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This paper aims to propose a parallel automated assembly line system to produce multiple products in a semi-continuous system.

The control system developed in this research consists of a manufacturing system for two-level hierarchical dynamic decisions of autonomous/automated/automatic-guided vehicles (AGVs) dispatching/next station selection and machining schedules and a station control scheme for operational control of machines and components. In this proposed problem, the assignment of multiple AGVs to different assembly lines and the semi-continuous stations is a critical objective. AGVs and station scheduling decisions are made at the assembly line level. On the other hand, component and machining resource scheduling are made at the station level.

The proposed scheduler first decomposes the dynamic scheduling problems into a static AGV and machine assignment during each short-term rolling window. It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as a minimum cost flow problem during each short-term scheduling window. A comprehensive decision making process and heuristics are developed for efficient implementation. A simulation study is worked out for validation.

Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The sequence of stations is pre-specified in each assembly line and the components of a product are kept in machine magazine. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs.

This paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system.

Considering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production.

The model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example.

A case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized.

The benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

The purpose of this study is to investigate the benefits of the turning point layout; a simulation model being applicable for strategic level is designed that compares systems with and without turning points. Specifically, the avoidance of deadlocks and prevention of conflicts is substantial.

Optimization process for different layouts and configuration of autonomous guided vehicles (AGVs) are worked out using statistical methods for design parameters. Regression analysis is used to find effective design parameters and analysis of variance is applied for adjusting critical factors. Also, the optimal design is then implemented in a manufacturing system for an industrial automation and the results are reported.

The outputs imply the effectiveness of the proposed approach for real industrial cases. This research will combine both simulation-based method and optimization technique to improve the quality of solutions.

In AGV systems, the begin-end combinations are usually connected by using a fixed layout, which is not the optimal path. The capability of these configurations is limited and often the conflict of multiple AGVs and deadlock are inevitable. By appearing more flexible layouts and advanced technology, the positioning and dispatching of AGVs increased. A new concept would be to determine each path dynamically. This would use the free paths for AGVs leading to overcome the conflicts and deadlocks.

The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities.

The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs.

As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time.

Predictive maintenance (PM) activities carry out the operations on machines and tools before the breakdown takes place and it helps to prevent failures before they happen.

This study aims to perform innovation analysis for a product based on market, design and process dimensions. This integrated approach provides sustainability for product design and development.

A significant aspect of innovation is investigated to provide energy from the wastes collected in the reverse chain. First, the indicators related to the product opportunity gap were collected and ranked by the structural equation modeling (SEM) method. Indicators with a factor loading above 0.6 are selected and inserted into the proposed mathematical model. The proposed mathematical model was implemented in GAMS 28.2.0 to maximize energy production from waste and minimize the cost of product innovation. A case study on pistachio new packaging process innovation is investigated.

The results showed that in today’s competitive world where sustainability and the environment are important, the index of converting waste into energy is one of the main indicators of innovation. Consequently, flammability is extracted from the mathematical model as one of the most significant indicators leading to higher energy production with the lowest innovation cost.

New product development (NPD) is significant to sustain market share and satisfy customer needs. Different approaches are proposed to handle NPD, mostly focusing on the customer and design requirements.

This paper presents a stepwise method for identification and analysis of innovative activities in production systems. The purpose of this paper is to provide a structure in order to propose the risk paradigms and factors corresponding to the innovative activities and evaluation of the impact of these activities on innovation decisions and investment.

The model used here is an analytical approach that evaluates the impact of innovative activities on innovation decision and investment using product opportunity gap (POG) concept. This framework is applied for innovative activities of Asian industrial field, and the risk of innovative activities is calculated by weighted risk analysis method. In this method, the risk weights and intensities are estimated by the average of experts’ opinions in interviews.

This implementation discovered some useful information being used by investors, innovators and policymakers for taking the best strategies and decisions in various innovation domains such as innovation management, risk management and innovation policy. The results of this study show that the product innovation is the most popular category of innovation that has occurred in Asian manufacturing industries, and the product innovation, marketing innovation and organizational innovation have the most influence on technology, economic and social changes intensity, respectively.

This study analyzed the risk of innovative activities after their occurrence and because of different views of experts, there were diverse and sometimes contradictory analyses of innovative activities risk.

This paper links two separate and important sectors of innovation domain: innovation risk and innovation decision making and investment. POG plays the role of a bridge to connect the two mentioned sectors and shows how innovation causes the technological, economic and social changes. This paper also provides useful and practical information for innovation investors and decision makers to take the best decisions and to avoid the probable failures and losses.

This paper aims to conduct a comprehensive fault tree analysis (FTA) on the critical components of industrial robots. This analysis is integrated with the reliability block diagram (RBD) approach to investigate the robot system reliability.

For practical implementation, a particular autonomous guided vehicle (AGV) system was first modeled. Then, FTA was adopted to model the causes of failures, enabling the probability of success to be determined. In addition, RBD was used to simplify the complex system of the AGV for reliability evaluation purpose.

Hazard decision tree (HDT) was configured to compute the hazards of each component and the whole AGV robot system. Through this research, a promising technical approach was established, allowing decision-makers to identify the critical components of AGVs along with their crucial hazard phases at the design stage.

As complex systems have become global and essential in today’s society, their reliable design and determination of their availability have turned into very important tasks for managers and engineers. Industrial robots are examples of these complex systems that are being increasingly used for intelligent transportation, production and distribution of materials in warehouses and automated production lines.

In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously.

Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions.

Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job.

Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

Due to the fact that the non-standard products, being used by customers, may cause failures in products with sales delays, which naturally affect the warranty policy. Thus, it seems to be necessary to study these two concepts simultaneously. The paper aims to discuss these issues.

In this paper, a model is developed for estimating the expected warranty costs under sales delay conditions when two operator costs (failing but not reported and non-failing but reported) are included.

The proposed model is validated using a numerical example for a two types of intermittent and fatal failures occur under a non-renewing warranty policy.

Sales delay is the time interval between the date of production and the date of sale. Most reported literature on warranty claims data analysis related to sales delay have mainly focussed on estimating the probability distribution of the sales delay.

However, due to the huge number of studies and on the other hand to be new and creative, the represented models and methods – as the two main parts of this field – have been got more complicated, which consequently have been turned into unpractical research studies for the realistic situations. Therefore, the purpose of this study is the representation of a novel and simple method to deal with the aforementioned gap.

To this end, Fazl-Tash method have been proposed, in which a thorough and complete model including 114 criteria and a simple technique to rank and select the best supplier have been presented. Sustainability and resiliency are considered in collecting criteria effective on supplier selection.

The method was carried out in a case study in an industrial company. The efficiency of the proposed method is evaluated in comparison with other conventional approaches.

As selecting the supplier plays a crucial role to bring some important advantages for companies, such as coping with the cost and time problems and influencing the majority of contemporary markets’ requirements, in recent years, there have been representing more effective studies in the supplier selection literature.