Expanding Finance-Based Scheduling to Devise Overall-Optimized Project Schedules

Construction contractors often finance projects using bank credit lines that allow contractors to withdraw money up to certain credit limits. Finance-based scheduling provides schedules that ensure that the contractor’s indebtedness at any time during the construction stage does not exceed the credit limit. Generally, constricted credit limits tend to yield prolonged schedules. Provided that credit limits can be adequately relaxed, compressed schedules of compressed-duration activities can be attained. Devising a compressed schedule calls for the incorporation of time-cost trade-off (TCT) analysis to strike a balance between the decreased overhead costs and the increased direct costs of the activities. Since employing TCT analysis usually causes great fluctuations in the daily resource requirements by mixing compressed-duration activities of high resource demand with others of low resource demand, therefore, the need for resource management techniques becomes inevitable to ensure efficient utilization of resources. This note used genetic algorithms to expand finance-based scheduling to devise schedules for relaxed credit limits. A prototype system was developed and coded using VISUAL BASIC, then demonstrated using a five-activity example project. The prototype was validated by comparing the results with those obtained by using the integer programming. Expanding finance-based scheduling to handle the whole spectrum of credit limits helps devise overall-optimized schedules that consider cash, time, cost, and resources.     

Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects

This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices.     

Work Continuity Constraints in Project Scheduling

Repetitive projects involve the repetition of activities along the stages of the project. Since the resources required to perform these activities move from one stage to the other, a main objective of scheduling these projects is to maintain the continuity of work of these resources so as to minimize the idle time of resources. This requirement, often referred to as work continuity constraints, involves a tradeoff between total project duration and the resource idle time. The contribution of this paper is threefold. First, we provide an extensive literature summary of the topic under study. Although most research papers deal with the scheduling of construction projects, we show that this can be extended to many other environments. Second, we propose an exact search procedure for scheduling repetitive projects with work continuity constraints. This algorithm iteratively shifts repeating activities further in time in order to decrease the resource idle time. We have embedded this recursive search procedure in a horizon-varying algorithm in order to detect the complete tradeoff profile between resource idle time and project duration. The procedure has been coded in Visual C++ and has been validated on a randomly generated problem set. Finally, we illustrate the concepts on three examples. First, the use of our new algorithm is illustrated on a small fictive problem example from literature. In a second example, we show that work continuity constraints involve a tradeoff between total project duration and the resource idle time. A last example describes the scheduling of a well-known real-life project that aims at the construction of a tunnel at the Westerschelde in The Netherlands.     

Resource Optimization Using Combined Simulation and Genetic Algorithms

This paper presents a new approach for resource optimization by combining a flow-chart based simulation tool with a powerful genetic optimization procedure. The proposed approach determines the least costly, and most productive, amount of resources that achieve the highest benefit/cost ratio in individual construction operations. To further incorporate resource optimization into construction planning, various genetic algorithms (GA)-optimized simulation models are integrated with commonly used project management software. Accordingly, these models are activated from within the scheduling software to optimize the plan. The result is a hierarchical work-breakdown-structure tied to GA-optimized simulation models. Various optimization experiments with a prototype system on two case studies revealed its ability to optimize resources within the real-life constraints set in the simulation models. The prototype is easy to use and can be used on large size projects. Based on this research, computer simulation and genetic algorithms can be an effective combination with great potential for improving productivity and saving construction time and cost.     

Using Elitist Particle Swarm Optimization to Facilitate Bicriterion Time-Cost Trade-Off Analysis

The present study develops a new optimization algorithm to find the complete time-cost profile (Pareto front) over a set of feasible project durations, i.e., it solves the time-cost trade-off problem. To improve existing methods, the proposed algorithm aims to achieve three goals: (1) to obtain the entire Pareto front in a single run; (2) to be insensitive to the scales of time and cost; and (3) to treat all existing types of activity time-cost functions, such as linear, nonlinear, discrete, discontinuous, and a hybrid of the above. The proposed algorithm modifies a population-based search procedure, particle swarm optimization, by adopting an elite archiving scheme to store nondominated solutions and by aptly using members of the archive to direct further search. Through a fast food outlet example, the proposed algorithm is shown effective and efficient in conducting advanced bicriterion time-cost analysis. Future applications of the proposed algorithm are suggested in the conclusion.     

