Integrated quadratic assignment and continuous facility layout problem

In this paper, an integrated layout model has been considered to incorporate intra and inter-department layout. In the proposed model, the arrangement of facilities within the departments is obtained through the QAP and from the other side the continuous layout problem is implemented to find the position and orientation of rectangular shape departments on the planar area. First, a modified version of QAP with fewer binary variables is presented. Afterward the integrated model is formulated based on the developed QAP. In order to evaluate material handling cost precisely, the actual position of machines within the departments (instead of center of departments) is considered. Moreover, other design factors such as aisle distance, single or multi row intra-department layout and orientation of departments have been considered. The mathematical model is formulated as mixed-integer programming (MIP) to minimize total material handling cost. Also due to the complexity of integrated model a heuristic method has been developed to solve large scale problems in a reasonable computational time. Finally, several illustrative numerical examples are selected from the literature to test the model and evaluate the heuristic

Closed loop supply chain mathematical modeling considering lean agile resilient and green strategies

The supply chain management is planning, implementation and effective control of supply chain operations considered as a key factor for the competitiveness of the organizations. To make these targets, four management strategies of lean, agile, resilient and green have been separately proposed. Recently, studies have been performed with a consideration of these four strategies simultaneously named LARG (Lean, Agile, Resilient and Green). However, due to the novelty of this subject, the mathematical modeling of SCND (Supply Chain Network Design) has not been addressed in LARG strategy. SCND is one of the most essential parts of supply chain management that strategic decisions of it have heavily effects in both overall and partial applicability of the supply chain. The goal of this paper is to design a closed loop supply chain network considering LARG strategy using multi-objective modeling with uncertain demand. The objective functions are total profit, customer satisfaction and total pollution. The model is formulated to determine which facility sites should be selected (strategic decisions), and find out the optimal number of parts and products in the network (tactical decisions). Finally, a real industrial case study is provided to illustrate the performance and applicability of the LARG strategy in SCND in practice

Robust Inventory System Optimization Based on Simulation and Multiple Criteria Decision Making

Inventory management in retailers is difficult and complex decision making process which is related to the conflict criteria, also existence of cyclic changes and trend in demand is inevitable in many industries. In this paper, simulation modeling is considered as efficient tool for modeling of retailer multiproduct inventory system. For simulation model optimization, a novel multicriteria and robust surrogate model is designed based on multiple attribute decision making (MADM) method, design of experiments (DOE), and principal component analysis (PCA). This approach as a main contribution of this paper, provides a framework for robust multiple criteria decision making under uncertainty

Complete / Incomplete Hierarchical Hub Center Single Assignment Network Problem

In this paper we present the problem of designing a three level hub center network. In our network, the top level consists of a complete network where a direct link is between all central hubs.  The second and third levels consist of star networks that connect the hubs to central hubs and the demand nodes to hubs and thus to central hubs, respectively. We model this problem in an incomplete network environment. In this case, the top level is an incomplete network where the direct link between all central hubs is not necessary and may lead to lower transportation costs. We propose mixed integer programming model for these problems and conduct a computational study for these two developed models by using the CAB data

Developing an integrated blood supply chain network in crisis conditions considering the concentration of sites in facilities and blood types substitution

