Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method

In the current competitive business world, viable companies are those that have flexible strategies and long-term plans, by which they can appropriately respond to a dynamic environment. These strategies are used to find the optimum allocation of company income to the main sources of development, for the expansion of company activities and for service expansions.     

A linear relaxation-based heuristic approach for logistics network design

We address the problem of designing and planning a multi-period, multi-echelon, multi-commodity logistics network with deterministic demands. This consists of making strategic and tactical decisions: opening, closing or expanding facilities, selecting suppliers and defining the product flows. We use a heuristic approach based on the linear relaxation of the original mixed integer linear problem (MILP). The main idea is to solve a sequence of linear relaxations of the original MILP, and to fix as many binary variables as possible at every iteration. This simple process is coupled with several rounding procedures for some key decision variables. The number of binary decision variables in the resulting MILP is small enough for it to be solved with a solver. The main benefit of this approach is that it provides feasible solutions of good quality within an affordable computation time.     

Supply chain network design under oligopolistic price and service level competition with foresight

In this paper a new bi-level model for designing the network structure of a competitive supply chain (SC) is presented with anticipating variable prices and service levels competition in markets under stochastic price and service level dependent elastic demands with the presence of existing, external rivals. The network structure of the new entrant SC would be designed under the limited production capacity of its producers in a way to maximize its future capturable profit in the competitive markets. The network of the new SC is assumed to be set “once and for all” but further price and service level adjustments are possible. Outer part of this bi-level model deals with strategic decisions of SC network design. Given the SC network structure assigned by the outer model in each iteration, the inner equilibrium model determines the equilibrium retail prices and service levels. Finally, we illustrate the model through several numerical examples.     

Supply chain network design under uncertainty with new insights from contracts

In this paper, the classical problem of supply chain network design is reconsidered to emphasize the rolc of contracts in uncertain environments. The supply chain addressed consists of four layers： suppliers, manufacturers, warehouses, and customers acting within a single period. The single owner of the manufacturing plants signs a contract with each of the suppliers to satisfy demand from downstream. Available contracts consist of long-term and option contracts, and unmet demand is satisfied by purchasing from the spot market. In this supply chain, customer demand, supplier capacity, plants and warehouses, transportation costs, and spot prices are uncertain. Two models are proposed here： a risk-neutral two-stage stochastic model and a risk-averse model that considers risk measures. A solution strategy based on sample average approximation is then proposed to handle large scale problems. Extensive computational studies prove the important role of contracts in the design process, especially a portfolio of contracts. For instance, we show that long-term contract alone has similar impacts to having no contracts, and that option contract alone gives inferior results to a combination of option and long-term contracts. We also show that the proposed solution methodology is able to obtain good quality solutions for large scale problems.     

A neuro-fuzzy approach for functional genomics data interpretation and analysis

The paper is concerned about the application of neuro-fuzzy techniques for the functional analysis of gene expression data from microarray experiments. The objective of this paper is to learn and predict functional classes of the E. coli genes using neuro-fuzzy based techniques, such as modular neuro and neuro-fuzzy networks. Methods of combining explicit and implicit knowledge in functional interpretation and analysis of gene expression data are proposed.     

A hierarchical optimization technique for the strategic design of distribution networks

The paper addresses the optimal design of the last supply chain branch, i.e., the Distribution Network (DN), starting from manufacturers till the retailers. It considers a distributed system composed of different stages connected by material links labeled with suitable performance indices. A hierarchical procedure employing direct graph (digraph) modeling, mixed integer linear programming, and the Analytic Hierarchy Process (AHP) is presented to select the optimal DN configuration. More in detail, a first-level DN optimization problem taking into account the definition and evaluation of the distribution chain performance provides a set of Pareto optimal solutions defined by digraph modeling. A second level DN optimization using the AHP method selects, on the basis of further criteria, the DN configuration from the Pareto face alternatives. To show the method effectiveness, the optimization model is applied to a case study describing an Italian regional healthcare drug DN. The problem solution by the proposed design method allows improving the DN flexibility and performance.     

