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In this research, the levels of safety, health, and environmental risks in a building stone processing company (BSPC) have been identified using the integrated approach of the EFMEA (Environmental Failure Mode and Effects Analysis) and the William Fine method, along with the TOPSIS technique for prioritizing organizational safety layers, examining potential incidents, and enhancing organizational efficiency. To achieve this, data and risk assessment information were first collected and evaluated, and then, with the formation of an expert task force, brainstorming sessions were held to identify and analyze environmental risks in the production process using the EFMEA technique. Additionally, with the assistance of the William Fine method, safety and health risks in the production process were identified and examined. In the next step, the costs of corrective actions were calculated, and the results obtained from the tables of both EFMEA and William Fine techniques were combined, and decisions were made regarding risks with high and very high levels. Subsequently, using TOPSIS, protective layers were prioritized based on two criteria: cost and time. Following the risk assessment using the EFMEA method, four risks were classified as high-risk, nine risks as medium-risk, and two risks as low-risk. Subsequently, employing the William Fine technique, a total of 41 hazards were evaluated across five worksheets. 5% of the hazards were categorized as very high-risk, 19% as high-risk, 27% as medium-risk, and 49% of the evaluated hazards were classified as low-risk. Ultimately, the results obtained from the integration of the William Fine and EFMEA techniques categorized two risks as very high-risk, 12 risks as high-risk, 20 risks as medium-risk, and 22 risks as low-risk. Furthermore, working at heights was selected as one of the risks with high-risk, and protective layers and control measures were proposed and examined. The use of helmets, shoes, harnesses, and the establishment of a safety platform, considering both time and cost criteria, is the first priority for controlling risks in working at heights activity. [ABSTRACT FROM AUTHOR]

One of the global ecological problems is the excessive carbon dioxide emissions generated by vehicles in the transport sector, including passenger transport. Therefore, the objective of this investigation was to develop a model that supports the prediction of vehicle variants that will be satisfactory to the customer in terms of: (i) quality level and (ii) environmental impact throughout the life cycle. This model was developed with the following techniques: TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), LCA (Life Cycle Assessment), SMARTER (Specific, Measurable, Achievable, Relevant, and Time-bound), Pareto–Lorenz, and the Multi-Criteria Decision Method rule (7 ± 2). A model test was carried out for production variants of the electric vehicle BEV (battery electric vehicle) for which the quality level and life cycle assessment were estimated. Vehicle quality levels ranged from 0.15 to 0.69, with a weight of 0.75. However, vehicle life cycle scores were estimated in the range of 0.25 to 0.57, with a weight of 0.25. Ultimately, the level of the vehicles' LCA ranged from 0.18 to 0.62. As a result, it was shown that on the basis of various modifications of the quality level of vehicle variants and the corresponding environmental impacts throughout their life cycle, it is possible to predict the vehicle variant that is most satisfactory for the customer and, at the same time, environmentally friendly. The originality of the model relies on supporting the making of sustainable design decisions and the planning of vehicle improvement actions according to customer expectations. Therefore, the model can be used to analyse different types of vehicles by producers and dealers of these products. [ABSTRACT FROM AUTHOR]

One of the global ecological problems is the excessive carbon dioxide emissions generated by vehicles in the transport sector, including passenger transport. Therefore, the objective of this investigation was to develop a model that supports the prediction of vehicle variants that will be satisfactory to the customer in terms of: (i) quality level and (ii) environmental impact throughout the life cycle. This model was developed with the following techniques: TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), LCA (Life Cycle Assessment), SMARTER (Specific, Measurable, Achievable, Relevant, and Time-bound), Pareto–Lorenz, and the Multi-Criteria Decision Method rule (7 ± 2). A model test was carried out for production variants of the electric vehicle BEV (battery electric vehicle) for which the quality level and life cycle assessment were estimated. Vehicle quality levels ranged from 0.15 to 0.69, with a weight of 0.75. However, vehicle life cycle scores were estimated in the range of 0.25 to 0.57, with a weight of 0.25. Ultimately, the level of the vehicles’ LCA ranged from 0.18 to 0.62. As a result, it was shown that on the basis of various modifications of the quality level of vehicle variants and the corresponding environmental impacts throughout their life cycle, it is possible to predict the vehicle variant that is most satisfactory for the customer and, at the same time, environmentally friendly. The originality of the model relies on supporting the making of sustainable design decisions and the planning of vehicle improvement actions according to customer expectations. Therefore, the model can be used to analyse different types of vehicles by producers and dealers of these products.

