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This paper aims to discuss the sequence-dependent forward setup time (FST) and backward setup time (BST) consideration for the first time in two-sided assembly lines. Sequence-dependent FST and BST values must be considered to compute all of the operational times of each station. Thus, more realistic results can be obtained for real-life situations with this new two-sided assembly line balancing (ALB) problem with setups consideration. The goal is to obtain the most suitable solution with the least number of mated stations and total stations.

The complex structure it possesses has led to the use of certain assumptions in most of the studies in the ALB literature. In many of them, setup times have been neglected or considered superficially. In the real-life assembly process, potential setup configurations may exist between each successive task and between each successive cycle. When two tasks are in the same cycle, the setup time required (forward setup) may be different from the setup time required if the same two tasks are in consecutive cycles (backward setup).

Algorithm steps have been studied in detail on a sample solution. Using the proposed algorithm, the literature test problems are solved and the algorithm efficiency is revealed. The results of the experiments revealed that the proposed approach finds promising results.

The sequence-dependent FST and BST consideration is applied in a two-sided assembly line approach for the first time. A genetic algorithm (GA)-based algorithm with ten different heuristic rules was used in this proposed model.

The performance of cellular manufacturing (CM) systems has been rigorously investigated during the last two decades. The findings from these studies need to be systematically tabulated, given that they span a wide range of systems and experimental conditions. Some of the findings have also not been in agreement with the prescriptive literature on group technology (GT). No such survey of research exists to date. Attempts to fill the void by providing a taxonomy that categorizes these studies into simulation‐based, analytical and empirical studies. Also discusses past work in the context of various experimental factors and conditions, such as system configurations assumed, setup and operation times, scheduling rules, lot sizes and performance measures used. Summarizes major findings from each stream of literature from the point of view of both researchers and practitioners.

The purpose of this paper is to uncover the significance of SMED in manufacturing environments.

The paper gives setup instructions and guidelines to prepare the standardized setup procedure without ignoring actual constraints in production environment. It uses a case study in a small-scale manufacturing unit of northern India to generate an integrated setup reduction approach, utilizing Single Minute Exchange of Die (SMED)-based industrial engineering tools to achieve faster setups. It describes the feasibility of quick changeovers in small enterprises based on an “SMED” approach. Finally, the paper carries out empirical analysis of the financial/non-financial benefits incurred from setup reductions.

Setup activities are a vital part of the production lead time of any product and so affect overall product cost. Industrial engineering techniques have been used to analyze the existing procedure of setups. A SMED approach can help eliminate unwanted activities, externalize the internal activities, if possible, and reduce them by simplification or standardization.

The paper demonstrates the practical application of SMED showing how it can bring real breakthroughs in reducing setup time in small-scale manufacturing.

There can be activities that cannot reduce times by conventional single minute exchange of die (SMED) tools. In this case more advanced tools are needed. The purpose of this paper is to integrate the fuzzy Taguchi method into the SMED method in order to improve the setup time. The reason for using fuzzy logic is the subjective evaluation of factor’s levels assessment by experts. Subjective assessment contains a certain degree of uncertainty and is vagueness. The fuzzy Taguchi method provides to determining optimal setup time parameters in an activity of SMED. So it is possible to reduce time more than the conventional SMED method.

In this study, the SMED method and the fuzzy Taguchi method are used.

In this study, it has been shown that the setup time is reduced (from 196 to 75 min) and the optimum value can be given at the intermediate value by the fuzzy Taguchi method.

In this limited literature research, the authors have not found a study using the fuzzy Taguchi method in the SMED method.

To compare the performance of a new hybrid manufacturing system (HMS) with a conventional cellular manufacturing system (CMS). The hybrid system is a combination of the cellular manufacturing and job shop.

A hypothetical manufacturing facility with eight machines and 20 parts is used as a case. Simulation models are developed for two manufacturing systems. A multi‐factor comparison is carried out to test the performance of the systems under different scenarios.

It was found that group scheduling rules (GSR) and the manufacturing system design factors have significant impact on the performance of the system. In particular, the hybrid system shows its best performance when the MSSPT GSR is applied, whereas the cellular system is superior when DDSI is implemented. The results also demonstrate that, by adding non‐family parts to the production schedule of the HMS, significant benefits in the performance measures can be attained.

The conclusion cannot be generalized, as the result is dependent upon the input data and the size of the problem.

