Optimal crew routing for linear repetitive projects using graph theory and entropy maximization metric

Construction projects that contain several identical or similar units are usually known as repetitive or linear projects. Repetitive projects are regarded as a wide umbrella that includes various categories of construction projects and represents a considerable portion of the construction industry, and contain uniform repetition of work. CPM has been proved to be inefficient in scheduling linear projects because CPM does not address two key aspects, which are maintaining crew work continuity, and achieving a constant rate of progress to meet a given deadline. Line-of-balance (LOB) is a linear scheduling methodology that produces a work schedule in which resource allocation is automatically performed to provide a continuous and uninterrupted use of resource. The fundamental principles of LOB have several shortfalls that raise many concerns about LOB, which need to be attuned and improved in order to suit the nature of construction projects. Hence, this thesis proposes a hybrid approach for scheduling linear projects that stresses on the limitation of LOB scheduling technique. To meet the physical limitation of resources in linear projects, this study presents a flexible optimization model for resolving resource constraint dilemma in linear scheduling problems .The proposed model utilizes a MATLAB code as the searching algorithm to automate the model formulation. The novelty of this model is supplementing a new optimization engine and a decision supporting system that formulate the optimal crews routing between different activities in different units and guarantee the optimal crew distribution for cost efficiency. This model investigates the mechanics of allocating a multi- task skilled workforce between different activities in different units that can lead to increased productivity, flexibility, and work continuity; besides, this model has the capability of accurately identifying the critical path in linear projects. Furthermore, to avoid day-to-day fluctuation in resource demands, this study encompasses a simulation model for handling the resource leveling in linear construction projects. The proposed model was implemented using crystal ball ribbon based on an entropy maximization metric. The entropy-maximization method accounts for such possibility of allowing activity duration to be stretched or crunched relying on activity type without affecting total completion date of a project and provides more optimized resource allocation solutions. A case study for a 4-km sewage pipeline is used to demonstrate the capability of the proposed models, which illustrates the implementation of the proposed models in construction projects

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Optimized Resource-Constrained Method for Project Schedule Compression

Construction projects are unique and can be executed in an accelerated manner to meet market conditions. Accordingly, contractors need to compress project durations to meet client changing needs and related contractual obligations and recover from delays experienced during project execution. This acceleration requires resource planning techniques such as resource leveling and allocation. Various optimization methods have been proposed for the resource-constrained schedule compression and resource allocation and leveling individually. However, in real-world construction projects, contractors need to consider these aspects concurrently. For this purpose, this study proposes an integrated method that allows for joint consideration of the above two aspects. The method aims to optimize project duration and costs through the resources and cost of the execution modes assigned to project activities. It accounts for project cost and resource-leveling based on costs and resources imbedded in these modes of execution. The method's objective is to minimize the project duration and cost, including direct cost, indirect cost, and delay penalty, and strike a balance between the cost of acquiring and releasing resources on the one hand and the cost of activity splitting on the other hand. The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting. The proposed method utilizes the fuzzy set theory (FSs) for modeling uncertainty associated with the duration and cost of project activities and genetic algorithm (GA) for scheduling optimization. The method has five main modules that support two different optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; sensitivity IV analysis module; and decision-support module. The two optimization methods use the genetic algorithm as an optimization engine to find a set of non-dominated solutions. One optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the other uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization method is coded in python as a stand-alone automated computerized tool to facilitate the needed iterative rescheduling of project activities and project schedule optimization. The developed method is applied to a numerical example to demonstrate its use and to illustrate its capabilities. Since the adopted numerical example is not a resource-constrained optimization example, the proposed optimization methods are validated through a multi-layered comparative analysis that involves performance evaluation, statistical comparisons, and performance stability evaluation. The performance evaluation results demonstrated the superiority of the NSGA-II against the dynamic weighted optimization genetic algorithm in finding better solutions. Moreover, statistical comparisons, which considered solutions’ mean, and best values, revealed that both optimization methods could solve the multi-objective time-cost optimization problem. However, the solutions’ range values indicated that the NSGA-II was better in exploring the search space before converging to a global optimum; NSGA-II had a trade-off between exploration (exploring the new search space) and exploitation (using already detected points to search the optimum). Finally, the coefficient of variation test revealed that the NSGA-II performance was more stable than that of the dynamic weighted optimization genetic algorithm. It is expected that the developed method can assist contractors in preparation for efficient schedule compression, which optimizes schedule and ensures efficient utilization of their resources

Fuzzy Optimal Allocation and Arrangement of Spaces in Naval Surface Ship Design.

