Joint pricing and ordering decisions for a loss-averse retailer with quantity-oriented reference point effect and demand uncertainty: a distribution-free approach

PurposeThis work examines the joint pricing and ordering (JPO) decisions for a loss-averse retailer with quantity-oriented reference point (RP) effect under demand uncertainty.Design/methodology/approachThe demand is assumed to be uncertain with the mean and variance as the only known information. The prospect theory is used to model the retailer's expected utility. An expected utility maximization model in the distribution-free approach (DFA) is then developed. Using duality theory, the expected utility under the worst-case distribution is transformed into tractable piece-wise functions. To examine the effectiveness of the DFA in coping with the demand uncertainty, a stochastic programming model is developed and its solutions are used as benchmarks.FindingsThe proposed model and solution approach can effectively hedge against the demand uncertainty. The JPO decisions are significantly influenced by the LA coefficient and the reference level. The LA has a stronger influence than the reference level does on the expected utility. An excessive LA is detrimental while an appropriate reference level is beneficial to the retailer.Practical implicationsThe results of this work are applicable to loss-averse retailers with the quantity-oriented RP when making JPO decisions with difficulty in predicting the demands.Originality/valueThe demand is assumed to be uncertain in this work, but a certain demand distribution is usually assumed in the existing literature. The DFA is used to study JPO decisions for the loss-averse retailer with quantity-oriented RP effect under the uncertain demand.

Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City

Urban traffic demand distribution is dynamic in both space and time. A thorough analysis of individuals’ travel patterns can effectively reflect the dynamics of a city. This study aims to develop an analytical framework to explore the spatiotemporal traffic demand and the characteristics of the community structure shaped by travel, which is analyzed empirically in New York City. It uses spatial statistics and graph-based approaches to quantify travel behaviors and generate previously unobtainable insights. Specifically, people primarily travel for commuting on weekdays and entertainment on weekends. On weekdays, people tend to arrive in the financial and commercial areas in the morning, and the functions of zones arrived in the evening are more diversified. While on weekends, people are more likely to arrive at parks and department stores during the daytime and theaters at night. These hotspots show positive spatial autocorrelation at a significance level of p = 0.001. In addition, the travel flow at different peak times form relatively stable community structures, we find interesting phenomena through the complex network theory: 1) Every community has a very small number of taxi zones (TZs) with a large number of passengers, and the weighted degree of TZs in the community follows power-law distribution; 2) As the importance of TZs increases, their interaction intensity within the community gradually increases, or increases and then decreases. In other words, the formation of a community is determined by the key TZs with numerous traffic demands, but these TZs may have limited connection with the community in which they are located. The proposed analytical framework and results provide practical insights for urban and transportation planning.

Exploring Taxi Demand Distribution of Comprehensive Land-Use Patterns Using Online Car-Hailing Data and Points of Interest in Chengdu, China

With the rapid advance of urbanization, land-use intensity is increasing, and various land-use forms gather to form comprehensive land-use patterns. Traffic demand shows variability and complexity under comprehensive land-use patterns. Accurate analysis of traffic demand in urban transportation is the key to active traffic control and road guidance. Researchers have widely studied the relationship between traffic demand and land-use patterns, while land-use intensity is ignored when classifying land-use patterns, and the traffic demand distribution in each land-use pattern is not studied specifically. Taxi is a flexible public mode in urban areas, and taxi demand is an important component in analyzing traffic demand and identifying traffic hotspots in cities. This paper explores taxi demand distribution of comprehensive land-use patterns using online car-hailing data and points of interest (POI) in Chengdu, China. The demand-driven traffic analysis zones are developed by clustering origin–destination points of online car-hailing services. Using POI data, comprehensive land-use patterns are classified with land-use forms and land-use intensity. The K-shape algorithm is adopted to extract the typical taxi demand distribution in each comprehensive land-use pattern. Finally, two indicators, total taxi demand (TTD) and taxi demand difference (TDD), are computed and further analyzed. Results show that taxi demand distribution is still differential even under the same land-use pattern. Three land-use patterns whose average hourly taxi demand reaches about 300 vehicles per square kilometer have the largest TTD and most uneven TDD. The findings can support traffic management, land-use combination, and land-use adjustment to avoid concentrated taxi demand and mismatched TDD.

