amir farshbaf

Nowadays, coordination between members in a supply chain has become very important and beneficial to channel members. Through cooperative advertising, manufacturers and retailers can jointly participate in promotional programs. This action not only reduces the cost of advertising, but also is important to create a link with local retailers in order to increase immediate sales at the retail level. In this article, the problem of cooperative advertising and pricing decisions in a multi-product manufacturer-retailer (oligopoly market) supply chain is investigated. Stackelberg game with leadership of the manufacturer is proposed to model the problem. In order to find optimal prices and advertising expenditure, the bi-level programming approach is implemented. Solutions for the first level are determined by a genetic algorithm and best responses of retailers to the generated solutions of the manufacturer are calculated by CPLEX. Finally, numerical experiments and sensitivity analysis are conducted in order to assess the efficiency of models and solution procedures. Results show that competition will lead to a lower retail price, which is preferable from the consumers’ point of view. Also, profit of the manufacturer and retailers will decrease if competition effect increases.

With the growth in customers’ demand diversification, mixed-model U-lines (MMUL) have acquired increasing importance in the area of assembly systems. There are generally two different approaches in the literature for balancing such systems. Some researchers believe that since the types of models can be very diverse, a balancing approach without simultaneously sequencing of models will not yield an optimum configuration. On the other hand, another group of researchers point to the high cost of balancing systems and prefer to do it only one time regardless of the models’ sequences. In this paper, we aim to compare these two approaches by introducing an economic indicator. To do so, two models as representatives of the two different viewpoints are taken from the literature. To check the validity of this methodology, it is implemented by Lingo 11.0, for small scale, and GA, for a large scale. The obtained results indicate that, from the proposed economic indicator point of view, mixed-model U-lines balancing and sequencing (MMUL/BS) is preferred to its counterpart, mixed-model U-lines balancing (MMULB). This paper offers economic guidelines for managers to choose between only balancing and implementing it by sequencing at the same time.

Cell formation problem mainly addresses how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand are changed in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customers’ requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage are considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problems. Sensitivity analysis is used to show the validity of the proposed model. Besides, numerical examples are used both, for small-sized and large-sized instances to show the efficiency of the method in finding near optimal solution.

In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness and maximizing reliability of suppliers are considered as objective functions in the proposed mathematic model. In order to have a more reliable model in case of disruption, the robustness measure is used in the model. Moreover, because of the lack of information, the economic factors such as tax rate, interest rate, and inflation are considered as uncertain factors in the model. An interactive possibilistic programming approach is applied for solving the multi-objective model. To solve larger size instances, genetic algorithm is proposed. Finally numerical examples are presented to show how the model works in practice.

One of the main problems of supply chain network design is uncertainty. To consider this, designing of a three-echelon supply chain in a fuzzy environment is discussed in this paper. Since satisfaction of some constraints in supply chain is vital and necessary, so this research proposes a direct solution approach to find the solution which represents the trade-off between feasibility degree of constraints and satisfaction degree of the goal. Furthermore, another novation of this paper is optimizing a supply chain network design problem containing both of the parameters and decision variables as fuzzy number. Each fuzzy mathematical programming model with fuzzy decision variables can be solved effectively by employing direct solution approach. A numerical example is discussed and analyzed in order to show efficiency of the proposed approach.