scholarly journals Discrete time-cost tradeoff model for optimizing multi-mode construction project resource allocation

2015 ◽  
Author(s):  
Shuangshuang Nie ◽  
Jihong Gao
Keyword(s):  
Resource Allocation ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Time Cost ◽  
Resource Constrained ◽  
Resource Leveling ◽  
Constraint Problem ◽  
Project Scheduling Problem

The project scheduling and resource allocation problems have been studied using different optimization methods. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently resource constraint problem. For example, resource-constrained project scheduling problem (RCPSP) was proposed to optimize scheduling under resource constraints. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.

scholarly journals Discrete time-cost tradeoff model for optimizing multi-mode construction project resource allocation

2015 ◽  
Author(s):  
Shuangshuang Nie ◽  
Jihong Gao
Keyword(s):  
Resource Allocation ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Time Cost ◽  
Resource Constrained ◽  
Resource Leveling ◽  
Constraint Problem ◽  
Project Scheduling Problem

The project scheduling and resource allocation problems have been studied using different optimization methods. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently resource constraint problem. For example, resource-constrained project scheduling problem (RCPSP) was proposed to optimize scheduling under resource constraints. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.

scholarly journals Discrete time-cost tradeoff model for optimizing project resource allocation

2015 ◽  
Author(s):  
Shuangshuang Nie ◽  
Jihong Gao
Keyword(s):  
Resource Allocation ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Time Cost ◽  
Resource Constrained ◽  
Resource Leveling ◽  
Constraint Problem ◽  
Project Scheduling Problem

The project scheduling and resource allocation problems have been studied using different optimization methods. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently resource constraint problem. For example, resource-constrained project scheduling problem (RCPSP) was proposed to optimize scheduling under resource constraints. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.

scholarly journals Discrete time-cost tradeoff model for optimizing multi-mode construction project resource allocation

2015 ◽  
Author(s):  
Shuangshuang Nie ◽  
Jihong Gao
Keyword(s):  
Discrete Time ◽  
Pareto Front ◽  
Optimization Technique ◽  
Time Cost ◽  
Resource Constrained ◽  
Baseline Model ◽  
Resource Leveling ◽  
Project Scheduling Problem ◽  
Resource Cost ◽  
Multi Mode

The resource-constrained project scheduling problem has received broad attentions and was evolved into various sub-problems such as resource-constrained discrete time-cost tradeoff problem. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently with RCPSP. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.

scholarly journals On some implementations of solving the resource constrained project scheduling problems

2019 ◽  
Vol 29 (1) ◽  
pp. 31-42 ◽  
Author(s):  
E.Kh. Gimadi ◽  
E.N. Goncharov ◽  
D.V. Mishin
Keyword(s):  
Renewable Resources ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Scheduling Problems ◽  
Makespan Minimization ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem ◽  
Technological Constraints

We consider a resource-constrained project scheduling problem with respect to the makespan minimization criterion. The problem accounts for technological constraints of activities precedence together with resource constraints. Activities pre- emptions are not allowed. The problem with renewable resources is NP-hard in the strong sense. We propose an exact branch and bound algorithm for solving the problem with renewable resources. It uses our new branching scheme based on the representation of a schedule in form of the activity list. We use two approaches of constructing the lower bound. We present results of numerical experiments, illustrating the quality of the proposed lower bounds. The test instances are taken from the library of test instances PSPLIB.

scholarly journals Comparison of Heuristic Priority Rules in the Solution of the Resource-Constrained Project Scheduling Problem

2021 ◽  
Vol 13 (17) ◽  
pp. 9956
Author(s):  
Osman Hürol Türkakın ◽  
David Arditi ◽  
Ekrem Manisalı
Keyword(s):  
Resource Allocation ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Priority Rules ◽  
Resource Constrained ◽  
Start Time ◽  
Project Duration ◽  
Resource Constrained Project Scheduling ◽  
Finish Time ◽  
Positional Weight

Resource-constrained project scheduling (RCPS) aims to minimize project duration under limited resource availabilities. The heuristic methods that are often used to solve the RCPS problem make use of different priority rules. The comparative merits of different priority rules have not been discussed in the literature in sufficient detail. This study is a response to this research gap. It compares 17 heuristic priority rules and seeks the best performing heuristic priority rule. This is the first study ever that compares heuristic priority rules by considering combinations of variations in (1) resource allocation procedures, (2) number of activities, (3) number of resource constraints, and (4) resource supply levels. The objective is to understand the relative merits of heuristic rules used in solving the RCPS problem. The findings indicate that the “minimum late finish time” rule generates the shortest predicted project duration when used in parallel resource allocation, whereas the “minimum late start time”, “minimum late finish time”, and the “highest rank of positional weight 2” rules perform best in serial resource allocation. It was also found that parallel resource allocation is slightly superior to serial resource allocation in most instances.

scholarly journals Compact MDDs for Pseudo-Boolean Constraints with At-Most-One Relations in Resource-Constrained Scheduling Problems

2017 ◽  
Author(s):  
Miquel Bofill ◽  
Jordi Coll ◽  
Josep Suy ◽  
Mateu Villaret
Keyword(s):  
Project Scheduling ◽  
Resource Constraints ◽  
Time Dependent ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Binary Decision ◽  
Compact Binary ◽  
Order Of Magnitude ◽  
Project Scheduling Problem ◽  
Multi Mode

Pseudo-Boolean (PB) constraints are usually encoded into Boolean clauses using compact Binary Decision Diagram (BDD) representations. Although these constraints appear in many problems, they are particularly useful for representing resource constraints in scheduling problems. Sometimes, the Boolean variables in the PB constraints have implicit at-most-one relations. In this work we introduce a way to take advantage of these implicit relations to obtain a compact Multi-Decision Diagram (MDD) representation for those PB constraints. We provide empirical evidence of the usefulness of this technique for some Resource-Constrained Project Scheduling Problem (RCPSP) variants, namely the Multi-Mode RCPSP (MRCPSP) and the RCPSP with Time-Dependent Resource Capacities and Requests (RCPSP/t). The size reduction of the representation of the PB constraints lets us decrease the number of Boolean variables in the encodings by one order of magnitude. We close/certify the optimum of many instances of these problems.

scholarly journals Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

2022 ◽  
Vol 7 (2) ◽  
pp. 95-110 ◽  
Author(s):  
Amir Golab ◽  
Ehsan Sedgh Gooya ◽  
Ayman Al Falou ◽  
Mikael Cabon
Keyword(s):  
Genetic Algorithms ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Heuristic Algorithms ◽  
Single Mode ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Heuristic Solution ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

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