The application may be limited to certain industry sectors. Further studies may be needed to identify the appropriate industry.

While the majority of the literature focuses on either a job shop or a pure CMS, this paper has a distinctive approach that allows the combined use of both systems. This could be a useful transitional approach from one system to the other.

The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness.

A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation.

The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop.

Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines.

The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error.

Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

This paper considers the decision problem for a minimum setup strategy of a production system arising in the assembly of printed circuit boards of different types, using a placement machine with multi‐slot feeders. We formulate the problem as a binary linear programming model, and propose a heuristic procedure to find the solution that consists of a board‐assembly sequence, an associated component loading and unloading strategy and a feeder‐assignment plan within reasonable computational effort. Computational results from solving the simulated problem instances by using the heuristic method and the mathematical model are provided and compared. The proposed heuristic procedure can be incorporated into the PCB scheduling optimization software to decrease cycle times and increase overall assembly throughput in a high‐mix, low‐volume PCB manufacturing environment.

The purpose of this paper is to present a hybrid meta-heuristic based on genetic algorithms (GAs), simulated annealing, variable neighborhood descent and path relinking for solving the variant of the unrelated parallel machine scheduling problem considering sequence-dependent setup times.

The authors carried out computational experiments on literature problem instances proposed by Vallada and Ruiz (2011) and Arnaout et al. (2010) to test the performance of the proposed meta-heuristic. The objective function adopted was makespan minimization, and the authors used relative deviation, average and population standard deviation as performance criteria.

The results indicate the competitivity of the proposed approach and its superiority in comparison with several other algorithms. In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances.

In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances.

The proposed approach presented high-quality results, with an innovative hybridization of a GA and neighborhood search algorithms, tested in diverse instances of literature. Furthermore, the case study demonstrated that the proposed approach is recommended for solving real-world problems.

In practical environments, machines subject to maintenance are prevalent in many production systems. This paper aims to find a schedule that minimizes the completion time (or equivalently, the total setup time) subject to maintenance and due dates.

An efficient heuristic is presented to provide the near‐optimal solution for the problem. The performance of the heuristic is evaluated by comparing its solution with the optimal solution obtained from the integer linear programming model.

In many production systems, the sequence‐dependent setup time of a job cannot be ignored when a switch between two different jobs occurs. The paper studies the sequence‐dependent setup time problem with periodic maintenance, where several maintenances are required. Computational results show that problems with larger time interval and smaller maintaining time can produce a smaller completion time.

Here an efficient heuristic is developed to provide the near‐optimal schedule for the problem. The proposed integer linear programming model is also presented to provide the optimal schedule. However, the proposed heuristic and the integer linear programming model developed in the paper are appropriate for those companies where maintenance is performed periodically and the sequence‐dependent setup times of their jobs are required.

The paper presents the heuristic and the integer linear programming model to deal with sequencing and maintenance problems.

The purpose of this paper is to address the gap between definition and practical aspects of production efficiency in mass customization (MC). The paper summarizes all major issues impacting efficiency in MC. Also, the paper reviews metrics, relationship between various parameters and provides a best practices benchmark toolkit to achieve higher machine efficiencies.

The paper identified and categorized multiple challenges impacting machine efficiency in MC through a literature review spanning over three decades, and also ranked the identified issue-based parameters. Top issues were found varying across different types of industries identified through the review. Metrics pertaining to efficiency and degree of MC are reviewed in the paper. A chronological review of issues is presented, and a chain diagram is built in the paper. Toolkit of best practices created with solution strategies and tools are summarized through the review.

The paper found that MC reasonably impacts machine efficiency which needs to be addressed. Major issues through literature review-based ranking are uncovered, and worldwide research trend and comparison are presented. Active research in this area is observed to be at its peak since 2010. The extensive use of strategies and benchmark toolkit for improving efficiency are summarized.

Ranking of issues has been done through a literature review; hence, there can be skewness depending on the frequency of issues researched by various authors in various areas of industries.

This paper is useful for manufacturing managers and companies willing to increase the size of their product portfolio and choices within their available resources without compromising machine efficiencies and, thereby, the cost. The identified issues help in providing a comprehensive issue list to the academia.

This paper describes what is believed to be the first study that explicitly examines the issues faced in achieving machine efficiency while manufacturing in an MC environment.