فهرست مطالب heibatolah sadeghi

In this paper, the economic production is considered by considering defective products during production, a percentage of which can be recycled at a fixed cost, also machine failure, and variable demand. It is assumed that the products are produced at a fixed rate, but the machine may break down during production. Machine failure during production is a random variable that follows an exponential distribution with a specified parameter. If the machine breaks down during production, production stops immediately and the machine is repaired, and this study assumes that the machine repair time is a fixed value. Also, unlike the classical models, the demand for the manufactured product is expressed as a non-incremental function of time. The main goal is to determine the optimal policies for reproducing the problem in such a way that the total annual cost is minimized. For this purpose, first, a mathematical model is presented, then, the average cost per unit of time is determined, and based on the concepts of global optimization, the optimal values are determined. Finally, by solving a numerical example, the problem is analyzed. The results show that, the repair time has a great impact on the cost of the entire system, and as it increases, the system cost increases exponentially.

In this study, we model a stochastic scheduling problem for a robotic cell with two unreliable machines susceptible to breakdowns and subject to the probability of machine failure and machine repair. A single gripper robot facilitates the loading/unloading of parts and cell-internal movement. Since it is more complicated than the other cycles, the focus has been on the S_2 cycle as the most frequently employed robot movement cycle. Therefore, a multi-objective mathematical formulation is proposed to minimize cycle time and operational costs. The -constraint method is used to solve small-sized problems. Non-dominated sorting genetic algorithm II (NSGA-II), is used to solve large-sized instances based on a set of randomly generated test problems. The results of several Test problems were compared with those of the GAMS software to evaluate the algorithm's performance. The computational results indicate that the proposed algorithm performs well. Compared to GAMS software, the average results for maximum spread (D) and non-dominated solutions (NDS) are 0.02 and 0.04, respectively.

Credit incentives are crucial tools in supply chain and inventory management. Using this strategy, the buyer could pay the purchase cost with a delay. Therefore, it will increase the order quantity and the buyer's satisfaction. This paper investigates the economic production model considering the incentive conditions for supplier credit, variable demand, deteriorating items, and shortages. It is assumed that the supplier sends the ordered items to the manufacturer on time; however, he receives the purchase price of the products after a permitted delay. Furthermore, the deterioration rate is a fixed percentage of the inventory level. Therefore, a nonlinear programming model is proposed for figuring out replenishment policy by minimizing the total inventory cost. The best replenishment policy is examined by employing Wolfram Mathematica. Moreover, a genetic algorithm is suggested due to the model's nonlinearity. Numerical analyses show that while the results do not significantly differ, the proposed GA reaches near-optimum solutions in less CPU time.

Today, the concept of JIT production has usage in production management and inventory control widely. In such an environment, tardiness or earliness is essential. Therefore, scheduling tries to minimize the sum of earliness and tardiness, which represents customer satisfaction, as well as inventory control. Most studies in scheduling adopt the assumption that machines are continuously available during the planning horizon. But in the real world, some machines may be temporarily unavailable for reasons such as breakdowns or preventive maintenance activities. So, considering the unavailability as a constraint is necessary for scheduling problems in the JIT production system. In this study, the unavailability constraint has been investigated with two flexible modes on a single machine. In each period, the duration of unavailability corresponding to the continuous working time of the machine changes in a discrete manner and can adopt two different values. Since the objective function is irregular, unforced idleness may be useful, increasing the complexity of the problem. First, a binary integer mathematical programming model is presented. Due to the NP-Hardness of the problem under consideration, a genetic algorithm is proposed to solve the problem in large dimensions. To examine the performance of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), several problem instances are generated and solved, and the obtained results are compared with those obtained from solving the mathematical model with the GAMS software. The computational results indicate the proposed algorithm has a good performance with an average deviation of 0.87% and a reasonable computational time.

Reducing inventory costs is among the essential strategies for survival and profitability in today’s competitive environment. Classical inventory models have been developed to minimize inventory costs. However, they have several limiting assumptions. In this study, a three-level logistic chain consisting of a manufacturer, a distributor, and a retailer is considered. The problem of optimizing the inventory model for the distributor is examined. The distributor orders the products from the manufacturer. However, the manufacturer does not simultaneously deliver the total order and sends them in several discrete instances. In other words, it employs the multi-delivery strategy. Furthermore, the retailer’s demand is discrete and equal to a fixed amount at each equal interval of time. The distributor aims to determine the optimal order quantity and the optimal plan of receiving orders to minimize the total costs. Finally, the proposed problem is analyzed in a numerical example, and the results are compared with the classical inventory model. The results show that the proposed model has better performance with increasing holding and fixed ordering costs.

