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To provide a new model to solve the assembly‐planning problem of a textile machine in a shopfloor which can help researchers and practitioners.

The assembly planning of a textile machine (repetitive manufacturing system) involves the allocation of operations to cross‐trained operators. Workflow is defined as the workloads assigned to the operators. Operators with smaller workloads are selected to be assigned new operations from the list of unscheduled operations. Three different scheduling strategies – random, shortest processing time, and longest processing time – are adopted for the selection of operations to be assigned to operators. Different combinations of these strategies are considered for the selection of both preceding and succeeding operations. A computer simulation program has been coded on an IBM/PC‐compatible system in the C++ language to study the performance of real data from the shopfloor.

The relative percentage of imbalance is adopted for evaluating the performance of these heuristics. The RL, SL and LL produced well balanced workload schedules with lesser RPI values for all operators other than heuristics.

Non‐traditional approaches like genetic algorithms can be applied to determine the robustness of the results obtained by this research.

The experiments on simulated and real data clearly indicate that the order of succeeding operations determines the balanced workflows to the assembly of operations among the operators.

The allocation of assembly operations to the operators is modeled into a parallel machine‐scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

WITH the approach of the European Management Conference at Torquay and the news that The Work Study Society will be holding a one‐day Conference in London on October 14th and another in Hereford on November 25th it looks as if work study men will have plenty of opportunities of exchanging points of view this autumn.

This paper applies the knowledge-based genetic algorithm to solve the optimization problem in complex products technological processes.

The knowledge-based genetic algorithm (KGA) is defined as a hybrid genetic algorithm (GA) which combined the GA model with the knowledge model. The GA model searches the feasible space of optimization problem based on the “neighborhood search” mechanism. The knowledge model discovers some knowledge from the previous optimization process, and applies the obtained knowledge to guide the subsequent optimization process.

The experimental results suggest that the proposed KGA is feasible and available. The effective integration of GA model and knowledge model has greatly improved the optimization performance of KGA.

The technological innovation of complex products is one of effective approaches to establish the core competitiveness in future. For this reason, the KGA is proposed to the technological processes optimization of complex products.

Discusses experiences from the design of parallellized flow assembly systems. Also discusses factors influencing the performance of collective work and relates these to the flow parameters in the Saab Automobile′s final assembly plant in Malmö and the Volvo Uddevalla final assembly plant. Illustrates this discussion by empirical data from the shopfloor of the Volvo Uddevalla plant. Focuses mainly on the evolution of the intra‐group work pattern from the start in the training workshop to the final intra‐group work pattern and the assembly performance achieved before the closing down of the plant. Reports on management′s initial design assumptions communicated during the design of the Uddevalla plant. Comments on and revises these design assumptions, since they were far too simple and lacked the support of theoretical analysis and empirical evidence. The lack of a scientific explanation for the production and work organization principles communicated within Volvo might be one of the reasons for the closing down – it certainly was not the performance of the plant.

This paper explains the development stages of a generic decision support system to leverage supply chain performance (GLE). The purpose of this paper is to identify and trade off the critical supply chain measures which are interrelated and in contradiction with each other.

The GLE was developed as an extension of the supply chain performance assessment tool proposed by Banomyong and Supatn (2011). It contained nine measures covering key activities along the supply chain under dimensions of cost, time and reliability. Their interrelations were figured out by causal linkages, whereas their contradictions were traded off as multi-objective optimization. It is solved using fuzzy goal programming along with a weighted max-min operator in order to acquire the Pareto-optimal solution.

The results from the GLE showed there were two critical supply chain measures including supply chain cost per sales and average order cycle time. They contradictorily influenced by a root-cause, namely product lot size. Its Pareto-optimal value was provided to achieve the minimized values of supply chain cost per sales and average order cycle time which were consistent with their relative weights.

As generic features, the GLE needs further validation in several industries under various supply chain strategies. The further validation may contribute the GLE to include multiple decision variables, multiple types of product and multiple periods of time. In addition, the GLE may consider a dimensional measure of environmental impact along the supply chain activities.

