Sharif Digital Repository

In this paper, the joint replenishment-location problem of some distribution centers (DCs) with a centralized decision maker who is responsible for ordering and dispatching shipments of a single product is modeled. The warehouse spaces of the DCs are limited and the product is sold under an incremental discount policy. The model seeks to minimize the total cost of the supply chain under the joint replenishment policy along with minimizing the cost of locating the DCs in potential sites as the first objective. The second objective is to minimize the warehouse space of all DCs using the revisable approach. As the proposed model is a bi-objective integer non-linear optimization problem... 

In this study, a single-period two-level inventory control problem is modeled in which the demand is a random variable and shortage is assumed as lost sales. The aim is to maximize the expected profit and the service level at the end of the season, simultaneously. The setting investigated in this research is unique in the sense that both all-units and incremental discount policies are considered under a budget constraint. The developed NP-hard bi-objective optimization problem cannot be solved using an exact method within a reasonable computational time. Thus, a meta-heuristic algorithm, namely multi-objective invasive weeds optimization algorithm (MOIWO) is developed to solve the proposed... 

In this research, an economic order quantity (EOQ) model is first developed for a two-level supply chain system consisting of several products, one supplier and one-retailer, in which shortages are backordered, the supplier's warehouse has limited capacity and there is an upper bound on the number of orders. In this system, the supplier utilizes the retailer's information in decision making on the replenishments and supplies orders to the retailer according to the well known (R, Q) policy. Since the model of the problem is of a non-linear integer-programming type, a genetic algorithm is then proposed to find the order quantities and the maximum backorder levels such that the total inventory... 

An aggregate production–distribution (P-D) planning generates an aggregate plan for regular time, overtime, outsourcing, hiring, and firing that takes into account distributing, inventory holding, backordering, and machine capacity for a definite planning horizon. A large number of P-D problems require decisions to be made in the presence of uncertainty. Previous research on this topic mainly utilized either stochastic programming or fuzzy programming to cope with the uncertainty. This may lead into huge challenges for supply chain managers that use non-robust P-D planning in uncertain environments. Moreover, there has been little discussion about robust optimization approach in aggregate... 

A novel bi-objective multi-server location-allocation (LA) model is developed in this paper, in which the facilities are modeled as an M/M/m queuing system. Further, capacity and budget limitations are provided to make the LA problem more realistic. The two objective functions include (1) minimizing aggregate waiting times and (2) minimizing the maximum idle time of all facilities. Since the proposed model is NP-hard, a meta-heuristic algorithm called multi-objective harmony search algorithm (MOHA) is developed to solve it. In this algorithm, a new presentation scheme that satisfies most of the model constraints is proposed. Since there is no benchmark available in the literature to validate... 

In many service and industrial applications of the facility location problem, the number of required facilities along with allocation of the customers to the facilities are the two major questions that need to be answered. In this paper, a facility location problem with stochastic customer demand and immobile servers is studied. Two objectives considered in this problem are: (1) minimizing the average customer waiting time and (2) minimizing the average facility idletime percentage. We formulate this problem using queuing theory and solve the model by a genetic algorithm within the desirability function framework. Several examples are presented to demonstrate the applications of the proposed... 

Similar to other existing classical production and inventory control models, the economic production quantity (EPQ) model is derived based upon some assumptions that cause its limited real-world applications. One of the assumptions in this model is that the orders are delivered in a continuous and constant rate. In order to make the EPQ model more applicable to real-world production and inventory control environments, in this research, an EPQ model is considered in which the orders are delivered discretely in the form of multiple pallets. Under this condition, the EPQ costs are derived and a new model is developed to find the optimum values of both the economic order quantity and the reorder... 

The economic production quantity (EPQ) is one of the most applicable model in production and inventory control environments. Like other classical production and inventory control models, it is derived based upon assumptions that cause limited real-world applications. Continuous and constant-rate delivery of orders, infinite availability of warehouse spaces, and applicability on a single product are some of these assumptions. In order to make the EPQ model more applicable to real-world production and inventory control problems, in this paper, we expand this model by assuming that the orders may be delivered discretely in the form multiple pallets. In addition, we may have more than one... 

In this paper, two methods to solve multi-response statistical problems are presented. In these methods, desirability function, genetic algorithm and simulation methodology are applied. The desirability function is responsible for modeling the multi-response statistical problem, the genetic algorithm tries to optimize the model and, finally, the simulation approach generates the required input data from a simulated system. The methods differ from each other in controlling the randomness of the problem. In the first method, replications control this randomness, while, in the second method, the randomness is controlled by a statistical test. Furthermore, these methods are compared by designed... 

12 mJ, 10-Hz green pulses from frequency doubled Nd:YAG laser was used for pumping Rhodamine 6G in a dye laser based on multiple-prism beam expander. In order to increase the stability of the output pulse, a unique dye cell with variable dimensions of active medium was designed and constructed. By using multiple-prism dispersion theory, conditions for zero-dispersion in two and four-prism beam expander configuration was calculated. In the case of two-prism configuration, tuning range of dye laser was 565–595 nm and the linewidth of pulse at 580 nm was about 1.8 nm and it is in good agreement with the calculated amount of 1.6 nm. Furthermore, using four-prism configuration tuning range of... 

Simultaneous reductions in inventory of raw materials, work-in-process, and finished items have recently become a major focus in supply chain management. Vendor-managed inventory is a well-known practice in supply chain collaborations, in which manufacturer manages inventory at the retailer and decides about the time and replenishment. In this paper, an integrated vendor-managed inventory model is presented for a two-level supply chain structured as a single capacitated manufacturer at the first level and multiple retailers at the second level. Manufacturer produces different products where demands are assumed decreasing functions of retail prices. In this chain, both the manufacturer and... 

The economic production quantity (EPQ) model is often used in manufacturing environments to assist firms in determining the optimal production lot size that minimizes the overall production-inventory costs. While there are some unrealistic assumptions in the EPQ model that limit its real-world applications, in this research, some of these assumptions such as (1) infinite availability of warehouse space, (2) all of the produced items being perfect, and (3) the existence of one product type are relaxed. In other words, we develop a multi-product EPQ model in which there are some imperfect items of different product types being produced such that reworks are allowed and that there is a... 

In this paper, a bi-objective multi-products economic production quantity (EPQ) model is developed, in which the number of orders is limited and imperfect items that are re-workable are produced. The objectives of the problem are minimization of the total inventory costs as well as minimizing the required warehouse space. The model is shown to be of a bi-objective nonlinear programming type, and in order to solve it two meta-heuristic algorithms namely, the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm, are proposed. To verify the solution obtained and to evaluate the performance of proposed algorithms, two-sample t-tests... 

This paper points out an application of the "two-echelon single-period product (newsboy) problem," in which within a limited budget the final product and the raw materials are purchased before the start of the selling period and depending on the demand, the raw materials may be transformed into the finished product during the period. The objective of this problem is to find the order quantities of both the raw materials and the final product at the beginning of the period such that the expected profit is maximized. A new model is first developed for this problem and then a Lagrangian method is proposed to solve it. Finally, a numerical example is given to illustrate the application of the... 

In this paper, a multi-item multiperiod inventory control problem with all-unit and/or incremental quantity discount policies under limited storage capacity is presented. The independent random demand rates of the items in the periods are known and the items are supplied in distinct batch sizes. The cost consists of ordering, holding, and purchasing. The objective is to find the optimal order quantities of all items in different periods such that the total inventory cost is minimized and the constraint is satisfied. A mixed binary integer programming model is first developed to model the problem. Then, a parameter-tuned genetic algorithm (GA) is employed to solve it. Since there is no... 

Adjusting control factors (independent variables) to achieve an optimal level of output (response variable) is usually required in many real-world manufacturing problems. Common optimization methods often begin with estimating the relationship between a response and independent variables. Among these techniques, response surface methodology (RSM), due to its simplicity, has recently attracted extensive attention. However, on the one hand, in some cases, the relationship between a response and independent variables is too complex to be estimated using polynomial regression models. On the other hand, solving the obtained optimization model is not easy by exact methods. This paper introduces a... 

In this paper, the joint replenishment-location problem of some distribution centers (DCs) with a centralized decision maker who is responsible for ordering and dispatching shipments of a single product is modeled. The warehouse spaces of the DCs are limited and the product is sold under an incremental discount policy. The model seeks to minimize the total cost of the supply chain under the joint replenishment policy along with minimizing the cost of locating the DCs in potential sites as the first objective. The second objective is to minimize the warehouse space of all DCs using the revisable approach. As the proposed model is a bi-objective integer non-linear optimization problem... 

This paper deals with a two-echelon inventory system for a non-repairable item where the system consists of one warehouse and m identical retailers and uses continuous-review (R, Q) ordering policy. To find an effective stocking policy for this system, a mathematical model with the objective of minimizing the total annual inventory investment subject to constraints on the average annual order frequency, expected number of backorders, and budget is formulated. The mathematical model of the problem at hand is shown to be nonlinear integer-programming and hence a parameter-tuned genetic algorithm is proposed to solve it efficiently. A numerical example is provided at the end to illustrate the... 

Bi-objective optimization of a multi-product multi-period three-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) each with uncertain services, and customer nodes is aimed in this paper. The two objectives are minimization of the total cost while maximizing the average number of products dispatched to customers. The decision variables are: (1) the number and the locations of reliable DCs in the network, (2) the optimum number of items produced by plants, (3) the optimum quantity of transported products, (4) the optimum inventory of products at DCs and plants, and (5) the optimum shortage quantity of the customer nodes. The problem is first formulated... 

Bi-objective optimization of a multi-product multi-period three-echelon supply-chain-network problem is aimed in this paper. The network consists of manufacturing plants, distribution centers (DCs), and customer nodes. To bring the problem closer to reality, the majority of the parameters in this network including fixed and variable costs, customer demand, available production time, set-up and production times, all are considered stochastic. The goal is to determine the quantities of the products produced by the manufacturing plants in different periods, the number and locations of the warehouses, the quantities of products transported between the supply chain entities, the inventory of...