A strategic model for supply chain design with logical constraints: formulation and solution

This paper proposes a strategic production–distribution model for supply chain design with consideration of bills of materials (BOM). Logical constraints are used to represent BOM and the associated relationships among the main entities of a supply chain such as suppliers, producers, and distribution centers. We show how these relationships are formulated as logical constraints in a mixed integer programming (MIP) model, thus capturing the role of BOM in the selection of suppliers in the strategic design of a supply chain. A test problem is presented to illustrate the effectiveness of the formulation and solution strategy. The results and their managerial implications are discussed.Scope and purposeSupply chain design is to provide an optimal platform for efficient and effective supply chain management. The problem is often an important and strategic operations management problem in supply chain management. This paper shows how the mixed integer programming modeling techniques can be applied to supply chain design problem, where some complicated relations, such as bills of materials, are involved. We discuss how to solve such a complicated model efficiently.     

A biased random-key genetic algorithm for wireless backhaul network design

This paper describes a biased random-key genetic algorithm for a real-world wireless backhaul network design problem. This is a novel problem, closely related to variants of the Steiner tree problem and the facility location problem. Given a parameter h, we want to build a forest where each tree has at most h hops from the demand nodes, where traffic originates, to the root nodes where each tree is rooted. Candidate Steiner nodes do not have any demand but represent locations where we can install cellsites to cover the traffic and equipment to backhaul the traffic to the cellular core network. Each Steiner node can cover demand nodes within a given distance, subject to a capacity constraint. The aggregate set of constraints may make it impossible to cover or backhaul all demands. A revenue function computes the revenue associated with the total amount of traffic covered and backhauled to the root nodes. The objective of the problem is to build a forest that maximizes the difference between the total revenue and the cost associated with the installed equipment. Although we will have a forest when we consider only the backhaul links and root nodes, the addition of demand vertices can induce undirected cycles, resulting in a directed acyclic graph. We consider instances of this problem with several additional constraints that are motivated by the requirements of real-world telecommunication networks.     

Supply network design: Risk-averse or risk-neutral?

In this paper, we investigate a supply network design in supply chain with unreliable supply with application in the pharmaceutical industry. We consider two types of decision making policies: (1) a risk-neutral decision-making policy that is based on a cost-minimization approach and (2) a risk-averse policy wherein, rather than selecting facilities and identifying the pertinent supplier–consumer assignments that minimize the expected cost, the decision-maker uses a Conditional Value-at-Risk (CVaR) approach to measure and quantify risk and to define what comprises a worst-case scenario. The CVaR methodology allows the decision-maker to specify to what extent worst-case scenarios should be avoided and the corresponding costs associated with such a policy. After introducing the underlying optimization models, we present computational analysis and statistical analysis to compare the results of the risk-averse and risk-neutral policies. In addition, we provide several managerial insights.     

Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs

Designing distribution networks - as one of the most important strategic issues in supply chain management - has become the focus of research attention in recent years. This paper deals with a two-echelon supply chain network design problem in deterministic, single-period, multi-commodity contexts. The problem involves both strategic and tactical levels of supply chain planning including locating and sizing manufacturing plants and distribution warehouses, assigning the retailers' demands to the warehouses, and the warehouses to the plants, as well as selecting transportation modes.We have formulated the problem as a mixed integer programming model, which integrates the above mentioned decisions and intends to minimize total costs of the network including transportation, lead-times, and inventory holding costs for products, as well as opening and operating costs for facilities. Moreover, we have developed an efficient Lagrangian based heuristic solution algorithm for solving the real-sized problems in reasonable computational time.     

A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path

A supply chain network design needs to consider the future probability of reconfiguration due to some problems of disaster or price changes. The objective of this article is to design a reconfigurable supply chain network by optimizing inventory allocation and transportation routing. A two-stage programming is composed according to Benders decomposition by allocating inventory in advance and anticipating the changes of transportation routings; thus the transportation routing is stochastic in nature. In addition, the fuzzy shortest path is developed to solve the problem complexity in terms of the multi-criteria of lead time and capacity with an efficient computational method. The results and analysis indicate that the proposed two-stage programming with fuzzy shortest path surpasses the performance of shortest path problem with time windows and capacity constraint (SPPTWCC) in terms of less computational time and CPU memory consumption. Finally, management decision-making is discussed among other concluding remarks.     

