INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2012 | Volume:23 | Issue:3|Papers Count:9

A two-machine permutation flow shop scheduling with independent jobs and different due dates is considered in this paper. Since this problem is shown to be NP-Hard, We use the simulated annealing to solve this problem. The objective is minimizing the weighted earliness and tardiness that cover JIT concept. We construct our algorithm in four scenarios with considering two Markov chains and two temperature reduction rates. The best scenario is proposed based on the results of computational experiments. A mathematical programming formulation is proposed for the problem, and the solution obtained by proposed SA algorithms are compared with the optimal ones obtained by mathematical model using LINGO software for small size instances. Also the performance of the best scenario is compared by the standard model of genetic algorithm for different sizes problems and its advantages are shown.

The Traveling Repairman Problem is a customer-oriented routing problem in which a repairman is visiting a set of geographically distributed customers. The objective function is to minimize the total waiting times of all customers. The importance of this problem can be found in its applications in the following areas: blood distributing, manufacturing systems, and transportation and logistics. Apart from its importance, research on this problem is very limited. In this paper a new mixed-integer programming formulation is developed, and several properties of model are studied. Additionally, by developing lower and upper bounds, a branch and bound algorithm is developed to solve the problems with up to 30 nodes. According to the computational experiments, the developed model is very competitive.

In this research, robotic cell scheduling problem with three CNC machines and with an operation on each machine with the purpose of optimizing manufacturing cost and time in S1 cycle has been investigated. To obtain the optimal total cost of production has been used the optimal processing time on machine operation. In this research, robotic cell scheduling is studied in three different situations. In the first situation the optimal processing time of operation in each machine, are on the lower bound. In the second situation the optimal processing time of operation in each machine, are on the upper bound. In The third situation the Optimal processing time of operation in each machine, are between upper bound and lower bound. The next step in this research is proposing solving approaches for the problems mentioned above. The Lagrange method is used, To solve this problem. The most important result of this research is modeling and solving scheduling problem for three-machine robotic cell in three different situations and finding optimized answers for the cost and time. It should be mentioned that in the literature, robotic cell scheduling problem for optimization of manufacturing cost and time is only solved and modeled for two-machine situation.

A mathematical model and heuristic method for solving multi-depot and multi-product vehicle routing problem with heterogeneous vehicle have been proposed in this paper. Customers can order several products and depots must deliver customer’s orders before due date with different vehicle. Hence mathematical model of multi-depot vehicle routing problem has been developed to represent these conditions. Aim of this model is to minimize total delivery distance or time spent in servicing all customers. As this problem is very complex, we have offered a heuristic method that includes four steps. Grouping, routing and vehicle selection, scheduling and packing of products and improvement are the aforementioned steps. Efficiency of heuristic has been tested by a case study and several numerical examples. Comparing the results of heuristic and optimal solving has revealed that the deviation of heuristic results from optimal answer is lower than eight perce.

This paper presents a novel metaheuristic method for solving an extended Markowitz portfolio selection model. In the extended model, the objective function has been modified to include realistic objectives and four additional sets of constraints, i.e., bounds on holdings, cardinality, minimum transaction lots, and liquidity constraints have been also included. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio is equal to a predefined number. The liquidity constraints reflect the investors' tendency to invest on those stocks that are more quickly tradeable.The extended model is classified as a multi-objective mixed-integer programming model necessitating the use of efficient heuristics to find the solution. In this paper, we propose a heuristic based on pareto combined ant colony optimization and simulated annealing approaches. The performance of the proposed approach is compared to some other approaches using Tehran Stock Exchange data. The computational results show that the proposed approach effectively outperforms other approaches subject to the computationtime needed and the quality of the obtained solutions especially in large-scale problems.

Nowadays transportation problems are drawing more attention with respect to increasing demand for receiving products and services due to population increase and expansion of cities. One of the most important issues of transportation problems which are drawing the attention of the most researchers is vehicle routing problem. In this research, a new linear integer programming for multi-depot vehicle routing problem with multiple consecutive periods is formulated such that there is no obligation for vehicle to return to the fist depot and the first and the last depot for each route, in each period are specified based on the customers of other periods. The main goal of the presented model is servicing the set of customers during different periods through via different depots. Then a met-heuristic algorithm (genetic algorithm) is analyzed as a solution approach of the problem. One of the main results of this research is the reduction of routing costs due to flexibility in determining the last depot of each route. Some of the generated benchmark instances are used to show the performance and validity of the algorithm.

This paper presents a new mathematical programming model for an integrated production and air transportation in supply chain management with sequence-dependent setup times in order to design an applied procedure for the production and distribution schedule. The aim of this model is to minimize the total supply chain cost consisting of the costs of distribution, production earliness and tardiness, and delivery. Because of the complexity and NP-hardness of this problem, two meta-heuristics based on genetic algorithm (GA) and variable neighborhood search (VNS) are proposed. The parameters of these algorithms and their appropriate operators are set and determined by the use of the Taguchi experimental design. Then, the quality of the results obtained by these algorithms is compared. The computational results show that the developed VNS outperforms the proposed GA.

This paper presents a new mathematical programming model for an integrated production and air transportation in supply chain management with sequence-dependent setup times in order to design an applied procedure for the production and distribution schedule. The aim of this model is to minimize the total supply chain cost consisting of the costs of distribution, production earliness and tardiness, and delivery. Because of the complexity and NP-hardness of this problem, two meta-heuristics based on genetic algorithm (GA) and variable neighborhood search (VNS) are proposed. The parameters of these algorithms and their appropriate operators are set and determined by the use of the Taguchi experimental design. Then, the quality of the results obtained by these algorithms is compared. The computational results show that the developed VNS outperforms the proposed GA.

Development of an optimized strategy for the maintenance of Iranian railway track maintenance in a form of management system was made in this research. Through reviewing the current maintenance management system in the USA, Europe and Asia as well as investigating the characteristics of railway structure and current maintenance approaches in Iran, a new algorithm was made in order to improve efficiency of maintenance decision system and maintenance planning in Iranian railway industry. The practicability and reliability of the new maintenance algorithm (model) were evaluated by applying the new proposed algorithm in a railway line in Tehran province. It was shown that the new proposed maintenance management system (algorithm) is effective in providing an optimized maintenance strategies and plans. It was also shown that track operation can be evaluated by sensitivity analyses of the proposed model.