Sustainability is at the heart of continuous product improvement, where social, environmental, and economic considerations are key. This is a difficult task, especially in the area of the following economic and environmental changes, as well as changes in customer expectations. Additionally, it has become crucial to make product decisions that will be based on their entire life cycle (LCA). Although these aspects of sustainability and LCA are popular, bringing them together in one analysis remains challenging. Therefore, the article's objective was to develop a simplified model that supports decisionmaking, taking into account the criteria of sustainable development and life cycle assessment (LCA). The originality is the proposed model, which operates based on a qualitative indicator (product quality level), an environmental indicator (the product's impact on the environment throughout its life cycle) and a price indicator (purchase price of the product). These indicators are created using the TOPSIS method and the modified ACJ-E method. As a result, a product ranking is created. A novelty is the combination of Life Cycle Assessment (LCA) criteria with quality criteria and the actual purchase price of the product. The model test was carried out on the example of CATERPILLAR mini excavators. However, the model can be dedicated to any company that wants to make decisions in accordance with the idea of sustainable development. [ABSTRACT FROM AUTHOR]

The reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm driven by the high responsiveness and performance efficiencies. In such system, machines, material handling units or machines components can be added, modified, removed or interchanged as needed. Hence, the design of RMS is based on reconfigurable machines capabilities and product specification. This paper addresses the problem of machines selections for RMS design under unavailability constraints and aims to develop an approach to ensure the best process plan according to the customised flexibility required to produce all parts of a given product. More specifically, we develop a flexibility-based multi-objective approach using an adapted version of the well-known non-dominated sorting genetic algorithm to select adequate machines from a set of candidate (potential) ones, in order to ensure the best responsiveness of the designed system in case of unavailability of one of the selected machines. The responsiveness is based on the flexibility of the designed system and a generated process plan, which guarantees the management of machines unavailability. It is defined as the ability and the capacity to adapt the process plan in response to machines unavailability. Two objectives are considered, respectively, the maximisation of the flexibility index of the system and the minimisation of the total completion time. To choose the best solution in the Pareto front, a multi-objective decision-making method called technique for order of preference by similarity to ideal solution is used. To demonstrate the applicability of the proposed approach, a simple example is presented and the numerical results are analysed. [ABSTRACT FROM PUBLISHER]