A new approach to generating, evaluating, and optimizing general arrangements of naval surface ships is presented. Beginning from a user editable database of spaces, the algorithms return an optimized arrangement. The user drives the design by quantitatively defining spaces’ goals and constraints for location, proximity, and shape. The arrangements task is undertaken in two parts: Allocation and Arrangement. Allocation is the assignment of a space to a region of a ship. The unit region used is dubbed a Zone-deck. The Zone-deck is the intersection of one deck and one watertight subdivision. The allocation solution evaluates Zone-deck area utilization and spaces’ relative and global position goals. Adjacency and separation distance between spaces is measured in increments of deck and subdivision. Global position is assessed by deck and subdivision. Each discrete distance and position has an editable default fuzzy preference value. Allocation is essentially a very large scale combinatorial bin packing problem. With the added complexity of relative location constraints, this also becomes a type of quadratic assignment problem. The independent variable vector is an ordered listing by space index number of each space’s assigned Zonedeck index number. A customized Genetic Algorithm optimizes the integer-coded chromosome. The second part arranges one Zone-deck at a time. Arrangement is done in two iterative steps: topology and geometry. Topology gives the relative longitudinal and transverse position of each space’s seed location. These locations are translated onto an orthogonal grid. In the second step, spaces are expanded to have size and shape filling the available area in the stochastic growth loop. Spaces are defined by up to three contiguous boxes allowing for L, T, C and Z shapes. The arrangement cost function evaluates each space’s required area satisfaction, aspect ratio, minimum overall dimension, minimum segment dimension, perimeter, connectivity to access, and proximity constraints to other spaces. Editable piecewise linear fuzzy utility functions translate each criteria measure to a fuzzy utility. The best of a modest number of geometry solutions returns joiner bulkhead locations and a cost function value to a Genetic Algorithm optimization of the topology chromosome.Ph.D.Naval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60736/1/enick_1.pd

Parametric Optimization for the Maximization of Hydrogen Production by Enterobacter Cloacae

The decrease of fossil fuel energy induces the development of sustaining renewable energy. One of the potential energy to be further developed is hydrogen energy. Most of the hydrogen resources currently come from fossil fuel energy. Besides, some biological processes also can produce hydrogen such as dark fermentation which is being focused on in this project. Enterobacter cloacae are used as the bacteria to be fermented in the nutrient broth. Since this process has yet to achieve economic sustainability, this project focuses on the maximization of the production of hydrogen gas by optimizing the parameters influencing the hydrogen production. The decision variables (process parameters) are the initial glucose concentration, Inoculum age and also the initial pH of the nutrient broth. By using data from the previous research, the parameters are optimized by using three numerical methods, simulated annealing, pattern search and Genetic algorithm. A comparison between these three algorithms used is done to compare the optimization results and discuss their advantages and disadvantages

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Methodology for Optimizing Commonality Decisions in Multiple Classes of Ships.

A methodology is presented for optimizing commonality decisions in multiple classes of ships with consideration of mission effectiveness, construction costs, and the savings that will result from the use of commonality. Currently the automobile and consumer product industries apply commonality methodologies in developing families of products that use a common platform. From the use of common components, a manufacturer can realize significant savings in a given line of products. If properly applied, the shipbuilding industry could also see significant cost savings from the use of commonality. An approach to the optimal use of commonality has been developed for the design of different classes of ships using a platform of common components. Unlike previous methodologies, this optimization process takes into explicit consideration the savings associated with the use of commonality. A ship synthesis model was adapted to create ship designs from independent design variables in order to predict the mission performance and calculate construction costs for each design. This was linked with a fleet commonality savings model reflecting savings from bulk purchases of equipment and the shipbuilding learning curve. The commonality items considered were the cruise engines, ship service generators, weapons, superstructure, and midship hull blocks. A multicriterion evolutionary algorithm is utilized to efficiently search the design space for feasible designs of multiple classes of ships. The criteria studied are the mission performance of ship class A/average cost, performance of class B/average cost, and net fleet savings through commonality. Through the use of dominance sorting and genetic operators, offspring solutions are developed in order maximize multiple design objectives from one generation to the next. The resulting solutions form a discrete Pareto front that allows the designer to choose a suitable set of designs to best meet the mission needs of multiple ship classes. The design methodology is demonstrated and tested through a problem modeled on the U.S. Coast Guard Deepwater High and Medium Endurance Fleet missions to enable interpretation of results. Results demonstrate that the methodology presented will prove to be a valuable tool in making good commonality decisions.Ph.D.Naval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/55684/2/mcorl_1.pd

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration