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This paper develops a fuzzy stochastic multi-objective linear programming model for a multi-level, capacitated lot-sizing problem (ML-CLSP) in a mixed assembly shop. The proposed model aims to minimize the total cost consisting of total variable production cost, inventory cost, backorder cost, and setup cost while maximizing the resource utilization rate simultaneously. To cope with inherent mixed fuzzy stochastic uncertainty associated with input data, e.g., the demand and process-related parameters, they are treated as fuzzy stochastic parameters. We conducted a numerical example from literature to illustrate the efficiency of the proposed method against other ones. To validate the expediency of the proposed ML-CLSP and solution method, a real case study was executed in a furniture company. The results demonstrate the usefulness of the proposed model and its solution approach. [ABSTRACT FROM AUTHOR]

The quality control of sub-assemble products (SAP) in a distributed manufacturing shop (DMS) becomes crucial and complicated when the production of SAPs involves a variety of production technology. In this case, traditional statistical process control methods are not sufficient to control such manufacturing system. Here, we design an intelligent web information system, where quality data are collected from DMS and stored in the central database. The processes of manufacturing SAPs in DMS are then controlled by clustering homogenous SAPs using the quality control of SAP in DMS (QCSD) and process smoothness factor based SAP predefined clustering (PSFSPC) algorithms, respectively. A prototype system called an intelligent web information system quality control (IWIS-QC) has been developed to trace the quality profiles of SAPs. Finally, a case study has been presented to illustrate and validate the proposed approach. [ABSTRACT FROM AUTHOR]

We develop a fuzzy multi-objective linear programming (FMOLP) model for solving multi-objective mixed-model assembly line problem. In practice, vagueness and imprecision of the goals in this problem make the fuzzy decision-making complicated. The proposed model considers minimizing total utility work, total production rate variation, and total setup cost, using a two-phase linear programming approach. In the first phase, the problem is solved using a max–min approach. The max–min solution not being efficient, in general, we propose a new model in the second phase to maximize a composite satisfaction degree at least as good as the degrees obtained by phase one. To show the effectiveness of the proposed approach, a numerical example is solved and the results are compared with the ones obtained by the fuzzy mixed integer goal programming and weighted additive methods. The computational results show that the proposed FMOLP model achieves lower objective functions as well as higher satisfaction degrees. [ABSTRACT FROM AUTHOR]