Development of a dynamic simulation model for inventory level optimization through supply chain

System dynamic is an approach to understanding the behavior of complex systems over time. Current research on system dynamics modeling in supply chain management focuses on inventory decision and policy development, time compression, demand amplification, supply chain design and integration, and international supply chain management. Less attention has been devoted on the inventory level improvement with fuzzy demand controller in dynamic simulation model. This study is aimed to consider customer demand fluctuation to improve the finished goods inventory level of Electronic Company by using dynamic simulation model. Dynamic and changes in demand and corresponding excess during time through the product life is considered as serious problems in supply chain. Data collection and analysis have been considered to find the current problems of the company. Dynamic model has been constructed by ITHINK software to represent the inventory level of company. The stock and flow diagrams are become visible to represent the structure of a system with more detailed information. Three different variables have been applied in Fuzzy controller to give the better level of inventory by MATLAB SIMULINK. Generated results have been compared by current company inventory level in ITHINK software and the best alternative was selected to suggest the company management

Development of dynamic simulation model for inventory level optimization through supply chain

System dynamic is an approach to understanding the behavior of complex systems over time. Current research on system dynamics modeling in supply chain management focuses on inventory decision and policy development, time compression, demand amplification, supply chain design and integration, and international supply chain management. Less attention has been devoted on the inventory level improvement with fuzzy demand controller in dynamic simulation model. This study is aimed to consider customer demand fluctuation to improve the finished goods inventory level of Electronic Company by using dynamic simulation model. Dynamic and changes in demand and corresponding excess during time through the product life is considered as serious problems in supply chain. Data collection and analysis have been considered to find the current problems of the company. Dynamic model has been constructed by ITHINK software to represent the inventory level of company. The stock and flow diagrams are become visible to represent the structure of a system with more detailed information. Three different variables have been applied in Fuzzy controller to give the better level of inventory by MATLAB SIMULINK. Generated results have been compared by current company inventory level in ITHINK software and the best alternative was selected to suggest the company management

Meta-heuristic Algorithms for an Integrated Production-Distribution Planning Problem in a Multi-Objective Supply Chain

In today's globalization, an effective integration of production and distribution plans into a unified framework is crucial for attaining competitive advantage. This paper addresses an integrated multi-product and multi-time period production/distribution planning problem for a two-echelon supply chain subject to the real-world variables and constraints. It is assumed that all transportations are outsourced to third-party logistics providers and all-unit quantity discounts in transportation costs are taken into consideration. The problem has been formulated as a multi-objective mixed-integer linear programming model which attempts to simultaneously minimize total delivery time and total transportation costs. Due to the complexity of the considered problem, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are developed within the LP-metric method and desirability function framework for solving the real-sized problems in reasonable computational time. As the performance of meta-heuristic algorithms is significantly influenced by calibrating their parameters, Taguchi methodology has been used to tune the parameters of the developed algorithms. Finally, the efficiency and applicability of the proposed model and solution methodologies are demonstrated through several problems in different size

A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network

Integrated production-distribution planning (PDP) is one of the most important approaches in supply chain networks. We consider a supply chain network (SCN) to consist of multi suppliers, plants, distribution centers (DCs), and retailers. A bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs in supply chain and transfer time of products for retailers. From different terms of evolutionary computations, this paper proposes a Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) to solve the problem. To validate the results obtained, a popular algorithm namely non-dominated sorting genetic algorithm (NSGA-II) is utilized as well. Since the solution-quality of proposed meta-heuristic algorithm severely depends on their parameters, the Taguchi method is utilized to calibrate the parameters of the proposed algorithm. Finally, in order to prove the validity of the proposed model, a numerical example is solved and conclusions are discussed

Reliable multi-product multi-vehicle multi-type link logistics network design: A hybrid heuristic algorithm

Abstract This paper considers the reliable multi-product multi-vehicle multi-type link logistics network design problem (RMLNDP) with system disruptions, which is concerned with facilities locating, transshipment links constructing, and also allocating them to the customers in order to satisfy their demand with minimum expected total cost (including locating costs, link constructing costs, and also expected transshipment costs in normal and disruption conditions). The motivating application of this class of problem is in multi-product, multi-vehicle, and multitype link logistics network design regarding to system disruptions simultaneously. In fact, the decision makers in this area are not only concerned with the facility locating costs, link constructing costs, and logistical costs of the system but also by focusing on the several system disruption states in order to be able to provide a reliable sustainable multi configuration logistic network system. All facility location plans, link construction plans and also link transshipment plans of demands in the problem must be efficiently determined while considering the several system disruptions. The problem is modeled as a mixed integer programming (MIP) model. Also, a hybrid heuristic, based on linear programming (LP) relaxation approach, is proposed. Computational experiments illustrate that the provided algorithm will be able to substantially outperform the proposed integer programming model in terms of both finding and verifying the efficient optimal (or near optimal) solution at a reasonable processing time

Effects of work injury cost to overall production cost with linear programming approach

Production planning is an important activity in manufacturing industries. The main goal of production planning is to minimize the cost under the condition that the customer requirement in terms of quality, quantity, and time is satisfied. An important player (human) is with little attention in traditional production planning. This thesis studied production planning with consideration of human factor, especially human work injuries as a result of performing a repetitive operation for a certain period of time in production systems. Production planning in this thesis only takes the minimization of total production cost as its goal. A linear programming technique was employed to incorporate the cost of work injury into the total production cost model. The LINDOTM software was used to solve the linear production planning model and to analyze the solution. Finally, the benefits of the production planning, which considers work injury, were discussed. Several conclusions can be drawn from this study: (1) the traditional production planning model, which only takes the material costs and labor costs into account, cannot deal with the cost related to work injury; (2) the work injury cost could be significant in those manual-intensive assembly systems, especially with high production rates; (3) the careful design of the worker’s postures can significantly reduce the work injury cost and thus the total cost of production. The significant contributions of this thesis are: (1) the development of a mathematical model for the total production cost including the work injury cost and (2) the finding that the work injury cost may be a significant portion in the total cost of production in the assembly system that has intensive manual works.