A Stackelberg–Nash–Cournot Equilibrium in a Pollution Reduction Scheme

This paper presents a Stochastic Stackelberg–Nash–Cournot Equilibrium model with continuous market demand distribution to examine the effectiveness of ambient charges as an effective policy measure for reducing nonpoint source pollution in a hybrid scheme. To do so, we consider the supply side of an energy market with hybrid technology that competes in an oligopoly market setting. Within such a setting, each power plant or firm uses a mix of fossil fuels (F) and renewable energy sources (R) to generate power at any given time. The demand for electricity is not realized at the time when the firm (leader) makes the decision. The competition between the two energy sources available to leader is assumed to be of Nash–Cournot equilibria, implying that they use one energy source to generate electricity, whilst holding the other energy source as a constant when the followers reactions are known. Based on the assumption that the demand function is affine and power plants cost functions are quadratic, we obtain the Stackelberg–Nash–Cournot equilibrium. Hence, our analysis provides an interesting insight into the effectiveness of using ambient charges, within the context of a Stochastic Stackelberg–Nash–Cournot competition, as an environmental economic policy measure when included within a robust hybrid scheme. From an economical point of view, this allows pollutants to develop specific control technologies by undertaking research and development (R&D) measures or production processes to maintain emissions standards in a hybrid scheme. From a policy implementations point of view, the environmental authority can use the pollution abatement technology ratio to set ambient charges and industry specific pollutant quantitative limits subject to technological variations.

Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design

The emergence of online retailers has brought new opportunities to the design of their distribution networks. Notably, for online retailers that do not operate offline stores, their target customers are more sensitive to the quality of logistic services, such as delivery speed and reliability. This paper is motivated by a leading online retailer for cosmetic products on Taobao.com that aimed to improve its logistics efficiency by redesigning its centralized distribution network into a multilevel one. The multilevel distribution network consists of a layer of primary facilities to hold stocks from suppliers and transshipment and a layer of secondary facilities to provide last-mile delivery. There are two major challenges of designing such a facility network. First, online customers can respond significantly to the change of logistics efficiency with the redesigned network, thereby rendering the network optimized under the original demand distribution suboptimal. Second, because online retailers have relatively small sales volumes and are very flexible in choosing facility locations, the facility candidate set can be large, causing the facility location optimization challenging to solve. To this end, we propose an iterative prediction-and-optimization strategy for distribution network design. Specifically, we first develop an artificial neural network (ANN) to predict customer demands, factoring in the logistic service quality given the network and the city-level purchasing power based on demographic statistics. Then, a mixed integer linear programming (MILP) model is formulated to choose facility locations with minimum transportation, facility setup, and package processing costs. We further develop an efficient two-stage heuristic for computing high-quality solutions to the MILP model, featuring an agglomerative hierarchical clustering algorithm and an expectation and maximization algorithm. Subsequently, the ANN demand predictor and two-stage heuristic are integrated for iterative network design. Finally, using a real-world data set, we validate the demand prediction accuracy and demonstrate the mutual interdependence between the demand and network design. Summary of Contribution: We propose an iterative prediction-and-optimization algorithm for multilevel distribution network design for e-logistics and evaluate its operational value for online retailers. We address the issue of the interplay between distribution network design and the demand distribution using an iterative framework. Further, combining the idea in operational research and data mining, our paper provides an end-to-end solution that can provide accurate predictions of online sales distribution, subsequently solving large-scale optimization problems for distribution network design problems.

HS-MOEA/D: An Oriented Algorithm for Delay and Reliability VNF-SC Deployment

Network function virtualization (NFV) technology can realize on-demand distribution of network resources and improve network flexibility. It has become one of the key technologies for next-generation communications. Virtual network function service chain (VNF-SC) deployment is an important problem faced by network function virtualization technology. In this paper, the problem, VNF deployment for VNF-SC, is investigated. First, a two-objective mathematical model, which maximizes balancing and reliability of SFC, is established. In this model, VNFs are divided into two classes, i.e., part of required VNFs in each VNF-SC is dependent, others are independent. Second, harmony search-based MOEA/D (HS-MOEA/D) is proposed to solve the model effectively. In HS-MOEA/D, Chebyshev decomposition mechanism is used to transform multiobjective optimization problem into a series of single-objective optimization subproblems. A new evolutionary strategy is deeply studied in order to propose a new harmony search (HS) algorithm. Finally, to show high performance of the proposed algorithm, a large number of experiments are conducted. The simulation results show that the proposed algorithm enhances the reliability of SFC and reduces the end-to-end delay.

