JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH IN PRODUCTION SYSTEMS (IERPS)
Year:2016 | Volume:3 | Issue:6|Papers Count:8

Flexible flow shop scheduling problem (FFS) with unrelated parallel machines contains sequencing in flow shop where, at any stage, there exists one or more unrelated parallel machines. The objective consists of minimizing the maximum completion time. A new Lagrangian relaxation (LR) method is developed to solve the candidate problem. To solve the sub-problems in LR, we use two approaches such as, simplicity of sub-problems and dominance rules. The results show that the both approaches can achieve the near-optimal solution in reasonable time; but there is no significant difference between them. On the other side, simplicity of sub-problems can achieve the solution in reasonable time.

Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran. The globalization trend causes the emergence of intense competition among manufacturers to gain more profits. In order to be competitive in today’s rapidly changing business world, organizations have shifted from a centralized single factory production to a decentralized multi-factory structure. We assume that production takes place in several factories, which may be geographically distributed in different locations, in order to comply with and to take advantage from the trend of globalization. This allows them to be closer to their customers, to employ professionals, to comply with local laws, to focus on a few product types, to produce and market their products more effectively, and respond to market changes more quickly. These can be attained by transporting the jobs from an overloaded factory to the factory which has fewer workloads. Obviously, considering these assumptions, as well as multi-objective scheduling are surely more practical than those scheduling problems which do not take them into account. In this research, after formulating the scheduling problem as a mixed integer linear programming for simultaneous minimization of the sum of the earliness and tardiness of jobs and the total completion time, a new exact method and a multi-objective metaheuristic algorithm are proposed. Finally, the heuristic algorithm and the output of particle swarm-based algorithm are reported.

The classical models of vehicle routing generally focused on minimizing total distance and travel time, however in green vehicle routing problem the main objective is minimizing total emissions and fuel consumption besides the other objects.In this paper, extension of GVRP with minimizing fuel and emission costs presented that considered pickup and delivery constraints with hard time windows. Travel time in this model is not constant and speed of vehicles would be determined in regards to customers’ time widows. In this paper, a heuristic based adaptive large neighborhood search proposed for solving the model. Construction algorithm in this method is heuristic based algorithm with proposed criterion according to pick up and delivery constrains and time windows with assumption of variable speed. Computational results confirms efficiency of this algorithm.

The vehicle routing problem (VRP) can be studied in variant cases, in which two related important problems are the VRP with hard time windows and the multi-depot VRP with heterogeneous vehicles. Most problems presented in this field are single-objective problems with the aim of the minimum cost; however, the complexity of real problems usually doubts the use of single objective problems. This paper considers not only the minimum travel cost, but also the distance travelled by the used vehicles and their loads. Since this problem is the NP-hard one, the non-dominated sorted genetic algorithm - II is used. To show its efficiency for solving small-sized problems, the obtained results are evaluated with the results obtained by the e-constraint method. The results show that the obtained gap of the objective function values is less than 4% in all the solved problems indicating the efficiency of the proposed algorithm.

Correct recognition and precise classification of significant patterns in statistical process control charts is unavoidable. Since these unnatural patterns associate out of control conditions. In fact, extraction of unnatural patterns increases the sensitivities of control charts in identification of out of control states. In recent years, because of the abilities of artificial neural networks in patterns recognition, these networks have been used to discriminate unnatural patterns in Shewart control charts. In most of such studies, the misclassification error of patterns is remarkable, especially when the desired sensitivity of process is at high value. This paper proposes a hybrid model for the recognition and analysis of the basic patterns in process control charts using LVQ and MLP networks along with examining the fitted line of sample points. In the presented model not only the misclassification error at different levels of sensitivities decreases considerably, but when basic patterns occur concurrently, recognition of patterns and assessment of their corresponding parameters will be possible also. The efficiency and effectiveness of the model have been tested by simulated samples.

Rescheduling is considered as a part of decision making process in supply chain of many manufacturing industries and it plays a significant role in fulfillment of consumers’ needs. Hereupon, this article addresses the issue of rescheduling in a three-stage supply chain with a focus on integration of the stages. The first stage includes suppliers, the second stage includes fleet of good transportation and the third stage includes manufacturers of final products. Therefore, the mixed integer model has been used for the mentioned problem with the aim of minimizing the total tardiness time of the orders. In the general case, a genetic algorithm has been provided for problem solving which has chromosomes with variable structures. Comparison between the results of the proposed algorithm with Random Search on a wide range of random problems and optimum solution on the small size problems shows the good performance of the proposed algorithm. Moreover, by relaxing of the some attributes of the problem, the proposed algorithm was compared with two existing heuristic algorithms in the literature. Results show the better performance of the suggested algorithm.

This paper develops a model based on game theory to analyze a two echelon supply chain consists of two manufacturers and two retailers, that are operating in a make-to-order environment. The considerable point in the defined supply chain is that the manufacturers by taking into account the discount to the retailer that exclusively sells their products want to seize the market. In this model, manufacturers’ brand value and customers loyalty to a brand has high importance. It should be noted that in the defined Stackelberg model retail prices would be determined after specifying the lead times. We show that the reduction in the proportion of goods received by a retailer from a manufacturer will increase its retail price, also the exclusive supplying of a good rising its wholesale price.