Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model

This paper presents a novel model for designing a reliable network of facilities in closed-loop supply chain under uncertainty. For this purpose, a bi-objective mathematical programming formulation is developed which minimizes the total costs and the expected transportation costs after failures of facilities of a logistics network. To solve the model, a new hybrid solution methodology is introduced by combining robust optimization approach, queuing theory and fuzzy multi-objective programming. Computational experiments are provided for a number of test problems using a realistic network instance.     

Dynamic network design for reverse logistics operations under uncertainty

The design of reverse logistics network has attracted growing attention with the stringent pressures from environmental and social requirements. In general, decisions about reverse logistics network configurations are made on a long-term basis and factors influencing such reverse logistics network design may also vary over time. This paper proposes dynamic location and allocation models to cope with such issues. A two-stage stochastic programming model is further developed by which a deterministic model for multiperiod reverse logistics network design can be extended to account for the uncertainties. A solution approach integrating a recently proposed sampling method with a heuristic algorithm is also proposed in this research. A numerical experiment is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution method.     

Multi-objective design of an organ transplant network under uncertainty

We propose a novel multi-period location–allocation model for the design of an organ transplant transportation network under uncertainty. The model consists of a bi-objective mathematical programming model that minimizes total cost and time, including waiting time in the queue for the transplant operation, while considering organs’ priorities. A fuzzy multi-objective programming based approach is presented to solve the small and medium size problems to optimality. For larger problems, we propose two meta-heuristics based algorithms. Lower bounds, and several numerical examples with managerial insights are discussed. A real case-study is provided, and the existing and the proposed optimal solutions are compared.     

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.     

A reverse logistics cost minimization model for the treatment of hazardous wastes

This study presents a cost-minimization model for a multi-time-step, multi-type hazardous-waste reverse logistics system. A discrete-time linear analytical model is formulated that minimizes total reverse logistics operating costs subject to constraints that take into account such internal and external factors as business operating strategies and governmental regulations. Application cases are presented to demonstrate the feasibility of the proposed approach. By using the proposed model coupled with operational strategies, it is shown that the total reverse logistics costs for the applications cases can be reduced by more than 49%.     

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.     

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.     

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.     

Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

This paper designs a reliable healthcare network. Under limited capacity, queue of patients may deteriorate the condition and leads to risk of death. Consequently, it is vital to investigate a queue system that considers the condition and changes over the time. Besides, treatment units just serve patients that are in their coverage threshold, while this threshold is affected by several factors. This paper considers number of patients and covering threshold under uncertainty. To handle uncertainty, an integrated approach is proposed. Two meta-heuristic algorithms are developed for the given problem. Finally, we carried out experiments to assess proposed model and approaches.     

2-Stage stochastic programming approach for hub location problem under uncertainty: A case study of air network of Iran

In this paper 2-stage stochastic programming has been developed for formulating stochastic uncapacitated multiple allocation hub location problem. This problem is studied under three cases. The first, stochastic demand, the second, stochastic transportation cost and the third, integrated stochastic, which is compounded of first and second cases. A case of air network in Iran is used to evaluate proposed formulations and computational results obtained by GAMS are presented. The results show that considering uncertainty into formulation could cause in different solutions.     

A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.     

A hybrid Tabu Search approach for the design of a paper recycling network

This paper addresses the design of a paper recycling network including external procurement, in-house recycling of paper, technology selection and selling or disposing of co-products. In contrast to the literature, we consider a combined continuous and discrete facility location problem solved by a hybrid Tabu Search approach to enhance candidate facility locations. We provide rules of thumb regarding the length of the tabu list and suggestions regarding runtime boundaries in cases where pre-evaluations are elaborate. The results based on a real-life application case show the potential savings of in-house paper recycling compared to the solely external procurement of recovered paper.     

Measuring the performance of multimode bus transit: A mixed structure network DEA model

The aim of this paper is to propose a mixed structure network data envelopment analysis (MSNDEA) model which allows a representation of a parallel production serial connection of consumption processes in a unified framework and hence can be used to simultaneously estimate the production efficiency, service effectiveness and operational effectiveness of multimode transit firms. To do this, Taiwan’s bus transit system is selected for the empirical applications. The main advantages of the MSNDEA model are in contexts where internal linkage and shared inputs between activities can be considered in this model, and the model structure is more realistic than the conventional one.     

A fully fuzzy network DEA-Range Adjusted Measure model for evaluating airlines efficiency: A case of Iran

Due to the competition among companies, evaluating the performance is one of the most important issues for managers. Data Envelopment Analysis (DEA) - Range Adjusted Measure model (RAM) is one of the non-radial models to evaluate the performance of companies. This paper presents a fully fuzzy network DEA-RAM model for evaluating airlines and extends the network DEA-RAM model in the fully fuzzy framework. First, a network structure is designed for airlines and then, a DEA-RAM model is presented for calculating efficiency scores. To handle uncertainty in data, fully fuzzy approach is considered and finally, a multi-objective network model is proposed to calculate airlines’ efficiencies. To solve the proposed fully fuzzy DEA-RAM model, the lexicographic approach is used and the efficiency scores are reported as interval numbers. To illustrate the capability of the proposed fully fuzzy DEA-RAM model, the actual data of 14 Iranian airlines are gathered and the interval efficiencies of airlines are evaluated.     

Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application

In this paper, we present a two-stage mixed integer programming (MIP) interdiction model in which an interdictor chooses a limited amount of elements to attack first on a given network, and then an operator dispatches trains through the residual network. Our MIP model explicitly incorporates discrete unit flows of trains on the rail network with time-variant capacities. A real coal rail transportation network is used in order to generate scenarios to provide tactical and operational level vulnerability assessment analysis including rerouting decisions, travel and delay costs analysis, and the frequency of interdictions of facilities for the dynamic rail system.     

A model of wine tourist behaviour: a festival approach

The study constructs a temporal model of wine tourist behaviour on the basis of the social psychologist' theory of consumer attitudes and related concepts with regard to past behaviour, satisfaction, perceived value and behavioural intentions. More importantly, this study added two dimensions to this model by proposing that satisfaction and perceived value had an impact on the attendees' intentions (i) to visit a local winery and (ii) to buy local wine products. Using a path analysis approach and data collected from the attendees at a regional wine festival, the study examined the above relationships. The results of this path analysis can be summarised as: (i) past behaviour influenced the intention to revisit and the level of perceived value, but had no effect on the level of satisfaction; (ii) perceived value strongly affected the level of satisfaction; (iii) satisfaction had a strong impact on future intentions to revisit and also an effect on intentions to visit local wineries and to buy local wine products; and (iv) perceived value affected the intentions to revisit the festival and to visit local wineries but did not influence the intentions to buy local wines. It is believed that the results of the present study will be useful to organisers of wine festivals and/or wine tourism developers. Copyright © 2008 John Wiley & Sons, Ltd.     

Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach

We propose an evaluation approach for a novel travel demand management strategy known as the downtown space reservation system (DSRS). This approach takes into account three perspectives, i.e., transportation service provider’s, the user’s, and the community’s and is based on network-Data Envelopment Analysis (DEA) where the perspectives are inter-related through intermediate inputs/outputs. Two types of network-DEA models (radial and slacks-based models) are considered. An example is provided using data propagated from a microscopic traffic simulation model of the DSRS. The results show that individual node performance can drive network DEA performance and that this information can inform future designs of the DSRS.     

Financial distress,survival network design strategies,and airline pricing: An event study of a merger between a bankrupt FSC and an LCC in Brazil

This paper develops an event study to investigate the airfare effects of the bankruptcy of a financially distressed full-service carrier (FSC) and its subsequent acquisition by a low-cost carrier (LCC) in Brazil. We account for the distressed carrier's survival network design strategies (SNDS) pursued during its reorganization—a suspected source of sample selection bias. Additionally, as rivals' pricing could be aimed at driving the distressed/bankrupt carrier out of the market, we treat the carrier's distress as endogenously determined with it. Our results do not uncover any survival pricing behavior stemming from SNDS, but reveal fiercer price competition from rivals in periods preceding both the distressed carrier's bankruptcy filing and acquisition. We also find evidence of enduring price competitiveness in the long run of the acquisition event, shedding light on the potential facilitating role played by bankruptcy protection regulations in keeping and sustaining market contestability after the bankruptcy-filing period.     

The effect of a roundabout corridor's design on selecting the optimal crosswalk location: A multi-objective impact analysis

Crosswalks located at mid-block segment between roundabouts can provide a good balance among delay, carbon dioxide (CO₂) emissions, and relative difference between vehicles and pedestrians speed. However, when considering local pollutant criteria, the optimal crosswalk location may be different to that obtained for CO₂. This paper described a multi-objective analysis of pedestrian crosswalk locations, with the objectives of minimizing delay, emissions, and relative difference between vehicles and pedestrians speed. Accounting for the difference between global (e.g., CO₂) and local pollutants (monoxide carbon, nitrogen oxides, and hydrocarbons) was one the main considerations of this work. Vehicle activity along with traffic and pedestrian flows data at six roundabout corridors in Portugal, one in Spain, and one in the United States were collected and extracted. A simulation environment using VISSIM, Vehicle Specific Power, and Surrogate Safety Assessment Model models was used to evaluate traffic operations along the sites. The Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II) was implemented to further search optimal crosswalk locations. The results yielded improvements to both delay and emissions by using site-optimized crosswalks. The findings also revealed that the spacing between intersections widely influenced the optimal crosswalk location along a mid-block section. If the spacing is low (<100 m), the crosswalk location will be approximately in 20%–30% of the spacing length. For spacing values between 140 and 200 m, crosswalks would be located at the midway position. When a specific pollutant criterion was considered, no significant differences were observed among optimal crosswalk data sets.     

A price forecasting model for predicting value of commercial airports: a case of three Korean airports

As the privatization of airports progresses throughout the world, the economic value of these strategic assets must be determined from a commercial perspective, rather than the economic impact perspective, which is typically used by governmental agencies to justify their construction. This paper applies financial theory of asset valuation to commercial airports. The authors have selected three airports for valuation purposes: Gimpo, Kimhae and Jeju. These airports are ranked top three in terms of annual traffic volume, among the 16 airports operated by Korea Airport Corporation (“KAC”). The authors estimate the intrinsic value of three major airports; Gimpo, Kimhae, and Jeju based on the discounted cash flow (DCF) model at $1.8 billion. The ultimate price achieved depends on the individual expectations of potential buyers, their opportunity costs and entrepreneurial abilities, as well as the ensuing negotiations. This paper, therefore, provides a benchmark for determining airport valuation in the event of privatization.