Showing papers in "Transportation Research Part E-logistics and Transportation Review in 2016"
TL;DR: Considering economic, environmental and social impacts, a new sustainable closed-loop location-routing-inventory model under mixed uncertainty is presented in this paper, where the environmental impacts of CO2 emissions, fuel consumption, wasted energy and the social impacts of created job opportunities and economic development are considered.
Abstract: 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.
269 citations
TL;DR: Wang et al. as discussed by the authors proposed an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery, and formulated it as a network min-cost flow problem and proposed various pruning techniques that can dramatically reduce the network size.
Abstract: In urban logistics, the last-mile delivery from the warehouse to the consumer’s home has become more and more challenging with the continuous growth of E-commerce. It requires elaborate planning and scheduling to minimize the global traveling cost, but often results in unattended delivery as most consumers are away from home. In this paper, we propose an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery. To efficiently solve the model, we formulate it as a network min-cost flow problem and propose various pruning techniques that can dramatically reduce the network size. Comprehensive experiments were conducted with Singapore and Beijing datasets. The results show that our solution can support real-time delivery optimization in the large-scale mobile crowd-sourcing problem.
242 citations
TL;DR: It is unveiled that CAN stays the most robust when low-degree nodes or high flight flow links are removed, which is similar to the Worldwide Airline Network (WAN), albeit less redundant.
Abstract: This paper encapsulates the Chinese Airline Network (CAN) into multi-layer infrastructures via the “k-core decomposition” method. The network is divided into three layers: Core layer, containing airports of provincial capital cities, is densely connected and sustains most flight flow; Bridge layer, consisting of airports in Tier 2 and Tier 3 cities, mainly connects two other layers; and Periphery layer, comprising airports of remote areas, sustains little flight flow. Moreover, it is unveiled that CAN stays the most robust when low-degree nodes or high flight flow links are removed, which is similar to the Worldwide Airline Network (WAN), albeit less redundant.
233 citations
TL;DR: In this paper, a multi-objective optimization model featuring a sustainability performance scoring method and a stochastic fuzzy goal programming approach is developed that can be used to perform a dynamic sustainability tradeoff analysis and design a resiliently sustainable supply chain.
Abstract: Sustainable supply chain management has become an integral part of corporate strategy for virtually every industry. However, little is understood about the broader impacts of sustainability practices on the capacity of the supply chain to tolerate disruptions. This article aims to explore the sustainability–resilience relationship at the supply chain design level. A multi-objective optimization model featuring a sustainability performance scoring method and a stochastic fuzzy goal programming approach is developed that can be used to perform a dynamic sustainability tradeoff analysis and design a “resiliently sustainable” supply chain. Important managerial and practical insights are obtained from an empirical case study.
227 citations
TL;DR: In this article, a mixed-integer, non-linear model is developed for designing robust global supply chain networks under uncertainty, and six resilience strategies are proposed to mitigate the risk of correlated disruptions.
Abstract: 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.
212 citations
TL;DR: In this article, the taxi market in the presence of a single taxi hailing app through an aggregate and static approach is modeled, and the existence and stability of equilibria are examined, and a partial-derivative-based sensitivity analysis is conducted to quantitatively evaluate the impacts of the platform's pricing strategies to taxi market performance.
Abstract: Taxi hailing apps that facilitate taxi-customer matching quickly become popular in recent years. By combining the theories of two-sided market and taxi market, this paper models the taxi market in the presence of a single taxi hailing app through an aggregate and static approach. Based on the equilibrium model, the existence and stability of equilibria are examined, and a partial-derivative-based sensitivity analysis is conducted to quantitatively evaluate the impacts of the platform’s pricing strategies to the taxi market performance. The features of desirable price perturbations that improve social welfare and/or the platform’s profitability are also characterized.
198 citations
TL;DR: In this paper, a hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the green vehicle routing and scheduling problem (GVRSP) which allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data.
Abstract: The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.
195 citations
TL;DR: In this article, the authors compared the game between the original equipment manufacturers (OEMs) and the third-party remanufacturers (3PRs) on equilibrium quantities, prices, and profits.
