Showing papers in "Transportation Research Part E-logistics and Transportation Review in 2014"

A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges

Abstract: This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.).

Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application

Abstract: This paper presents a robust network design model for the supply of blood during and after disasters. A practical optimization model is developed that can assist in blood facility location and allocation decisions for multiple post-disaster periods. The application of the proposed model is investigated in a case problem where real data is utilized to design a network for emergency supply of blood during potential disasters. Our analysis on the tradeoff between solution robustness and model robustness arrives at important practical insights. The performance of the proposed ‘robust optimization’ approach is also compared with that of an ‘expected value’ approach.

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

Abstract: 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.

Enhancing metro network resilience via localized integration with bus services

Abstract: This paper advances the field of network disruption analysis by introducing an application to a multi-modal transport network, capitalizing on the redundancies and improved connectivity of an integrated metro-bus network. Metro network resilience to disruptions can be enhanced by leveraging on public bus services. To ensure better acceptance among operators and commuters, we focus on introducing localized integration with bus services instead of designing an entirely new bus network to achieve the desired resilience to potential disruptions. This is accomplished by increasing the capacity of bus services that run in parallel with affected metro lines as well as those connecting to different metro lines. Our analysis starts with a network representation to model the integrated metro and bus system. A two-stage stochastic programming model is further developed to assess the intrinsic metro network resilience as well as to optimize the localized integration with bus services. The approach is applied to a case study based on the Singapore public transit system and actual travel demand data. The results show that the metro network resilience to disruptions can be enhanced significantly from localized integration with public bus services.

Coordination of a socially responsible supply chain using revenue sharing contract

Abstract: This paper explores coordination of a corporate social responsible (CSR) manufacturer–retailer chain by considering two cases, CSR retailer and CSR manufacturer. In manufacturer-Stackelberg game setting revenue sharing (RS) contract is used to coordinate the channel. It is found that CSR retailer’s perfect welfare maximizing motive resolves channel conflict, otherwise RS contract coordinates the channel. Wholesale price of RS contract is higher than marginal production cost above a threshold of CSR in one case and is negative above a threshold of CSR in the other. Also, CSR manufacturer’s pure profit is negative above a threshold of CSR.

Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake

Abstract: The effective distribution of critical relief in post disaster plays a crucial role in post-earthquake rescue operations. The location of distribution centers and vehicle routing in the available transportation network are two of the most challenging issues in emergency logistics. This paper constructs a nonlinear integer open location-routing model for relief distribution problem considering travel time, the total cost, and reliability with split delivery. It proposes the non-dominated sorting genetic algorithm and non-dominated sorting differential evolution algorithm to solve the proposed model. A case study on the Great Sichuan Earthquake in China expounds the application of the proposed models and algorithms in practice.

A three-stage stochastic facility routing model for disaster response planning

Abstract: This paper presents a three-stage mixed-integer stochastic programming model for disaster response planning, considering the opening of local distribution facilities, initial allocation of supplies, and last mile distribution of aid. The vehicles available for transportation, the state of the infrastructure and the demand of the potential beneficiaries are considered as stochastic elements. Extensive computational testing performed on realistic instances shows that the solutions produced by the stochastic programming model are significantly better than those produced by a deterministic expected value approach.

Quality management, environmental management maturity, green supply chain practices and green performance of Brazilian companies with ISO 14001 certification: Direct and indirect effects

Abstract: This study aims to test a new conceptual model based on the relationship between quality management (QM), environmental management maturity (EMM), adoption of external practices of green supply chain management (GSCM) (green purchasing and collaboration with customers) and green performance (GP) with data from 95 Brazilian firms with ISO 14001. To our knowledge, such links and relationships are not simultaneously identified and tested in the literature. The results indicate the validation of all of the research hypotheses. This paper highlights that an improvement in green performance will require attention to quality management, environmental management maturity, and green supply chain.

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

Abstract: 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.

Solving a static repositioning problem in bike-sharing systems using iterated tabu search

Abstract: In this paper, we study the static bike repositioning problem where the problem consists of selecting a subset of stations to visit, sequencing them, and determining the pick-up/drop-off quantities (associated with each of the visited stations) under the various operational constraints. The objective is to minimize the total penalties incurred at all the stations. We present an iterated tabu search heuristic to solve the described problem. Experimental results show that this simple heuristic can generate high quality solutions using small computing times.

Robustness of the air transport network

Abstract: This paper presents a methodology for the detection of critical airports (those whose isolation would cause the largest losses in network connectivity) in the global air transport network (ATN), based on simulating an attack on selected ATN airports using different adaptive selection criteria. The performances of several node selection criteria are compared, together with a new criterion based on Bonacich power centrality. The results show that most critical airports can be detected with an adaptive strategy based on betweenness centrality. The detection of such airports may help the development of contingency plans to develop an appropriate response to any airport closure.

