An integrated modeling framework for design of logistics networks with expedited shipment services

This paper studied an integrated logistics network problem that determines optimal supplier locations, assignments of these suppliers to terminal facilities, expedited shipment configurations, and inventory management strategies in an uncertain environment. We studied the problem structure and proposed mathematical models to determine the optimal network design that minimizes the expected total system cost. We developed a customized solution approach based on Lagrangian relaxation that can solve these models efficiently and accurately. Numerical examples are conducted to draw managerial insights into how problem settings and key parameter values affect the optimal design results.     

Supply chain design under quality disruptions and tainted materials delivery

Events such as the 2008 Heparin tragedy, in which patients lost their lives due to tainted pharmaceuticals, highlight the necessity for supply chain designers and planners to consider the risk of even low probability incidents in supply chains. The goal of this research is to design a single-period, single-product supply chain model with capacitated facilities to hedge against the possibility of sending tainted materials to consumers. Given that our mixed-integer stochastic model is NP-hard, we develop efficient heuristic and metaheuristic algorithms to obtain acceptable solutions. Computational experience is presented and discussed.     

A supply chain network design considering transportation cost discounts

This study addresses an integrated facility location and inventory allocation problem considering transportation cost discounts. Specifically, this article considers two types of transportation discounts simultaneously: quantity discounts for inbound transportation cost and distance discounts for outbound transportation cost. This study uses an approximation procedure to simplify DC distance calculation details, and develops an algorithm to solve the aforementioned supply chain management (SCM) problems using nonlinear optimization techniques. Numerical studies illustrate the solution procedures and the effects of the model parameters on the SCM decisions and total costs. Results of this study serve as a reference for business managers and administrators.     

Retail supply chain network design under operational and disruption risks

Designing robust and resilient retail networks under operational and disruption risks can create substantial competitive advantage. In this paper, a deterministic multiple set-covering model is first proposed. Then, it is extended to a possibilistic scenario-based robust model by scenario generation and disruption profiling to design a robust and resilient retail network. The developed models are validated through randomly generated examples and a real case study in retailing. Numerical results demonstrate that designing retail chains without considering operational and disruption risks is really misleading. Also, multiple covering of retail stores as the measure of redundancy increases the network’s resilience significantly.     

Designing a new supply chain for competition against an existing supply chain

We develop a bi-level model for designing an entrant supply chain (SC) in the presence of a pre-existing competing SC where demand is elastic with respect to price and distance. The model assumes dynamic competition between the new and pre-existing SCs in retailers’ level and probabilistic customers’ behavior. Strategic facility location and flow decisions are made while considering inventory carrying costs incurred on the operational level. We formulate the problem and propose exact and metaheuristic algorithms to solve it. The model is solved using data from a real-life case and also randomly generated test problems to extract managerial insights.     

Robust supply chain strategies for recovering from unanticipated disasters

Recovering from unanticipated disasters is critical in today’s global market. This paper examines the effectiveness of popular recovery strategies used to address unpredictable disasters that derail supply chains. We create a formal model to portray dynamic operational performance among supply chain firms facing disruptions caused by natural and man-made disasters. Our analysis shows that a supply chain recovers best if member firms adopt a radical, rapid, costly recovery strategy that immediately resolves the disruption. This observation is robust to various resource consumption requirements. We apply our methodology in the case of Taiwan’s 2011 food contamination scandal and provide managerial insights.     

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.     

Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty

Considering economic, environmental and social impacts, this paper presents a new sustainable closed-loop location-routing-inventory model under mixed uncertainty. The environmental impacts of CO₂ emissions, fuel consumption, wasted energy and the social impacts of created job opportunities and economic development are considered in this paper. The uncertain nature of the network is handled using a stochastic-possibilistic programming approach. Furthermore, for large-sized problems, a hybrid meta-heuristic algorithm and lower bounds are developed and discussed. Finally, a real case study is provided to demonstrate the applicability of the model in real-world applications, and several in-depth analyses are conducted to develop managerial implications.     

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.     

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.     

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.     

