Multi-stage Supply Chain Network Research Articles

An analytical method for cost analysis in multi-stage supply chains: A stochastic network model approach

This paper studies the cost distribution characteristics in multi-stage supply chain networks. Based on the graphical evaluation and review technique, we propose a novel stochastic network mathematical model for cost distribution analysis in multi-stage supply chain networks. Further, to investigate the effects of cost components, including the procurement costs, inventory costs, shortage costs, production costs and transportation costs of supply chain members, on the total supply chain operation cost, we propose the concept of cost sensitivity and provide corresponding algorithms based on the proposed stochastic network model. Then the model is extended to analyze the cost performance of supply chain robustness under different order compensation ability scenarios and the corresponding algorithms are developed. Simulation experiment shows the effectiveness and flexibility of the proposed model, and also promotes a better understanding of the model approach and its managerial implications in cost management of supply chains.

A fuzzy linear programming model for the optimization of multi-stage supply chain networks with triangular and trapezoidal membership functions

Supply chain management (SCM) is concerned with a complex business relations network that contains interrelationships between various entities, such as suppliers, manufacturers, distribution centers and customers. SCM integrates these entities and manages their interrelationships through the use of information technology to meet customer expectations (i.e., higher product variety and quality, lower costs and faster responses) effectively along the entire value chain. Thus, one of the vital issues in supply chain management is the design of the value chain network. In this paper, a fuzzy linear programming model for the optimization of the multi-stage supply chain model with triangular and trapezoidal membership functions is presented. The model determines the fuzzy capacities of the facilities (plants or distribution centers (DCs)) and the design of the network configuration with a minimum total cost. The total cost involves the shipping cost from suppliers; transportation costs between plants and DCs; distribution costs between DCs and customer zones; and opportunity costs from not having the material at the right time. The developed model is solved by a professional software package (LINDO), and the computational results are discussed.

Particle Swarm Optimisation for fixed-charge transportation problem in a Multistage Supply Chain Network

This work develops a mathematical model for a Multistage Supply Chain Network (MSCN) with a fixed charge for each route and proposes novel solution procedure based on Particle Swarm Optimisation (PSO). The MSCN problem involves selection of facilities to open, and the distribution network design must satisfy demand at minimum cost. The problem becomes Nondeterministic Polynomial-time (NP) hard while considering fixed charges. The novel idea in this work is the adoption of fixed charges for each production flow between stages. Numerical results are presented and compared with approximate and lower bound values to demonstrate the effectiveness of the proposed approach.

A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment

The configuration of the supply chain network has a strong influence on the overall performance of the supply chain. A well designed supply chain network provides a proper platform for efficient and effective supply chain management. The supply chain network should be designed in the way that could meet the customer needs with an efficient cost. This paper studies the responsive, multi-stage supply chain network design (SCND) problem under two conditions: (1) when direct shipment is allowed and (2) when direct shipment is prohibited. First, two mixed integer programming models are proposed for multi-stage, responsive SCND problem under two abovementioned conditions. Then, to escape from the complexity of mixed integer mathematical programming models, graph theoretic approach is used to study the structure of the SCND problems and it is proven that both of SCND problems considered in this paper could be modeled by a bipartite graph. Finally, since such network design problems belong to the class of NP-hard problems, a novel heuristic solution method is developed based on a new solution representation method derived from graph theoretic view to the structure of the studied problem. To assess the performance of the proposed heuristic solution method, the associated results are compared to the exact solutions obtained by a commercial.

Modeling defaults of companies in multi-stage supply chain networks

The interest in supply chain networks and their analysis as complex systems is rapidly growing. The physical approach to the topic draws on the concept of heterogenous interacting agents. The interaction among agents is considered as a repeated process of orders and production. The dynamics of production in the supply chain network which we observe is nonlinear due to the random failures in processes of orders and production. We introduce an agent-based model of a supply chain network which represents in more detail the real economic environment in which firms operate. We focus on the influence of local processes on the global economic behavior of the system and study how the proposed modifications change the general properties of the model. We observe collective bankruptcies of firms, which lead to self-emerging network structures. Our results give insight into the dynamics of default processes in supply chain networks, which have important implications both for risk managers and policy makers. Based on the simulations we show that agent-based modeling is a powerful tool for optimization of supply chain networks.

A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs

The design of configuration and the transportation planning are crucial issues to the effectiveness of multi-stage supply chain networks. The decision makers are interested in the determination the optimal locations of the hubs and the optimal transportation routes to minimize the total costs incurred in the whole system. One may formulate this problem as a 0-1 mixed integer non-linear program though commercial packages are not able to efficiently solve this problem due to its complexity. This study proposes a new spanning tree-based Genetic Algorithm (GA) using determinant encoding for solving this problem. Also, we employ an efficient heuristic that fixes illegal spanning trees existing in the chromosomes obtained from the evolutionary process of the proposed GA. Our numerical experiments demonstrate that the proposed GA outperforms the other previously published GA in the solution quality and convergence rate.

A steady-state genetic algorithm for multi-product supply chain network design

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents a solution procedure based on steady-state genetic algorithms (ssGA) with a new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes.