This paper introduces a new method for organizational performance measurement. The model integrates the QEST (Quality factor + Economic, Social & Technical dimensions) and the BSC (Balanced Scorecard) models. Although the model is originally defined to measure performance in the banking industries, it can be used for almost any organization and any multi-attribute decision-making problem. It not only has a new look to the BSC perspectives but enables organizations to obtain a more reliable assessment. The model takes advantage of the QEST-3D and the BSC that lets the proposed model be easy to generalize. The research’s motivation is to obtain performance measurement based on not only the values of the indicators but the proportion of the values ​​of the leading indicators and the lagging ones obtained by BSC. The financial perspective is considered a lagging indicator, while the other indicators are considered leading. To obtain reliability analysis, the model has been tested in 17 branches of a bank in Kurdistan province for three recent years, as a real case. The results that are compared with what is obtained from the pre-qualified TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) successfully illustrate the accuracy and practicality of the proposed model. The results show that based on the correct logic proposed in the proposed method for separating and analyzing the perspectives taken from the BSC, the strategy-oriented ranking made by the proposed method is more reliable and logical than the ranking performed by the TOPSIS.

Allocating redundancy components is one of the most efficient and well-known ways to increase the reliability of factories; which plays an important role in responding appropriately to customer demand, timely delivery of products and cost reduction. This leads to the creation of a stable and reliable supply chain. In the present study, the subject of simultaneous optimization of facility location-inventory-redundancy allocation has been investigated. In this regard, a single-period, three-level supply chain including supplier, distributor and retailer is considered. It is assumed that demand for each retailer is stochastic and normally distributed. Also, in order to deal with the fluctuations of demand, the risk pooling strategy has been applied, as a result of which, inventory will be held only in distribution centers. For this purpose, a nonlinear integer programming model is proposed to optimize the total cost of the supply chain as well as its reliability.Due to the complexity and NP-hardness of facility location-inventory and redundancy allocation problems, a multi-objective metaheuristic algorithm based on the simulated annealing algorithm, called AMOSA, was developed to solve the foregoing problem. Finally, to validate and accredit the algorithm, its results are compared with the results of the complete enumeration of all feasible solutions.

This paper presents an economic production quantity (EPQ) model with a periodic order quantity (POQ) policy, product reliability and periodic demand. The machine reliability has decreased over time; therefore, the rates of perfect and defective products reduce and increase over time, respectively. A fixed percentage of these products are reworked while the rest is wasted. Some equipment in their early days operates with excellent efficiency, but its performance deteriorates over time; therefore, operating costs increase. It is assumed the machine is inspected and repaired at the end of each production cycle; then, the machine returns to its original state. The demand for the final products is discrete, periodic and constant for each period. We use the POQ policy to meet customers' demand. In POQ procedures, orders for replenishment occur at fixed intervals. A Mixed Integer Non-Linear Program (MINLP) model is suggested. Then, a computational experiment is presented to discuss the optimally of the profit function. By analyzing the data, it is found that under different conditions, the manufacturer can use either Single Setup Single Period (SSSP) or Single Setup Multi-Period (SSMP) policy.

This paper deals with a two-period inventory planning and control problem with stochastic demand. In this system, a distributor can order the products in a preseason period and sell them in two subsequent selling periods. Demand during the first period is a random variable with a known probability distribution, while the demand for the remaining products at the end of the first period depends on their selling price during the second period. Therefore, the selling price of end-of-period products is very important so that a low price will achieve more sales. It is also assumed that the purchase price of the products in the preseason period is a time-dependent and follows a random process. For the considered problem, a mathematical model is suggested and then using the probabilistic dynamic planning approach, the optimal selling price at the second period, the optimal ordering quantity, and the purchase price of the products are determined to maximize the system profit. Finally, a numerical example is presented to investigate the performance of the model, and the sensitivity analysis of the main parameters.

Pricing and inventory strategies are two important decisions of a retailer which directly affect her/his profitability. They are even more prominent when the considered product undergoes deterioration. In this paper, the problem of joint pricing and inventory control is investigated in a competitive environment. In particular, we consider a number of competing retailers who sell substitutable deteriorating items to a common market. In addition, shortage is allowed and partially backlogged at the retailers. Each retailer aims to maximize their profit determining their price and inventory policies. At first, the profit function of the retailers is derived. Then, the existence of Nash Equilibrium solution is shown. Next, a solution procedure is developed to obtain the equilibrium quantities of price and replenishment policy. Furthermore, a numerical study is carried out to analyze the effect of the main parameters on the equilibrium solution. Numerical results show that the increase of competition intensity would decrease the summation of the retailers' profits, while it would increase the total satisfied demands. Moreover, in contrast to the monopoly models, changing the deterioration rate at a retailer does not significantly impact his/her selling price.

This paper considers multi-period serial production systems with Periodic order quantity (POQ) policy, lead-time uncertainties and demand dependent to the price. It is assumed that actual lead-time for each stage is probabilistic with known distribution and ordering system is multi period. During the production at each stage, the items may be produced in a longer time than it was schedule, causing a delay in production at this stage and this may result in backorders of the finished product. It is assumed in this case that a fixed percentage of the shortage is backlogged and other sales are lost. The objective of this paper is to find the pricing of unit product, planned lead-time and periodicity with quantity (POQ) policy in order to maximize the total system profit.