Abstract: Many original equipment manufacturers (OEMs) allow third-party remanufacturers (3PRs) to perform remanufacturing operations of branded or patented products – through either outsourcing or authorization. This study compares these two modes by modeling the game between the OEM and the 3PR on equilibrium quantities, prices, and profits. The results suggest that when consumers perceive the remanufactured products with a low value, the 3PR prefers the authorization approach; otherwise the 3PR prefers the outsourcing approach. However, in both scenarios, the OEM obtains higher profit through outsourcing than through authorization. Our further analysis compares two modes’ impacts on consumer surplus, social welfare, and environment.
165 citations
TL;DR: In this article, the authors analyzed the interrelationships between risks faced by third party logistics service providers (3PLs) in relation to one of its customers using DEMATEL and found that the 3PLs need to improve internal processes related to quality management, flexibility of its operations and also geographical coverage of their services.
Abstract: This paper analyses the interrelationships between risks faced by third party logistics service providers (3PLs) in relation to one of its customers using DEMATEL. Novel analysis of both within and between risk categories and generation of threshold value to prioritize risks generate useful insights. Results show that arms-length relationship between the customer and the 3PLs has strong influence on other risks and there is a need for collaborative relationships between 3PLs and its customers. Moreover, analysis indicates that the 3PLs need to improve internal processes related to quality management, flexibility of its operations and also geographical coverage of their services.
160 citations
TL;DR: In this article, the authors considered a three-echelon supply chain model where the supplier makes semi-finished products and transports to manufacturer for finished products, and the manufacturer transports products by single-setup-multi-delivery policy to multi-retailer.
Abstract: Several industries controls carbon emission during transporting products due to increased transportation for obtaining the best transportation way with reduced cost. This study considers a three-echelon supply chain model where the supplier makes semi-finished products and transports to manufacturer for finished products. The manufacturer transports products by single-setup-multi-delivery policy to multi-retailer. The aim of the model is to reduce the supply chain cost by considering variable transportation and carbon emission costs are considered due to several shipments. An algebraic approach is employed to obtain the closed-form solution. Numerical example, sensitivity analysis, and graphical representations are given to illustrate the model.
139 citations
TL;DR: 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.
Abstract: 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.
TL;DR: In this article, an original quality function deployment approach was developed to enhance maritime supply chain resilience, taking both customer requirements and maritime risks into consideration, and empirical analysis was carried out through in-depth studies of three major shipping lines and their respective major shippers.
Abstract: Being international and involving numerous organizations as the basic nature, maritime supply chains are exposed to various natural and man-made risks. This paper aims to develop an original quality function deployment approach to enhance maritime supply chain resilience, taking both customer requirements and maritime risks into consideration. The empirical analysis is carried out through in-depth studies of three major shipping lines and their respective major shippers. The top three resilience measures are contingency plan, monitoring and maintenance, and supply chain relationship management. The study also unveils the relatively low visibility and integration in maritime supply chains.
TL;DR: In this article, a two-stage location-routing model with recourse for integrated preparedness and response planning under uncertainty is proposed for risk management in disaster situations where there are uncertainties in demand and the state of the infrastructure.
Abstract: We propose a two-stage location-routing model with recourse for integrated preparedness and response planning under uncertainty. The model is used for risk management in disaster situations where there are uncertainties in demand and the state of the infrastructure. We solve the two-stage model by converting it into a single-stage counterpart. The latter is then implemented in an illustrative example. Comparative analyses are run to investigate the (1) value of planning location and routing in a single model, (2) value of transshipment, (3) differences when an expected-value objective is used, and (4) value of transshipment in the expected-value model.
TL;DR: In this paper, the authors investigated the effect of risk aversion on the optimal policies of a dual-channel supply chain under complete information and asymmetric information cases, and determined that the optimal value added only depends on the value-added cost.
