Exploring impact factors of shippers’ risk prevention activities: A European survey in transportation

This paper reports results of a survey examining the impact factors for shippers to implement risk prevention activities. A regression analysis illustrates the relations between risk and quality related impact factors and the shippers’ ability to implement risk prevention activities. The study identifies the corporate risk culture and product vulnerability as main impact factors for the implementation of prevention activities in transportation. This paper will be beneficial for transportation managers considering the implementation of risk prevention activities in the transportation field, and will support further empirical research in the transportation management and supply chain risk management research area.     

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.     

Effects of carbon emission reduction policies on transportation mode selections with stochastic demand

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.     

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 MILP model for planning the trips of dynamic positioned tankers with variable travel time

The transportation of the crude oil produced in offshore oilfields to onshore terminals is performed by vessels, known as shuttle tankers. Scheduling shuttle-tanker operations entails solving complex problems to ensure a timely offloading of the platforms, taking into account several logistics and inventory constraints. This work proposes a new MILP formulation that advances previous works by considering variable travel time between platforms and terminals. The combination of the MILP formulation with an optimization solver constitutes a decision-support tool to aid engineers reach optimal decisions for a planning horizon. To handle large-scale instances, rolling-horizon and relax-and-fix strategies are proposed.     

Delivery leadtime and channel structure decisions for make-to-order duopoly under different game scenarios

We develop game-theoretic models to explore the quoted delivery leadtime, price, and channel structure decisions for a make-to-order duopoly system under three game scenarios. Under the integrated-manufacturer first scenario, we find that (i) decentralization of the supply chain increases quoted leadtime; and (ii) both manufacturers may choose different channel structures under symmetric duopoly. By comparing with the symmetric scenario and the retailer first scenario, we find that a manufacturer facing a decentralized rival adopts decentralization when leadtime sensitivity, leadtime cost, and price elasticity are very small; the effect of decentralization on quoted leadtime largely depends on game scenario.     

Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium

This paper presents a bi-level robust optimization model, where a food company maximizes its profit and minimizes post-harvest loss by optimally deploying grain processing/storage facilities and determining grain purchase price, while a group of spatially distributed non-cooperative farmers determine harvest time, shipment, storage, and market decisions under yield uncertainty and market equilibrium. The non-cooperative behavior of the food company and the farmers is represented by a bi-level Stackelberg leader follower’s game model with mixed-integer decision variables. The proposed model and solution approach are applied to case studies for Illinois and Brazil.     

Optimal transit fare and service frequency of a nonlinear origin-destination based fare structure

This paper proposes a new nonlinear distance-based transit fare structure, which is measured by a function of the Euclidean distance between the origin and destination stations, termed as Origin-Destination (OD)-based fare. The novel fare structure encourages passengers to freely choose the most efficient trip plan. An optimization model is formulated based on a three-party game (involving the transport authority, transit company, and passenger) to determine the optimal fare function and frequency. An artificial bee colony algorithm is adopted to solve the model. Finally, a numerical example is provided to verify the proposed method.     

Advancing destination image: The destination content model

Knowledge of the mental representations that individuals hold about tourist destinations are important to understand their intentions. These mental destination representations have often been investigated by applying the concept of destination image. This study argues that the extant literature is often rather atheoretical and lacks operational rigor. These are major shortcomings which undoubtedly hinder the development of academic and managerial insights. In response, this study draws on contemporary psychology to develop the destination content model, comprising three informational components held in individuals‘ minds about destinations. The present study further outlines preferable methods and measures for each component, thus aiding researchers to investigate mental destination representations.     

Insurance ratemaking using a copula-based multivariate Tweedie model

The Tweedie distribution, featured with a mass probability at zero, is a convenient tool for insurance claims modeling and pure premium determination in general insurance. Motivated by the fact that an insurance policy typically provides multiple types of coverage, we propose a copula-based multivariate Tweedie regression for modeling the semi-continuous claims while accommodating the association among different types. The proposed approach also allows for dispersion modeling, resulting in a multivariate version of the double generalized linear model. We demonstrate the application in insurance ratemaking using a portfolio of policyholders of automobile insurance from the state of Massachusetts in the United States.