A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling

Abstract: We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO2 emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO2 emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation.

A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation

Abstract: This paper focuses on the planning of road restoration efforts during disaster response and recovery. The primary objective is to maximize network accessibility for all locations in the area during the restoration process so that survivors are evacuated and road side debris is removed as soon as possible. We propose a dynamic path based mathematical model that identifies criticality of blockages and clears them with limited resources. This model is more efficient than link based models and can solve restoration problems for realistic size networks within reasonable time. Algorithm performance is demonstrated using two instances based on districts in Istanbul.

A dynamic carsharing decision support system

Abstract: This paper proposes a dynamic optimization–simulation model as a decision support system for one-way carsharing organizations. To reduce the vehicle imbalance in one-way systems, a Vehicle Relocation Optimization model is solved successively in a discrete event simulation. Each event is the arrival of a new user. The model is compared to an a priori benchmark model. Autoshare is chosen as a case study. Results show that increasing the reservation time (time between requesting and picking up a vehicle) from 0 to 30 min reduces fleet size by 86%. The model captures a tradeoff between vehicle relocation hours and fleet size.

Sustainable hub location under mixed uncertainty

Abstract: This paper addresses a novel sustainable hub location problem (SHLP) in which two new environmental-based cost functions accounting for air and noise pollution of vehicles are incorporated. To cope with uncertain data incorporated in the model, a mixed possibilistic–stochastic programming approach is proposed to construct the crisp counterpart. A simulated annealing (SA) and an imperialist competitive algorithm (ICA) with a new solution representation are developed to solve real-sized instances whose performances are compared with a proposed lower bound. Finally, some computational experiments are provided to demonstrate the effectiveness of the proposed model and solution approaches.

Importance measures for inland waterway network resilience

Abstract: This work demonstrates a time-dependent paradigm for resilience and associated stochastic metrics in a waterway transportation context. We deploy two stochastic resilience-based component importance measures that highlight the critical waterway links that contribute to waterway network resilience and develop an optimization approach that determines the order in which disrupted links should be recovered for improved resilience. A data-driven case study illustrates these metrics to describe commodity flows along the various links of the US Mississippi River Navigation System.

Urban freight demand forecasting: A mixed quantity/delivery/vehicle-based model

Abstract: The research germinates from the statement that the cities have to solve the impacts due to freight transport in order to improve their sustainability implementing sets of city logistics measures. But city logistics measures involve several actors and choice dimensions. It is therefore important to have methods and models able to assess the effectiveness of the measures to be implemented. The current models were mainly developed to simulate some aspects of urban freight transport, and are not able to forecast many impacts of implementing traffic and transportation measures at an urban scale. This paper presents a modelling approach that tries to point out the relations existing among city logistics measures, actors and choice dimensions. It comprises three model sub-systems to estimate the quantity O–D matrices by transport service type (e.g. retailer on own account or wholesaler on own account or by carrier), the delivery O–D matrices by delivery time period, and the vehicle O–D matrices according to delivery tour departure time and vehicle type. This modelling system is a multi-stage model and considers a discrete choice approach for each decisional level. It was first tested using some data collected in the inner area of Rome, including traffic counts and interviews with retailers and truck-drivers. The model estimations were also compared with the experimental ones, and quite satisfactory results were obtained.

Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem

Abstract: Bikesharing suffers from the effects of fluctuating demand that leads to system inefficiencies. We propose a framework to solve the dynamic bikesharing repositioning problem based on four core models: a demand forecasting model, a station inventory model, a redistribution needs model, and a vehicle-routing model. The approach is proactive instead of reactive, as bike repositioning occurs before inefficiencies are observed. The framework is tested using data from the Hubway Bikesharing system. Simulation results indicate that system performance improvements of 7% are achieved reducing the number of empty and full events by 57% and 76%, respectively, during PM peaks.

Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts

Abstract: Understanding disruptions and how their effects propagate through the supply chain is critical to promote security and efficient movement of goods. This research proposes a system dynamics model as an effective quantitative approach for analyzing the effects of the materialization and simultaneous propagation of disruptions produced by terrorist acts on global supply chains performance. The article shows that the impact on inventory levels in the supply chain can increase 600% compared to normal operating conditions as a result of increasing the security measures on international borders. Finally, useful conclusions for designing more resilient supply chains and future research are exposed.

