Search results

The purpose of this paper is to consider various possible constraints of the problem of production and maintenance planning control for a multi-machine under subcontracting constraint, in order to bring the manufacturer industry closer to real mode. In this paper, we present an efficient and feasible optimal solution, by comparing optimization procedures.

Our manufacturing system is composed of parallel machines producing a single product, to satisfy a random demand under a given service level. In fact, the demand is greater than the total capacity of the set of machines; hence there rises a necessity of subcontracting to complete the missing demand. In addition, we consider that the unit cost of subcontracting is a variable depending on the quantity subcontracted. As a result, we have developed a stochastic optimal control model. Then, to solve the problem we compared three optimization methods: (exact/approximate), the genetic algorithm (GA), the Pattern Search (PS) and finally fmincon. Thus, we validate our approach via a numerical example and a sensitivity analysis.

This paper defines an internal production plan, a subcontracting plan and an optimal maintenance strategy. The optimal solution presented in this paper significantly improves the ability of the decision maker to consider larger instances of the integrated model. In addition, the decision maker can answer the following question: Which is the most optimal subcontractor to choose?

The approach developed deals with the case of the real-mode manufacturing industry, taking into consideration different constraints and determining decision variables which allow it to expand the profits of the manufacturing industry in different domains such as automotive, aeronautics, textile and pharmacies.

This paper is one of the few documents dealing with the integrated maintenance in subcontracting constraint production which considers the complex aspect of the multi-machine manufacturing industry. We also dealt with the stochastic aspect of demand and failures. Then, we covered the impact of the unit cost variation of subcontracting on the total cost. Finally, we shed light on a comparison between three optimization methods in order to arrive at the most optimal solution.

The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed.

A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered.

For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit.

A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application.

Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations.

Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost.

Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.

The welding areas of the workpiece must be consistent with high precision to ensure the welding success during the welding of automobile parts. The purpose of this paper is to design an automatic high-precision locating and grasping system for robotic arm guided by 2D monocular vision to meet the requirements of automatic operation and high-precision welding.

A nonlinear multi-parallel surface calibration method based on adaptive k-segment master curve algorithm is proposed, which improves the efficiency of the traditional single camera calibration algorithm and accuracy of calibration. At the same time, the multi-dimension feature of target based on k-mean clustering constraint is proposed to improve the robustness and precision of registration.

A method of automatic locating and grasping based on 2D monocular vision is provided for robot arm, which includes camera calibration method and target locating method.

The system has been integrated into the welding robot of an automobile company in China.

A method of automatic locating and grasping based on 2D monocular vision is proposed, which makes the robot arm have automatic grasping function, and improves the efficiency and precision of automatic grasp of robot arm.

Presents a review on implementing finite element methods on supercomputers, workstations and PCs and gives main trends in hardware and software developments. An appendix included at the end of the paper presents a bibliography on the subjects retrospectively to 1985 and approximately 1,100 references are listed.

The purpose of this paper is to propose a rolling horizon procedure (RHP) to deal with the large‐scale and strongly NP‐hard problem on cooperative lockage‐timetables and service‐policies of the five locks in the Three Gorges Project.

RHP based approach is composed of a series of periodical short‐time sub‐optimizations instead of global optimization. Each of the sub‐optimizations is modeled by a mixed‐integer‐programming and solved by a branch and bound algorithm with terminal penalty.

The RHP method is found to be effective and efficient. The size of the rolling horizon is positively correlated to the total weighted area of ships.

Precision of short‐time predictions is the main limitation.

This procedure promotes the navigation capacity of the Three Gorges Project and proposes a solution to the lockage‐scheduling problem of the Three Gorges Project.

This paper aims to study the feasibility of proposed method to focus the electroporation ablation by mean of multi-output multi-electrode system.

