Saman Hassanzadeh Amin

An intuitionistic fuzzy goal programming approach for finding pareto-optimal solutions to multi-objective programming problems

We propose a new intuitionistic fuzzy approach for solving multi-objective problems.The approach is an interactive procedure.The approach considers the degrees of satisfaction and dissatisfaction of objectives.An illustrative example is presented to discuss the properties of the approach. Multi-objective optimization in the intuitionistic fuzzy environment is the process of finding a Pareto-optimal solution that simultaneously maximizes the degree of satisfaction and minimizes the degree of dissatisfaction of an intuitionistic fuzzy decision. In this paper, a new method for solving multi-objective programming problems is developed that unlike other methods in the literature, provides compromise solutions satisfying both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. This method combines the advantages of the intuitionistic fuzzy sets concept, goal programming, and interactive procedures, and supports the decision maker in the process of solving programming problems with crisp, fuzzy, or intuitionistic fuzzy objectives and constraints. A characteristic of the proposed method is that it provides a well-structured approach for determining satisfaction and the dissatisfaction degrees that efficiently uses the concepts of violation for both objective functions and constraints. Another feature of the proposed method comes from its continuous interaction with the decision maker. In this situation, through adjusting the problems parameters, the decision maker would have the ability of revisiting the membership and non-membership functions. Therefore, despite the lack of information at the beginning of the solving process, a compromise solution that satisfies the decision makers preferences can be obtained. A further feature of the proposed method is the introduction of a new two-step goal programming approach for determining the compromise solutions to multi-objective problems. This approach ensures that the compromise solution obtained during each iterative step satisfies both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. The application of the proposed model is also discussed in this paper.

Cooperative advertising in a supply chain with retail competition

This research assesses the effects of cooperative (coop) advertising in a channel with competing retailers considering both advertising and pricing as decision variables. We develop a game-theoretic model and provide equilibrium solutions for two games. In Game 1, the manufacturer and the retailers do not use cooperative advertising (status quo); and in Game 2, coop advertising is implemented. We also obtain optimal solutions for the case where the channel is coordinated. Contrary to the results provided for one-manufacturer, one-retailer channels, we find that coop advertising may not be profitable for the retailers or for the channel, especially when the market is characterised by low levels of price competition and high advertising competition between retailers. Although it benefits the manufacturer, the total effect of cooperative advertising on the channel profit might be negative under such conditions. The results also show that coop advertising stimulates retailers’ spending but may result in lower advertising expenditures than for a fully coordinated channel. Finally, when coop advertising benefits the entire channel, it does not fully achieve results from vertical integration.

Effects of uncertainty on a tire closed-loop supply chain network

In a closed-loop supply chain (CLSC) network, there are both forward and reverse supply chains. In this research, a tire remanufacturing CLSC network is designed and optimized based on tire recovery options. The objective of the optimization model is to maximize the total profit. The optimization model includes multiple products, suppliers, plants, retailers, demand markets, and drop-off depots. The application of the model is discussed based on a realistic network in Toronto, Canada using map. In addition, a new decision tree-based methodology is provided to calculate the net present value of the problem in multiple periods under different sources of uncertainty such as demand and returns. Furthermore, the discount cash flow is considered in the methodology as a novel innovative approach. This methodology can be applied in comparing the profitability of different design options for CLSCs.

A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry

Abstract With the emerging trend of green supply chain management, supplier selection and order allocation based on green criteria have become very important in this competitive world. During the selection process of the eligible suppliers, qualitative as well as quantitative factors should be considered. In this paper, a novel mathematical model is developed to select a set of suppliers, and assign the order quantity. Due to the importance of environmental concerns, both qualitative and quantitative environmental criteria are taken into account in this research. The proposed model comprises two phases namely a two-stage QFD, and a stochastic multi-objective mathematical model. The stochastic (scenario) approach helps to manage the uncertainty in the order allocation process. Furthermore, trapezoidal fuzzy numbers are utilized to handle the vagueness in human thoughts. The application of the proposed model is shown in beverages industry.

Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty

Abstract This paper develops a possibilistic optimization model for a multi-period and multi-objective sustainable blood supply chain with uncertain data due to an uncertain condition during a disaster and after it. The components considered in this study are donor groups, blood collection facilities, distribution centers, and hospitals as the demand points. The minimization of the total cost, environmental effects, in addition to the maximization of social effects are considered as the objectives to increase the efficiency of the network. Then ϵ -constraint method is utilized to transfer the multi-objective mathematical model to a mono objective one. In order to validate the proposed model, some test problems are investigated. For large-sized problems, a meta-heuristic algorithm, namely simulated annealing (SA) is provided for solving the model. Some numerical examples are solved and evaluated and the performance of the SA algorithm is compared with harmony search (HS) algorithm. Finally, the obtained results are discussed, and the conclusions are provided.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.