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1 – 10 of over 3000
Article
Publication date: 4 January 2022

Xiang Li, Ming Yang, Hongguang Ma and Kaitao (Stella) Yu

Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the…

Abstract

Purpose

Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.

Design/methodology/approach

The authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.

Findings

Case studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.

Originality/value

This study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

Details

Industrial Management & Data Systems, vol. 122 no. 10
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 9 April 2021

Omid Kebriyaii, Marzieh Hamzehei and Mohammad Khalilzadeh

The number of natural and man-made disasters is remarkable and threatened human lives at the time of occurrence and also after that. Therefore, an efficient response following a…

Abstract

Purpose

The number of natural and man-made disasters is remarkable and threatened human lives at the time of occurrence and also after that. Therefore, an efficient response following a disaster can eliminate or mitigate the adverse effects. This paper aims to help address those challenges related to humanitarian logistics by considering disaster network design under uncertainty and the management of emergency relief volunteers simultaneously.

Design/methodology/approach

In this paper, a robust fuzzy stochastic programming model is proposed for designing a relief commodity supply chain network in a disaster by considering emergency relief volunteers. To demonstrate the practicality of the proposed model, a case study is presented for the 22 districts of Tehran and solved by an exact method.

Findings

The results indicate that there are many parameters affecting the design of a relief commodity supply chain network in a disaster, and also many parameters should be controlled so that, the catastrophe is largely prevented and the lives of many people can be saved by sending the relief commodity on time.

Practical implications

This model helps decision-makers and authorities to explore optimal location and allocation decisions without using complex optimization algorithms.

Originality/value

To the best of the authors’ knowledge, employee workforce management models have not received adequate attention despite their role in relief and recovery efforts. Hence, the proposed model focuses on the problem of managing employees and designing a disaster logistics network simultaneously. The robust fuzzy stochastic programming method is applied for the first time for controlling the uncertainties in the design of humanitarian relief supply chains.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 11 no. 3
Type: Research Article
ISSN: 2042-6747

Keywords

Article
Publication date: 27 December 2021

Sara Nodoust, Mir Saman Pishvaee and Seyed Mohammad Seyedhosseini

Given the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem…

Abstract

Purpose

Given the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem in order to distribute first aid relief items in the post disaster phase, where routes are subject to disruption.

Design/methodology/approach

To cope with such kind of uncertainty, the demand rate of relief items is considered as a random fuzzy variable and a robust scenario-based possibilistic-stochastic programming model is elaborated. The results are presented and reported on a real case study of earthquake, along with sensitivity analysis through some important parameters.

Findings

The results show that the demand satisfaction level in the proposed model is significantly higher than the traditional scenario-based stochastic programming model.

Originality/value

In reality, in the occurrence of a disaster, demand rate has a mixture nature of objective and subjective and should be represented through possibility and probability theories simultaneously. But so far, in studies related to this domain, demand parameter is not considered in hybrid uncertainty. The worth of considering hybrid uncertainty in this study is clarified by supplementing the contribution with presenting a robust possibilistic programming approach and disruption assumption on roads.

Open Access
Article
Publication date: 30 April 2013

Hongjoo Lee and Hosang Jung

In this paper, we propose a scenario based global supply chain planning (GSCP) process considering demand uncertainty originated from various global supply chain risks. To…

Abstract

In this paper, we propose a scenario based global supply chain planning (GSCP) process considering demand uncertainty originated from various global supply chain risks. To generate the global supply chain plan, we first formulate a GSCP model. Then, we need to generate several scenarios which can represent various demand uncertainties. Lastly, a planning procedure for considering those defined scenarios is applied. Unlike the past related researches, we adopt the fuzzy set theory to represent the demand scenarios. Also, a scenario voting process is added to calculate a probability (possibility) of each scenario. An illustrative example based on a real world case is presented to show the feasibility of the proposed planning process.

