s. h. r. pasandideh

The economic production quantity (EPQ) model considers the production rate, demand rate, setup costs, holding costs, and shortage costs to find the production quantity that minimizes the sum of these costs. The goal is to balance the costs associated with production, holding inventory, and potential shortages. In this paper, two objectives include the costs of production and ordering and others in a separate objective function. In the objectives of the other costs, The cost of storage space as a supply is defined to be minimized. This study considers scrap and reworks in the EPQ model. This inventory model accounts for many items on a single machine. The production capacity is reduced, and there are shortages when only one machine exists. By determining the quantities of the products produced by the manufacturing facility, the storage space for each product, cycle time, and product scarcity, we can reduce both the overall cost and the supply cost of warehouse space due to non-linearity and the inability to solve commercial software in large dimensions, a multi-objective meta-heuristic algorithm, namely the non-dominated sorting genetic algorithm (NSGA-II), is used. The findings are further validated using the non-dominated ranking genetic algorithm (NRGA). Also, the obtained Pareto front is studied with several indicators. To perform these two algorithms at the best condition, we employed the Taguchi approach and related orthogonal arrays and performed algorithms for each array considering several factors. Also, to validate the mathematical model, we used the augmented epsilon-constraint method executed in the GAMS environment. It is clear that GAMS commercial software yields better results; however, these two algorithms are justifiable when the problem becomes bigger. Finally, by performing a sensitivity analysis for these indicators and the objective functions, the behavior of the proposed algorithms is compared and examined in detail. Also, the superior algorithm is chosen using the TOPSIS as a multi-criteria decision-making method. Numerical examples show how the presented model and the proposed algorithms may be used efficiently. A surveying literature review clarifies that the related objective functions, constraints, and solution approaches have not been investigated until now.

The rapid growth of the population has resulted in an increasing demand for healthcare services, which forces managers to use costly resources such as operating rooms effectively. The surgery-scheduling problem is a general title for problems that consists of the patient selection and sequencing of the surgeries at the operational level, setting their start times, and assigning the resources. Hospital managers usually encounter elective surgeries that can be delayed slightly and emergency surgeries whose arrivals are unexpected, and most of them need quick access to operating rooms. Reserving operating room capacity for handling incoming emergency surgeries is expensive. Moreover, emergency surgeries cannot afford long waiting times. This paper deals with the problem of surgery scheduling in the presence of emergency surgeries with a focus on balancing the efficient use of operating room capacity and responsiveness to emergency surgeries. We proposed a new algorithm for surgery scheduling with a specific operating room capacity planning and analyzed it through a simulation method based on real data. This algorithm respects working hours and availability of staff and other resources in a surgical suite.

Changing the structure of supply chains to move towards less polluting industries and better performance has attracted many researchers in recent studies. Design of such networks is a process associated with uncertainties and control of the uncertainties during decision-making is of particular importance. In this paper, a two-stage stochastic programming model was presented for the design of a green closed-loop supply chain network. In order to reach the environmental goals, an upper bound of emission capability that helps governments and industries to control greenhouse gas emissions was considered. During the reverse logistics of this supply chain, waste materials are returned to the forward flow by the disassembly centers. To control the uncertainty of strategic decisions, demand and the upper bound of emission capacity with three possible scenarios is considered. To solve the model, a new accelerated Benders decomposition algorithm along with Pareto-Optimal-Cut was used. The efficiency of the proposed algorithm was compared with the regular Benders algorithm. The effect of different numerical values of parameters and probabilities of scenarios on the total cost was also examined.

In this paper, two-echelon newsvendor problem is considered. Many real-life situations like fashion, food industries, and healthcare services match newsvendor problem. Our problem is determining levels of inventory in order to optimize the profit and service level in selling a product. This product is made up of several raw materials. Only the distribution of demand is known, and the hot season of selling the product is just a short period and after that, the price of the product drops dramatically. The storage space and initial budget are limited. We modeled and solved the problem as an unconstrained nonlinear optimization problem using two nonlinear techniques, the sequential unconstrained minimization technique (SUMT) and steepest descent (SD).

Economic Production Quantity Model (EPQ) is one of the classic models of inventory control and it has a very important and practical role in manufacturing industry. As the most important things to reduce these internal risks, an efficient inventory control management and production planning can be mentioned, then as results of them, the best service level, cost reduction and optimal use of available resources can be achieved. For this purpose, an EPQ model is developed according to real-world conditions in this research. Shortage in this model is considered a mixture of backordered demand and lost sales and also, the products are divided into two categories of perfectly hale products and non-repairable defective products that fall into the category of scrap. In the other words, the important indicators studied in proposed model are the partial shortage and scrap. Costs related to the backorder demand are taken as fixed and time-dependent. In the proposed model, Inventory cycle length, the length of positive inventory cycle and backordered demand rate are determined during the shortage period to minimizing the total cost of inventory, So that all the stochastic and deterministic constraints of the model including holding costs, lost sales, backorder, budget, screening of products, disposal of scraps, total number of productions and average shortage times should be satisfied. Hence, in proposed model, due to the uncertainty of the real world situation and uncertainty in the availability of resources, a stochastic approach has been used. The presented multi-product model is in form of a single-objective nonlinear programming problem. Then, to solve the proposed model two methods including sequential quadratic programming and interior point algorithm are used. Furthermore, twenty numerical examples are solved by these two methods and SAS software, and the performance of the solution methods are compared using the Tukey's hypothesis test in terms of objective functions, the number of iterations need to achieve the optimal answer and infeasibility. Finally, in this paper, choosing the best method is done by applying the TOPSIS test.

Supply chain management and integration of its components are a key issue for sustainable economy. One of the most important in optimization supply chain modeling is production- distribution planning problem. Several authors have developed models for the production-distribution problem when only a percentage of solution procedure is in exact area. Most of these models were solved with the meta-heuristic method. In this paper, we are extended a production-distribution nonlinear programming problem in a two-echelon supply chain network, including manufacturers and distributors, and are solved with a mixed of exact solution and a meta-heuristic algorithm. The aim of this research is to determine the value of products delivered and the carrying amount of each vehicle such that the profit average, including sales price, production costs and transportation costs, is maximized. The model is for multiple distributors and all manufacturers in which all manufacturers are produced a type of product and are sent it to distributors. The mathematical model of the production-distribution problem is derived for which the objective function is proved to be convex, and the constraints being in linear forms are convex too. So, the proposed model is a convex nonlinear programming problem and its local maximum is the global maximum. Then, the proposed nonlinear programming problem is solved by two methods of a genetic algorithm and, Sequential Unconstrained Minimization Technique (SUMT) approach along with steepest descent method. The SUMT is the usual way in which constrained problems are converted to an unconstrained form and solved that way. It makes use of barrier methods as well to find a suitable initial point that over satisfies the inequality constraints. In this study, the genetic algorithm is used to validate the SUMT nonlinear programming approach. The numerical example is provided to illustrate the solution methods. Finally, future research and conclusion recommendations come in the last section of paper.