A two-phase approach for solving the multi-skill resource-constrained multi-project scheduling problem: a case study in construction industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Hossein Hosseinian ◽  
Vahid Baradaran
Keyword(s):  
Control Chart ◽  
Project Scheduling ◽  
Test Problems ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Two Phase ◽  
Content Type ◽  
Phase Approach ◽  
Multi Objective ◽  
Project Scheduling Problem

PurposeThe purpose of this research is to study the Multi-Skill Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where (1) durations of activities depend on the familiarity levels of assigned workers, (2) more efficient workers demand higher per-day salaries, (3) projects have different due dates and (4) the budget of each period varies over time. The proposed model is bi-objective, and its objectives are minimization of completion times and costs of all projects, simultaneously.Design/methodology/approachThis paper proposes a two-phase approach based on the Statistical Process Control (SPC) to solve this problem. This approach aims to develop a control chart so as to monitor the performance of an optimizer during the optimization process. In the first phase, a multi-objective statistical model has been used to obtain control limits of this chart. To solve this model, a Multi-Objective Greedy Randomized Adaptive Search Procedure (MOGRASP) has been hired. In the second phase, the MSRCMPSP is solved via a New Version of the Multi-Objective Variable Neighborhood Search Algorithm (NV-MOVNS). In each iteration, the developed control chart monitors the performance of the NV-MOVNS to obtain proper solutions. When the control chart warns about an out-of control state, a new procedure based on the Conway’s Game of Life, which is a cellular automaton, is used to bring the algorithm back to the in-control state.FindingsThe proposed two-phase approach has been used in solving several standard test problems available in the literature. The results are compared with the outputs of some other methods to assess the efficiency of this approach. Comparisons imply the high efficiency of the proposed approach in solving test problems with different sizes.Practical implicationsThe proposed model and approach have been used to schedule multiple projects of a construction company in Iran. The outputs show that both the model and the NV-MOVNS can be used in real-world multi-project scheduling problems.Originality/valueDue to the numerous numbers of studies reviewed in this research, the authors discovered that there are few researches on the multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with the aforementioned characteristics. Moreover, none of the previous researches proposed an SPC-based solution approach for meta-heuristics in order to solve the MSRCMPSP.

scholarly journals Heuristics for project scheduling with discounted cash flows optimisation

2015 ◽  
Vol 63 (3) ◽  
pp. 613-622 ◽  
Author(s):  
M. Klimek ◽  
P. Łebkowski
Keyword(s):  
Project Scheduling ◽  
Cash Flows ◽  
Standard Test ◽  
Test Problems ◽  
Scheduling Problem ◽  
Flow Networks ◽  
Resource Constrained ◽  
Numerical Example ◽  
Project Scheduling Problem ◽  
Active Schedule

Abstract The article presents the resource-constrained project scheduling problem with the maximisation of discounted cash flows from the contractor’s perspective: with cash outflows related to starting individual activities and with cash inflows for completing project stages (milestones). The authors propose algorithms for improving a forward active schedule by iterative one-unit right shifts of activities, taking into account different resource flow networks. To illustrate the algorithms and problem, a numerical example is presented. Finally, the algorithms are tested using standard test problems with additionally defined cash flows and contractual milestones.

An effective teaching-learning-based optimization algorithm for the multi-skill resource-constrained project scheduling problem

2019 ◽  
Vol 14 (4) ◽  
pp. 1064-1087
Author(s):  
Dheeraj Joshi ◽  
M.L. Mittal ◽  
Milind Kumar Sharma ◽  
Manish Kumar
Keyword(s):  
Project Scheduling ◽  
Scheduling Problem ◽  
Exploration And Exploitation ◽  
Resource Constrained ◽  
Content Type ◽  
Resource Constrained Project Scheduling ◽  
Self Study ◽  
Teaching Learning Based Optimization ◽  
Project Scheduling Problem ◽  
Teaching Learning

Purpose The purpose of this paper is to consider one of the recent and practical extensions of the resource-constrained project scheduling problem (RCPSP) termed as the multi-skill resource-constrained project scheduling problem (MSRCPSP) for investigation. The objective is the minimization of the makespan or total project duration. Design/methodology/approach To solve this complex problem, the authors propose a teaching–learning-based optimization (TLBO) algorithm in which self-study and examination have been used as additional features to enhance its exploration and exploitation capabilities. An activity list-based encoding scheme has been modified to include the resource assignment information because of the multi-skill nature of the algorithm. In addition, a genetic algorithm (GA) is also developed in this work for the purpose of comparisons. The computational experiments are performed on 216 test instances with varying complexity and characteristics generated for the purpose. Findings The results obtained after computations show that the TLBO has performed significantly better than GA in terms of average percentage deviation from the critical path-based lower bound for different combinations of three parameters, namely, skill factor, network complexity and modified resource strength. Research limitations/implications The modified TLBO proposed in this paper can be conveniently applied to any product or service organization wherein human resources are involved in executing project activities. Practical implications The developed model can suitably handle resource allocation problems faced in real-life large-sized projects usually administered in software development companies, consultancy firms, R&D-based organizations, maintenance firms, big construction houses, etc. wherein human resources are involved. Originality/value The current work aims to propose an effective metaheuristic for a more realistic version of MSRCPSP, in which resource requirements of activities may be more than one. Moreover, to enhance the exploration and exploitation capabilities of the original TLBO, the authors use two additional concepts, namely, self-study and examination in the search process.

Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms

Computing ◽  
2019 ◽  
Vol 101 (6) ◽  
pp. 547-570 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Alireza Goli ◽  
Milad Hematian ◽  
Arun Kumar Sangaiah ◽  
Tao Han
Keyword(s):  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Multi Objective ◽  
Project Scheduling Problem ◽  
Multi Mode

scholarly journals A multi-objective multi-agent optimization algorithm for the multi-skill resource-constrained project scheduling problem with transfer times

2021 ◽  
Author(s):  
Amirhossein Hosseinian ◽  
Vahid Baradaran
Keyword(s):  
Optimization Algorithm ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Multi Objective ◽  
Proposed Model ◽  
Multi Agent ◽  
Project Scheduling Problem ◽  
Transfer Times

This paper addresses the Multi-Skill Resource-Constrained Project Scheduling Problem with Transfer Times (MSRCPSP-TT). A new model has been developed that incorporates the presence of transfer times within the multi-skill RCPSP. The proposed model aims to minimize project’s duration and cost, concurrently. The MSRCPSP-TT is an NP-hard problem; therefore, a Multi-Objective Multi-Agent Optimization Algorithm (MOMAOA) is proposed to acquire feasible schedules. In the proposed algorithm, each agent represents a feasible solution that works with other agents in a grouped environment. The agents evolve due to their social, autonomous, and self-learning behaviors. Moreover, the adjustment of environment helps the evolution of agents as well. Since the MSRCPSP-TT is a multi-objective optimization problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used in different procedures of the MOMAOA. Another novelty of this paper is the application of TOPSIS in different procedures of the MOMAOA. These procedures are utilized for: (1) detecting the leader agent in each group, (2) detecting the global best leader agent, and (3) the global social behavior of the MOMAOA. The performance of the MOMAOA has been analyzed by solving several benchmark problems. The results of the MOMAOA have been validated through comparisons with three other meta-heuristics. The parameters of algorithms are determined by the Response Surface Methodology (RSM). The Kruskal-Wallis test is implemented to statistically analyze the efficiency of methods. Computational results reveal that the MOMAOA can beat the other three methods according to several testing metrics. Furthermore, the impact of transfer times on project’s duration and cost has been assessed. The investigations indicate that resource transfer times have significant impact on both objectives of the proposed model

Modeling and algorithm for resource-constrained multi-project scheduling problem based on detection and rework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongwei Zhu ◽  
Zhiqiang Lu ◽  
Chenyao Lu ◽  
Yifei Ren
Keyword(s):  
Tabu Search ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Assembly Process ◽  
Resource Constrained ◽  
Content Type ◽  
Baseline Schedule ◽  
Execution Sequence ◽  
Project Scheduling Problem ◽  
Critical Chain

Purpose To meet the requirement of establishing an effective schedule for the assembly process with overall detection and rework, this paper aims to address a new problem named resource-constrained multi-project scheduling problem based on detection and rework (RCMPSP-DR). Design/methodology/approach First, to satisfy both online and offline scheduling, a mixed integer programming model is established with a weighted bi-objective minimizing the expected makespan and the solution robustness. Second, an algorithm that combines a tabu search framework with a critical chain-based baseline generation scheme is designed. The tabu search framework focuses on searching for a reasonable resource flow representing the execution sequence of activities, while the critical chain-based baseline generation scheme establishes a buffered baseline schedule by estimating the tradeoff between two aspects of bi-objective. Findings The proposed algorithm can get solutions with gaps from −4.45% to 2.33% when compared with those obtained by the commercial MIP solver CPLEX. Moreover, the algorithm outperforms four other algorithms in terms of both objective performance and stability over instances with different weighting parameters, which reveals its effectiveness. Originality/value The represented RCMPSP-DR considering the overall detection and rework is an extension of the scheduling problem for large-scale equipment. An effective algorithm is proposed to establish the baseline schedule and determine the execution sequence of activities for the assembly process, which is significant for practical engineering applications.

Sign in / Sign up

Export Citation Format

Share Document