reza bashirzadeh

Environmental issues are unavoidable in supply chain management. Providing different level of green products is a major subject in this area for distinctive customer segments. In this research, a capacitated network design problem with different levels of green products for their specific demands is considered. This network has different levels with multiple products. The forward/reverse network consists of plants, hybrid warehouse/disposal centers, and customers. Two types of vehicles are considered to transport the products among the network. Each customer segment has specific needs based on their attitude to the greenness subject and their willingness to pay more money in order to attain a product with higher degree of greenness. A quadratic function is assumed for extra money to produce green products. To evaluate the reliability of the model, a real example proposed. Large size sample problems are solved using an efficient Lagrangian relaxation method. The results presents that the proposed method solved large size sample problems in a reasonable time.

Nurses’ scheduling problems have attracted a significant amount of healthcare research, indicating the importance of these issues. In this paper, it has tried to present a multi-objective model for the assignment of nurses and anesthesiologists to surgical teams, considering frequency and fairness in allocating time to staff members. Since idle time is inevitable, we seek to divide idle time equally among staff. In addition, the break time of each staff member have an almost regular frequency during the shift. Minimizing overtime costs and maximizing attention to the willingness of surgical staff members to work overtime are other objectives of the problem. Three metaheuristic algorithms NSGA-II, MOPSO, and SPEA-II used to solve the presented model. A hybrid multi-objective genetic algorithm based on variable neighborhood search is also presented. The comparison of the solutions of 4 algorithms shows that the proposed hybrid algorithm has a significant superiority compared to other algorithms in terms of the average value of the solution, the quality of the Pareto solution set, and execution time. The presented model is compared with the real data of the surgical department of elective patients of a government hospital in Qazvin province. The obtained results show that the presented model has significantly created equality in the amount of working time of nurses and anesthesiologists in the elective surgery department. It has also spread the idle time of each staff member during the work shift, which has caused different time breaks for each one.

Nowadays, given the competitive environment of business world, designing a supply chain (SC) that is compatible with the needs of the consumer market seems crucial. Due to its long-term impact on the company's performance, making decisions related to fulfilling the customer demand is an important issue in SC design and management. The present research tries to design a closed-loop supply chain network (SCN) with possible partial disruption in distribution centers during servicing. The objectives of this model are to minimize the total cost of SCN and maximize the system reliability, which is, in turn, dependent on the strategy chosen to cope with the partial disruption. Thus, in case of a partial disruption, some centers should be selected to compensate for disruption that, in addition to reducing costs, will be able to increase the system reliability. A weighted goal programming approach is used for solving the proposed multi-objective model, and a non-dominated sorting genetic meta-heuristic algorithm along with the exact method are developed in order to solve the problem. The results indicated that the proposed algorithm has appropriate performance in achieving near-exact solutions in large scales problems.

This paper introduces a Travel Demand Management (TDM) model in order to decrease the transportation externalities by affecting on passengers’travel choices. Thus, a bi-objective bi-modal optimization model for road pricing is developed aiming to enhance environmental and social sustainability by considering to minimize the air pollution and maximize the social welfare as its objectives. This model determines optimal prices (bus fare and car toll) and optimal bus frequency simultaneously in an integrated model. The model is based on discrete choice theory and consideres the modes’ utility functions in its formulation. The proposed model is solved by two meta-heuristic methods (Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objectives Harmony Search (MOHS)) and the numerical results of a case study in Tehran are presented. The main managerial insights resulted from this case study is that its results support the idea of “free public transportation” or subsidizing the public transport as an effective way to decrease the transport related air pollution

In this paper, a tri-level defense facility location model for full coverage in r-interdiction median problem is delivered. The purpose of this model is to design a proper service system in a way that after a worst case scenario of disturbance, they can utilize their full capacity of providing services. Hence, we have considered the defense facilities to provide extra protection for service facilities, and the goal is to optimally locate these facilities. The tri-level model is proposed based on leader-follower games as defender-attacker-defender framework. After the disturbance caused by the attacker, with the purpose of ensuring the operation of service facilities, the defender tries to establish a number of defense facilities in potential locations. Locating these facilities is carried with respect to the establishment of fixed cost of facilities and system’s current cost. It should be noted that each service facility must be at least within the coverage range of at least one defense facility (first level).So, system’s current costs can be defined based on the worst-case scenario of disturbance caused by the attacker. The problem is modeled as a static Stackelberg game between the attacker (level 2) and defender (level 3). In order to solve the model, two approaches have been used. In the first approach, explicit enumeration method is used for the first and second levels and an exact approach is used for the third level. In the second approach, hybrid methods consisting of genetic algorithm, explicit exact enumeration and exact approach have been used to solve the problem in a reasonable time. Comparing the proposed meta-heuristic to the exact approach in some samples, the numerical results show a quite satisfactory of this algorithm.

In this paper, we present a simulated annealing (SA) and a genetic algorithm (GA) based on heuristics for scheduling problem of jobs in virtual cellular manufacturing systems. A virtual manufacturing cell (VMC) is a group of resources that is dedicated to the manufacturing of a part family. Although this grouping is not reflected in the physical structure of the manufacturing system, but machines are spread on the shop floor physically. In this paper, there are multiple jobs with different manufacturing processing routes. First, we develop the mathematical model for the problem, and then we present the suggested algorithms. The scheduling objective is weighed tardiness and total travelling distance minimization. The problem is divided into two branches: small scale and large scale. For small scale, the results of GA and SA are compared to GAMS. For large scale problems, due to the time limitation of 3600 seconds, the results of GA and SA are compared to each other. Computational results show that both SA ad GA algorithms perform properly but SA is likely to turn out well in finding better solutions in shorter times especially in large scale problems.

Different manufacturing enterprises use regularly scheduling algorithms in order to help meeting demands over time and reducing operational costs. Nowadays, for a better useofresources and manufacturingin accordance withcustomer needs and given the level ofcompetitionbetweencompanies, employing asuitablescheduling programhasa double importance. Conventional productionmethods are constantly substituted with new ones for improving the efficiency and effectiveness of the entire production system. In this paper, two Meta-heuristic algorithms, Genetic and simulated annealing, have been used in order to solve the group scheduling problem of jobs in a single stage No-wait flow shop environment in which setup times are sequence dependent,. The purpose of solving the proposed problem is to minimize the maximum time needed to complete the jobs (Makespan). The results show that Genetic algorithm is efficient in problems with small and large dimensions, with respect to time parameter of problem solving.