Finance-Based Scheduling of Construction Projects Using Integer Programming

Construction scheduling is the process of devising schemes for sequencing activities. A realistic schedule fulfills the real concerns of users, thus minimizing the chances of schedule failure. The minimization of total project duration has been the concept underlying critical-path method/program evaluation and review technique (CPM/PERT) schedules. Subsequently, techniques including resource management and time-cost trade-off analysis were developed to customize CPM/PERT schedules in order to fulfill users’ concerns regarding project resources, cost, and time. However, financing construction activities throughout the course of the project is another crucial concern that must be properly treated otherwise, nonrealistic schedules are to be anticipated. Unless contractors manage to procure adequate cash to keep construction work running according to schedule, the pace of work will definitely be relaxed. Therefore, always keeping scheduled activities in balance with available cash is a potential contribution to producing realistic schedules. This paper introduces an integer-programming finance-based scheduling method to produce financially feasible schedules that balance the financing requirements of activities at any period with the cash available during that same period. The proposed method offers twofold benefits of minimizing total project duration and fulfilling finance availability constraints.     

Scheduling Resource-Constrained Projects with Ant Colony Optimization Artificial Agents

The paper presents a methodology to schedule resource-constrained construction projects by use of algorithms based on ant colony optimization (ACO); an artificial agent inspired by the collective behavior of natural ant colonies as they optimize their path from an origin (ant nest) to a destination (food source) by use of previously acquired knowledge. Further, to an application of the ACO artificial agent to a resource-unconstrained network topology, the method is applied to a resource-constrained network and utilized in examining the effects of resource availability constraints to critical path calculations and project completion time.     

Constraint Programming Approach to Precast Production Scheduling

Current scheduling practices in precast plants are fairly basic and depend greatly on experience. This may lead to inefficient resource utilization, over-inventory, and/or missing delivery dates. Computer assisted scheduling may therefore be useful in producing better production schedules. This paper shows how constraint programming (CP) can be applied in production scheduling for precast plants. The paper describes a constrained precast scheduling model that incorporates the key constraints and objectives considered by production schedulers. A capacity-based backward-scheduling earliest due date rule and a CP approach are developed to solve the model. The CP approach is computationally efficient, even though it incorporates many problem-derived constraints. The efficiency of the CP approach lies in the fact that the representation (model) is separated from the algorithm (solver). Strategies to improve the performance of the CP approach are identified, and the CP approach is compared against commonly used heuristic rules on an example problem.     

Comparing Schedule Generation Schemes in Resource-Constrained Project Scheduling Using Elitist Genetic Algorithm

An issue has arisen with regard to which of the schedule generation schemes will perform better for an arbitrary instance of the resource-constrained project scheduling problem (RCPSP), which is one of the most challenging areas in construction engineering and management. No general answer has been given to this issue due to the different mechanisms between the serial scheme and the parallel scheme. In an effort to address this issue, this paper compares the two schemes using a permutation-based Elitist genetic algorithm for the RCPSP. Computational experiments are presented with multiple standard problems. From the results of a paired difference experiment, the algorithm using the serial scheme provides better solutions than the one using the parallel scheme. The results also show that the algorithm with the parallel scheme takes longer to solve each problem than the one using the serial scheme.     

Irrigation Scheduling with Genetic Algorithms

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets that may be serviced simultaneously. This problem has an analogy with the classical multimachine earliness/tardiness scheduling problem in operations research (OR). In previously published work, integer programming was used to solve irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding. This is widely reported in OR literature. Hence integer programs (IPs) can be used only to solve relatively small problems typically in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. However, these need to be formulated carefully and tested thoroughly. The current research explores the potential of genetic algorithms to solve the simultaneous irrigation scheduling problem. For this purpose, two models are presented: the stream tube model and the time block model. These are formulated as genetic algorithms, which are then tested extensively, and the solution quality is compared with solutions from an IP. The suitability of these models for the simultaneous irrigation scheduling problem is reported.     