Of late, attempts are being made to optimise production system problems by minimum cost. A good available device in this area is response surface methodology. This methodology combines experimental designs and statistical techniques for empirical model building and optimising. In most situations simulated models for real world problems are non‐linear multi‐response, while responses are conflicting. The simultaneous optimisation of several conflicting responses is computationally expensive. So this makes the problem solving extremely complex. Since few attempts have been made to scrutinise this domain, in this paper the nonlinear continuous multi‐response problem is investigated. In order to tackle multi‐response optimisation difficulties, we propose a new hybrid meta heuristic based on the imperialist competitive algorithm. It simulates a socio–economical procedure, imperialistic competition. When there are some non‐dominated solutions in searching space, a technique for order performance by similarity to ideal solution is used to identify which non‐dominated solutions are imperialists and which ones belong to colonial societies. A particle swarm‐like mechanism is employed to model the influence of imperialists on colonies. The algorithm will continue until only one imperialist obtains all countries’ possessions. In order to prevent carrying out extensive experiments to find optimum parameters of the algorithm, we apply the Taguchi approach. Since there is no standard benchmark in this field, we use three case studies from distinguished papers in the multi‐response optimisation field. Comparing the results with some works mentioned in the literature reveals the superiority of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This research focuses on solving the multistage process push/pull junction point location problem. An aim is to implement a hybrid push/pull production system that can satisfy both high service‐levels and low inventory levels. Simultaneously, we consider sophisticated variability, such as multi‐products, random setup, indiscriminate break‐downs, yield loss, batch processes, and other contingencies. The problem can be solved by a multiple criteria decision‐making (MCDM) method. A technique for order‐preference by similarity‐to‐ideal solution (TOPSIS) is used to select a suitable option. The optimisation involves evaluation of stochastic performance measures within alternative scenarios among candidate junction‐point locations using a discrete event simulation model. A practical thin film transistor‐liquid crystal display (TFT‐LCD) process case‐study is utilised to illustrate the proposed method. After implementing a hybrid push/pull production strategy, simulation results indicate that the inventory level was reduced by over 18% while the service level remained about the same. For another scenario, a 3.4% decrease in service‐level can be paid off by a 46% decrease in inventory level and 34% improvement in lead time. [ABSTRACT FROM PUBLISHER]

Reconfigurable Manufacturing Systems provide the functionality and capacity needed, when needed. The Reconfigurable Machine Tool (RMT) plays a pivotal role in the fulfilment of this objective through their modular structure consisting of basic and auxiliary modules along with the open architecture software. In the present work, a novel approach based on the module interactions and machine capability is proposed to measure the machine reconfigurability and operational capability of an RMT. The developed performance measures along with cost are considered as the multiple objectives for the optimal machine assignment for a single part flow line allowing paralleling of similar machines. The multi-objective optimisation problem in hand is targeted in two phases. In the first phase, non-dominated sorting genetic algorithm II is applied to obtain the non-dominated solutions. In the subsequent stage, a multiple attribute decision-making approach is employed to rank the pareto frontiers. The proposed solutions are ranked based on Shannon entropy weight and Technique for Order Preference by Similarity to Ideal Solution method. The study reveals that the developed performance measures along with the hybrid approach have a great potential in handling the RMS optimisation and cost–benefit issues. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a multi-objective differential evolution algorithm (MODEA) to solve the multi-objective simple assembly line balancing problem type-2 (SALBP-2). This problem arises when in an existing assembly line, changes in the production process or demand structure take place and the organisation wants to produce the optimum number of items using a fixed number of workstations, which is associated with optimally assigning the tasks to an ordered sequence of stations such that the precedence relations are not violated and some measures of performance are optimised. The two considered objectives are: minimising the cycle time and the smoothness index of the assembly line. To that purpose, we develop a MODEA which unlike the existing algorithms deals with the considered objectives separately in selecting the next population members by proposing a new acceptance scheme based on the Pareto dominance concept and a new evaluation scheme based on TOPSIS. Also, by using the Taguchi method, we tune the effective factors of the developed algorithm. Then its efficiency is tested over available assembly line balancing benchmarks and compared to a new algorithm provided recently in the bi-objective SALBP-2 literature. Computational experiments indicate that the developed algorithm outperforms the existing meta-heuristic over a large group of benchmarks. [ABSTRACT FROM AUTHOR]

