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In fuzzy mathematical programming literature, most of the transformation approaches were mainly focused on integer linear programs (ILPs) with fuzzy parameters/variables. However, ILP-based solution approaches may be inadequate for solving large-scaled combinatorial fuzzy optimization problems, like project scheduling under mixed fuzzy-stochastic environments. Moreover, many real-life project scheduling applications may contain different types of uncertainties such as fuzziness, stochasticity, and dynamism simultaneously. Based on these motivations, this paper presents a novel constraint programming (CP)-based transformation approach for solving a multi-objective and multi-mode, fuzzy-stochastic resource investment project scheduling problem (FS-MRIPSP) which is a well-known NP-complete problem. In fact, the proposed approach mainly depends on a bound and decomposition principle which divides fuzzy components of the problem into the crisp middle, lower, and upper level problems. Thus, it reduces the problem dimension and does not need to use any standard fuzzy arithmetic and ranking operations directly. Furthermore, the stochastic nature of the problem is also taken into account by using a multi-scenario-based stochastic programming technique. Finally, a weighted additive fuzzy goal program is embedded into the proposed CP-based transformation approach to produce compromise fuzzy project schedules that trade-off between expected values of project makespan and total resource usage costs. To show the validity and practicality of the proposed approach, a real-life application is presented for the production-and-operations management module implementation process of an international Enterprise Resource Planning software company. The fuzzy-stochastic project schedules generated by the proposed CP-based approach are also compared to the results of a similar ILP-based method. Computational results have shown that the CP-based approach outperforms the ILP-based method in terms of both solution quality and computational time. [ABSTRACT FROM AUTHOR]

Nowadays, there has been an increasing attention to enterprise resource planning (ERP) software implementation projects due to the rapid improvements in information technologies. In this context, project managers may face with several uncertainties during the project planning and scheduling phases. In this paper, scheduling of a multi-objective ERP implementation project is targeted under human resource constraints and uncertainty. Through the proposed interval programming based solution approach, uncertainties of the project parameters/inputs as well as the project schedules/outputs which represent the starting or completion times of the activities can be easily handled. Therefore, several alternative project schedules can be generated by making use of the proposed approach according to the risk appetite of the project managers. Additionally, efficiency and reliability factors of the human resources are also taken into account. Finally, computational results of a real-life application have shown that more applicable and reliable compromise solutions can be generated by the proposed approach considering both project makespan and effective utilization of the human resource capacities. [ABSTRACT FROM AUTHOR]

• A fully uncertain multi-mode RIPSP under mixed interval and stochastic uncertainty. • A hybrid interval programming and chance constrained optimization based approach. • Compromise solutions to the tradeoff between makespan and human resource costs. • A real-life application for an ERP implementation project scheduling problem. • More reliable and risk-free solutions according to project manager's risk attitude. Most of the real-life project scheduling cases may involve different types of uncertainties simultaneously such as randomness, fuzziness and dynamism. Based on this motivation, the present paper proposes a novel interval programming and chance constrained optimization based hybrid solution approach for a fully uncertain, multi-objective and multi-mode resource investment project scheduling problem (MRIPSP). The classical discrete-time binary integer programming formulation of the problem is extended by incorporating both the interval-valued and interval-stochastic project parameters as well as variables. In addition to the uncertain project parameters/inputs, the completion times of the activities which represent the project schedule and the availabilities of the renewable project resources are also stated as uncertain project variables and represented by interval numbers. Then, the proposed interval-stochastic multi-mode resource investment project scheduling (IS-MRIPSP) model is converted into its crisp equivalent form by using the proposed approach. The proposed approach is also able to consider different types of project scheduling risks and produces more reliable and risk-free solutions according to the project manager's attitude toward risks. Furthermore, in addition to the classical makespan objective, effective and efficient utilization of the renewable project resources, i.e., human resources, is also targeted. The efficiency and reliability factors of the human resources are also taken into consideration. In order to generate balanced project schedules which tradeoff between the project time and total human resource costs, compromise programming approach is adapted. Finally, in order to test the validity and practicality of the proposed approach, a real-life application is presented for an enterprise resource planning (ERP) implementation project scheduling problem of an international industrial software company. [ABSTRACT FROM AUTHOR]