PRODUCTION AND OPERATIONS MANAGEMENT
Year:2017 | Volume:8 | Issue:1 (14) |Papers Count:10

This paper presents a multi-objective simulated annealing algorithm for mixed-model two-sided assembly line balancing with multi skilled operators. The objectives of the proposed model are minimizing the number of mated-stations, the number of total stations and total human cost for a given cycle time. Also, maximizing the weighted line efficiency and minimizing the weighted smoothness index are considered for the problem. An example is solved with the proposed approach in detail and the performance of this algorithm is tested on a set of test problems and changing neighborhood solution rules. The results show the proposed algorithm can be used as a good algorithm to solve the problem.

Multi-sided assembly line is a typical production line in factories, where tasks are performed parallel in different sides of the assembly line. This type of line is normally found in producing large products such as cars. It is very important for the production line to be balanced in order to improve the production productivity. This paper presents a new approach based on simulated annealing algorithm to vertical balancing of multi-sided assembly lines. Zoning constraints, cycle time, working time and precedence relationships are considered as hard constraints while positional constraints are considered as soft constraints. To show the applicability of the proposed approach, it is applied on a real sample assembly line. In order to find the most suitable values for the parameters of the algorithm, different scenarios have been run on the sample assembly line. Findings indicate that the proposed approach is highly capable to achieve the predetermined goals of the line balancing problem.

In this paper, an integrated approach of MADM and fault tree analysis (FTA) is provided for determining the most reliable combination of suppliers for a strategic product in IUT University. At first, risks of suppliers is estimated by defining the indices for evaluating them, determining their relative status indices and using satisfying and SAW methods. Then, intrinsic risks of utilized equipments in the products are qualified and the final integrated risk for equipments is determined. Finally, through all the different scenarios, the best composition of equipment suppliers is selected by defining the palpable top events and fault tree analysis. The contribution of this paper is about proposing an integrated method of MADM and FTA to determine the most reliable suppliers in order to minimize the final risk of providing a product.

This paper presents a bi-objective mathematical model for a four-echelon supply chain, including suppliers, producer, distributors and retailers. This model finds the flow among the different levels of the supply chain that minimizes the total cost of the supply chain and maximizes the service level under some restrictions in order to trade-off and justify the obtained solutions. Then, by using a numerical example, the presented model is solved by the STEM method and LINDO software. Finally, the flow among the different levels of the supply chain and the related objective function values are reported.

In competitive markets, attracting potential customers and keeping current customers is a survival condition for each company. So, paying attention to the requests of customers is important and vital. In this paper, the problem of order acceptance and scheduling has been studied, in which two types of customers or agents compete in a single machine environment. The objective is maximizing sum of the total profit of first agent's accepted orders and the total revenue of second agent. Therefore, only the first agent has penalty and its penalty function is lateness and the second agent's orders have a common due date and this agent does not accept any tardy order. To solve the problem, a mathematical programming, a heuristic algorithm and a pseudo-polynomial dynamic programming algorithm are proposed. Computational results confirm the ability of solving all problem instances up to 70 orders size optimally and also 93.12% of problem instances up to 150 orders size by dynamic programming.

Suppliers selection in supply chain as a multi-criteria decision making problem (contain both qualitative and quantitative criteria) is one of the main factors of a successful supply chain. Data Envelopment Analysis (DEA) is a method to determine the efficiency that has been used widely for this purpose. In the hybrid DEA model of Toloo and Nalchigar, it is claimed that this model can determines the most efficient (best) supplier in presence of imprecise data. In this paper, it will be shown that the model just can find an efficient supplier and could not find the most efficient supplier. This paper by explaining the other drawbacks of the paper, proposes a comprehensive model that removes the drawbacks and also contains the weight restrictions. In the other words, the proposed model can find the most efficient supplier by solving just one mixed integer linear programming in presence of both imprecise data and weight restrictions. For determining and ranking other efficient suppliers a new algorithm proposed. Application of proposed approach explained with considering imprecise data for 18 suppliers.

University course timetabling problem is a challenging and time-consuming task on the overall structure of timetable in every academic environment. The problem deals with many factors such as the number of lessons, classes, teachers, students and working time, and these are influenced by some hard and soft constraints. The aim of solving this problem is to assign courses and classes to teachers and students, so that the restrictions are held. In this paper, a constraint programming method is proposed to satisfy maximum constraints and expectation, in order to address university timetabling problem. For minimizing the penalty of soft constraints, a cost function is introduced and AHP method is used for calculating its coefficients. The proposed model is tested on department of management, University of Isfahan dataset using OPL on the IBM ILOG CPLEX Optimization Studio platform. A statistical analysis has been conducted and shows the performance of the proposed approach in satisfying all hard constraints and also the satisfying degree of the soft constraints is on maximum desirable level. The running time of the model is less than 20 minutes that is significantly better than the non-automated ones.

Nowadays, the focus of industries is changed from efficiency of workers to efficiency of knowledge workers. Thus, the performance appraisal process of the knowledge workers is important. A basic drawback of previous methods for performance evaluation is their biased results due to influence of rater's personal motivations. In this study, by developing a Result Base Method (RBM) approach, the criteria of performance appraisal of knowledge workers in outsourcing conditions is identified in Hedayat-e-Farhikhtegan-e-Javan Institution (HFJ). Different models of data envelopment analysis (DEA) are validated on 25 knowledge workers working at the research and development department at the institution. The DEA was able to classify knowledge workers into efficient and inefficient ones. Based on project career development plans, a set of efficient knowledge workers was used to establish an internal best practice benchmark for improving the performance of other inefficient knowledge workers. The findings show that 4 knowledge workers are efficient. Our findings indicate that the non-radial model of data envelopment analysis owns more separation power comparing with other radial models.

In this paper we propose an integrated algorithm based on combination of a discrete- event simulation and genetic algorithm. The simulation model is considered as a constraint-satisfaction procedure and if the streaming operations are initiated, then the meta-heuristic takes predefined steps to improve the solution. The latter is constructed through an interface, namely control matrix, implemented as interaction between the simulation model and refined solution of meta-heuristic. In run-time, the control matrix is accessed via simulation model for further modifications.The proposed method is implemented on classical job-shop problems with objective of makespan and results are compared with mixed integer programming model. Moreover, the appropriate dispatching priorities are achieved for dynamic job-shop problem minimizing a multi-objective criteria. The results show that simulation-based optimization are highly capable to capture the main characteristics of the shop and produce optimal/ near-optimal solutions with highly credibility degree.

One of the major issues that investors are facing with in capital markets is decision making about selecting an appropriate stock exchange for investment and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand, in portfolio selection problems if the assets' expected returns are normally distributed, variance and standard deviation are used as a risk measure. However, the expected returns on assets are not necessarily normal and sometimes have significant differences from normal distribution. This paper offers an optimal portfolio by introducing conditional value at risk (CVaR) as a measure of risk in a nonparametric framework considering a given expected return. This method is compared with the linear programming method.The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393.The results of this study show the superiority of the nonparametric method over the linear programming method while the nonparametric method is much faster than the linear programming method.