Project scheduling under uncertainty using fuzzy modelling and solving techniques

In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

SLS-PLAN-IT: A knowledge-based blackboard scheduling system for Spacelab life sciences missions

The primary scheduling tool in use during the Spacelab Life Science (SLS-1) planning phase was the operations research (OR) based, tabular form Experiment Scheduling System (ESS) developed by NASA Marshall. PLAN-IT is an artificial intelligence based interactive graphic timeline editor for ESS developed by JPL. The PLAN-IT software was enhanced for use in the scheduling of Spacelab experiments to support the SLS missions. The enhanced software SLS-PLAN-IT System was used to support the real-time reactive scheduling task during the SLS-1 mission. SLS-PLAN-IT is a frame-based blackboard scheduling shell which, from scheduling input, creates resource-requiring event duration objects and resource-usage duration objects. The blackboard structure is to keep track of the effects of event duration objects on the resource usage objects. Various scheduling heuristics are coded in procedural form and can be invoked any time at the user's request. The system architecture is described along with what has been learned with the SLS-PLAN-IT project

A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem

In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

The main goal of manufacturing industry is to produce the end products on time with good quality and keep the resource wastage low. However, manufacturing industry face several challenges such as bottle necks in the workflow, unsynchronized production, and sudden increase in product demands. In this paper, we are proposing a management platform for textile manufacturing plants with following modules: (1) sewing workflow optimization (2) quality assurance workflow optimization and (3) finishing workflow optimizations. We have used Genetic Programming (GP) approach, to optimize the workflows, considering different factors that affect each workflow. Our results show that, using our proposed platform, the manufacturing workflows can be optimized and reduce the bottle necks in the workflows and resource wastage in the manufacturing plant

The Resource Leveling Problem with multiple resources using an adaptive genetic algorithm

Resource management ensures that a project is completed on time and at cost, and that its quality is as previously defined; nevertheless, resources are scarce and their use in the activities of the project leads to conflicts in the schedule. Resource Leveling Problems consider how to make the resource consumption as efficient as possible. This paper presents a new Adaptive Genetic Algorithm for the Resource Leveling Problem with multiple resources, and its novelty lies in using the Weibull distribution to establish an estimation of the global optimum as a termination condition. The extension of the project deadline with a penalty is allowed, avoiding the increase in the project criticality punishing the shift of activities. The algorithmis tested with the standard Project Scheduling Problem Library PSPLIB, and a complete analysis and benchmarking test instances are presented. The proposed algorithm is implemented using VBA for Excel 2010 in order to provide a flexible and powerful decision support system that enables practitioners to choose between different feasible solutions to a problem, and in addition it is easily adjustable to the constraints and particular needs of each project in realistic environments.This study was partially funded by the Spanish Ministry of Science and Innovation (research project BIA2011-23602).Ponz Tienda, JL.; Yepes Piqueras, V.; Pellicer Armiñana, E.; Moreno Flores, J. (2013). The Resource Leveling Problem with multiple resources using an adaptive genetic algorithm. Automation in Construction. 29(1):161-172. doi:10.1016/j.autcon.2012.10.003S16117229