Multi-Mode Resource Constrained Project Scheduling Using Differential Evolution Algorithm

Project scheduling is a tool that manages the work and resources associated with delivering a project on time. Project scheduling is important to organize, keep track of the finished and in-progress tasks and manage the quality of work delivered. However, many problems arise during project scheduling. Minimizing project duration is the primary objective. Project cost is also a critical matter, but there will always be a trade off between project time and cost (Ghoddousiet et al., 2013), so scheduling activities can be challenging due to precedence activities, resources, and execution modes. Schedule reduction is heavily dependent on the availability of resources (Zhuo et al., 2013). There have been several methods used to solve the project scheduling problem. This dissertation will focus on finding the optimal solution with minimum makespan at lowest possible cost. Schedules should help manage the project and not give a general estimate of the project duration. It is important to have realistic time estimates and resources to give accurate schedules. Generally, project scheduling problems are challenging from a computational point of view (Brucker et al., 1999). This dissertation applies the differential evolution algorithm (DEA) to multi mode, multi resource constrained project scheduling problems. DEA was applied to a common 14- task network through different scenarios, which includes Multi Mode Single Non Renewable Resource Constrained Project Scheduling Problem (MMSNR) and Multi Mode Multiple Non Renewable Resource Constrained Project Scheduling Problem (MMMNR). DEA was also applied when each scenario was faced with a weekly constraint and when cost and time contingencies such as budget drops or change in expected project completion times interfere with the initial project scheduling plan. A benchmark problem was also presented to compare the DEA results with other optimization techniques such as a genetic algorithm (GA), a particle swarm optimization (PSO) and ant colony optimization (ACO). The results indicated that our DEA performs at least as good as these techniques as far as the project time is concerned and outperforms them in computational times and success rates. Finally, a pareto frontier was investigated, resulting in optimal solutions for a multi objective problem focusing on the tradeoff of the constrained set of parameters

An Integrated Model for Multi-Mode Resource-Constrained Multi-Project Scheduling Problems Considering Supply Management with Sustainable Approach in the Construction Industry under Uncertainty Using Evidence Theory and Optimization Algorithms

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors; the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model’s performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample problems with different dimensions, as well as a case study on residential apartment construction projects by a contracting company. In this respect, the answers obtained from solving the model using the multi-objective DE algorithm were better and superior to the NSGA-II algorithm and had a more favorable performance. Generally, the results indicate that using the integrated multi-objective mathematical model in the present research for managing and scheduling multi-mode resource-constrained multi-project problems, especially in the construction industry, can lead to an optimal state consistent with the desired objectives and can significantly improve the progress and completion of projects

Time-Cost Tradeoff and Resource-Scheduling Problems in Construction: A State-of-the-Art Review

Duration, cost, and resources are defined as constraints in projects. Consequently, Construction manager needs to balance between theses constraints to ensure that project objectives are met. Choosing the best alternative of each activity is one of the most significant problems in construction management to minimize project duration, project cost and also satisfies resources constraints as well as smoothing resources. Advanced computer technologies could empower construction engineers and project managers to make right, fast and applicable decisions based on accurate data that can be studied, optimized, and quantified with great accuracy. This article strives to find the recent improvements of resource-scheduling problems and time-cost trade off and the interacting between them which can be used in innovating new approaches in construction management. To achieve this goal, a state-of-the-art review, is conducted as a literature sample including articles implying three areas of research; time-cost trade off, constrained resources and unconstrained resources. A content analysis is made to clarify contributions and gaps of knowledge to help suggesting and specifying opportunities for future research

Optimal Ship Maintenance Scheduling Under Restricted Conditions and Constrained Resources

The research presented in this dissertation addresses the application of evolution algorithms, i.e. Genetic Algorithm (GA) and Differential Evolution algorithm (DE) to scheduling problems in the presence of restricted conditions and resource limitations. This research is motivated by the scheduling of engineering design tasks in shop scheduling problems and ship maintenance scheduling problems to minimize total completion time. The thesis consists of two major parts; the first corresponds to the first appended paper and deals with the computational complexity of mixed shop scheduling problems. A modified Genetic algorithm is proposed to solve the problem. Computational experiments, conducted to evaluate its performance against known optimal solutions for different sized problems, show its superiority in computation time and the high applicability in practical mixed shop scheduling problems. The second part considers the major theme in the second appended paper and is related to the ship maintenance scheduling problem and the extended research on the multi-mode resource-constrained ship scheduling problem. A heuristic Differential Evolution is developed and applied to solve these problems. A mathematical optimization model is also formulated for the multi-mode resource-constrained ship scheduling problem. Through the computed results, DE proves its effectiveness and efficiency in addressing both single and multi-objective ship maintenance scheduling problem

A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment

Project scheduling models with resource constraints and multi-mode activities aims to create a schedule for carrying out activities considering precedence constraints and available resources in order to minimize the project duration. In the real world, we face uncertainty related to projects, where there are no historical data, hence, we should rely on the experts' judgements to estimate activity durations. For this purpose, in this paper, the 99-simulation method is used to deal with uncertainty. The exact mathematical programming model is presented in this paper and the hybrid algorithm based on Genetic Algorithm is used to solve this type of project scheduling problem which finds the near-optimal solution in a short computational time. Finally, the effectiveness of the proposed model is examined with a numerical example.Peer reviewedFinal article published.MRCPSPSimulationUncertaintyGenetic algorith

A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem

Multiple work shifts are commonly utilized in construction projects to meet project requirements. Nevertheless, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project success. In this study, a novel approach called “Multiple Objective Symbiotic Organisms Search” (MOSOS) to solve multiple work shifts problem is introduced. The MOSOS algorithm is new meta-heuristic based multi-objective optimization techniques inspired by the symbiotic interaction strategies that organisms use to survive in the ecosystem. A numerical case study of construction projects were studied and the performance of MOSOS is evaluated in comparison with other widely used algorithms which includes non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC). The numerical results demonstrate MOSOS approach is a powerful search and optimization technique in finding optimization of work shift schedules that is it can assist project managers in selecting appropriate plan for project

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

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

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm

Modeling and Solving Project Portfolio and Contractor Selection Problem Based on Project Scheduling under Uncertainty

AbstractIn this paper a new formulation of the project portfolio selection problem based on the project schedules in uncertain circumstances have been proposed. The project portfolio selection models usually disregard the project scheduling, whereas is an element of the project selection process. We investigate a project portfolio selection problem based on the schedule of the projects, so that the minimum expected profit would be met in the shortest possible time period. Also due to uncertain nature of durations of the activities, this duration considered as the semi-trapezoidal fuzzy numbers. Finally, a fuzzy linear programming model is developed for the problem, where the results indicated the validity of the presented model