Abstract: We investigated the effect of risk aversion on the optimal policies of a dual-channel supply chain under complete information and asymmetric information cases. We determined that the optimal value added only depends on the value-added cost. The optimal prices under a risk-averse case are lower than those in a risk-neutral case. Information asymmetry increases wholesale and retail prices but reduces direct sale price, and tends to engender inefficiency. The value of information increases with the mean of the manufacturer’s estimation about the retailer’s risk aversion.
TL;DR: In this article, a retailer's ordering and transportation mode selection problem using stochastic customer demand is studied and the optimal ordering and transport mode selection decisions under different carbon emission reduction policies are investigated.
Abstract: We are witnessing more frequent extreme weather events due to the global warming. There is an urgent need for governments, industries, general public, and academics to take coordinated actions in order to tackle the challenges imposed by the climate change. It is essential to incorporate the environmental objective in the transportation mode selection problem as transportation is a main contributor to carbon emissions. With this in mind, our paper studies the retailer’s ordering and transportation mode selection problem using stochastic customer demand and investigates the optimal ordering and transportation mode selection decisions under different carbon emission reduction policies. Our analytical results reveal that there are some important transportation mode shifting thresholds under different carbon emissions reduction policies. These findings do not only help firms to make optimal decisions under different carbon emission reduction policies but also support policy makers to develop effective policies on carbon emissions reduction.
TL;DR: In this paper, the authors conduct an extensive computational study to quantify the impact of different types of participants' flexibility on the performance of a single-driver, single-rider ride-sharing system and show that small increases in flexibility, e.g., in terms of desired departure time or maximum detour time, can significantly increase the expected matching rate, especially when the number of trip announcements in the system is small.
Abstract: We conduct an extensive computational study to quantify the impact of different types of participants’ flexibility on the performance of a single-driver, single-rider ride-sharing system. Our results consistently show that small increases in flexibility, e.g., in terms of desired departure time or maximum detour time, can significantly increase the expected matching rate, especially when the number of trip announcements in the system is small. The insights gained from our study can provide the basis for the design of information campaigns and incentives schemes aimed at increasing the performance and success of ride-sharing systems.
TL;DR: In this paper, the authors proposed two integer programming models for optimizing an automated taxi (AT) system for last mile of train trips, where trip reservations are accepted or rejected by the operator according to the profit maximization, and any reservation on a selected zone by the model must be satisfied.
Abstract: We propose two integer programming models for optimizing an automated taxi (AT) system for last mile of train trips. Model S1: trip reservations are accepted or rejected by the operator according to the profit maximization; model S2: any reservation on a selected zone by the model must be satisfied. Models were applied to a case-study. Results indicate that fleet size influences the profitability of the taxi system: a fleet of 40 ATs is optimal in S1 and 60 ATs in S2. Having electric ATs constrains the system for small fleets because ATs will not have time for charging.
TL;DR: A unified simulation-based algorithm is developed to solve the problem of passenger flow organization in subway stations under uncertain demand and data envelopment analysis (DEA) and genetic algorithms (GA) are embedded in this algorithm to increase computing speed.
Abstract: This paper proposes a problem of passenger flow organization in subway stations under uncertain demand. The existing concepts of station service capacity are extended and further classified into three in different demand scenarios. Mathematical models are put forward to measure the three capacities and a unified simulation-based algorithm is developed to solve them. To increase computing speed, data envelopment analysis (DEA) and genetic algorithms (GA) are embedded in this algorithm. A case study will demonstrate the performance of the proposed algorithm and give a detailed procedure of passenger flow control based on station service capacity in various demand scenarios.
TL;DR: In this paper, the authors studied a time-constrained heterogeneous vehicle routing problem on a multigraph where parallel arcs between pairs of vertices represent different travel options based on criteria such as time, cost, and distance.
Abstract: We study a time-constrained heterogeneous vehicle routing problem on a multigraph where parallel arcs between pairs of vertices represent different travel options based on criteria such as time, cost, and distance. We formulate the problem as a mixed-integer linear programming model and develop a tabu search heuristic that efficiently addresses computational challenges due to parallel arcs. Numerical experiments show that the heuristic is highly effective and that freight operators can achieve advantages in cost and customer service by considering alternative paths, especially when route duration limits are restrictive and/or when vehicles of smaller capacity are dispatched to serve remote customers.
