سال:1390 | دوره: | شماره: |تعداد مقالات:8

The task of balancing of assembly lines is well-known in mass production system but this problem is NP-hard even for the simple straight line. Therefore, utilizing heuristic methods for these problems is totally unquestionable. Furthermore, in line with balancing problems, heuristic methods are the foundation of the metaheuristic methods, thus it seems to be necessary to use more efficient heuristic methods. This paper presents a new heuristic method based on the famous critical path method (CPM) for the simple assembly line balancing problem (SALBP) that the only precedence constraints between tasks and cycle time have been considered. In this research regarding the objectives of minimizing the number of workstation, maximizing smoothness index and maximizing line performance, the efficiency of the suggested method and 11 popular methods are compared at SALBP-1. For comparative evaluation, 11 networks are collected from open literature, and are used with different cycle times. On the whole, 40 problems have been solved and it is found that the heuristic method based on critical path is more efficient than the others.

In this paper, the portfolio selection problem is considered, where fuzziness and randomness appear simultaneously in optimization process. Since return and dividend play an important role in such problems, a new model is developed in a mixed environment by incorporating fuzzy random variable as multi-objective nonlinear model. Then a novel interactive approach is proposed to determine the preferred solution. Finally a numerical example is presented to illustrate the proposed model.

In industries, Preventive Maintenance (PM) is not a new practice to minimise the sudden breakdown of production machine. PM will be performed at predetermine intervals to provide a balance between failure cost and component utilisation (aging). However, most of PM interval is based on experience or original equipment manufacturer (OEM) recommendations. Consequently, the benefits from PM are not fully obtained because the current machine state is not considered. The aim of this paper is to propose a maintenance management decision model for PM application. The main objective of the model is to revise the PM interval based on current machine state. The proposed model provides step-by-step procedure from data identification, classification, evaluation until determination of the revised PM interval.

In this paper, a genetic algorithm is presented for an identical parallel-machine scheduling problem with family setup time that minimizes the total weighted flow time (P/STsi,b/Swjfj). No set-up is necessary between jobs belonging to the same family. A set-up must be scheduled when switching from the processing of family i jobs to those of another family j, i ¹ j, the duration of this set-up being the sequence-independent set-up time sj for family j. This problem is shown to be NP-hard in the strong sense and obtaining an optimal solution for the large-sized problems in reasonable computational time is extremely difficult. Further, it is computationally evaluated the performance of the proposed genetic algorithm solutions obtained using a mixed integer programming (MIP) with the Lingo 8.0 software.

In the explosive growth of Business to Business (B2B) Electronic Trades, electronic markets have received a great deal of attention recently. The obtained profit of trading in E-B2B market encourage market participants to remain in the market. Market participants consist of: sellers, buyers, and market owner. In this paper the expected profit function for each market participant has been defined in a neutral market based on double auction. Also, the model is simulated and results are shown. Linear programming in game matrix is used to exhibit the capability of the proposed model to support the decision making process. Then the model is exemplified.

Short-term prediction of traffic flow is central to alleviating congestion and controlling the negative impacts of environmental pollution resulting from vehicle emissions on both inter- and intra-urban highways. The strong need to monitor and control congestion time and costs for metropolis in developing countries has therefore motivated the current study. This paper establishes the application of neuro-fuzzy to predict traffic volume of vehicles on a busy traffic corridor. Using a case drawn from metropolitan Lagos, Nigeria, a traffic prediction system is designed such that the predicted values (output) can be accessed by the public through mobile phones. The best route to a particular route will also be advised by the system. In addition, the expected fuel consumption and travel time will be included in the output. Input data is pre-processed based on acquired real time traffic data, the network is trained and the fuzzifier module categorized the numerical output of the model. The advisory module of the traffic prediction model then computes the expected travel time and the fuel consumption cost. The results obtained established the non-linear nature of traffic flow along the routes and indicates that predicting the traffic situation is non-algorithmic. The travel time along the routes is averaged at 23.5 minutes, while the fuel cost is estimated at an average of $2.03. Thus, proper control of traffic time and cost could be obtained if monitoring is aided with neuro-fuzzy as a tool.

In this paper, the researchers have proposed a multi-dimensional knapsack model for project capital budgeting problem in uncertain situation which has been modeled through fuzzy sets. The optimistic and pessimistic situations were considered and associated deterministic models were yielded. Numerical example has been supplied toillustrate the performance of proposed model. The results were promising in the sense of helping the decision makers.

This article addresses a multi-stage flowshop scheduling problem with equal number of unrelated parallel machines. The objective is to minimize the makespan for a given set of jobs in the system. This problem class is NP-hard in the strong sense, so a hybrid heuristic method for sequencing and then allocating operations of jobs to machines is developed. A number of test problems are randomly generated and results obtained by proposed heuristic are compared with optimal solutions reported by the Lingo 8.0 package applying the branch & bound approach. The results show that the proposed hybrid method is more efficient when the problem sizes have been increased.