جستجوی مقالات مرتبط با کلیدواژه "برنامه ریزی خطی عدد صحیح مختلط" در نشریات گروه "مدیریت"

Home Healthcare provides a wide range of home care services from prevention to rehabilitation and primary care to professional nursing services. This research presents a two-objective mathematical model based on the mixed integer linear programming approach for the home healthcare routing and scheduling problem (HHCRSP) with the objectives of minimization the nurses’ travel costs and the maximal working time difference among nurses. Considering the multimodal transportation, several initial health centers and one final health center and patient and nurse time windows are important features of the studied mathematical model. Small-sized problems have solved by using the Ɛ-constraint method on GAMS software. Also, due to the NP-hardness of the problem, MOPSO and NSGA-II algorithms have used to solve the medium and large-sized problems. The statistical results showed that the NSGA-II performed better than the MOPSO for medium and large sizes of problems in both MID and NOS performance metrics. Overall, Results of the performance metrics on different sizes of problems indicate the efficient and effective performance of NSGA-II in solving the understudied problem.

In the Milkran logistics system, vehicles are sent to collect orders from suppliers and deliver them to ‎assembly lines, according to pre-planned routes. In this way, the vehicle goes to the location of ‎several suppliers to pick up orders and then is sent to one or more destinations for delivery. In this ‎logistics system, cargoes are aggregated within the vehicle and through various nodes in the logistics ‎network. This paper introduces a mixed integer linear programming model for the Milkran logistics ‎problem that takes into account considerations such as three-dimensional loading of the order ‎pallets into vehicles, 50% higher cost for returning empty pallets, order timewindows, and ‎heterogeneous fleets. Given the nature of the problem, an algorithm based on grouping evolutionary ‎strategy is introduced that uses heuristic methods to ensure vehicles’ loading and routing feasibility. ‎The effectiveness of the introduced mathematical model and meta-heuristic algorithm is measured ‎using data collected from Saipa Automotive Group. The computational results show that Milkran ‎Logistics has the ability to reduce costs by 24.5% (on average), compared to the direct shipping ‎strategy pursued by Saipa.‎

In this paper the integrated Master Surgical Scheduling program and Case Mix Planning is investigated. A new two-step approach is proposed for creating and updating this program. In the first step, a model is proposed for creating master surgical schedule considering the demand distributions of different surgery kind of each surgeon. In the second step, having the weekly waiting list of patients, a model is proposed for updating this program in order to cope with demand fluctuations and maximize the use of operating rooms capacity in weekly period. In this paper the limitation of down-stream resources is also considered. Three objectives are considered for this problem: minimizing over time cost and idle time cost of operating rooms, maximizing the surgeons’ preferences and minimizing the not fulfilled demand. The real data from Al-Zahra hospital of Isfahan, Iran is used to evaluate the models and analyze the results. The proposed approach is evaluated using these real data in several problem instances. The experiments show that the proposed approach leads to better results than real program of hospital with significant different which displays the efficiency of the proposed approach.

In recent years, global concerns about environmental and social issues have made consumers, government organizations, companies and universities more active, and their focus has increasingly been on the design of the supply chain network as the most important part of the supply chain. The main objective of this paper is to present a supply chain modeling model for Hamadan Glass Manufacturing Company considering the dimensions of sustainability.

In this paper, a Fuzzy Multi-objective Mixed Integral Programming is presented to design a closed loop supply chain under uncertainty conditions in order to minimize environmental impacts and maximize social impacts and economic benefits. In this model, both the constraints and the parameters of the problem are fuzzy, which is determined by the Jimenez method, and the algorithms of NSGA-II and MOPSO have been used to solve the model.

The proposed model was solved with two multi-objective genetic algorithms and multi-objective particle swarm optimization, and the necessary comparisons were made between the results. Finally, Pareto's solutions were determined. According to the results, the two algorithms differ in the time criterion that the NSGA-II is superior to MOPSO. Also, there are two different algorithms in the MID standard that MOPSO excels over NSGA-II and does not have any significant superiority over the remaining criteria.

Based on the results of the research, simultaneous consideration of economic, environmental and social dimensions and uncertainty in some parameters such as demand and returns lead to improved supply chain performance in terms of profitability and customer satisfaction.

One of the important issues in supply chain management is the evaluation and selection of efficient suppliers, which leads to the creation of competitive advantage and improving the performance of the organization. This becomes more important when several suppliers with the possibility of discount are also considered. The purpose of the present research is to evaluate and select of suppliers in multiple conditions (multi-product/ multi-supplier) whit quantity discounts in Isfahan’s Mobarakeh Steel Company. This research is applied and due to it's nature and method is descriptive-survey. To solving the supplier selection problem, a hybrid method including a Fuzzy Analytical Hierarchy Process (FAHP) and a mathematical model with Mixed Integer Linear Programming (MILP) method has been developed. This method operates in two stages. In the first stage, the relative weights of each criterion for each type of item are determined via FAHP technique. In the second stage, the outputs of the first stage are used as inputs in the MILP model to determine the suppliers and the quantities to be provided. The proposed model has been implemented in the purchase department of Isfahan’s Mobarakeh Steel Company and selection problem of best supplier from among the four companies, to purchase four products and regarding four criteria's investigated. The results indicate that applying the proposed model enables decision makers not only to improve their decisions by integrating quantitative and non-quantitative criteria, but to do it systematically and with a higher level of confidence.

The two-stage assembly flow shop with setup times is one of the new production scheduling problems. In this problem, the parts are produced in the first stage and then they are assembled in the second stage. The objective is to sequence parts in the first stage to minimize the makespan. First, a mathematical model in form of a mixed integer linear programming model is developed. Then, to solve the problem, two metaheuristics, imperialist competitive and genetic algorithms, are proposed. The performance of the model and the two algorithms are evaluated by numerical experiments. The result show that the imperialist algorithm works more effectively.