tool for managers.

scholarly journals An Efficient Particle Swarm Optimizer with Application to Man-Day Project Scheduling Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Frode Eika Sandnes
Keyword(s):  
Project Scheduling ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Np Hard ◽  
Scheduling Problems ◽  
Particle Swarm Optimizer ◽  
Np Hard Problem ◽  
Hard Problems ◽  
Project Scheduling Problem ◽  
Mode Assignment

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfully.

scholarly journals An Enhanced Discrete Bees Algorithm for Resource Constrained Optimization Problems

2019 ◽  
Vol 22 (64) ◽  
pp. 123-134
Author(s):  
Mohamed Amine Nemmich ◽  
Fatima Debbat ◽  
Mohamed Slimane
Keyword(s):  
Project Scheduling ◽  
Optimization Problems ◽  
Solution Space ◽  
Bees Algorithm ◽  
Problem Instance ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Proposed Model ◽  
Project Scheduling Problem

In this paper, we propose a novel efficient model based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed model, named Enhanced Discrete Bees Algorithm (EDBA), iteratively solves the RCPSP by utilizing intelligent foraging behaviors of honey bees. The potential solution is represented by the multidimensional bee, where the activity list representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighboring technique, based on the swap operator, which takes into account the specificity of the solution space of project scheduling problems and reduces the number of parameters to be tuned. The proposed EDBA is tested on well-known benchmark problem instance sets from Project Scheduling Problem Library (PSPLIB) and compared with other approaches from the literature. The promising computational results reveal the effectiveness of the proposed approach for solving the RCPSP problems of various scales.

scholarly journals An Efficient Simulation Algorithm for Resource-Constrained Project Scheduling Problem

2014 ◽  
Vol 8 (1) ◽  
pp. 9-13
Author(s):  
L. Peng ◽  
P. Wuliang
Keyword(s):  
Project Scheduling ◽  
Large Scale ◽  
Exact Algorithms ◽  
Scheduling Problem ◽  
Simulation Algorithm ◽  
Resource Constrained ◽  
Resource Conflict ◽  
Resource Constrained Project Scheduling ◽  
Np Hard Problem ◽  
Project Scheduling Problem

Since Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP-hard problem, it is difficult to solve large-scale practical cases by using traditional exact algorithms. Genetic algorithm (GA) is a kind of intelligent algorithm for approximate optimization, which can ascertain global optimization or suboptimal solution within a reasonable time. This article presented a new simulation algorithm by using GA for solving Resource-Constrained Project Scheduling Problem. In the algorithm, the activity adjacency matrix and priority-based preemptive resource conflict resolution are used to prevent chromosome from generating infeasible schedules. Finally, the method was tested with an actual machine and electricity project case, and the results show that the presented method is efficient and practical for practical project cases.

A Multi-Agent Optimization Method for Preemptive Resource-Constrained Project Scheduling

2019 ◽  
Vol 10 (1) ◽  
pp. 21-33
Author(s):  
Yongyi Shou ◽  
Wenjin Hu ◽  
Changtao Lai ◽  
Ying Ying
Keyword(s):  
Project Scheduling ◽  
Optimization Method ◽  
Negotiation Process ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Project Schedule ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Multi Agent ◽  
Project Scheduling Problem

A multi-agent optimization method is proposed to solve the preemptive resource-constrained project scheduling problem in which activities are allowed to be preempted no more than once. The proposed method involves a multi-agent system, a negotiation process, and two types of agents (activity agents and schedule agent). The activity agents and the schedule agent negotiate with each other to allocate resources and optimize the project schedule. Computational experiments were conducted using the standard project scheduling problem sets. Compared with prior studies, results of the proposed method are competitive in terms of project makespan. The method can be extended to other preemptive resource-constrained project scheduling problems.

scholarly journals A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

2017 ◽  
Vol 233 (1) ◽  
pp. 293-305 ◽  
Author(s):  
Daniel Morillo Torres ◽  
Federico Barber ◽  
Miguel A Salido
Keyword(s):  
Energy Consumption ◽  
Project Scheduling ◽  
Current Trend ◽  
Limited Resource ◽  
Problem Instance ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

This article focuses on obtaining sustainable and energy-efficient solutions for limited resource programming problems. To this end, a model for integrating [Formula: see text] and energy consumption objectives in multi-mode resource-constrained project scheduling problems (MRCPSP-ENERGY) is proposed. In addition, a metaheuristic approach for the efficient resolution of these problems is developed. In order to assess the appropriateness of theses proposals, the well-known Project Scheduling Problem Library is extended (called PSPLIB-ENERGY) to include energy consumption to each Resource-Constrained Project Scheduling Problem instance through a realistic mathematical model. This extension provides an alternative to the current trend of numerous research works about optimization and the manufacturing field, which require the inclusion of components to reduce the environmental impact on the decision-making process. PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/ .

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

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Daniel Morillo ◽  
Federico Barber ◽  
Miguel A. Salido
Keyword(s):  
Energy Consumption ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Exact Methods ◽  
Approximate Methods ◽  
Resource Constrained Project Scheduling ◽  
Np Hard Problem ◽  
Project Scheduling Problem ◽  
Metaheuristic Techniques

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.

A RESOURCE OPTIMISATION BASED HEURISTIC FOR RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM

2019 ◽  
Vol XVI (4) ◽  
pp. 115-124
Author(s):  
Mazhar Ali ◽  
Saif Ullah ◽  
Mirza Jahanzaib
Keyword(s):  
Project Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Shared Resources ◽  
Penalty Cost ◽  
Scarce Resources ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem ◽  
And Shifts

Resource constrained project scheduling problem has significant application in industries. Although several heuristic solutions have been developed in the literature to address this problem, most of these have lesser focus on scheduling of shared and scarce resources. The presented study proposes a resource optimisation based heuristic (ROBH) to optimise the utilisation of shared resources so as to minimise the penalty cost of projects. The proposed ROBH identifies shared resources within the project activities and shifts the activities from the bottleneck resource to the residual resources. The performance of the proposed ROBH was tested using the standard benchmark instances of project scheduling problems available in the existing literature. The results were compared with those obtained from the heuristics available in the project scheduling problem library. This comparison indicated that the results provided by ROBH are significant as compared to the results obtained from the heuristics available in the literature.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Junjie Chen ◽  
Shurong Tong ◽  
Hongmei Xie ◽  
Yafei Nie ◽  
Jingwen Zhang
Keyword(s):  
Project Scheduling ◽  
Optimization Problems ◽  
Programming Model ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Enumeration Algorithm ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

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

Sign in / Sign up

Export Citation Format

Share Document