Price promotion with reference price effects in supply chain

We consider the price promotion in a supply chain comprising one manufacturer and one retailer, who take into account the reference price effects of consumers. The problem is analyzed as a manufacturer-lead Stackelberg game. The results indicate that reference price effects could mitigate “double marginalization” effects, and improve the channel efficiency. We also show that the optimal price promotion benefits the manufacturer, retailer and consumers in consumer promotion model. Furthermore, we provide the conditions under which the retailer has an interest in offering price promotion to consumers. Finally, we employ numerical analysis to demonstrate more managerial insights.     

A supply chain network design model for biomass co-firing in coal-fired power plants

We propose a framework for designing the supply chain network for biomass co-firing in coal-fired power plants. This framework is inspired by existing practices with products with similar physical characteristics to biomass. We present a hub-and-spoke supply chain network design model for long-haul delivery of biomass. This model is a mixed integer linear program solved using benders decomposition algorithm. Numerical analysis indicates that 100 million tons of biomass are located within 75 miles from a coal plant and could be delivered at $8.53/dry-ton; 60 million tons of biomass are located beyond 75 miles and could be delivered at $36/dry-ton.     

Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies

In this study, an approach to re-planning the multi-stage supply chain (SC) subject to disruptions is developed. We analyze seven proactive SC structures, compute recovery policies to re-direct material flows in the case of two disruption scenarios, and assess the performance impact for both service level and costs with the help of a SC (re)planning model containing elements of system dynamics and linear programming. In the result, an explicit connection of performance impact assessment and SC plan reconfiguration issues with consideration of the duration of disruptions and the costs of recovery has been achieved.     

Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study

A mixed-integer, non-linear model is developed for designing robust global supply chain networks under uncertainty. Six resilience strategies are proposed to mitigate the risk of correlated disruptions. In addition, an efficient parallel Taguchi-based memetic algorithm is developed that incorporates a customized hybrid parallel adaptive large neighborhood search. Fitness landscape analysis is used to determine an effective selection of neighborhood structures, while the upper bound found by Lagrangian relaxation heuristic is used to evaluate quality of solutions and effectiveness of the proposed metaheuristic. The model is solved for a real-life case of a global medical device manufacturer to extract managerial insights.     

Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain

This study presents a mathematical model that designs a reliable multi-modal transportation network for a biofuel supply chain system, where intermodal hubs are subject to site-dependent probabilistic disruptions. The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. Numerical analysis show that the model selects to use intermodal hubs located in areas with low disruption probabilities. In case of a disaster, the reliable solution results in 6.21% savings over the minimum cost solution.     

Design and planning for green global supply chains under periodic review replenishment policies

We quantify the impact of jointly optimizing strategic network design and tactical inventory planning on the cost and CO₂ emissions of multi-echelon logistics networks. The obtained insights indicate that longer optimized replenishment cycles reduce a node’s transportation cost and CO₂ emissions but increase its inventory costs. Moreover, under a fixed replenishment cycle, a node’s service level increases when supplied by a satellite warehouse. Finally: (i) the costs of implementing optimal green network design decisions could be misleading if inventory planning is neglected, (ii) greening of supply chains could become expensive, (iii) current legislative CO₂ ton prices hardly influence logistics networks.     

Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing

This paper describes an integrated model that jointly optimizes the strategic and tactical decisions of a closed-loop supply chain (CLSC). The strategic level decisions relate to the amounts of goods flowing on the forward and reverse chains. The tactical level decisions concern balancing disassembly lines in the reverse chain. The objective is to minimize costs of transportation, purchasing, refurbishing, and operating the disassembly workstations. A nonlinear mixed integer programming formulation is described for the problem. Numerical examples are presented using the proposed model.     

The value of information on supply risk under random yields

For companies facing challenge of managing unreliable supply sources, one of the operational goals is to increase the overall profit by improving performance of uncertain supply. We here develop stochastic decision frameworks which evaluate necessary information about uncertainties and help newsvendor and suppliers to enhance supply reliability. We first analyze the newsvendor’s purchasing decision under supply uncertainty and the derived decision framework is used to identify stochastic dominance conditions. We found that partial supply risk information is sufficient to determine regular ordering quantity, but to improve the overall profit, it is important to gather more information about stochastic dominance conditions.     

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.