Generic Scheduling Optimization Model for Multiple Construction Projects

El-Abbasy, Mohammed S.; Elazouni, Ashraf; Zayed, Tarek

doi:10.1061/(asce)cp.1943-5487.0000659

Cited by 52 publications

(29 citation statements)

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“…In the recent work of El‐Abbasy et al. () and El‐Abbasy et al. (), a generic, multi‐objective optimization model that covers all types of objectives and constraints, for a portfolio of projects, was introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers have considered variants to this problem earlier (e.g., El‐Abbasy et al., , ; Alavipour and Arditi, ); however, and to the authors' knowledge, no exact technique has been used to solve this problem; instead, researchers have relied on meta‐heuristics to solve this problem. A common excuse to avoid using exact methods, especially IP, is the time needed to solve this problem compared to genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

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The contractor time–cost–credit trade‐off problem: integer programming model, heuristic solution, and business insights

Al‐Shihabi¹,

AlDurgam

2020

Int Trans Operational Res

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Contractors in the construction sector face several trade‐offs between time and cost. The time–cost trade‐off (TCT) is one of these trade‐offs where contractors can reduce a project completion time by assigning more resources to activities, which means spending more money, to shorten the execution times of project activities. On the other hand, contractors who finance their projects through credit lines from banks such that if they reach their credit limits, then the start times of some project activities can be delayed until cash is available again, which might lead to an increase in the project execution time; thus, contractors need to consider the time–credit trade‐off. In this work, we simultaneously consider these two trade‐offs that affect the project completion time and use mixed integer linear programming (MILP) to model the contractor time–cost–credit trade‐off (TCCT) problem. The MILP model minimizes the project execution time given the contractor's budgetary and financial constraints. In addition to the MILP model, we also develop a heuristic solution algorithm to solve the problem. Through a set of benchmark instances, we study the effectiveness of the heuristic algorithm and the computation time of the exact model. It is found that a good upper bound for the MILP results in less computation time. We also study some practical aspects of the problem where we highlight the importance of expediting contractor payments in addition to selecting a financially stable contractor. Finally, we use our MILP model to help a contractor bid for a project.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The contractor time–cost–credit trade‐off problem: integer programming model, heuristic solution, and business insights

Al‐Shihabi¹,

AlDurgam

2020

Int Trans Operational Res

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“…e activities can be executed with the several combinations of modes using various construction methods, materials, crew size, and overtime policy. Under this situation, each combination will have different project performances regarding time, cost, and quality [5]. Although RCMPSP and MRCMPSP play a vital role in project management, there are not many fruits on the topic.…”

Section: 3mentioning

confidence: 99%

“…Deng et al [57] applied particle swarm optimization (PSO) to search the optimal schedule for the RCMPSP. A nondominated sorting genetic algorithm II (NSGA-II) is proposed to obtain optimal tradeoffs between different projects objectives [5]. Abido and Elazouni [58] introduced strength Pareto evolutionary algorithm (SPEA) to minimize the financing costs, duration of a group of projects, and the required credit.…”

Section: 3mentioning

confidence: 99%

“…e projects may vary in size, importance, and the skill required at various stages and still use the same pool of resources [4]. Multiproject scheduling is a fundamental problem for enterprises to reasonably allocate the limited resources to optimize the performance of the project [5]. Herroelen [6] states that even a small improvement in multiproject management will yield a significant benefit to the project management field.…”

Section: Introductionmentioning

confidence: 99%

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Resource Unconstrained and Constrained Project Scheduling Problems and Practices in a Multiproject Environment

Kannimuthu

Palaneeswaran

Raphael

et al. 2018

Advances in Civil Engineering

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Construction companies execute many projects simultaneously. In such situations, the performance of one project may influence the others positively or negatively. Construction professionals face difficulties in managing multiple projects in limited resource situations. e purpose of this study is to identify the problems in multiproject scheduling from the practitioner's perspective and to discover current practices under resource unconstrained and constrained settings. e specific objectives are (1) determining the most challenging issues being faced in handling multiproject environment, (2) enumerating the practices adopted in the industry, and finally (3) identifying the practitioners' perceptions on the multiproject scheduling aspects such as network modeling approaches; activity execution modes; concept of sharing, dedicating, and substituting resources; centralized and decentralized decision-making models; solution approaches; and tools and techniques. An online questionnaire survey was conducted to address the objectives above. e top challenging issues in managing multiproject environment are identified. Factor analysis identified the factors by grouping the variables (a) decision-related, (b) project environment-related, (c) project management-related, and (d) organization-related factors. Resource-unconstrained situation mainly faces the issue of underutilization and wastage of resources leading to lower profit realization. e following findings were identified to overcome the unconstrained resource situation such as identifying the work front, adopting pull planning approach, creating a common resource pool, and allotting it on a rental basis. On the contrary, resource-constrained situation faces the issues of prioritization of resources, coordination, communication, collaboration, quality issues, and rework. e findings suggest the strategies such as topup via subcontracting, proactive pull planning, introducing buffers, training the culture of the organization towards better communication, coordination, and collaboration, to improve the reliability of achieving baseline project performances. Various multiproject aspects suggested for effective management. e identified problems, practices, and various multiproject aspects are expected to contribute better management of multiproject resource unconstrained and constrained project scheduling.

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A genetic algorithm for subcontractors selection and allocation in multiple building projects

2021

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In construction industry general contractors (GCs) need to manage and conduct numerous projects simultaneously. To this aim, they usually have subcontractors conduct available tasks and projects.So, subcontractor management is becoming a major challenge. To deal with this challenge, GCs aim to reduce total costs of projects including employment /subcontracting costs, indirect costs, tardiness penalties, and the money which must be paid for movement of workforce from one project to another. The aim of current research study is to propose a model for selecting subcontractors and assigning available tasks in the project to them in order to reduce the costs of the GC. Then, a genetic algorithm (GA) is proposed to solve a real problem. The proposed algorithm is innovative from three points of view: (1) generation of initial population; (2) subcontractor assignment approach and (3) the objective function. The problem was also solved by means of an exact method. Then, the results of proposed algorithm were compared to the outcomes of the exact method. This comparison shows that the proposed algorithm can efficiently help GCs select subcontractors and assign available tasks to each subcontractor when several projects must be carried out simultaneously.

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Generic Scheduling Optimization Model for Multiple Construction Projects

Cited by 52 publications

References 50 publications

The contractor time–cost–credit trade‐off problem: integer programming model, heuristic solution, and business insights

The contractor time–cost–credit trade‐off problem: integer programming model, heuristic solution, and business insights

Resource Unconstrained and Constrained Project Scheduling Problems and Practices in a Multiproject Environment

A genetic algorithm for subcontractors selection and allocation in multiple building projects

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