Journal of Optimization in Industrial Engineering
Volume:4 Issue: 7, Winter and Spring 2011

This paper is in search of designing the cellular manufacturing systems (CMSs) under dynamic and flexible environment. CM is proper for small-to-medium lot production environment that helps the companies to produce variable kind of productions with at least scraps. The most important benefits of CM are decline in material handling, reduction in work-in-process, reduction in set-up time, increment in flexibility, improved quality, and shorter lead time. In this research A multi-objective mixed integer model is presented that considers some real-world critical conditions same as costs of multi-period cell formation and production planning, human resource assignment to cells and balancing workload of cells. This model groups the parts and machines concurrently with labor assignment This study aims to 1) minimize various costs including reassignment cost of human resource, the batch inter-cell material handling cost, constant and variable cost of machines, relocation and purchase cost of machines, 2) minimize cell load variation and 3) maximize utilization rate of human resource. The model is complicate, so it is verified with Lingo 8. 0. Soft ware. Since particle swarm optimization approach less than many other metaheuristic approaches have been applied to solve multi-objective CMS problems so far, we utilize this method to solve our model. The results are presented at the last part.

Lean manufacturing is a systematic approach to identifying and eliminating wastes (non-value added activities) through continuous improvement by conveying the product at the pull of the customer in pursuit of production. In a more basic term, more value with less work. Since lean manufacturing eliminates many of the problems associated with poor production scheduling and line balancing, lean manufacturing is particularly appropriate for companies that do not have ERP systems in place or do not have strong material requirements planning (MRP), production scheduling, or production allocation systems in place. This is particularly significant in Bangladesh, where many private Bangladeshi garment manufacturing companies are operating significantly below their potential capacity, or experiencing a high level of late-deliveries, due to problems with their current production scheduling and production management systems. Considering all those facts this paper provides a roadmap as well as a framework to those manufacturing companies who are really operating significantly below their potential capacity. In this work, the existing layouts were studied and then layouts are proposed to enhance the production system and value stream mapping (VSM) is used as a basic lean manufacturing tool and some cellular manufacturing philosophies to find out the improved level of performance and productivity particularly in the garments section of Bangladesh. At the final stage, research work is reinforced by using a simulation software ARENA to judge the sustainability of proposal.

Most of data in Multi-attribute decision making (MADM) problems are changeable rather than constant and stable. Therefore, sensitivity analysis after problem solving can effectively contribute to making accurate decisions. In this paper, we offer a new method for sensitivity analysis in multi-attribute decision making problems in which if the weights of one attribute changes, then we can determine changes in the results of the problem. These changes involve changes in the weight of other attributes and the change in the final rank of alternatives. This analysis was conducted for Technique for order-preference by similarity to ideal solution (TOPSIS) technique, one of the most frequently used multi-attribute decision making techniques, and the formulas were obtained. The paper continues with a numerical example and at last conclusions and suggestions for future researches are offered.

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification tasks. Nevertheless, there are still a lot of problems especially when dealing with numerical (continuous valued) attributes. Some of those problems can be solved using fuzzy decision trees (FDT). Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, a few researchers independently have proposed to utilize fuzzy representation in decision trees to deal with similar situations. Fuzzy representation bridges the gap between symbolic and non symbolic data by linking qualitative linguistic terms with quantitative data. In this paper, a new method of fuzzy decision trees is presented. This method proposed a new method for handling continuous valued attributes with user defined membership. The results of crisp and fuzzy decision trees are compared at the end.

In this article, a finite horizon, multi product and multi period economic order quantity like seasonal items is considered where demand rate is deterministic and known but variable in each period. The order quantities of items come in batch sizes and the end of the period order quantity and, consequently, demand of customers are zero. In addition, storage space is constrained and the problem was considered under all units discount (AUD) policy. The modeling technique used for this problem is mixed binary integer programming. The objective was to find the minimization optimal order quantities under time value of money over the finite horizon. The inventory control system costs include three costs: ordering cost, holding cost, and purchase cost. In order to solve the proposed model, a genetic algorithm (GA) is applied. Finally, we provide a number of examples in order to illustrate the algorithms further.

Task assignment problem (TAP) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. TAP is a combinatorial optimization problem and NP-complete. This paper proposes a hybrid meta-heuristic algorithm for solving TAP in a heterogeneous distributed computing system. To compare our algorithm with previous ones, an extensive computational study on some benchmark problems was conducted. The results obtained from the computational study indicate that the proposed algorithm is a viable and effective approach for the TAP.

Risk response planning is one of the main phases in the project risk management and has major impacts on the success of a large-scale project. Since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. The conventional approaches tend to be less effective in dealing with the impreciseness of risk response planning. This paper presents a new decision-making methodology in a fuzzy environment to evaluate and select the appropriate responses for project risks. To this end, two fuzzy well-known decision-making techniques, namely, decision tree and TOPSIS (technique for order preference by similarity to ideal solution), are extended based on multiple selected criteria, simplifying parameterized metric distance and fuzzy similarity measure. Finally, a case study in an oil and gas project in Iran is provided to show the suitability of the proposed fuzzy methodology in large-scale practical situations.

Facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. In this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with probabilistic customer's locations and fuzzy customer’s demands under the Hurwicz criterion is proposed. This model is formulated as α-cost minimization model according to different criteria. Since our problem is strictly Np-hard, a new hybrid intelligent algorithm is presented to solve the stochastic-fuzzy model. The proposed algorithm is based on a vibration damping optimization (VDO) algorithm which is combined with the simplex algorithm and fuzzy simulation (SFVDO). Finally, a numerical example is presented to illustrate the capability of the proposed solving methodologies.