Manufacturing industry plays a very significant role in the economic functioning of any country. In recent times, reverse engineering (RE) has become an integral part of manufacturing set-up owing to its numerous applications. The quality of RE product primarily depends on the quality of digitization i.e., part measurement. There is a diverse range of digitization devices which can be employed in RE. These machines have variability in terms of cost, accuracy, ease of use, accessibility, scanning time, etc. Therefore, the decision regarding the selection of a suitable device becomes important in a particular RE application. The decisions taken in the planning stage for RE can have a long lasting impact on the functionality, quality and the economics of components to be used by manufacturing industries. To accomplish the selection procedure, a comparative study of three digitization techniques has been carried out. The determination of an appropriate digitization system is basically a multi-criteria decision making (MCDM) problem. MCDM techniques are yet to be applied in the selection of digitization systems for RE. MCDM is one of the most widely used decision methodologies in business and engineering spheres. The aim of this work is to describe various MCDM methods in the selection of digitization systems for RE. This paper intends to employ combinations between different MCDM methods such as group eigenvalue method (GEM), analytic hierarchy process (AHP), entropy method, elimination and choice expressing reality (ELECTRE), technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighing (SAW) method. In this work, GEM, AHP, Entropy methods has been used to elicit weights of various selection criteria, while TOPSIS, ELECTRE and SAW have been applied to rank the alternatives. A comparative analysis has also been performed to determine the efficacies of different approaches. The conclusion of the paper reveals the best digitization system as well as the characteristics of different MCDM methods and their suitability in RE application. [ABSTRACT FROM AUTHOR]

Facility layout design, a NP hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new sustainable stochastic dynamic facility layout problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated are then analyzed by data envelopment analysis to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi attribute decision-making techniques such as technique for order preference by similarity to ideal solution, interpretive ranking process and analytic hierarchy process, Borda-Kendall and integer linear programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size $$N=12$$ for time period $$T=5$$ having Gaussian demand are considered. [ABSTRACT FROM AUTHOR]

Manufacturing organisations are witnessing a transformation in the manufacturing paradigm due to the increasing competition. Agile manufacturing (AM) is an operations concept that is intended to improve the competitiveness of firms. When market conditions are unfavourable, a firm needs to stay competitive in order to function well and remain in good health. In such situations, it becomes essential that an organisation optimises its manufacturing processes so that it would adapt to changes in an unpredictable market scenario and remain competitive. AM principles enable an organisation to sustain in the competitive market scenario. Concept selection for an AM system is a typical multi-criteria decision making (MCDM) problem. In order to enhance the effectiveness of concept selection, a unique combination of fuzzy decision making trial and evaluation laboratory (DEMATEL), fuzzy analytical network process (ANP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was used in the study. The study is aimed at selecting the best concept design of an automobile component. The selected design was subjected to implementation in the case organisation. [ABSTRACT FROM AUTHOR]

Due to the increasing competition, modern manufacturing organisations are adopting agile manufacturing (AM) paradigm. AM enables the speedy production of customised, high-quality products in varying lot sizes. Concept selection in the context of AM is a typical multi-criterion decision making problem as it involves several agility criteria. The case organisation needs to select the best concept among five alternative designs. VIKOR (VlseKriterijumskaOptimizacija I KompromisnoResenje in Serbian, meaning multi-criteria optimization and compromise solution) has been used to select the best concept design. Based on the inputs gathered from decision makers, concept design (A3) has been found the best concept design from the perspective of agility. The result derived from Fuzzy VIKOR has been compared with fuzzy TOPSIS (technique for order preference by similarity to an ideal solution) methodology. The selected concept design has been subjected to implementation in the case organisation. [ABSTRACT FROM AUTHOR]

Transfer molding is used extensively in electronic packaging. To achieve a high production rate and high molding quality, it is necessary to have strict control on the epoxy resin characteristics as well as identify the optimal process conditions of the transfer molding. In this paper, process simulations of transfer molding for electronic packages were conducted according to Taguchi experiments. The simulation results were then used to derive quality indexes by using TOPSIS (techniques for order preference by similarity of ideal solution) algorithm. Through the analysis of the quality indexes, optimal process conditions can be identified. Two verification tests were carried out and results of the tests are found very close to the predictions. This project has demonstrated that numerical simulation combining with Taguchi method and TOPSIS can be a useful tool for optimization of process conditions for transfer molding of electronic packages. [Copyright &y& Elsevier]