Supply chain-based barriers for truck-engine remanufacturing in China

Abstract: This paper introduces a research framework to identify barriers from a remanufacturing supply chain perspective including strategic (governmental) and operational dimensions. Using responses from expert practitioners in a truck engine remanufacturer in China, a grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is applied to examine the cause-effect relationships among various implementation barriers. The results identify that lack of strong financial support for remanufacturing technologies or equipment updates and innovation are key implementation barriers. Lack of quality standards of remanufactured products, adequate availability of used truck engines, and quality guarantee marketing of remanufactured engines are also major barriers.

Integrated inventory control and transportation decisions under carbon emissions regulations: LTL vs. TL carriers

Abstract: This study analyzes an integrated inventory control and transportation planning problem with carbon emissions regulations. We investigate the economic order quantity model with less-than-truckload (LTL) and truckload (TL) transportation under carbon cap, cap and trade, cap and offset, and taxing policies. We find the retailer’s optimal order quantity under each regulation with LTL and TL carriers. Analytical and numerical results comparing LTL and TL carriers are documented. We illustrate that the retailer’s carrier preference depends on regulation parameters. The tools provided enable analyzing the effects of regulations, transportation costs and emissions on the retailer’s costs and emissions with each carrier.

New SP-values of time and reliability for freight transport in the Netherlands

Abstract: This paper discusses the methods used in a study on the values of time and reliability in freight transport in the Netherlands. SP surveys were carried out among more than 800 shippers and carriers. A novel feature is that both for the value of time and reliability two additive components are distinguished: a transport cost and a cargo component. Specific instructions were given to make sure that the carriers provide the former and shippers that contract out the latter component. The resulting values that will be used in CBA in The Netherlands are presented and compared against the international literature. © 2014 Elsevier Ltd.

Linking rival and stakeholder pressure to green supply management: Mediating role of top management support

Abstract: Drawing upon the Schumpeterian view of competition and stakeholder theory, the purpose of our study is to examine how issues of rivalry and stakeholder pressure motivate firms to implement green supply management practices. We also consider the role of top management support as an important enabler to how firms react to competitive pressures to pursue green supply management practices. Our model is tested using a sample of supply chain professionals. Our results indicate that environmental pressure from rivals and stakeholders influences green supply management implementation through the mediating role of top management support for environmental initiatives.

Analyzing the Impact of Intermodal-Related Risk to the Design and Management of Biofuel Supply Chain

Abstract: 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.

A comprehensive evacuation planning model and genetic solution algorithm

Abstract: We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.

Corporate governance, financial management decisions and firm performance: Evidence from the maritime industry

Abstract: This study investigates the relation between corporate governance with (i) financial management decisions such as earnings management and sub-optimal investment, and (ii) firm performance in maritime firms. The study reveals that important corporate governance measures, such as insider ownership, board size, presence of corporate governance committees, the percentage of directors serving on the boards of other firms and CEO duality, are associated with financial management decisions and firm performance. The associations revealed can potentially assist in mitigating agency problems and improving financial management decisions and performance in maritime firms.

Multi-objective design of an organ transplant network under uncertainty

Abstract: 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.

Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

Abstract: In this paper, the number of goods subject to inspection at European Border Inspections Post are predicted using a hybrid two-step procedure. A hybridization methodology based on integrating the data obtained from autoregressive integrated moving averages (SARIMA) model in the artificial neural network model (ANN) to predict the number of inspections is proposed. Several hybrid approaches are compared and the results indicate that the hybrid models outperform either of the models used separately. This methodology may become a powerful decision-making tool at other inspection facilities of international seaports or airports.

A latent class choice based model system for railway optimal pricing and seat allocation

Abstract: In this paper, discrete choice methods in the form of multinomial logit and latent class models are proposed to explain ticket purchase timing of passenger railway. The choice model and demand functions are incorporated into a revenue optimization problem which jointly considers pricing and seat allocation. The framework provides insightful policy implications in term of fare and capacity distribution derived from actual passenger behavior. It shows that accepting short-haul demand provides greater revenue than long-haul demand using the same capacity. Revenue improvement ranges from 16.24% to 24.96% in multinomial logit models and from 13.82% to 21.39% in latent class models respectively.

Integration of logistics and cloud computing service providers: Cost and green benefits in the Chinese context

Abstract: Drawing on the innovation diffusion theory and data from 236 Chinese small and medium-sized logistics service providers (SMLSPs), this study developed and tested a conceptual model for empirically examining the green and cost benefits of integration between cloud service providers and SMLSPs in the Chinese context. The study posits that the perceived green and cost benefits drive the need for cloud computing (CC) adoption by Chinese SMLSPs. The results indicate that Chinese SMLSPs are attracted by CC to reduce cost in a short term and to gain sustainability through green benefits in a long term. The study extends CC capabilities and enterprise integration literature in the context of logistics services.