Simulation optimization of an inventory control model for a reverse logistics system

Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.

Surfactant Stock Optimization for Cost Minimization in Neonatal Intensive Care Units

Surfactant deficiency in newborns is a result of a respiratory insufficiency condition, which is a major cause of illness and death. In terms of maintaining vital functions that require emergency intervention, it is crucial that surfactant is available for treatment upon request. The unknown times between patient arrivals and the patients’ stochastic weight changes in the hospital cause difficulties in determining the surfactant doses needed. The surfactant dose treatment for patients must be calculated according to the patient’s weight. In this study, a mathematical model that minimizes the purchase, order, holding, and waste costs of the surfactant has been developed while finding the optimum vial by considering random variables such as the time between a patient’s arrival and weight changes. With cost and demand affecting each other, the model uses a continuous inventory control policy, including calculating how much each preparation and vial should be used for, the reorder point, and the optimum order quantity. Also, the validity of the optimum values obtained with the mathematical model of a 66-bed neonatal intensive care unit in a hospital was tested with real data.

Reusability Evaluation Models In Reverse Logistics

This study focuses on the problem of reusability evaluation in reverse logistics. To deal with it, products are categorized into two types: well established products, and products with fast innovations. An innovation reliability based model (model 1) is suggested to evaluate reusability of returns for the first category. For the second category, a fuzzy multiple participant-multiple criteria (MPMC) decision making model is presented, which is a modified combination of two previous researches: the disposal cause analysis matrix (Umeda et al., 2005), and the fuzzy analytical hierarchy process (AHP) method (van Laarhoven and Pedrycz, 1983). To present the application of model 1, a green manufacturing system with an (s, Q) inventory control policy is simulated using Arena. With the aid of it, the system is analyzed in two situations: with recovery operations, and without recovery operations to investigate the effects of both model 1 and recovery operations on the system parameters.

Reusability Evaluation Models In Reverse Logistics

This study focuses on the problem of reusability evaluation in reverse logistics. To deal with it, products are categorized into two types: well established products, and products with fast innovations. An innovation reliability based model (model 1) is suggested to evaluate reusability of returns for the first category. For the second category, a fuzzy multiple participant-multiple criteria (MPMC) decision making model is presented, which is a modified combination of two previous researches: the disposal cause analysis matrix (Umeda et al., 2005), and the fuzzy analytical hierarchy process (AHP) method (van Laarhoven and Pedrycz, 1983). To present the application of model 1, a green manufacturing system with an (s, Q) inventory control policy is simulated using Arena. With the aid of it, the system is analyzed in two situations: with recovery operations, and without recovery operations to investigate the effects of both model 1 and recovery operations on the system parameters.

Omni-Chanel Network Design towards Circular Economy under Inventory Share Policies

In this paper, we study inventory share policies in an omni-channel supply network, to contribute to the circular economy (CE) concept. Lateral inventory share implementation provides flexibility and profitability in the supply chain by allowing inventory share between the same echelon locations in a network. Total holding costs and transportation costs can be reduced by lateral inventory share applications, which also contribute to decreased material usage for production over time, as well as reduced CO2 emission released by transportation. Technological and Internet of Things (IoT) developments make it possible for companies to share their real-time information with each other for uninterrupted marketing experiences. With such a connected network, companies aim to increase their profitability and responsiveness to their customers. We explore a well-designed inventory share policy towards the CE concept under an (s, S) inventory control policy. We simulate several pre-defined share designs by Arena 16.0 commercial software and compare their performances in terms of cost, responsiveness, transportation frequency, inventory held, etc. The results show that, by the implementation of a well-designed lateral inventory share policy, an omni-channel network can benefit from decreased holding cost and transportation cost, contributing to the CE concept.

Efficient Algorithms for a Large-Scale Supplier Selection and Order Allocation Problem Considering Carbon Emissions and Quantity Discounts

This paper considers a multi-period supplier selection and order allocation problem for a green supply chain system that consists of a single buyer and multiple heterogeneous suppliers. The buyer sells multiple products to end customers and periodically replenishes each item’s inventory using a periodic inventory control policy. The periodic inventory control policy used by the buyer starts every period with an order size determination of each item and the subsequent supplier selection to fulfill the orders. Because each supplier in the system is different from other suppliers in the types of carrying items, delivery distance, item price, and quantity discount schedule, the buyer’s problem becomes a complicated optimization problem. For the described order size and supplier selection problem of the buyer, we propose a nonlinear integer programming model and develop two different algorithms to enhance the usability of the model in a real business environment with a large amount of data. The algorithms are developed to considerably cut computational time and at the same time to generate a good feasible solution to a given supplier selection and order allocation problem. Computational experiments that were conducted to test the efficiency of the algorithms showed that they can cut as much as 99% of the computational time and successfully find feasible solutions, deviating not more than 3.4% from the optimal solutions.

Simulation of a stochastic multi-echelon distribution supply chain under a continuous inventory control policy

Multi Echelon Distribution System (MEDS) is a multifaceted system focusing on integration of all factors involved in the entire distribution process of finished goods to customers. This paper proposes a simulation framework at the operational level of MEDS. The proposed model includes three echelons, based on discrete-event simulation approach, where the performed operations within our system are depending on several key variables that often seem to have strong interrelationships. It is necessary to simulate such complicated system, in order to understand the whole mechanism, to analyze the interactions between various components and eventually to provide information without decomposing the system. The simulation framework is used to evaluate the performance of the considered system at initial conditions and to compare it with different scenarios generated by simulation running. The study concludes with an analysis of system performance and the finding results according to each scenario.