43 research outputs found
100% screening economic order quantity model under shortage and delay in payment
It is for a long time that the Economic Order Quantity(EOQ) model has been successfully applied to inventory management. This paper studies a multiproduct EOQ problem in which the defective items will be screened out by 0 screening process and will be sold after the screening period. Delay in payment is permissible though payment should be made during the grace period and the warehouse capacity is limited. Otherwise, there will be an additional penalty cost for late payment so the retailer would not be able tobuy products at discount prices.All-units and incremental discounts are considered for the products which dependon the order’s quantity just like the permissible delay in payment. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to solve the proposed model and numerical examples are provided for better illustrations
A New Model for Location-Allocation Problem within Queuing Framework
This paper proposes a bi-objective model for the facility location problem under a congestion system. The idea of the model is motivated by applications of locating servers in bank automated teller machines (ATMS), communication networks, and so on. This model can be specifically considered for situations in which fixed service facilities are congested by stochastic demand within queueing framework. We formulate this model with two perspectives simultaneously: (i) customers and (ii) service provider. The objectives of the model are to minimize (i) the total expected travelling and waiting time and (ii) the average facility idle-time. This model represents a mixed-integer nonlinear programming problem which belongs to the class of NP-hard problems. In addition, to solve the model, two metaheuristic algorithms including non-dominated sorting genetic algorithms (NSGA-II) and non-dominated ranking genetic algorithms (NRGA) are proposed. Besides, to evaluate the performance of the two algorithms some numerical examples are roduced and analyzed with some metrics to determine which algorithm works better
Increasing the reliability and the profit in a redundancy allocation problem
This paper proposes a new mathematical model for multi-objective redundancy allocation problem (RAP) without component mixing in each subsystem when the redundancy strategy can be chosen for individual subsystems. Majority of the mathematical model for the multi-objective redundancy allocation problems (MORAP) assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. The proposed model for MORAP simultaneously maximizes the reliability and the net profit of the system. And finally, to clarify the proposed mathematical model a numerical example will be solved. Keywords: Redundancy Allocation Problem, Serial-Parallel System, Redundancy Strategies, MORAP
A Bi-objective Optimization for Vendor Managed Inventory Model
Vendor managed inventory is a continuous replenishment program that is designed to provide major cost saving benefits for both vendors and retailers. Previous research on this area mainly included single objective optimization models where the objective is to minimize the total supply chain costs or to maximize the total supply chain benefits. This paper presents a bi-objective mathematical model for single-manufacture multi-retailer with multi-product in order to maximize their benefits. It is assumed that demand is a decreasing and convex function of the retail price. In this paper, common replenishment cycle is considered for the manufacturer and its retailers. Then, the proposed model converts to the single-objective optimization problem using a weighted sum method. A genetic algorithm (GA) is applied to solve it and response surface methodology is employed to tune the GA parameters. Finally, several numerical examples are investigated to demonstrate the applicability of the proposed model and solution approach
Stochastic single machine scheduling problem as a multi-stage dynamic random decision process
In this work, we study a stochastic single machine scheduling problem in which the features of learning effect on processing times, sequence-dependent setup times, and machine configuration selection are considered simultaneously. More precisely, the machine works under a set of configurations and requires stochastic sequence-dependent setup times to switch from one configuration to another. Also, the stochastic processing time of a job is a function of its position and the machine configuration. The objective is to find the sequence of jobs and choose a configuration to process each job to minimize the makespan. We first show that the proposed problem can be formulated through two-stage and multi-stage Stochastic Programming models, which are challenging from the computational point of view. Then, by looking at the problem as a multi-stage dynamic random decision process, a new deterministic approximation-based formulation is developed. The method first derives a mixed-integer non-linear model based on the concept of accessibility to all possible and available alternatives at each stage of the decision-making process. Then, to efficiently solve the problem, a new accessibility measure is defined to convert the model into the search of a shortest path throughout the stages. Extensive computational experiments are carried out on various sets of instances. We discuss and compare the results found by the resolution of plain stochastic models with those obtained by the deterministic approximation approach. Our approximation shows excellent performances both in terms of solution accuracy and computational time
A Continuous Review inventory Control Model within Batch Arrival Queuing Framework: A Parameter-Tuned Imperialist Competitive Algorithm
In this paper, a multi-product continues review inventory control problem within batch arrival queuing approach (MQr/M/1) is modeled to find the optimal quantities of maximum inventory. The objective function is to minimize summation of ordering, holding and shortage costs under warehouse space, service level, and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Np-Hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, a simulated annealing algorithm has been utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analyzed using some numerical illustrations
Layout of Cellular Manufacturing System in Dynamic Condition
Cellular manufacturing system (CMS) is highly important in modern manufacturing methods. Given the ever increasing market competition in terms of time and cost of manufacturing, we need models to decrease the cost and time of manufacturing. In this study, CMS is considered in condition of dynamic demand in each period. The model is developed for facing dynamic demand that increases the cost of material flow. This model generates the cells and location facilities at the same time and it can move the machine(s) from one cell to another cell and can generate the new cells for each period. Cell formation is NP-Complete and when this problem is considered in dynamic condition, surly, it is strongly NP- Complete. In this study, genetic algorithm (GA) is used as a meta-heuristic algorithm for solving problems and evaluating the proposed algorithm, Branch and Bound (B & B) is used as a deterministic method for solving problems. Ultimately, the time and final solution of both algorithms are compared
A location-allocation model for quality-based blood supply chain under IER uncertainty
Providing blood with high quality at the lowest cost and the shortest time is main challenge of blood supply chain management. This paper presents a new model for designing a dynamic and three level blood supply chain incorporating the quality issues. The proposed model intends to locate facilities, and to determine the best strategy for blood allocation by minimizing both cost and time and maximizing the customer satisfaction based on quality of blood delivery. In order to deal with consideration of real world, intricacies such as blood freshness, both separation and apheresis extraction methods, Cross match to Transfusion ratio (C/T) and equipment failure have been involved. Also, Interval Evidential Reasoning (IER) approach is applied to handle the uncertainty of blood product demand. Since the proposed model is NP-hard, MOPSO and NSGAII algorithms are utilized to solve it. Finally, to demonstrate the applicability of the problem some numerical examples are designed in different sizes and the most favorable algorithm is determined using TOPSIS method