Four-Dimensional Visualization of Construction Scheduling and Site Utilization

Four-dimensional (4D) models link three-dimensional geometrical models with construction schedule data. The visual link between the schedule and construction site conditions is capable of facilitating decision making during both the planning and construction stages. The emphases of these 4D developments have often been placed at the level of construction components. Practical features assisting site management are at times lacking in the following areas: (1) generation of site usage layouts; (2) estimation of quantities of construction materials; and (3) cost evaluation. In order to pinpoint these deficiencies, the objective of this work is to enable visual study of the effects of job progress on the logistics and resource schedules. This paper presents a 4D visualization model that is intended both to help construction managers plan day-to-day activities more efficiently in a broader and more practical site management context and to thereby add to our knowledge and understanding of the relevance of modern computer graphics to the responsibilities of the construction site manager. A brief site trial of the software is described at the conclusion of the paper.     

Use of Genetic Algorithms in Resource Scheduling of Construction Projects

This paper presents an augmented Lagrangian genetic algorithm model for resource scheduling. The algorithm considers scheduling characteristics that were ignored in prior research. Previous resource scheduling formulations have primarily focused on project duration minimization. Furthermore, resource leveling and resource-constrained scheduling have traditionally been solved independently. The model presented here considers all precedence relationships, multiple crew strategies, total project cost minimization, and time-cost trade-off. In the new formulation, resource leveling and resource-constrained scheduling are performed simultaneously. The model presented uses the quadratic penalty function to transform the resource-scheduling problem to an unconstrained one. The algorithm is general and can be applied to a broad class of optimization problems. An illustrative example is presented to demonstrate the performance of the proposed method.     

Object-Oriented Scheduling for Repetitive Projects with Soft Logics

The application of network techniques of project scheduling to repetitive projects has been criticized for the inability of network techniques to help maintain work continuity. Moreover, current network techniques require a large number of activities to represent a repetitive project and presume that there is only one logical sequence. This makes schedules time consuming to develop as well as maintain. Further, the logic chosen by the planner might be far from the shortest possible duration. This paper, utilizing the soft logic sequencing principles developed by Fan et al., develops a system which provides an easy input module in addition to scheduling and work-continuity-maintenance modules. The system eases the network generation and update processes, which in turn provides the shortest possible duration logics and the start and finish dates required to maintain work continuity.     

Minimizing Cutting Wastes of Reinforcement Steel Bars Using Genetic Algorithms and Integer Programming Models

Materials that are in the form of one-dimensional stocks such as steel rebars, structural steel sections, and dimensional lumber generate a major fraction of the generated construction waste. Cutting one-dimensional stocks to suit the construction project requirements result in trim or cutting losses, which is the major cause of the one-dimensional construction waste. The optimization problem of minimizing the trim losses is known as the cutting stock problem (CSP). In this paper, three approaches for solving the one-dimensional cutting stock problem are presented. A genetic algorithm (GA) model, a linear programming (LP) model, and an integer programming (IP) model were developed to solve the one-dimensional CSP. Three real life case studies from a steel workshop have been studied. The generated cutting schedules using the GA, LP, and IP approaches are presented and compared to the actual workshop’s cutting schedules. The comparison shows a high potential of savings that could be achieved using such techniques. Additionally, a user friendly Visual Basic computer program that utilizes genetic algorithms for solving the one-dimensional CSP is presented.     

Simulation Model to Forecast Project Completion Time

Project cost is most sensitive to its schedule. The construction project environment comprising dynamic, uncertain, but predictable, variables such as weather, space congestion, workmen absenteeism, etc., is changing continuously, affecting activity durations. The reliability of project duration forecast can be enhanced by an explicit analysis to determine the variation in activity durations caused by the dynamic variables. A computer model is used to simulate the expected occurrence of the uncertainty variables. From the information that is collected normally for a progress update of the tactical plan and by simulating the project environment, the combined impact of the uncertainty variables is predicted for each progress period. By incorporating the combined impact in the duration estimates of each activity, the new activity duration distribution is generated. From these activity duration distributions, the probability of achieving the original project completion time and of completing the project at any other time is computed.     