The proposed method has been developed based on a previously designed electroporation system, which has the capabilities to modify the electric field distribution in real time, and to estimate the impedance distribution. Taking into consideration the features of the system and biological tissues, the problem has been addressed in three phases: modeling, control system design and simulation testing. In the first phase, a finite element analysis model has been proposed to reproduce the electric field distribution within the hepatic tissue, based on the characteristics of the electroporation system. Then, a control strategy has been proposed with the goal of ensuring complete ablation while minimizing the affected volume of healthy tissue. Finally, to check the feasibility of the proposal, several representative cases have been simulated, and the results have been compared with those obtained by a traditional system.

The proposed method achieves the proposed goal, as part of a complex electroporation system designed to improve the targeting, effectiveness and control of electroporation treatments and serve to demonstrate the feasibility of developing new electroporation systems capable of adapting to changes in the preplanning of the treatment in real-time.

The work presents a thorough study of control method to multi-output multi-electrode electroporation system by mean of a rigorous numerical simulation.

This paper aims to propose an improved Moth Flame Optimization (I-MFO) algorithm to optimize the cost-oriented two-sided assembly line balancing (2S-ALB). Prior to the decision to assemble a new product, the manufacturer will carefully study and optimize the related cost to set up and run the assembly line. For the first time in ALB, the power cost is modeled together with the equipment, set up and labor costs.

I-MFO was proposed by introducing a global reference flame mechanism to guide the global search direction. A set of benchmark problems was used to test the I-MFO performance. Apart from the benchmark problems, a case study from a body shop assembly was also presented.

The computational experiment indicated that the I-MFO obtained promising results compared to comparison algorithms, which included the particle swarm optimization, Cuckoo Search and ant colony optimization. Meanwhile, the results from the case study showed that the proposed cost-oriented 2S-ALB model was able to assist the manufacturer in making better decisions for different planning periods.

The main contribution of this work is the global reference flame mechanism for MFO algorithm. Furthermore, this research introduced a new cost-oriented model that considered power consumption in the assembly line design.

Agile manufacturing (AM) has evolved as a revolutionary way of manufacturing the products while managing the uncertainties, product introduction time, responsiveness, innovation, superior quality, etc. along the supply chain to satisfy the ever increasing customer demand and to maximize the profit. The purpose of this paper is to critically analyze the literature related to various dimensions of AM and to report the findings.

This paper presents the reviews of 300 scholarly articles from 1993 to 2016 by various researchers and practitioners on AM collected from different sources, i.e. Google Scholar and ResearchGate. The information related to AM is also captured during industrial visits to different Indian manufacturing plants.

AM definitions are reported along divergent scopes and objectives in the literature. The researchers have given importance on performance measurement and process analysis through empirical and descriptive analysis whereas its implementation issues are neglected. It is also observed that the effort made on AM is significant for manufacturing industries which is overlooked in service industries.

This literature review has identified many research gaps in AM which were not paid attention before. Researchers can address these research gaps for strengthening the AM implementation.

In total, 300 research papers are reviewed and analyzed to capture the various aspects of AM and its related issues but not restricted to research methodologies, author profiles, types of industries, tools/techniques/methodology used, etc.

Several previous studies have described the use of maintenance management (MM) at polytechnic institutions. However, a major drawback of those studies is that they did not reveal the actual support for MM as practiced using the conventional method. To describe the full situation at polytechnics, the gaps in the conventional method must be determined. Thus, the purpose of this study is to analyse the gaps in the conventional method for actual MM as practiced at Malaysian polytechnics.

The existence of these gaps must be explored further to reach a better understanding of the utilisation of the conventional method for MM. Primary data were gathered through interviews to develop the proposed system. The major conclusion drawn from the interview results is that there is a lack of comprehensive MM systems that integrate the operation and maintenance processes of facilities management and software programming to guide the decision-making processes. The interview results also reveal irregularities within the MM database used by Malaysian polytechnics related to e-complaints, as well as the fact that the database is unsystematic.

The findings show the need for sophisticated MM at polytechnics.

The identified gaps will guide future improvements to develop MM systems that meet the solution needs.