A systematic approach for supply chain improvement using design structure matrix

Supply chain is a complex system that involves many system elements from various functional areas. Performance of a supply chain heavily depends on the effectiveness of communication and coordination among these system elements and functional areas. However, a large and complex supply chain usually makes it difficult to coordinate and thus degrades its performance. This paper focuses on the development of a systematic approach with the following objectives: (1) to identify and quantify the interactions among the system elements in a supply chain; (2) to decompose the large interdependent group of system elements into smaller and manageable sub-groups; and thus (3) to improve the structure of the supply chain system. A supply chain system is first decomposed into subsystems and system elements from which the interactions (i.e., independent, dependent and interdependent relationships) are studied and documented by design structure matrix (DSM). Next, the interaction strengths among the related system elements are quantified. Cluster analysis is used to decompose the large interdependent group into smaller ones in order to provide a better supply chain system structure. The effectiveness of this systematic approach is demonstrated by an illustrative example. The result shows that it is able to improve the system structure of a supply chain that will be useful for the supply chain reengineering.     

A hybrid genetic algorithm based heuristic for an integrated supply chain problem

Supply chain management is becoming an essential component of efficient decision-making for companies, as they are increasingly implementing optimization strategies in order to improve their business performance. In this paper we develop a capacitated multi-echelon joint location-inventory model, according to which a single product is distributed from a manufacturer to retailers through a set of warehouses, the locations of which are to be determined by the model. Each retailer is assigned exactly one warehouse, while each warehouse can serve multiple retailers. The model decides where to locate warehouses, assigns retailers to the warehouses and decides the times between orders at the warehouses and the retailers, so as to minimize the cost of operating the supply chain. Notably, the model considers capacity constraints for each warehouse, ensuring that the reorder quantity is below the capacity limit. We develop a genetic algorithm (GA) based heuristic to solve the problem and the GA is validated on small size problems by comparing its solution to the optimal solution obtained by the General Algebraic Modeling System (GAMS). We focus our attention on specifically customizing the GA and thus an experimental analysis is carried out to find the optimal parameter setting for the GA as well as to obtain insights on the effect of the various GA parameters. Finally, a sensitivity analysis is conducted to show the effect of the capacity constraints.     

A computational study for common network design in multi-commodity supply chains

In this paper, we study a supply chain network design problem which consists of one external supplier, a set of potential distribution centers, and a set of retailers, each of which is faced with uncertain demands for multiple commodities. The demand of each retailer is fulfilled by a single distribution center for all commodities. The goal is to minimize the system-wide cost including location, transportation, and inventory costs. We propose a general nonlinear integer programming model for the problem and present a cutting plane approach based on polymatroid inequalities to solve the model. Randomly generated instances for two special cases of our model, i.e., the single-sourcing UPL&TAP and the single-sourcing multi-commodity location-inventory model, are provided to test our algorithm. Computational results show that the proposed algorithm can solve moderate-sized problem instances efficiently.     

考虑偏好与合作的供应链网络设计与优化

提出了考虑核心企业决策偏好与成员企业合作意愿的双目标供应链网络设计与优化问题。应用模糊多目标规划方法,将决策者对各个目标的不精确期望水平考虑到供应链网络的设计与优化模型中。研究了集中决策模式下核心决策者的不同目标偏好与分散决策模式下各组织成员群体决策的合作意愿,对供应链的各个目标及网络优化配置的影响。结果表明,“极大极小”算子体现了核心决策者对各个目标的偏好,但大权重目标以牺牲小权重目标的值为代价的。新的“模糊与”补偿算子能使供应链的各个目标值获得理想的均衡效果,体现了供应网络中核心企业与其他成员企业群体协商决策的管理思想与行为特征。     

Financial ratings with scarce information: A neural network approach

On a wake of Basel II Accord in 2004, banks and financial institutions can build an internal rating system. This work focuses on Italian small firms that are more hard to judge because quite often financial data are not simply available. The aim of this paper is to propose a simulation model for assigning rating judgements to these firms, using poor financial information.The proposed model produces a simulated counterpart of Bureau van Dijk-K Finance (BvD) rating judgements. It is clear that there are problems when small firms must be judged because it is difficult to obtain financial data; indeed in Italy these enterprises must deposit the balance-sheet in reduced form. Suggested methodology is a three-layer process where each of them is formed by, respectively, one, two and four feed-forward artificial neural networks with back-propagation algorithm. The proposed model is a good solution for evaluating small firms with poor financial information but not only: the research underlines and supports the ability of artificial neural networks of learning and reproducing some aspects or some features or behaviours of reality.     