Details

Journal of International Logistics and Trade, vol. 11 no. 1
Type: Research Article
ISSN: 1738-2122

Keywords

Article
Publication date: 17 July 2020

Arya Sohrabi, Mir Saman Pishvaee, Ashkan Hafezalkotob and Shahrooz Bamdad

Prepaid mobile Internet is one of the most profitable services that are composed of multiple attributes. The overall utility of Internet service can be broken down into the sum of…

Abstract

Purpose

Prepaid mobile Internet is one of the most profitable services that are composed of multiple attributes. The overall utility of Internet service can be broken down into the sum of the utility of individual attribute levels. Based on the multi-attribute theory, rational consumers choose the service that yields the highest utility from a number of possible alternatives. Determining the optimal attribute levels that satisfy consumers' preferences and maximize the total revenue of the firm is a challenging multi-attribute decision problem for any mobile operator. When designing mobile Internet services, adopting a robust composition of services against different realizations of competitors' strategies can bring advantages for network operators. The purpose of this study is to determine the optimal attribute levels of prepaid mobile Internet packages with the aim of maximizing the total revenue of the firm by considering the paradigms of multi-attribute utility theory about consumer choices and the issue of uncertainty in counterpart services offered by the competitors.

Design/methodology/approach

This paper formulates the problem of multi-attribute pricing and design of mobile Internet plans in a competitive environment by developing deterministic and robust scenario-based mathematical models and considering the paradigms of multi-attribute utility theory about consumer choices. The proposed robust scenario-based models are based on three different paradigms, including maximizing expected revenue, minimizing the negative deviation from expected revenue and minimizing the maximum regret. A comprehensive numerical analysis is conducted to evaluate and compare the efficiency of the proposed models.

Findings

The evaluations reveal that deploying recourse policy can result in higher revenue for the firm when facing uncertainty. By doing sensitivity analysis, this paper shows that consumer preferences for brand attribute and consumers' purchase frequency can influence the revenue of network operators.

Originality/value

This paper develops a novel deterministic multi-attribute product line design (PLD) model to address the problem of determining the price and composition of prepaid mobile Internet plans. Furthermore, the issue of uncertainty in counterpart services offered by the competitors is studied for the first time in the PLD literature.

Details

Journal of Enterprise Information Management, vol. 33 no. 5
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 13 November 2009

Weida Xu and Tianyuan Xiao

The purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.

Abstract

Purpose

The purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.

Design/methodology/approach

Scenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min‐max related; and the other is α‐worst scenario based. Corresponding optimization models are formulated, respectively. A genetic algorithm‐based robust optimization framework is designed. Comprehensive computational experiments are done to study the effect of these robust approaches.

Findings

With min‐max related robust criteria, the solutions can provide an optimal worst‐case hedge against uncertainties without a significant sacrifice in the long‐run performance; α‐worst scenario‐based criteria can generate flexible robust solutions: through rationally tuning the value of α, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment.

Research limitations/implications

This paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future.

Practical implications

In an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios.

Originality/value

Using robust optimization approaches to balance mixed model assembly line.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 28 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 31 December 2007

Abraham Bernstein, Peter Vorburger and Patrice Egger

People are subjected to a multitude of interruptions. In order to manage these interruptions it is imperative to predict a person's interruptability – his/her current readiness or…

Abstract

Purpose

People are subjected to a multitude of interruptions. In order to manage these interruptions it is imperative to predict a person's interruptability – his/her current readiness or inclination to be interrupted. This paper aims to introduce the approach of direct interruptability inference from sensor streams (accelerometer and audio data) in a ubiquitous computing setup and to show that it provides highly accurate and robust predictions.

Design/methodology/approach

The authors argue that scenarios are central for evaluating the performance of ubiquitous computing devices (and interruptability predicting devices in particular) and prove this on the setup employed, which was based on that of Kern and Schiele.

Findings

The paper demonstrates that scenarios provide the foundation for avoiding misleading results, and provide the basis for a stratified scenario‐based learning model, which greatly speeds up the training of such devices.

Practical implications

The direct prediction seems to be competitive or even superior to indirect prediction methods and no drawbacks have been observed yet.

