Global supply chain design: A literature review and critique

In this paper, we review decision support models for the design of global supply chains, and assess the fit between the research literature in this area and the practical issues of global supply chain design. The classification scheme for this review is based on ongoing and emerging issues in global supply chain management and includes review dimensions for (1) decisions addressed in the model, (2) performance metrics, (3) the degree to which the model supports integrated decision processes, and (4) globalization considerations. We conclude that although most models resolve a difficult feature associated with globalization, few models address the practical global supply chain design problem in its entirety. We close the paper with recommendations for future research in global supply chain modeling that is both forward-looking and practically oriented.     

Supply chain network design under profit maximization and oligopolistic competition

In this paper, we model the supply chain network design problem with oligopolistic firms who are involved in the competitive production, storage, and distribution of a homogeneous product to multiple demand markets. The profit-maximizing firms select both the capacities associated with the various supply chain network activities as well as the product quantities. We formulate the governing Nash–Cournot equilibrium conditions as a variational inequality problem and identify several special cases of the model, notably, a generalization of a spatial oligopoly and a classical oligopoly problem to include design capacity variables. The proposed computational approach, which is based on projected dynamical systems, fully exploits the network structure of the problems and yields closed form solutions at each iteration. In order to illustrate the modeling framework and the algorithm, we also provide solutions to a spectrum of numerical supply chain network oligopoly design examples.This paper makes a contribution to game theoretic modeling of competitive supply chain network design problems in an oligopolistic setting.     

Design of international assembly systems and their supply chains under NAFTA

The purpose of this paper is to provide a decision support aid for the strategic design of an assembly system in the international business environment created by NAFTA. The strategic design problem is to prescribe a set of facilities, including their locations, technologies, and capacities, as well as strategic aspects of the supply chain, selecting suppliers; locating distribution centers; planning transportation modes; and allocating target levels (i.e., amounts) for production, assembly, and distribution. The objective is to maximize after-tax profits. This paper presents a mixed integer programming model that represents the complexities of the international design problem as well as relevant enterprise-wide decisions in the US–Mexico business environment under NAFTA. It deals with a broad set of design issues (e.g., bill-of-material restrictions, international financial considerations, and material flow through the entire supply chain) using effective modeling devices (e.g., linearizing non-linearities that arise in modeling transfer prices and allocating transportation charges). Examples demonstrate how managers might use the model as a decision support aid.     

Establishment and application of fuzzy decision rules: an empirical case of the air passenger market in Taiwan

This paper develops a framework for creating fuzzy decision rules by socio‐economic variables, transactional record variables and customer benefit variables. Fuzzy decision rules may be applied on marketing systems of businesses. This research uses fuzzy k‐means algorithm and C4.5 decision tree algorithm to generate fuzzy decision rules. The framework is applied on the air passenger market of Taiwan for an empirical case study. Results of this research found two non‐fuzzy (crisp) decision rules and two fuzzy decision rules, which can be applied on the customer relationship management systems of airlines. Copyright © 2010 John Wiley & Sons, Ltd.     

Impact of decision sequence on reliability enhancement with supply disruption risks

This paper considers a supply chain that consists of a manufacturer and a supplier who faces disruption risks. We investigate the impact of decision sequence on the supplier’s endogenous reliability enhancement and the firms’ equilibrium pricing strategies. The supply chain reliability achieves a higher level under the supplier–leader game, but this does not always lead to a higher payoff for the supply chain. Each firm prefers to make the decision first, while any decision sequence can become dominant for the supply chain. We also show that the supply chain can achieve coordination via the revenue sharing contract.     

A multi-population genetic algorithm for transportation scheduling

This study considers the integration of production and transportation scheduling in a two-stage supply chain environment. The objective function minimizes the total tardiness and total deviations of assigned work loads of suppliers from their quotas. After modeling the problem as a mixed integer programming problem, a genetic algorithm with three populations, namely, a multi-society genetic algorithm (MSGA), is proposed for solving it. MSGA is compared with the optimum solutions for small problems and a heuristic and a random search approach for larger problems. Additionally, an MSGA is compared with a generic genetic algorithm. The experimental results show the superiority of the MSGA.     