Preliminary Cost Estimation Model Using Case-Based Reasoning and Genetic Algorithms

This study proposes a preliminary cost estimation model using case-based reasoning (CBR) and genetic algorithm (GA). In measuring similarity and retrieving similar cases from a case base for minimum prediction error, it is a key process in determining the factors with the greatest weight among the attributes of cases in the case base. Previous approaches using experience, gradient search, fuzzy numbers, and analytic hierarchy process are limited in their provision of optimal solutions. This study therefore investigates a GA for weight generation and applies it to real project data. When compared to a conventional construction cost estimation model, the accuracy of the CBR- and GA-based construction cost estimation model was verified. It is expected that a more reliable construction cost estimation model could be designed in the early stages by using a weight estimation technique in the development of a construction cost estimation model.     

Comparison of Using Mixed-Integer Programming and Genetic Algorithms for Construction Site Facility Layout Planning

The use of modular construction has gained wide acceptance in the industry. For a specific construction facility layout problem such as site precast standardized modular units, it requires the establishment of an on-site precast yard. Arranging the precast facilities within a construction site presents real challenge to site management. This complex task is further augmented with the involvement of several resources and different transport costs. A genetic algorithm (GA) model was developed for the search of a near-optimal layout solution. Another approach using mixed-integer programming (MIP) has been developed to generate optimal facility layout. These two approaches are applied to solve with an example in this paper to demonstrate that the solution quality of MIP outperforms that of GA. Further, another scenario with additional location constraints can also be solved readily by MIP, which, however, if modeled by GA, the solution process would be complicated. The study has highlighted that MIP can perform better than GA in site facility layout problems in which the site facilities and locations can be represented by a set of integer variables.     

Permutation-Based Elitist Genetic Algorithm for Optimization of Large-Sized Resource-Constrained Project Scheduling

The resource-constrained project scheduling problem (RCPSP) has received the attention of many researchers because its general model can be used in a wide variety of construction planning and scheduling applications. The exact procedures and priority-rule-based heuristics fail to search for the optimum solution to the RCPSP of large-sized project networks in a reasonable amount of time for successful application in practice. This paper presents a permutation-based elitist genetic algorithm for solving the problem in order to fulfill the lack of an efficient optimal solution algorithm for project networks with 60 activities or more as well as to overcome the drawback of the exact solution approaches for large-sized project networks. The proposed algorithm employs the elitist strategy to preserve the best individual solution for the next generation so the improved solution can be obtained. A random number generator that provides and examines precedence feasible individuals is developed. A serial schedule generation scheme for the permutation-based decoding is applied to generate a feasible solution to the problem. Computational experiments using a set of standard test problems are presented to demonstrate the performance and accuracy of the proposed algorithm.     

Electimize: New Evolutionary Algorithm for Optimization with Application in Construction Engineering

In nonlinear construction optimization problems, the capability of current optimization algorithms to find an optimal solution is usually limited by their inability to evaluate the effects of changing the value of each decision variable on reaching the optimal solution. This paper presents fundamental research aimed at developing a novel evolutionary optimization algorithm, named Electimize, that mimics the behavior of electrons flowing, through electric circuit branches with the least electric resistance. In the proposed algorithm, solutions are represented by electric wires and are evaluated on two levels: a global level, using the objective function, and a local level, evaluating the potential of each generated value for every decision variable. The paper presents (1) the research philosophy and scope, (2) the research methodology, and (3) the development of the algorithm. The proposed algorithm has been validated and applied successfully to an NP-hard cash flow optimization problem. The algorithm was able to find a better optimal solution and identified ten alternative optimal solutions for the same problem. This should prove useful in enhancing the optimization of complex large-scale problems.