TL;DR: A multi-objective MILP model is formulated to find the optimal choice of suppliers and their order quantity allocation under disruption risk and suggests that the supplier failure probability affects the expected total cost more than supplier flexibility and loss cost.
Abstract: We formulate a multi-objective MILP model to find the optimal choice of suppliers and their order quantity allocation under disruption risk. Suppliers are evaluated and ranked, based on the preference values obtained using a hybrid fuzzy AHP-fuzzy PROMETHEE. Multi-objective Particle Swarm Optimization is then applied to yield a set of Pareto-optimal solutions for the choice of suppliers and their order allocation. Numerical experimentation suggests that the supplier failure probability affects the expected total cost more than supplier flexibility and loss cost. Sensitivity analysis is performed on the failure probability, the output flexibility, and loss cost of the suppliers.
TL;DR: In this article, a bi-objective, bilevel optimization model for the location of relief distribution centers (DCs) in humanitarian logistics is proposed, where the upper-level decision-maker selects locations for capacitated DCs.
Abstract: We propose a bi-objective, bilevel optimization model for the location of relief distribution centers (DCs) in humanitarian logistics. The upper-level decision-maker (an aid-providing organization) selects locations for capacitated DCs. On the lower level, beneficiaries choose a DC according to distance and amount of supply to be expected. This effects a user equilibrium on the lower decision level. Upper level objectives are to minimize total opening cost for the DCs and total uncovered demand. We develop an exact algorithm for determining the Pareto frontier of the problem, integrating the adaptive epsilon-constraint method, a branch-and-bound procedure, and the Frank–Wolfe procedure.
TL;DR: In this article, the authors explore risk management of logistics systems in several critical areas, namely disruption risk management, operational risk control, disaster and emergency management, and logistics service risk analysis.
Abstract: Nowadays, with the globalization of business operations, logistics systems are threatened by all kinds of uncertainties and disruptions. Almost every month, serious accidents in transportation and natural disasters all around the world are reported in the media. As a result, an effective and efficient risk management scheme is of a top most priority in the mind of all professionals in logistics management. This paper concisely explores risk management of logistics systems in several critical areas, namely disruption risk management, operational risk control, disaster and emergency management, and logistics service risk analysis. The papers featured in the special issue are also introduced and examined. This paper ends with a proposal of various future research directions for advancing risk management of logistics systems.
TL;DR: The logistic field has seen an increasing usage of electric vehicles and the resulting distribution planning problems present new computational challenges, and this paper addresses these problems.
Abstract: To minimize greenhouse gas emissions, the logistic field has seen an increasing usage of electric vehicles. The resulting distribution planning problems present new computational challenges. We address a problem, called Electric Traveling Salesman Problem with Time Windows. We propose a mixed integer linear formulation that can solve 20-customer instances in short computing times and a Three-Phase Heuristic algorithm based on General Variable Neighborhood Search and Dynamic Programming. Computational results show that the heuristic algorithm can find the optimal solution in most small-size instances within a tenth of a second and achieves goods solutions in instances with up to 200 customers.
TL;DR: In this article, the authors assess two strategies to improve the resilience of air traffic networks and show that an adaptive reconfiguration strategy is superior to a permanent re-routing solution.
Abstract: Air traffic networks are essential to today’s global society. They are the fastest means of transporting physical goods and people and are a major contributor to the globalisation of the world’s economy. This increasing reliance requires these networks to have high resilience; however, previous events show that they can be susceptible to natural hazards. We assess two strategies to improve the resilience of air traffic networks and show an adaptive reconfiguration strategy is superior to a permanent re-routing solution. We find that, if traffic networks have fixed air routes, the geographical location of airports leaves them vulnerable to spatial hazard.
TL;DR: In this article, a fashion quick response program with social media observations, demand forecast updating, and a boundedly rational retailer is studied, and the likelihood of having good social media comments on the product plays a critical role in affecting the value of quick response, and its impact is mediated by the fashion retailer's prior attitude towards the market demand.