Supplier hoarding,government intervention,and timing for post-disaster crop supply chain recovery

This work presents a multi-methodological approach to address the issue of post-disaster crop supply chain recovery under the influences of government intervention and supplier hoarding intention. A conceptual model which characterizes the antecedents of supplier hoarding intention is proposed, and validated using survey data. Grounded in the empirical study, an analytical model is then proposed for decision analysis of a two-tier crop supply chain recovering from post-disaster supply disruptions. Analytical results indicate that hoarding behavior reduces the time taken by supply chain members for supply recovery; and however, contributes to mixed effects on the expected profits of supply chain members.     

Freight transport network design using particle swarm optimisation in supply chain–transport supernetwork equilibrium

This paper presents a discrete network design problem for optimally designing freight transport network in terms of the efficiency of supply chain. Modelling is undertaken within the framework of mathematical programmes with equilibrium constraints, which first incorporates both supply chain and transport networks explicitly. The upper level determines the best set of actions for transport network improvement, while the lower-level decision is based on a supply chain–multimodal transport supernetwork equilibrium. New variants of particle swarm optimisation are developed to approximately solve the upper level. Numerical tests reveal their superior performance and the effective freight transport-related actions.     

Bioethanol supply chain system planning under supply and demand uncertainties

A mixed integer stochastic programming model is established to support strategic planning of bioenergy supply chain systems and optimal feedstock resource allocation in an uncertain decision environment. The two-stage stochastic programming model, together with a Lagrange relaxation based decomposition solution algorithm, was implemented in a real-world case study in California to explore the potential of waste-based bioethanol production. The model results show that biowaste-based ethanol can be a viable part of sustainable energy solution for the future.     

Strategic design and operational management optimization of a multi stage physical distribution system

Design and management of logistic networks is one of the most critical issues in supply chain management. However, the literature does not contain any effective models, methods, and applications that simultaneously support management decisions in the strategic design of the distribution system, in the operational planning and organization of vehicles, and in container trips organization adopting different modes of transportation. The aim of this paper is to illustrate an original framework for the design and optimization of a multi echelon and multi level production/distribution system that combines mixed-integer linear programming modeling with cluster analysis, heuristic algorithms, and optimal transportation rules. A significant case study is illustrated revealing the effectiveness of the approach and tools proposed.     

A hybrid decomposition algorithm for designing a multi-modal transportation network under biomass supply uncertainty

This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generate scenarios from prediction errors of the historical and forecasted biomass supply availabilities. We solve the model using a hybrid decomposition algorithm that combines Sample average approximation with an enhanced Progressive hedging algorithm. The proposed algorithm is validated via a real-world case study using data from Mississippi and Alabama. Computational results indicate that the proposed algorithm is capable of producing high quality solutions in a reasonable amount of time.     

A location-inventory-pricing model in a supply chain distribution network with price-sensitive demands and inventory-capacity constraints

This paper presents a location-inventory-pricing model for designing the distribution network of a supply chain with price-sensitive demands and inventory-capacity constraints. The supply chain has market power and uses markup pricing. An efficient Lagrangian relaxation algorithm is proposed to solve the model. Our numerical study shows that by moderately increasing the number of possible values for pricing decisions, the model can be used to find near-optimal solutions of a similar location-inventory-pricing problem with continuous pricing decisions. The approach used here to incorporate pricing decisions can be applied to other supply-chain design and planning problems with price-sensitive demands.     

A note on supply chain network equilibrium models

This paper is concerned with the supply chain network equilibrium models proposed by Nagurney et al. [Nagurney, A., Dong, J., Zhang, D., 2002. A supply chain network equilibrium model. Transportation Research 38E, 281–303] and Dong et al. [Dong, J., Zhang, D., Nagurney, A., 2004. A supply chain network equilibrium model with random demands. European Journal of Operational Research 156, 194–212]. It demonstrates that these models possess the unconstrained continuously differentiable minimization formulations, whose any stationary point is the solution of the corresponding model. Accordingly, not only is the Quasi-Newton algorithm capable of finding a solution of the model, but also it can overcome the difficulty experienced by the modified projection method in choosing an appropriate predetermined step size. In addition, 11 benchmark examples are employed to show the advantage of the unconstrained minimization formulation.     

Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties

This paper attempts to design a reverse supply chain network (SCN), add it to an existing multi-product forward SCN and simultaneously redesign the existing forward supply chain (SC). The problem considers uncertainty on products demand and and also returned products in multi-period context. Benders’ decomposition is applied to solve the stochastic mixed-integer model to optimality. The scenarios are generated based on the demand distribution function using Cholesky’s factorization method to consider correlation among different products’ demands. To decrease the computational effort, the number of scenarios is reduced using k-means clustering algorithm. The method is tested on a cell phone SC.     

An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain

This paper proposes a multi-objective possibilistic programming model to design a sustainable medical supply chain network under uncertainty considering conflicting economic, environmental and social objectives. Effective social and environmental life cycle assessment-based methods are incorporated in the model to estimate the relevant environmental and social impacts. An accelerated Benders decomposition algorithm utilizing three efficient acceleration mechanisms is devised to cope with computational complexity of solving the proposed model. Computational analysis is also provided by using a medical industrial case study to present the significance of the proposed model as well as the efficiency of the accelerated Benders decomposition algorithm.     

The influence of green practices on supply chain performance: A case study approach

The main objective of this exploratory paper is to investigate the relationships between green practices of supply chain management and supply chain performance. This relationship is investigated in the context of the automotive industry. Five research propositions are suggested and tested with empirical data derived from five case studies taken from the Portuguese automotive supply chain. The data analysis identifies the most important green practices considered by managers, as well as the performance measures that are most appropriate and most widely used as means to evaluate the influence of green practices on supply chain performance. A conceptual model was derived from the data analysis and it can be used to assess the influence of green practices on supply chain performance. This model provides evidence as to which green practices have positive effects on quality, customer satisfaction and efficiency. It also identifies the practices which have negative effects on supply chain performance.     

Internet book retailing and supply chain management: an analytical study of inventory location speculation and postponement

Internet retailing models support supply chains where consumer order locations are decoupled from inventory locations. In this setting, retailers dynamically consider inventory location speculation and postponement to fulfill their orders. Particularly, retailers can manage inventory to fulfill orders through two opposing strategies: in-stock inventory and drop-shipping. This paper extends the supply chain management literature by modeling Internet retailers' decisions to balance their offerings between these two strategies. The results show how retailers depend more on both of these strategies as their market share and product popularity increase. Thus, both inventory management strategies may be considered simultaneously to better manage Internet retailers' inventory.     

Optimizing a bi-objective inventory model of a three-echelon supply chain using a tuned hybrid bat algorithm

This paper presents a bi-objective VMI problem in a single manufacturer-single vendor multi-retailer (SM-SV-MR) supply chain, which a redundancy allocation problem is incorporated. In the hybridized problem, a manufacturer produces a single item using several machines that work in series, and stores it in a warehouse to replenish one vendor who delivers it to several retailers using the shortest possible route. A novel meta-heuristic, called hybrid bat algorithm (HBA), with calibrated parameters is utilized to find a near-optimum solution. To show the efficiency of HBA, the results are compared to the ones using the traditional BA and a genetic algorithm.     

Optimizing shipment,ordering and pricing policies in a two-stage supply chain with price-sensitive demand

In this paper we focus on a two-stage supply chain consisting of one vendor and one buyer. We develop an integrated production–inventory–marketing model to determine the relevant profit-maximizing decision variable values. The model proposed is based on the joint total profit of both the vendor and the buyer, and it finds out the optimal ordering, shipment and pricing policies. We are able to ascertain the optimal decision variable values employing an analytical solution procedure. The numerical evidence suggests that it is more beneficial for the buyer and the vendor to cooperate with each other when the demand is more price sensitive.     

Probabilistic modeling of freight consolidation by private carriage

A program of freight consolidation is a systematic attempt to decrease total transportation cost between a given origin and destination. Fewer shipments of larger weight are dispatched, while recognizing the inventory carrying cost of holding the first-arriving orders before dispatching the combined load. Here we employ probabilistic modeling to choose the maximum holding time and desired dispatch quantity. We obtain practical decision rules for temporal consolidation for transportation in one's own truck. Final results are expressed visually through a nomograph (four linked graphs) relating decision variables, probability and demand parameters, and objective-function values. The nomograph permits an intuitive sensitivity analysis.