Multimode Resource-Constrained Research Articles

An efficient hybrid differential evolution based serial method for multimode resource-constrained project scheduling

The Multimode Resource-Constrained (MRC) problem aims at finding the start times and execution modes for the activities of a project that minimizes project duration under current precedence constraints and resource limitations. This study integrates the fuzzy c-means clustering technique and the chaotic technique into the Differential Evolution to develop the Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) algorithm, an efficient hybrid approach to solving MRC and other related problems. Within the FCDE, the chaos prevents the optimization algorithm from premature convergence and the fuzzy c-means clustering acts as several multi-parent crossover operators for utilizing population information efficiently and enhance convergence efficiency. Further, this study applies a serial method to reflect individual-user priorities into the active schedule and the project duration calculations. Experiments run indicate that the proposed FCDE-MRC obtains optimal results more reliably and efficiently than the benchmark algorithms considered. The FCDE-MRC is a promising alternative methodology to handling resource-constrained problems.

Evolutionary Algorithm for Solving Multi-Mode Resource Constrained Project Scheduling Problems through Deterministic Mode Selection

This work describes a novel approach towards solving Multimode Resource Constrained Project Scheduling (MRCPS) Problems and an algorithm developed to implement this approach. The algorithm is intended to be used as an alternative to the various genetic algorithms currently being used to solve such problems. Using a deterministic approach that aims to select the optimum modes for execution as efficiently as possible, this algorithm has given deviations far below those of previous efforts in J30 dataset as published by PSPLib. This algorithm is expected to have great implications in the field of Optimisation.

Integrated scheduling and batch ordering for construction project

Multi-Mode Resource Constrained Project Scheduling Problem and material batch ordering for construction project are integrated to help project manager consider various trade-offs among several costs, such as renewable resources’ cost, material price, ordering cost, back-ordering cost, inventory holding cost and reward/penalty for early/late project completion. Therefore, we prove a mixed integer programming model and impel to calculate inventory holding cost and back order cost in objective function. Moreover, a hybrid algorithm combined adapted harmony search and genetic algorithm is proposed correspondingly. In order to inherit elitist solution and maintain population’s diversity simultaneously, we add a selection operator when the harmony memory is initialized and modify the replacement operator based on distance. Besides, genetic algorithm is adopted based on a ‘012’ coding scheme. Finally, algorithm and model performance is presented and several project instances are provided with different network structures and realizations to discuss the factors on total cost.

Scheduling reconstruction operations with modes of execution

Since the decline of the Cold War, the risk of major conflict between powerful, industrialized nations has decreased. Insecurities in the 21st century are more apt to arise from the debris of imploding states. Such situations may require intervention—military or otherwise—by concerned states. To meet this new operational challenge, nations must adapt their planning procedures to account for Security, Stabilization, Transition, and Reconstruction Operations (SSTRO). This paper develops a project scheduling framework for post-conflict reconstruction that schedules reconstruction activities to maximize the positive impact to the local economy during the initial phase of SSTRO. Specifically, this paper builds on the Multimode Resource Constrained Project Scheduling Problem with Generalized Precedence Relations (MM-RCPSP-GPR) using goal programming to maximize the reconstruction operations’ positive impact to local population's economic welfare. This MM-RCPSP-GPR variant is applied to a notional example to illustrate its potential use in post-conflict SSTRO. The basic framework can be extended to other settings.