Data Sharing in a Multi-Tiered Supply Chain Network

As competition has intensified for both domestic and international firms, so has the need to increase operating and production efficiency and reduce costs, implying an increasing focus on integrating processes and sharing data between business partners (Edwards, Peters, & Sharman, 2001). The purpose of this study is to examine the state of data sharing practices between companies within a supply chain configuration and to assess the pervasiveness with which data moves beyond company boundaries.Our findings indicate that the number of tiers data shared correlates significantly with firm performance, but that the type of data shared varies depending on whether the partner is a customer or supplier. Customers appear more likely to receive inventory data and suppliers are most often provided forecast data. The sharing of data is most profound with adjacent supply chain partners. However, it is not uncommon for data to be shared directly with a nonadjacent supply chain member. The number of tiers moderated the relationship between both partnering and process improvement elements as they influenced firm performance.

An efficient memetic algorithm for the multi-stage supply chain network problem

A supply chain is dynamic and involves the constant flow of information, production, services, and funds from suppliers to customers between different stages. In this paper, a memetic algorithm (MA, a hybrid genetic algorithm) is developed to find the strategy that can give the lowest cost of the physical distribution flow. The proposed MA is combined with the genetic algorithm (GA), a multi-greedy heuristic method (GH), three local search methods (LSMs): the pairwise exchange procedure (XP), the insert procedure (IP), and the remove procedure (RP), the Fibonacci number procedure, and the linear programming technique (LP) to improve the tradition genetic algorithm (GA). Preliminary computational experiments demonstrate the efficiency and performance of the proposed MA.

Supply chain networks, electronic commerce, and supply side and demand side risk

In this paper, we develop a supply chain network model in which both physical and electronic transactions are allowed and in which supply side risk as well as demand side risk are included in the formulation. The model consists of three tiers of decision-makers: the manufacturers, the distributors, and the retailers, with the demands associated with the retail outlets being random. We model the optimizing behavior of the various decision-makers, with the manufacturers and the distributors being multicriteria decision-makers and concerned with both profit maximization and risk minimization. We derive the equilibrium conditions and establish the finite-dimensional variational inequality formulation. We provide qualitative properties of the equilibrium pattern in terms of existence and uniqueness results and also establish conditions under which the proposed computational procedure is guaranteed to converge. We illustrate the supply chain network model through several numerical examples for which the equilibrium prices and product shipments are computed. This is the first multitiered supply chain network equilibrium model with electronic commerce and with supply side and demand side risk for which modeling, qualitative analysis, and computational results have been obtained.

A hybrid heuristic algorithm for the multistage supply chain network problem

In recent years, many developments in logistics were connected to the need for information in an efficient supply chain flow. The supply chain is often represented as a network called a supply chain network (SCN) that is comprised of nodes that represent facilities (suppliers, plants, distribution centers and customers). Arcs connect these nodes along with the production flow. A multistage SCN (MSCN) is a sequence of multiple SCN stages. The flow can only be transferred between two consecutive stages. The MSCN problem involves the choice of facilities (plants and distribution centers) to be opened and the distribution network design must satisfy the demand with minimum cost. In this paper, a revised mathematical model is first proposed to correct the fatal error appearing in the existing models. An efficient hybrid heuristic algorithm (HHA) was developed by combining a greedy method (GM), the linear programming technique (LP) and three local search methods (LSMs) (always used in solving the scheduling problem). The pair-wise exchange procedure (XP), the insert procedure (IP) and the remove procedure (RP) to solve the MSCN problem. Preliminary computational experiments demonstrate the efficiency and performance of the proposed HHA.

Design of Six Sigma Supply Chains

Variability reduction and business-process synchronization are acknowledged as keys to achieving sharp and timely deliveries in supply-chain networks. In this paper, we introduce a new notion, which we call six sigma supply chains to describe and quantify supply chains with sharp and timely deliveries, and develop an innovative approach for designing such networks. The approach developed in this paper is founded on an intriguing connection between mechanical design tolerancing and supply-chain lead-time compression. We show that the design of six sigma supply chains can be formulated as a mathematical programming problem, opening up a rich new framework for studying supply-chain design optimization problems. To show the efficacy of the notion and the design methodology, we focus on a design optimization problem, which we call the inventory optimization (IOPT) problem. Given a multistage supply-chain network, the IOPT problem seeks to find optimal allocation of lead time variabilities and inventories to individual stages, so as to achieve required levels of delivery performance in a cost-effective way. We formulate and solve the IOPT problem for a four-stage make-to-order liquid petroleum gas supply chain. The solution of the problem offers rich insights into inventory-service level tradeoffs in supply-chain networks and proves the potential of the new approach presented in this paper. Note to Practitioners-This paper builds a bridge between mechanical design tolerancing and supply-chain management. In particular, the paper explores the use of statistical tolerancing techniques in achieving outstanding delivery performance through variability reduction. Informally, a six sigma supply chain is that which delivers products within a customer specified delivery window, with at most 3.4 missed deliveries per million. The innovations in this paper are the following: 1) to define two performance metrics delivery probability and delivery sharpness to describe the precision and accuracy of deliveries, in terms of process capability indexes C/sub p/,C/sub pk/, and C/sub pm/; 2) to formulate the supply-chain design optimization problem using the process capability indices; 3) to suggest an efficient solution procedure for the design optimization problem. The paper presents the case study of a two-echelon distribution network and using the framework developed in the paper shows the role of inventory in controlling lead time variability and achieving six sigma levels of delivery performance.