A novel approach for planning of shipbuilding processes

Shipbuilding is acknowledged as an uncertain, complex, and unique industrial effort that yields massive products consisting of numerous parts and is vulnerable to unexpected events. The industry is also dominated by customer requirements through designs tailor-made for a specific ship. Planning in shipbuilding is therefore considered a formidable process. Consequently, many studies have been conducted to develop a planning framework for the industry to efficiently handle planning process. Yet none of these studies are deemed substantial enough to be regarded as holistic, straightforward, well-accepted, and compatible with the nature of shipbuilding. This study is therefore an important contribution by presenting a novel, hybrid, and integrated general-purpose planning framework applicable to all shipbuilding processes. The novel method exploits historical ship construction scheduling data, synthesizing hierarchical planning, dynamic scheduling, and discrete-event simulation, which is validated through an empirical study in this paper

Solving crew scheduling problem in offshore supply vessels, heuristics and decomposition methods

For the efficient utilisation of resources in various transportation settings, scheduling is a significant area of research. Having crew as the main resource for operation maintenance, scheduling crew have been a powerful decision making tool for optimisation studies. This research provides a detailed real case study analysis regarding the difficulties in planning crew in maritime industry. As a special case study, this thesis researches crew scheduling in offshore supply vessels which are used for specific operations of a global scaled company in oil and gas industry deeply with modified formulations, heuristics and decomposition methods.An extended version of computational study for a simple formulation approach (Task Based Model) is applied as deeper analysis to Leggate (2016). Afterwards, more realistic approach to the same problem is revised. Following the revision, a customized and thorough computational study on the heuristic method with various settings is designed and implemented in C++. After elaborated analysis completed on the suggested models firstly, a modification on Time Windows model is presented to increase the efficacy. This modification provides a sharp decrease in upper bounds within a short time compared to the previously suggested models. Through this suggestion, more economic schedules within a short period of time are generated.Achieving high performances from the modified model, an application of a decomposition algorithm is provided. We implemented a hybrid solution of Benders Decomposition with a customized heuristic for the modified model. Although this hybrid solution does not provide high quality solutions, it evaluates the performance of possible decomposed models with potential improvements for future research. An introduction to robust crew scheduling in maritime context is also given with a description of resources of uncertainty in this concept and initial robust formulations are suggested.For the efficient utilisation of resources in various transportation settings, scheduling is a significant area of research. Having crew as the main resource for operation maintenance, scheduling crew have been a powerful decision making tool for optimisation studies. This research provides a detailed real case study analysis regarding the difficulties in planning crew in maritime industry. As a special case study, this thesis researches crew scheduling in offshore supply vessels which are used for specific operations of a global scaled company in oil and gas industry deeply with modified formulations, heuristics and decomposition methods.An extended version of computational study for a simple formulation approach (Task Based Model) is applied as deeper analysis to Leggate (2016). Afterwards, more realistic approach to the same problem is revised. Following the revision, a customized and thorough computational study on the heuristic method with various settings is designed and implemented in C++. After elaborated analysis completed on the suggested models firstly, a modification on Time Windows model is presented to increase the efficacy. This modification provides a sharp decrease in upper bounds within a short time compared to the previously suggested models. Through this suggestion, more economic schedules within a short period of time are generated.Achieving high performances from the modified model, an application of a decomposition algorithm is provided. We implemented a hybrid solution of Benders Decomposition with a customized heuristic for the modified model. Although this hybrid solution does not provide high quality solutions, it evaluates the performance of possible decomposed models with potential improvements for future research. An introduction to robust crew scheduling in maritime context is also given with a description of resources of uncertainty in this concept and initial robust formulations are suggested

Modelling activity times by hybrid synthetic method

Uncertain (manual) activity times impact a number of manufacturing system modules: plant and layout design, capacity analysis, operator assignment, process planning, scheduling and simulation. Direct observation cannot be used for non-existent production lines. A hybrid direct observation/synthetic method derived from Method Time Measurement available in industry is proposed. To determine accurate activity times required by heuristics and metaheuristics optimisation, manufacturing system modules are modelled by MILP and operator efficiency parameters are used for time standardisation. Among human factors considered are skill and ergonomics. Application to the sterilisation of reusable medical devices is extensively described. Experimental data taken from observation on the field and a worst-case date have shown the model direct applicability for professionals also to non-manufacturing cases

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

YesProposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problem

Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing

Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multi-objective production scheduling algorithm is developed to obtain Pareto-optimal solutions, and then TOPSIS based multi-criteria decision-making is performed to recommend an optimal solution to facilitate filed operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid.Comment: 13 pages, 6 figures, 4 table

Numerical Integration and Dynamic Discretization in Heuristic Search Planning over Hybrid Domains

In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required that each state on the trajectory induced by the execution of plans complies with a given set of global constraints. We approach the computation of plans for such domains as the problem of searching over a deterministic state model. In this model, some of the successor states are obtained by solving numerically the so-called initial value problem over a set of ordinary differential equations (ODE) given by the current plan prefix. These equations hold over time intervals whose duration is determined dynamically, according to whether zero crossing events take place for a set of invariant conditions. The resulting planner, FS+, incorporates these features together with effective heuristic guidance. FS+ does not impose any of the syntactic restrictions on process effects often found on the existing literature on Hybrid Planning. A key concept of our approach is that a clear separation is struck between planning and simulation time steps. The former is the time allowed to observe the evolution of a given dynamical system before committing to a future course of action, whilst the later is part of the model of the environment. FS+ is shown to be a robust planner over a diverse set of hybrid domains, taken from the existing literature on hybrid planning and systems.Comment: 17 page

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS