Commodity flow estimation for a metropolitan scale freight modeling system: supplier selection considering distribution channel using an error component logit mixture model

Freight forecasting models have been significantly improved in recent years, especially in the field of goods vehicle behavior modeling. On the other hand, the improvements to commodity flow modeling, which provide inputs for goods vehicle simulations, were limited. Contributing to this component in urban freight modeling systems, we propose an error component logit mixture model for matching a receiver to a supplier that considers two-layers in supplier selection: distribution channels and specific suppliers. The distribution channel is an important element in freight modeling, as the type of distribution channel is relevant to various aspects of shipments and vehicle trips. The model is estimated using the data from the Tokyo Metropolitan Freight Survey. We demonstrate how typical establishment survey data (i.e. establishment and outbound shipment records) can be used to develop the model. The model captures the correlation structure of potential suppliers defined by business function and provides insights on the differences in the supplier choice by distribution channel. The reproducibility tests confirm the validity of the proposed approach, which is currently integrated into a metropolitan-scale agent-based freight modeling system, for practical use.

     

Future planning for benchmarking and ranking sustainable suppliers using goal programming and robust double frontiers DEA

Decision-making for selecting sustainable suppliers has become an intricate duty. To rank sustainable suppliers and select benchmarks this paper proposes an efficiency improvement plan. Two levels of improvement plans including goals and benchmarks are presented for the suppliers. To this end, the first-level goals are obtained using goal programming (GP) and data envelopment analysis (DEA). Since inputs and outputs of the first-level goals might be imprecise, robust Charnes-Cooper-Rhodes (CCR) model is run. As a result, the benchmarks of the second-level are obtained. Then, a robust CCR inefficiency model is applied for ranking the suppliers. In fact, such a ranking capability is made by creating double-frontiers including CCR efficiency and inefficiency frontiers. Accordingly, the suppliers are ranked using the first-level goals. In the new ranking, uncertainty of the goals is considered by running robust optimization technique. The proposed approach provides technical and planning capabilities which are demonstrated by a case study.     

Environmentally friendly cost efficient and effective sea transport outsourcing strategy: The case of Statoil

Statoil is a completely integrated oil and gas Norwegian company which established in 1972. The company is one of the most technology-intensive world’s leading energy producers and covers more than 35 countries across the globe. This study proposes how Statoil coordinate with environmentally friendly, cost efficient and effective sea transport supplier. The study used the theories of supply chain management such as model of industrial purchasing process, model of strategic triangle and Kraljic purchasing portfolio model. The result of the study identified that the emission of environmentally harmful gases from sea transport are dependent on the size and the speed of the ships, and the type of fuel they consume. The best solution for Statoil to get environmentally friendly sea transport service is using suppliers that are certified by the preferred standard of ITTC 7.5-04-01-01.2 or ISO 15016:2002. The requirement of environmentally friendly sea transport supplier by Statoil maximizes the suppliers’ risk. Therefore, making partnership with the supplier is the effective and giving the contract with a cost reimbursable agreement of fuel price adjustment (escalation clause) is the cost efficient coordination of Statoil.     

Determining vehicle dispatch frequency when shipping frequency differs among suppliers

Collecting is a way to consolidate freight that involves trucks picking up material from more than one supplier on each trip to a single destination. Earlier research resulted in a method that optimizes collecting vehicle dispatch frequency and that assumes that all suppliers are visited on each dispatch. This paper develops a method that determines the optimal dispatch frequency when large suppliers are visited more frequently than are small suppliers. This added flexibility allows the combined inventory and transportation costs of collecting to be reduced. The method is developed analytically and illustrated with an example.     

A two-sided market platform analysis for the electric vehicle adoption: Firm strategies and policy design

This study explores how to facilitate the electric vehicle (EV) diffusion from a two-sided market platform competition. We develop a stylized model depicting the platform competition between electric and gasoline vehicles by combining indirect network effects of consumer and energy supplier sides as well as vehicle manufacturers’ profits. The findings of this study provide several meaningful strategic and policy implications for EV manufacturers and policymakers who wish to enhance EV diffusion. First, EV sales are significantly influenced by indirect network effects from the energy supplier side to the consumer side, and vice versa. This implies that EV manufacturers who wish to boost EV diffusion should implement a strategy providing energy suppliers with incentives to willingly join the EV platform. Second, the dynamic nature of the effects of energy costs on platform competition might render counter-intuitive evidence that the drop in oil prices does not always negatively influence EV sales. This requires EV manufacturers to prepare a contingent strategy adjusting to such unexpected conditions. Third, governments should consider the energy supplier side as well as the consumer side in designing EV diffusion policies. When governments have a very challenging EV diffusion target, a balanced policy, which treats both gasoline and electric vehicle technologies fairly, may be more effective than a consumer subsidy policy.     

Impact analysis of climate change on rail systems for adaptation planning: A UK case

Climate change poses critical challenges for rail infrastructure and operations. However, the systematic analysis of climate risks and the associated costs of tackling them, particularly from a quantitative perspective, is still at an embryonic phase due to the kaleidoscopic nature of climate change impacts and lack of precise climatic data. To cope with such challenges, an advanced Fuzzy Bayesian Reasoning (FBR) model is applied in this paper to understand climate threats of the railway system. This model ranks climate risks under high uncertainty in data and comprehensively evaluates these risks by taking account of infrastructure resilience and specific aspects of severity of consequence. Through conducting a nationwide survey on the British railway system, it dissects the status quo of primary climate risks. The survey implies that the top potential climate threats are heavy precipitation and floods. The primary risks caused by the climate threats are bridges collapsing and bridge foundation damage due to flooding and landslips. The findings can aid transport planners to prioritise climate risks and develop rational adaptation measures and strategies.     

Uncertainty modeling for bus selection and allocation in a private transportation system

ABSTRACT

This paper describes the development of a probabilistic formulation that provides global optimum selection and allocation of a fleet of buses in a private transportation system of an organization where a third party is hired to provide transportation for its employees and their dependents. In this private transportation system, a fleet of buses is to be selected and allocated to serve employees and their independents on different prescheduled trips along different routes from the organization’s headquarters and residential compound where round-trip times of scheduled trips are subject to uncertainty due to random delays. We propose a probabilistic approach based on 0-1 integer programming for the selection and allocation to determine the optimal number and size of buses assigned to a set of prescheduled trips in a particular time interval. Examples and a case study are presented to illustrate the applicability and suitability of the proposed approach.     

An optimal aircraft fleet management decision model under uncertainty

Decision planning for an efficient fleet management is crucial for airlines to ensure a profit while maintaining a good level of service. Fleet management involves acquisition and leasing of aircraft to meet travelers' demand. Accordingly, the methods used in modeling travelers' demand are crucial as they could affect the robustness and accuracy of the solutions. Compared with most of the existing studies that consider deterministic demand, this study proposes a new methodology to find optimal solutions for a fleet management decision model by considering stochastic demand. The proposed methodology comes in threefold. First, a five‐step modeling framework, which is incorporated with a stochastic demand index (SDI), is proposed to capture the occurrence of uncertain events that could affect the travelers' demand. Second, a probabilistic dynamic programming model is developed to optimize the fleet management model. Third, a probable phenomenon indicator is defined to capture the targeted level of service that could be achieved satisfactorily by the airlines under uncertainty. An illustrative case study is presented to evaluate the applicability of the proposed methodology. The results show that it is viable to provide optimal solutions for the aircraft fleet management model. Copyright © 2013 John Wiley & Sons, Ltd.     

A stochastic fleet size and mix model for maintenance operations at offshore wind farms

A fleet of vessels and helicopters is needed to support maintenance operations at offshore wind farms. The cost of this fleet constitutes a major part of the total maintenance costs, hence keeping an optimal or near-optimal fleet is essential to reduce the cost of energy. In this paper we study the vessel fleet size and mix problem that arises for the maintenance operations at offshore wind farms, and propose a stochastic three-stage programming model. The stochastic model considers uncertainty in vessel spot rates, weather conditions, electricity prices and failures to the system. The model is tested on realistic-sized problem instances, and the results show that it is valuable to consider uncertainty and that the proposed model can be used to solve instances of a realistic size.     

Truck backhauling on two terminal networks

Truck backhauling reduces empty truck-miles by having drivers haul loads on trips back to their home terminal. This paper 1) examines the impact on backhauling opportunities of terminal locations and directional imbalances in the flow of freight from the terminals, and 2) develops a method for determining which truckloads should be backhauled. Backhauling is studied for two terminals sending full truckloads to many customers under steady-state conditions. This research develops two backhauling models. The first is a continuous model that makes simplifying assumptions about customer locations and travel distances. It results in formulae showing that 1) savings from backhauling increase at a decreasing rate as the directional flow of freight between two terminals becomes more balanced and 2) backhauling is an important, but often ignored, factor in terminal (e.g. trucking terminal, warehouse, or plant) location and supplier selection decisions. The second model is a more general discrete model that determines which loads should be backhauled to minimize empty truck-miles.     

Visualisation of uncertainty in probabilistic traffic models for policy and operations

This study investigates different methods to visualise uncertainty in static representations of probabilistic traffic models predictions on road-networks. Although various graphical cues may be used to represent uncertainty it is not a priori clear which of them are most suited for this purpose, since their legibility, intelligibility and the degree to which they interfere with other graphical elements in a representation differ widely. Several graphical uncertainty representations were therefore developed and analysed in expert sessions. A selection of the initial set of uncertainty visualisations was further evaluated in a cognitive alternative task-switching experiment. The results show that graphical representations are able to convey uncertainty information relatively accurately, while some uncertainty visualisations outperform others. It depends on the model and scenario which representation is most suited for a given application. This paper presents an overview of possible graphic uncertainty representations and the considerations involved when applying them to uncertainty in traffic model visualisations.     

The price of uncertainty in pavement infrastructure management planning: An integer programming approach

Currently there is a true dichotomy in the pavement maintenance and rehabilitation (M&R) literature. On the one hand, there are integer programming-based models that assume that parameters are deterministically known. On the other extreme, there are stochastic models, with the most popular class being based on the theory of Markov decision processes that are able to account for various sources of uncertainties observed in the real-world. In this paper, we present an integer programming-based alternative to account for these uncertainties. A critical feature of the proposed models is that they provide – a priori – probabilistic guarantees that the prescribed M&R decisions would result in pavement condition scores that are above their critical service levels, using minimal assumptions regarding the sources of uncertainty. By construction of the models, we can easily determine the additional budget requirements when additional sources of uncertainty are considered, starting from a fully deterministic model. We have coined this additional budget requirement the price of uncertainty to distinguish from previous related work where additional budget requirements were studied due to parameter uncertainties in stochastic models. A numerical case study presents valuable insights into the price of uncertainty and shows that it can be large.     

A fuzzy-stochastic optimization model for the intermodal fleet management problem of an international transportation company

ABSTRACT

In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO₂ emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model.     

Uncertainty analysis of the large zone economic module of the simple,efficient, elegant,and effective model (SE3M) of land use and transportation

Integrated land use and transportation forecasting models are used to assist decision-makers in the policy analysis and infrastructure capital improvement selection process. These models are typically given precise, point-estimate inputs that are mathematically linked, through a series of submodels, to forecasted model outputs. These point-estimate inputs represent an unrealistic level of precision and a growing body of research is focusing on statistical techniques to model uncertainty in model inputs and parameters and tracking the effects of this uncertainty through the various submodels to the model outputs. This paper presents an uncertainty analysis of the Large Zone Economic Module (LZEM) of the Simple, Efficient, Elegant, and Effective Model (SE³M) of land use and transportation. Three case-study implementations of the model are used to obtain a reasonably sound approximation of how uncertainty affects LZEM outputs: Guam, Puerto Rico, and Oahu, Hawaii. These case studies were the subject of an early transferability study with SE³M and were selected based on both their insularity and diverse physical, economic, and demographic geographies. The findings of this research demonstrate that LZEM has a robust framework, with the potential to estimate error both in the positive and negative direction under uncertain input/parameter conditions.     

Myopic loss aversion in the response of electric vehicle owners to the scheduling and pricing of vehicle charging

Upward expectations of future electric vehicle (EV) growth pose the question about the future load on the electricity grid. While existing literature on EV charging demand management has focused on technical aspects and considered EV-owners as utility maximizers, this study proposes a behavioural model incorporating psychological aspects relevant to EV-owners facing charging decisions and interacting with the supplier. The behavioural model represents utility maximization under myopic loss aversion (MLA) within an ultimatum game (UG) framework where the two players are the EV-owner and the electricity supplier. Experimental economics allowed testing the validity of the behavioural model by designing three experiments where a potential EV-owner faces three decisions (i.e., to postpone EV charging to off-peak periods for a discount proposed by the supplier, the amount of discount to request for off-peak charging at times decided by the supplier, and the amount of discount to accept for supplier-controlled charging) under two contract durations (i.e., short-term, long-term). Findings from the experiments show that indeed potential EV-owners perform charging decisions while being affected by MLA resulting from monetary considerations and the UG participation, and that presenting long-term contracts help potential EV-owners to curtail MLA behaviour and minimise cost even though the assumption of utility maximization is violated.     

Real options and flexibility analysis in design and management of one-way mobility on-demand systems using decision rules

This study explores the concepts of real options and flexibility analysis as an approach to address uncertain demand growth in mobility on-demand (MoD) vehicle-sharing systems, with the goal of improving expected lifecycle performance. As MoD systems are gaining popularity worldwide, they inevitably face significant uncertainty in terms of needs and customer demands. Designing, planning capacity deployment, and operating such system can be challenging, and require significant capital investments for companies and cities. Two distinct real options analysis (ROA) models are developed to evaluate and optimize flexible strategies for these systems, relying on a novel methodological approach to value flexibility based on decision rules. The decision-rule-based approach differs from standard ROA approaches used to quantify the value of flexibility in irreversible investment projects, typically based on dynamic programming. It emulates the decision-making process by capturing mathematically a triggering mechanism that determines when it is best to exercise the flexibilities embedded in the system design. Two prevalent types of MoD systems are studied in this paper as demonstration of the methodological framework: a station-based system where customers must pick up and return the vehicle at specific locations, and a free-floating system, where customers may pick up and drop the car anywhere within a certain area. A simulation-based approach is used to analyze the station-based system, which models the rebalancing operations from a micro-level perspective. The approach consists of a discrete event simulator for performance estimation, and an optimization algorithm for design space exploration that integrates a population-based search algorithm with Optimal Computing Budget Allocation (OCBA). For the free-floating system, an analytical model is developed where the decision rule is formulated into and solved using stochastic mixed integer programming (MIP). The study provides guidance to system operators on potential strategies for deploying MoD systems, considering explicitly uncertainty and flexibility as a value enhancing mechanism.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

The supply side of the automated people mover market: A spectrum of choices

A survey of the supply side of the Automated People Mover (APM) market over the past ten to fifteen years shows some significant trends. In the nineteen eighties, suppliers concentrated on designing, developing, and marketing unique technologies. Thus, each supplier tended to specialize in a single technology, with some variations in size and capacity. With the present growth of the APM market, several APM suppliers have developed, either through mergers or acquisitions, a more diversified range of technologies to meet a variety of system requirements in terms of capacity, service and budget considerations. As a result of this trend, the transit industry now has a full range of technologies that are well proven and capable of meeting the different needs, special operational requirements, and budgets of its potential clients.     

Welfare effects of road pricing and traffic information under alternative ownership regimes

This paper models strategic interactions between a road supplier, a provider of traffic information, and road users, with stochastic travel times. Using a game-theoretical analysis of suppliers’ pricing strategies, we assess the social welfare effects of traffic information under various ownership regimes. The results show that the distortive welfare effect of monopolistic information pricing appears relatively small. Collusion of the road operator and information provider yields higher social welfare than independent pricing by two firms. The intuition behind this result resembles that behind the welfare effects of double marginalization, but is not exactly the same, as traffic information is not strictly complementary to road use.     

Flight and passenger delay assignment optimization strategies

This paper compares different optimization strategies for the minimization of flight and passenger delays at two levels: pre-tactical, with on-ground delay at origin, and tactical, with airborne delay close to the destination airport. The optimization model is based on the ground holding problem and uses various cost functions. The scenario considered takes place in a busy European airport and includes realistic values of traffic. A passenger assignment with connections at the hub is modeled. Statistical models are used for passenger and connecting passenger allocation, minimum time required for turnaround and tactical noise; whereas uncertainty is also introduced in the model for tactical noise. Performance of the various optimization processes is presented and compared to ration by schedule results.