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This study aims to deal with the multi-mode resource-constrained project scheduling problem (MRCPSP) with the ability to transport resources among multiple sites, aiming to minimize the total completion time and the total cost of the project simultaneously.

To deal with the problem under consideration, a bi-objective optimization model is developed. All activities are interconnected by finish-start precedence relations, and pre-emption is not allowed. Then, the ɛ-constraint optimization method is used to solve 24 different-sized instances, ranging from 5 to 120 activities, and report the makespan, total cost and CPU time. A set of Pareto-optimal solutions are determined for some instances, and sensitivity analyses are performed to find the impact of changing parameters on objective values.

Results highlight the importance of resource transportability assumption on project completion time and cost, providing useful insights for decision makers and practitioners.

A novel bi-objective optimization model is proposed to deal with the multi-site MRCPSP, considering both the cost and time of resource transportation between multiple sites. To the best of the authors’ knowledge, none of the studies in the project scheduling area has yet addressed this problem.

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.

The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.

The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.

There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).

A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.

Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.

It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.

An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

A case study approach identified the activity execution modes in building construction projects in India to support multi-mode resource-constrained project scheduling. The data required to compute time, cost and quality of each activity are compiled from real construction projects. A binary integer-programming model has been developed to perform multi-objective optimization and identify Pareto optimal solutions. The RR-PARETO3 algorithm was used to identify the best compromise trade-off solutions. The effectiveness of the proposed framework is demonstrated through sample case study projects.

Results show that good compromise solutions are obtained through multi-objective optimization of time, cost and quality.

Case study data sets were collected only from eight building construction projects in India.

It is feasible to adopt multi-objective optimization in practical construction projects using time, cost and quality as the objectives; Pareto surfaces help to quantify relationships among time, cost and quality. It is shown that cost can be reduced by increasing the duration, and quality can be improved only by increasing the cost.

The use of different activity execution modes compiled from multiple projects in optimization is illustrated, and good compromise solutions for the multi-mode resource-constrained project scheduling problems using multi-objective optimization are identified.

Construction 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.

Due 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.

According to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.

ICA/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.

In 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.

Most projects are facing delays, and accelerating the pace of project progress is a necessity. Project managers are responsible for completing the project on time with minimum cost and with maximum quality. This study provides a trade-off between time, cost, and quality objectives to optimize project scheduling.

The current paper presents a new resource-constrained multi-mode time–cost–quality trade-off project scheduling model with lags under finish-to-start relations. To be more realistic, crashing and overlapping techniques are utilized. To handle uncertainty, which is a source of project complexity, interval-valued fuzzy sets are adopted on several parameters. In addition, a new hybrid solution approach is developed to cope with interval-valued fuzzy mathematical model that is based on different alpha-levels and compensatory methods. To find the compatible solution among conflicting objectives, an arithmetical average method is provided as a compensatory approach.

The interval-valued fuzzy sets approach proposed in this paper is denoted to be scalable, efficient, generalizable and practical in project environments. The results demonstrated that the crashing and overlapping techniques improve time–cost–quality trade-off project scheduling model. Also, interval-valued fuzzy sets can properly manage expressions of the uncertainty of projects which are realistic and practical. The proposed mathematical model is validated by solving a medium-sized dataset an adopted case study. In addition, with a sensitivity analysis approach, the solutions are compared and the model performance is confirmed.

This paper introduces a new continuous-based, resource-constrained, and multi-mode model with crashing and overlapping techniques simultaneously. In addition, a new hybrid compensatory solution approach is extended based on different alpha-levels to handle interval-valued fuzzy multi-objective mathematical model of project scheduling with influential uncertain parameters.

The purpose of this paper is to evaluate project activities' efficiency in different execution modes for the optimization of time–cost-quality and environmental impacts trade-off problem.

This paper presents a parallel Data Envelopment Analysis (DEA) method for evaluation of project activities with different execution modes to select the best execution mode and find a trade-off between objectives. Also, according to the nature of the project activities, outputs are categorized into desirable (quality) and undesirable (time, cost and environmental impacts) and analyzed based on the DEA model. In order to rank efficient execution modes, the ideal and anti-ideal virtual units method is used. The proposed model is implemented on a real case of a rural water supply construction project to demonstrate its validity.

The findings show that the use of the efficient execution mode in each activity leads to an optimal trade-off between the four project objectives (time, cost, quality and environmental impacts).

This study help project managers and practitioners with choosing the most efficient execution modes of project activities taking time–cost-quality-environmental impacts into account.

In this paper, in addition to time and cost optimization of construction projects, quality factors and environmental impacts are considered. Further to the authors' knowledge, there is no method for evaluating project activities' efficiency. The efficiency of different activity modes is also evaluated for the first time to select the most efficient modes. This research can assist project managers with choosing the most appropriate execution modes for the activities to ultimately accomplish the project with the lowest time, cost and environmental impacts along with the highest quality.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to provide a comprehensive overview of literature and methods that can be used for deconstruction project planning of buildings. Furthermore, shortcomings of the identified planning methods are presented and research gaps are identified.

Requirements to consider for the planning of deconstruction projects are defined, to help in the classification of planning methods. With the help of these requirements, in a detailed literature review strategic and operational planning methods for deconstruction projects are investigated and discussed. Requirements which are not met by any of the identified planning methods can be interpreted as research and/or documentation gaps.

On the one hand, the literature review shows that recent approaches deal with planning methods for deterministic time and resource scheduling. Furthermore, project costs can be well planned by several methods. On the other hand, the literature review reveals that recent approaches mostly do not consider risks and uncertainties, environmental hazards or specific safety issues. A major shortcoming is that applied planning methods can only calculate up to a specific level of detail, e.g. with a limited number of activities, due to a very high computational effort in solving such project planning problems exactly.

To the authors’ knowledge, this is the first study that provides an overview of literature and methods for the deconstruction project planning of buildings and it is also the first study that unveils research gaps for future research. Furthermore, the classified planning methods assist in identifying suitable methods for the planning of future deconstruction projects.

This research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.

The method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.

The case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.

The contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.