Abstract: In this paper, we study the fashion quick response program with social media observations, demand forecast updating, and a boundedly rational retailer. We analytically find that the likelihood of having good social media comments on the product plays a critical role in affecting the value of quick response, and its impact is mediated by the fashion retailer’s prior attitude towards the market demand. We then demonstrate how a Pareto improving situation can be achieved under quick response, and uncover that manipulating social media comments can benefit the manufacturer under the surplus sharing contract, but not under the two-part tariff contract.
TL;DR: In this paper, train services on a double-track high speed railway are rescheduled in a disrupted situation, where one track of a segment is temporarily unavailable, and the uncertain duration of the disruption is handled.
Abstract: This paper reschedules train services on a double-track high speed railway in a disrupted situation, where one track of a segment is temporarily unavailable. We have to decide the sequence of train services passing through the blocked segment, the arrival and departure time of each train service at each station, and the canceled train services. Three practical train rescheduling strategies are explicitly compared and formulated by three MILP models. The uncertain duration of the disruption is handled. A rolling horizon approach is applied to solve our models. The models are tested on a real-world instance of the Beijing-Shanghai high speed railway.
TL;DR: In this article, the authors examined the existence of dynamic volatility spillovers within and between the dry-bulk and tanker freight markets by employing the multivariate DCC-GARCH model and the volatility spillover index developed by Diebold and Yilmaz (2012, 2009).
Abstract: This paper examines the existence of dynamic volatility spillovers within and between the dry-bulk and tanker freight markets by employing the multivariate DCC-GARCH model and the volatility spillover index developed by Diebold and Yilmaz (2012, 2009). This methodology is invariant to ordering the variables when estimating a VAR model and allows for the disaggregation of volatility spillovers in total, directional, net and net pairwise. Results reveal the existence of large time-varying volatility spillovers across shipping freight markets, which are more intense during and after the global financial crisis.
TL;DR: In this paper, the authors developed a generalized Nash equilibrium network model for post-disaster humanitarian relief by nongovernmental organizations (NGOs), where NGOs derive utility from providing relief supplies to victims of the disaster at demand points in a supply chain context while competing with each other for financial funds provided by donations.
Abstract: We develop a Generalized Nash Equilibrium network model for post-disaster humanitarian relief by nongovernmental organizations (NGOs). NGOs derive utility from providing relief supplies to victims of the disaster at demand points in a supply chain context while competing with each other for financial funds provided by donations. The shared constraints consist of lower and upper bounds for demand for relief items at the demand points to reduce materiel convergence or congestion. This game theory problem is reformulated as an optimization problem and numerical examples and a theoretical case study on Hurricane Katrina given.
TL;DR: In this article, the authors investigated the network design problem arising from the regional hazardous waste management system, where the problem is to identify the locations of various waste facilities, and determine the transportation routes of hazardous wastes and waste residues between those waste facilities.
Abstract: This paper investigates the network design problem arising from the regional hazardous waste management system. The problem is to identify the locations of various waste facilities, and determine the transportation routes of hazardous wastes and waste residues between those waste facilities. Aiming at minimizing jointly the total cost and total risk, the problem is formulated as a multi-objective mixed integer linear programming model. By exploiting the advantages of the model, three multi-objective optimization approaches are customized to find highly qualified non-dominated solutions. The effectiveness and efficiency of the approaches are examined both on a hypothetical case and a realistic case.
TL;DR: This work proposes an adaptive large-neighborhood search with several specifically designed operators and features, showing the excellent performance of the algorithm in terms of solution quality and computational efficiency.
Abstract: This problem involves optimizing product collection and redistribution from production locations to a set of processing plants over a planning horizon. This horizon consists of several days, and the collection-redistribution is performed on a repeating daily basis. A single routing plan must be prepared for the whole horizon, taking into account the seasonal variations in the supply. We model the problem using a sequence of periods, each corresponding to a season. We propose an adaptive large-neighborhood search with several specifically designed operators and features. The results show the excellent performance of the algorithm in terms of solution quality and computational efficiency.