In the management of the blood supply chain network, the existence of a coherent and accurate program can help increase the efficiency and effectiveness of the network. This research presents an integrated mathematical model to minimize network costs and blood delivery time, especially in crisis conditions. The model incorporates various factors such as the concentration of blood collection, processing, and distribution sites in facilities, emergency transportation, pollution, route traffic (which can cause delivery delays), blood type substitution, and supporter facilities to ensure timely and sufficient blood supply. Additionally, the model considers decisions related to the location of permanent and temporary facilities at three blood collection, processing, and distribution sites, as well as addressing blood shortages. The proposed model was solved for several problems using the Augmented epsilon-constraint method. The results demonstrate that deploying advanced processing equipment in field hospitals, concentrating sites in facilities, and implementing blood type substitution significantly improve network efficiency. Therefore, managers and decision-makers can utilize these proposed approaches to optimize the blood supply chain network, resulting in minimized network costs and blood delivery time.IntroductionOne of the most important aspects of human life is health, which has a significant impact on other aspects of life. In this study, a two-objective mathematical programming model is proposed to integrate the blood supply chain network for both normal and crisis conditions at three levels: blood collection, processing and storage, and blood distribution. The proposed two-objective mathematical model simultaneously minimizes network costs and response time. The model is solved using the augmented epsilon-constraint method. To enhance the responsiveness to patient demand in healthcare facilities and address shortages, the model considers the concentration of levels (collection, processing and storage, and distribution of blood to patients) in facilities, blood type substitution, and supporter facilities. In blood type substitution, not every blood type can be used for every patient. Among several compatible blood groups, there is a prioritization for blood type substitution, allowing for an optimal allocation of blood groups based on the specific needs.Materials and MethodsIn this research, a two-objective mathematical programming model is proposed to design an integrated blood supply chain network at three levels: collection, processing, and distribution of blood in crisis conditions. The proposed model determines decisions related to the number and location of all permanent and temporary facilities at the three levels of blood collection, processing, and distribution, the quantity of blood collection, processing, and distribution, inventory levels and allocation, amount of blood substitution, and transportation method considering traffic conditions. Achieving an optimal solution for the developed two-objective model, which minimizes both objective functions simultaneously while considering the trade-off between the objective functions, is not feasible. Therefore, multi-objective solution methods can be used to solve problems considering the trade-off between objectives. In this research, the augmented epsilon-constraint method is employed to solve the proposed two-objective mathematical model. In this method, all objective functions, except one, are transformed into constraints and assigned weights. By defining an upper bound for the transformed objective functions, they are transformed into constraints and solved.Discussion and ResultsAlthough the two-objective mathematical model is transformed into a single-objective model using the augmented epsilon-constraint method, this approach can still yield Pareto optimal points. Therefore, managers and decision-makers can create a balanced blood supply chain network considering the importance of costs and blood delivery time. Sensitivity analysis was conducted to examine the effect of changes in the weights of the objective functions and the blood referral rate (RD parameter) on the values of the objective functions for three numerical examples. With changes in the weights of the objective functions relative to each other, the trend of changes in the values of the first and second objective functions for all three solved problems is similar. Specifically, when reducing the weight of the first objective function from 0.9 to 0.1, the values of the first objective function increase, while the values of the second objective function decrease when the weight of the second objective function increases from 0.1 to 0.9. The total amount of processed blood in field hospitals and main blood centers was compared for equal weights and time periods for the three problems. Additionally, the amount of processed blood in field hospitals is significantly higher than in main blood centers. This indicates that eliminating the cost and time of blood transfer in field hospitals (due to the concentration of blood collection, processing, and distribution levels) results in an increased amount of processed blood compared to main blood centers (single-level facilities), ultimately leading to a reduction in network costs.ConclusionThis study presents a two-objective mathematical model for the blood supply chain network, integrating pre- and post-crisis conditions. Decisions are proposed for the deployment of four types of facilities, including temporary blood collection centers, field hospitals, main blood centers, and treatment centers, at three levels of blood collection, processing, and distribution. Additionally, inventory, allocation, blood group substitution, blood shortage, transportation mode, and route traffic (delivery delays) are considered for four 24-hour periods in the model. For the first time in this field, knowledge of concentration levels in facilities is utilized, with simultaneous existence of the three levels of blood collection, processing, and distribution in field hospitals. This problem is formulated in a mixed-integer linear programming model with two objective functions aiming to minimize system costs and blood delivery time. The proposed model is solved using the augmented epsilon-constraint evolution method. Sensitivity analysis is conducted for the weights of the objective functions, and additional experiments (RD parameter) are performed. The sensitivity analysis on the weights of the objective functions reveals that reducing the weight of the first objective function leads to a decrease in blood delivery time, while increasing the weight of the second objective function results in an increase in network costs. The investigation of the impact of reducing the amount of additional testing (RD parameter) on the values of the objective functions confirms that advanced equipment at the processing sites of field hospitals reduces network costs and blood delivery time

A New Fuzzy TOPSIS-TODIM Hybrid Method for Green Supplier Selection Using Fuzzy Time Function

Today green supply chain is considered all around the world and supplier selection has been changed regarding these green and carbon emission criteria, so green supplier selection has been a major problem in this area. In this study we use fuzzy time function to assist managers in green supplier selection under uncertainty and ambiguity. This function will consider derivation from the goal during the time and by using it, and we will be able to have the best supplier in every period after having some modification in legal limitations for green supplier selection criteria. We use a fuzzy TOPSIS to have better initial weighting in TODIM, a discrete multicriteria method based on prospect theory in uncertainty (known as TODIM in Portuguese) decision making method. The results indicated that our proposed approach can easily and effectively accommodate criteria with gains and loss functions during time and also by using this method we will have a more reasonable predict of our suppliers ranking in future and that will help us in future investment in these suppliers. Finally it has been shown in car industries in Iran

Dynamic pricing in a two-echelon stochastic supply chain for perishable products

Supply chain management of perishable products has to use some mechanisms to control the product waste amount. Dynamic pricing and cooperation of the chain members are some mechanisms which mitigate the waste amount. This paper studies the dynamic pricing problem of a perishable product supply chain with one manufacturer, one retailer, and two periods: production and selling periods. The problem considers price markdown policy to manage the total quality-dependent stochastic demand: dividing the selling period into two different terms and offering two selling prices. This paper analyzes the problem heuristically via Stackelberg and cooperation games. Obtained results demonstrate that the cooperation scenario allocates the maximum profits to the chain members and customers due to the least selling prices. Also, in the Stackelberg cases, both members gain higher profits under the manufacturer-led Stackelberg game; however, the retailer-led Stackelberg game represents lower selling prices and the greatest price markdowns which is profitable to customers