A decision support model for construction cash flow management

The excessive level of construction business failures and their association with financial difficulties has placed financial management in the forefront of many business imperatives. This has highlighted the importance of cash flow forecasting and management that has given rise to the development of several forecasting models. The traditional approach to the use of project financial models has been largely a project-oriented perspective. However, the dominating role of “project economics” in shaping “corporate economics” tends to place the corporate strategy at the mercy of the projects. This article approaches the concept of cash flow forecasting and management from a fresh perspective. Here, the use of forecasting models is extended beyond their traditional role as a guideline for monitoring and control of progress. They are regarded as tools for driving the project in the direction of corporate goals. The work is based on the premise that the main parties could negotiate the terms and attempt to complement their priorities. As part of this approach, a model is proposed for forecasting and management of project cash flow. The mathematical component of the model integrates three modules: an exponential and two fourth-degree polynomials. The model generates a forecast by potentially combining the outcome of data analysis with the experience and knowledge of the forecaster/organization. In light of corporate objectives, the generated forecast is then manipulated and replaced by a range of favorable but realistic cash flow profiles. Finally, through a negotiation with other parties, a compromised favorable cash flow is achieved. This article will describe the novel way the model is used as a decision support tool. Although the structure of the model and its mathematical components are described in detail, the data processing and analysis parts are briefly described and referenced accordingly. The viability of the model and the approach are demonstrated by means of a scenario

Railway scheduling reduces the expected project makespan.

The Critical Chain Scheduling and Buffer Management (CC/BM) methodology, proposed by Goldratt (1997), introduced the concepts of feeding buffers, project buffers and resource buffers as well as the roadrunner mentality. This last concept, in which activities are started as soon as possible, was introduced in order to speed up projects by taking advantage of predecessors finishing early. Later on, the railway scheduling concept of never starting activities earlier than planned was introduced as a way to increase the stability of the project, typically at the cost of an increase in the expected project makespan. In this paper, we will indicate a realistic situation in which railway scheduling improves both the stability and the expected project makespan over roadrunner scheduling.Railway scheduling; Roadrunner scheduling; Feeding buffer; Priority list; Resource availability;

An efficient particle swarm optimizer with application to Man-Day project scheduling problems

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfull

Exact and heuristic reactive planning procedures for multi-mode resource-constrained projects.

The multi-mode resource-constrained project scheduling problem (MRCPSP) involves the determination of a baseline schedule of the project activities, which can be executed in multiple modes, satisfying the precedence relations and resource constraints while minimizing the project duration. During the execution of the project, the baseline schedule may become infeasible due to activity duration and resource disruptions. We propose and evaluate a number of dedicated exact reactive scheduling procedures as well as a tabu search heuristic for repairing a disrupted schedule. We report on promising computational results obtained on a set of benchmark problems.Project scheduling; Uncertainty; Reactive scheduling; Multi-mode RCPSP;

A parallel simulated annealing (PSA) for solving project scheduling problem with discounted cash flow policy in pricing strategy of the project suppliers

Problem programiranja projekta s ograničenim sredstvima u literaturi je poznat kao NP-Hard problem. U ovom istraživanju prvi se puta predlaže politika diskontne solventnosti za rješavanje problema programiranja projekta s ograničenim sredstvima. U klasičnim modelima pretpostavlja se da je cijena potrebnih resursa za izvršenje aktivnosti fiksna i resursi se mogu pripremiti samo po jednoj cijeni na tržištu. Problem je usmjeren na određivanje optimalnog polaznog vremena aktivnosti projekta uzimajući u obzir ograničenja prioriteta i dostupne resurse u svrhu skraćenja vremena završetka projekta. Kako bi se riješio predloženi model predlaže se hibridni algoritam zasnovan na dva algoritma, genetskom i simuliranog žarenja. U toj metodi genetski algoritam služi kao glavni okvir predložene metode a metoda simuliranog žarenja kao novi operater i u svrhu poboljšanja lokalnog pretraživanja glavnog algoritma. Budući da vrijednosti parametara znatno utječu na učinkovitost tih algoritama, daje se novi statistički pristup temeljen na stepenastoj regresiji (stepwise regression) za postavljanje parametara predloženih algoritama. Rezultati proračuna pokazuju visoku učinkovitost predloženog algoritma u odnosu na vrijeme donošenja rješenja i optimalnih rješenja.Resource-constrained project scheduling problem is known as a NP-Hard problem in literature. In this research, discounted cash flow policy is suggested for the resources-constrained project scheduling problem for the first time while in classic models, it has been assumed that price of the required resources is fixed for performing the activities and resources can be prepared only with one price rate in the market. Goal of this problem is to determine optimal starting time of the project activities considering precedence constraints and the available resources such that the project completion time can be minimized. In order to solve the proposed model, a hybrid algorithm based on two algorithms i.e. genetic and simulated annealing has been suggested. In this method, genetic algorithm has been designed as the main framework of the proposed method and simulated annealing method as a new operator and in order to improve local search of the main algorithm. Since values of the parameters have considerable effect on efficiency of these algorithms, therefore, a new statistical approach based on the stepwise regression has been presented to set the proposed algorithms parameters. Results of the calculations show high efficiency of proposed algorithm in terms of solution time and optimal solutions

Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

[EN] This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm(GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.This paper has been partially supported by the Spanish Research Projects TIN2013-46511-C2-1-P and TIN2016-80856-R.Morillo-Torres, D.; Barber, F.; Salido, MA. (2017). Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem. Mathematical Problems in Engineering. 2017:1-15. https://doi.org/10.1155/2017/4627856S1152017Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). 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