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.     

Optimizing an equilibrium supply chain network design problem by an improved hybrid biogeography based optimization algorithm

This paper presents a new equilibrium optimization method for supply chain network design (SCND) problem under uncertainty, where the uncertain transportation costs and customer demands are characterized by both probability and possibility distributions. We introduce cost risk level constraint and joint service level constraint in the proposed optimization model. When the random parameters follow normal distributions, we reduce the risk level constraint and the joint service level constraint into their equivalent credibility constraints. Furthermore, we employ a sequence of discrete possibility distributions to approximate continuous possibility distributions. To enhance solution efficiency, we introduce the dominance set and efficient valid inequalities into deterministic mixed-integer programming (MIP) model, and preprocess the valid inequalities to obtain a simplified nonlinear programming model. After that, a hybrid biogeography based optimization (BBO) algorithm incorporating new solution presentation and local search operations is designed to solve the simplified optimization model. Finally, we conduct some numerical experiments via an application example to demonstrate the effectiveness of the designed hybrid BBO.     

A case-based method for service-oriented value chain and sustainable network design

PurposeThe purpose of this research is to present a case-based analytic method for a service-oriented value chain and a sustainable network design considering customer, environmental and social values. Enterprises can enhance competitive advantage by providing more values to all stakeholders in the network.Design/methodology/approachOur model employs a stylized database to identify successful cases of value chain application under similar company marketing conditions, illustrating potential value chains and sustainable networks as references. This work first identifies economic benefits, environmental friendliness and social contribution values based on prior studies. Next, a search engine which is developed based on the rough set theory will search and map similarities to find similar or parallel cases in the database. Finally, a visualized network mapping will be automatically generated to possible value chains.FindingsThis study applies a case-based methodology to assist enterprises in developing a service-oriented value chain design. For decision makers, this can reduce survey time and inspire innovative works based on previous successful experience. Besides, successful ideas from prior cases can be reused. In addition to customer values, this methodology incorporates environment and social values that may encourage a company to build their value chain in a more comprehensive and sustainable manner.Research implicationsThis is a pilot study which attempts to utilize computer-aided methodology to assist in service or value-related design. The pertinent existing solutions can be filtered from an array of cases to engage the advantages from both product-oriented and service-oriented companies. Finally, the visualized display of value network is formed to illustrate the results.Practical implicationsA customized service-oriented value chains which incorporates environment and social values can be designed according to different conditions. Also, this system engages the advantages from both product-oriented and service-oriented companies to build a more comprehensive value network. Apart from this, the system can be utilized as a benchmarking tool, and it could remind the decision makers to consider potential value from a more multifaceted perspective.Originality/valueThis is the first paper that applied a computer-aided method to design service-oriented value chains. This work also can serve as a decision support and benchmarking system because decision makers can develop different value networks according to various emphasized values. Finally, the visualized display of value network can improve the communication among stakeholders.     

Computational comparison of two formulations for dynamic supply chain reconfiguration with capacity expansion and contraction

The strategic dynamic supply chain reconfiguration (DSCR) problem is to prescribe the location and capacity of each facility, select links used for transportation, and plan material flows through the supply chain, including production, inventory, backorder, and outsourcing levels. The objective is to minimize total cost. The network must be dynamically reconfigured (i.e., by opening facilities, expanding and/or contracting their capacities, and closing facilities) over time to accommodate changing trends in demand and/or costs. The problem involves a multi-period, multi-product, multi-echelon supply chain. Research objectives of this paper are a traditional formulation and a network-based model of the DSCR problem; tests to promote intuitive interpretation of our models; tests to identify computational characteristics of each model to determine if one offers superior solvability; and tests to identify sensitivity of run time relative to primary parameters.