The GLE is a unique decision support system to identify and trade off the critical, interrelated and contradicting supply chain measures. More uniqueness is obtained when the GLE offers an option of inputting a set of relative weights for the interrelated supply chain measures.

The purpose of this paper is to present a model, which assesses the wide range of data offered in non-financial reports, and enables benchmarking of these data between different organizations.

This work uses aspects of fuzzy logic and qualitative comparative analysis to build fuzzy sets, which form the basis of the benchmarking tool.

The model presented permits the identification of both negative and positive aspects of an organization’s CSR actions, and shows where improvements can be made by highlighting the standards reached by others.

The model offers a benchmarking tool that allows analysis of non-financial reporting, something missing from the field of CSR until now. Additionally, it offers a new approach where data sets are constructed to measure environmental/social impact in function of each unit of economic value generated. This approach aligns social/environmental and economic performance, thereby emphasizing the interconnectivity of a company’s financial, social and environmental bottom lines.

This paper develops a decision-analysis model to prioritize and select the site to establish a new hospital over different indicators such as cost, market conditions, environmental factors, government factors, locations and demographics. In this way, an integrated model is proposed under the intuitionistic fuzzy information (IFI), the standard deviation (SD), the rank-sum (RS) and the measurement of alternatives and ranking using the compromise solution (MARCOS) approach for ranking hospital sites (HSs).

The IF-SD-RS model is presented to obtain the combined weight with the objective and subjective weights of diverse sub-criteria and indicators for ranking sites to establish the hospital. The IF-MARCOS model is discussed to prioritize the various sites to establish the hospital over several crucial indicators and sub-criteria.

The authors implement the developed model on a case study of HSs assessment for the construction of new hospital. In this regard, inclusive set of 6 key indicators and 18 sub-criteria are considered for the evaluation of HSs. This study distinguished that HS (h₂) with combined utility function 0.737 achieves highest rank compared to the other three sites for the given information. Sensitivity analysis is discussed with different parameter values of sub-criteria to examine how changes in weight parameter ratings of the sub-criteria affect the prioritization of the options. Finally, comparative discussion is made with the diverse extant models to show the reasonability of the developed method.

This study aims to develop an original hybrid weighting tool called the IF-SD-RS model with the integration of IF-SD and IF-RS approaches to find the indicators' weights for prioritizing HSs. The developed integrated weighting model provides objective weight by IF-SD and subjective weight with the IF-RS model. The model presented in the paper deals with a consistent multi-attribute decision analysis (MADA) concerning the relations between indicators and sub-criteria for choosing the appropriate options using the developed IF-SD-RS-MARCOS model.

We consider an oriented network of activities characterized by activity times. Applying the theory of relatively closed systems, the network nodes are interpreted as elements and completion times as element outputs. The behavioural relation of each element is written algebraically in operator form. It is shown how the behavioural relation of the system as a whole may be solved in such a way that explicit dependencies of all completion times on the given activity times are obtained. In this process a matrix containing maximum operators is inverted and the operators in the inverse matrix retain their interpretation.

In accordance with his/her psycho‐physical abilities and degree of skill, regulating system in the operator's brain will match the speed of sewing operations, e.g. actual machine stitching speed, using a simple movement of the foot, to adapt it to his/her sensory and motoric reaction abilities. Workpiece in sewing is fed employing three degrees of freedom movement, i.e. using three independent regulating circuits: position of the seam, edges and workpiece layer length. Cybernetic bases of the regulating system man‐machine‐workpiece are presented, as well as the development of the necessary and maximum possible sewing machine operator's reactions in joining curved seams. The procedure is explained by constructing a mathematical model and obtaining the equations used in calculating necessary and maximum operator's reaction abilities. Measurements necessary were performed in the Department of Clothing Technology, Faculty of Textile Technology, University of Zagreb, using modern measuring equipment while garment‐sewing processes were recorded under actual in‐plant conditions. The results obtained were computer‐processed and a 3D graphic representation was obtained together with a mathematical model appropriate to calculate interdependence of the mentioned parameters. Appropriate software package was developed, which allows a quick, accurate and continuous determination of normal machine‐hand times, as dependent on all the influential parameters.