PRODUCTION AND OPERATIONS MANAGEMENT
Year:2018 | Volume:8 | Issue:2 (15) |Papers Count:8

Presently, due to bio-ecological concerns, reverse logistics, recycling, reproducing and reusing the product returns is gaining interest in business and research worldwide. One of the important issues in reverse logistics is determination the price of the final product as well as the cost of recollecting used (recycled) products. This study proposes a profit maximization-modeling framework for an integrated, forward and reverse logistics network design problems.A mixed integer non-linear programming (MINLP) formulation is presented for designing an integrated direct, reverse multi-level, single period and multi product logistic network. Product demands and returns are considered stochastic with a continuous function of price. Based on the stochastic demand and product returns, a two-stage stochastic programming is developed by using scenario-based stochastic approach. Moreover, the price of the returned products is considered as the decision variable dependent on its quality level.In order to solve the model, GAMS software is used. The proposed model is justified by a numerical example and implemented for real case YAZD TIRE Company.

In this paper, modeling and solving an open selective vehicle routing problem with pricing are introduced. We will discuss optimal pricing when using a homogeneous fleet of vehicles. Furthermore, in some real world applications, companies prefer to distribute their products using rented vehicles so returning to the depot is not required. Therefore we face an open routing problem. Despite the applicability of such problem, we did not find any published research that examines it. also an Improved Imperialist Competitive Algorithm (IICA) is proposed to solve proposed model. For validating this method, some small scale problems are solved and results are compared to the results of an exact method and Simulated Annealing (SA) algorithm. The comparison of results shows that the proposed method is suitable for solving the model. For investigating its efficiency in dealing with real world problems, some large scale problems are solved and the results are compared to the results of Simulated Annealing (SA) algorithm. Results show that IICA is more efficient than SA.

Hub location problem is one of the new issues in location problems. This kind of location problem is widely used in transportation systems. In this paper, we investigate p-hub center location allocation problem under capacity constraint. The aim of the proposed model is to determine the location of hub nodes and the allocation of non-hub nodes to the hub in such a way that the maximum traveling time is minimized. In addition, since the problem is an NP hard problem, two metaheuristic algorithms including simulated annealing algorithm and ant colony are developed for solving large size real world problems. The performances of the proposed algorithms are examined via some numerical examples taken from known related benchmark sets (AP dataset). The best solutions found using metaheuristic algorithms are also compare to the results achieved using Lingo software. The results demonstrate that the proposed algorithms are able to find optimum or near optimum solutions in acceptable run times.

In this paper, integrated production and batch delivery scheduling problem for make to order production system and one customer in supply chain has been addressed. One manufacture received n orders from one customer. Orders must be processed by single machine and sent in batches to customer. Sending several jobs as a batch leads to less transportation cost but may increase the cost of tardiness jobs. The objective is determining the production and delivery scheduling so that the related costs is minimized. The problem is strongly NP-hard. In this paper, one new math programming model including Mixed Integer Programming (MIP) model, Ant Colony System (ACS) and Elastic Ant System (EAS) are presented for solving it. In order to evaluate the efficiency of these two methods computational tests based on full factorial experimental design has been conducted. Computational test is performed for evaluation of these methods. The obtained results show that the heuristic algorithm is efficient which has been verified by using. Analysis of variance (ANOVA) technique. The results showed that the ACS is the most efficient method.

In the single-mode resource-constrained project scheduling problem, it is assumed that each activity has a specified known execution time and resource consumption can be done only in one way. In practice, there are many cases in which the make span can be reduced by providing additional resources activities. In this case, each activity can be done in one of the procedures, which is called the multi-mode resource-constrained project scheduling. In this paper, the problem includes determination of the basic schedule for the project activities which may be done in several models and the precedence relations are met, While the project make-span, cost and resource fluctuations are minimal. In this research project scheduling problem network will model using ED simulation software and the results of the simulation and a meta-heuristic algorithm has been compared. Finally, management strategies to optimize the scheduling, i.e such minimize total time, cost and resource leveling, will be offered.

Maximal covering location problem is one of the most important network location problems that is used in various applications. To improve the application of the maximal covering location problem (MCLP), several capacitated MCLP models were proposed. However, most of these models assume only one fixed capacity level for facilities. This assumption may limit the application of the capacitated MCLP. In this paper, a dynamic modular capacitated maximal covering location problem (DMCMCLP) is proposed and formulated that the limited capacities of facilities is designed into two levels of determining allocated modules and their sizes for facilities at each potential site. To evaluate the performance of DMCMCLP, a numerical example has been solved and DMCMCLP is compared with dynamic maximal covering location problem (DMCLP). The results confirm superiority of the proposed model comparing to the DMCLP model. Moreover the sensitivity analysis confirm the performance accuracy of the proposed model.

In this paper, a bi-objective integrated of production distribution planning problem model in a multi echelon supply chain is presented. This model is considered in a four echelon supply chain includes suppliers, manufactures, distribution centers and customers with several types of raw material and product in multi-time period. In addition to minimize the total cost of supply chain including transportation and supply of materials costs, preparation and production of products, inventory holding of materials and products in manufacture and distribution centers, transportation and purchase of products for distributors and customers, and backorder cost, the second objective is to decrease the transfer time of products in order to increasing the service level for customers. Two Pareto-based multi-objective meta-heuristic algorithms namely non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed to solve the model. Since the solution quality of all meta-heuristic algorithms severely depended on their parameters, Taghuchi method has been utilized to tune the parameters of algorithms. Finally, computational results obtained by implementing the algorithms on several problems of different sizes demonstrate the performance of proposed methodologies.

Coordination and integration between components and subsystems in complex products such as computers, special simulators, aircraft has especial importance. Therefore, it is necessary to pay special attention to the selection of the suppliers for components and subsystems of these products. In other words, in the selection of suppliers for critical and complex components and subsystems, other criteria such as levels of compliance and coordination between suppliers should be considered. The main reason for the inclusion of these criteria in the supplier selection problem is that in the occurrence of supply disruption in sensitive and complex components and subsystems, it’s not easy to switch to other similar suppliers. In this article the amount of compatibility and co-evolutionary between suppliers and their ability to establish interaction with each other as a criteria in supplier selection is modeled using mathematical programming and past performance of suppliers in disruption situation to preserve the most compatibility.