Originality/value

The paper introduces a method for accurately predicting a person's interruptability directly from simple sensors without any intermediate steps/symbols.

Details

International Journal of Pervasive Computing and Communications, vol. 3 no. 4
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 14 February 2022

Cay Oertel, Ekaterina Kovaleva, Werner Gleißner and Sven Bienert

The risk management of transitory risk for real assets has gained large interest especially in the past 10 years among researchers as well as market participants. In addition, the…

Abstract

Purpose

The risk management of transitory risk for real assets has gained large interest especially in the past 10 years among researchers as well as market participants. In addition, the recent regulatory tightening in the EU urges financial market participants to disclose sustainability-related financial risk, without providing any methodological guidance. The purpose of the study is the identification and explanation of the methodological limitations in the field of transitory risk modeling and the logic step to advance toward a stochastic approach.

Design/methodology/approach

The study reviews the literature on deterministic risk modeling of transitory risk exposure for real estate highlighting the heavy methodological limitations. Based on this, the necessity to model transitory risk stochastically is described. In order to illustrate the stochastic risk modeling of transitory risk, the empirical study uses a Markov Switching Generalized Autoregressive Conditional Heteroskedasticity model to quantify the carbon price risk exposure of real assets.

Findings

The authors find academic as well as regulatory urgency to model sustainability risk stochastically from a conceptual point of view. The own empirical results show the superior goodness of fit of the multiregime Markov Switching Generalized Autoregressive Conditional Heteroskedasticity in comparison to their single regime peer. Lastly, carbon price risk simulations show the increasing exposure across time.

Practical implications

The practical implication is the motivation of the stochastic modeling of sustainability-related risk factors for real assets to improve the quality of applied risk management for institutional investment managers.

Originality/value

The present study extends the existing literature on sustainability risk for real estate essentially by connecting the transitory risk management of real estate and stochastic risk modeling.

Details

Journal of Property Investment & Finance, vol. 40 no. 4
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 12 September 2023

Mohammad Hossein Dehghani Sadrabadi, Ahmad Makui, Rouzbeh Ghousi and Armin Jabbarzadeh

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and…

Abstract

Purpose

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

Design/methodology/approach

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

Findings

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Originality/value

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

Book part
Publication date: 6 November 2013

John L. Stanton, James Wiley and Peter Charette

There is always a significant amount of speculation as to how the American diet changes over time. Some of speculation is based on what’s good for people, and others base their…

Abstract

There is always a significant amount of speculation as to how the American diet changes over time. Some of speculation is based on what’s good for people, and others base their speculation on various supply and demand issues and the impact of world social changes. However one approach to forecasting the demand for various food products in the American diet is to extrapolate how America’s eating habits would change based on two different scenarios. The first scenario is an cohort model. In this scenario individuals continue their eating habits as they get older. The second scenario is the aging model, with which it is assumed that as people age they adopt the eating habits of the group that they’re moving into.

In this chapter we will evaluate these two scenario-based extrapolation models for projecting food consumption. Data comes from the National Health and Nutrition Examination survey (NHANES), which is conducted regularly by the National Center for Health Statistics (NCHS). It measures levels of consumption with a level accuracy usually not associated with traditional business data services. The specific food items consumed in a 24-hour period are collected for over 30,000 people along with an extensive list of biometric, anthropometric, social, and clinical variables.

The models we evaluate assume that new consumers will enter the market based on projected population growth rates and that consumers “exit” the market based on projected death rates. This chapter applies the models to a subset of the total food variables in the database. Food groups that are pertinent to current issues were selected, such as beef, carbonated soft drinks, and snack foods.

The models forecast food consumption of the by 5 year increments from age 1 to age 85+ an aging cohort model extrapolate how eating habits could be expected to changeover this time interval. The implications of this exercise are essential to the forecasting and management of food processors. The extrapolations may provide guidance for potential changes in capital investment or entry into other markets.

Details

Applications of Management Science
Type: Book
ISBN: 978-1-78190-956-0

Keywords

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