Resiliency management of layer poultry farm business during COVID-19 pandemic in the Yogyakarta Special Province

<p class="MDPI17abstract"><strong><span lang="EN-US">Objective: </span></strong><span lang="EN-GB">The objective of the research was to identify crises on layer poultry farms specifically in Yogyakarta Special Province during COVID-19 pandemic and investigate business resiliency management to be utilised by the farmers to deal with the situation.</span></p><p class="MDPI17abstract"><strong><span lang="EN-US">Methods: </span></strong><span lang="EN-US">The research was conducted on Yogyakarta Special Province in April-October 2020 with qualitative approach method and data collection was taken using in depth interview method. Research respondents were 2 layer farmers who were also chairman of farmers association, namely <em>Pinsar Petelur Nasional</em> and 10 farmers purposively selected using snow-ball sampling method. Data collection was taken by face to face indepth and telephone interview and subsequently data was analysed descriptively and by using content analysis</span><span lang="EN-GB">.</span><strong></strong></p><p class="MDPI17abstract"><strong><span lang="EN-US">Results</span></strong><strong><span lang="EN-US">: </span></strong><span lang="EN-GB">Layer poultry farmers of Yogyakarta Special Province had faced challenges on their business environment that was becoming business crisis source due to disease outbreaks, natural disasters, monetary crisis, to COVID-19 pandemic. Impacts caused by the pandemic on layer poultry industry of Yogyakarta Special Province affected market demand, distribution, egg price fluctuation and feed price, respectively. Learning through self-awareness on environmental changes and conducting community and organisation coordinations that was PPN and the adaptational management that adopted by farmers to overcome the impact of COVID-19 in business.</span><strong></strong></p><p class="MDPI17abstract"><strong><span lang="EN-US">Conclusions: </span></strong><span lang="EN-GB">Experience and endurance levels were the keys to resolve risks throughout running a farming business is important asset in conducting resiliency management in mitigating impacts of COVID-19 pandemic. Presence of community, group, and organisation networks will be usefull for the growth of capacity and opportunity of farmers in utilising novel and communication technology to negotiate with relevant farming industry stakeholders to maintain the existence of layer farmers in industry during and after the pandemic. Government supports and appropriate policy are also needed in supporting a hospitable business climate for the continuity of layer poultry farms.</span></p>

Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon

The ongoing COVID-19 pandemic is creating disruptive changes in urban mobility that may compromise the sustainability of the public transportation system. As a result, worldwide cities face the need to integrate data from different transportation modes to dynamically respond to changing conditions. This article combines statistical views with machine learning advances to comprehensively explore changing urban mobility dynamics within multimodal public transportation systems from user trip records. In particular, we retrieve discriminative traffic patterns with order-preserving coherence to model disruptions to demand expectations across geographies and show their utility to describe changing mobility dynamics with strict guarantees of statistical significance, interpretability and actionability. This methodology is applied to comprehensively trace the changes to the urban mobility patterns in the Lisbon city brought by the current COVID-19 pandemic. To this end, we consider passenger trip data gathered from the three major public transportation modes: subway, bus, and tramways. The gathered results comprehensively reveal novel travel patterns within the city, such as imbalanced demand distribution towards the city peripheries, going far beyond simplistic localized changes to the magnitude of traffic demand. This work offers a novel methodological contribution with a solid statistical ground for the spatiotemporal assessment of actionable mobility changes and provides essential insights for other cities and public transport operators facing mobility challenges alike.

Heterodox Macroeconomics: algunas implicaciones para las opciones de política económica en México y los Estados Unidos y en el estudio de los modelos à la Harrod

Nota bibliográfica de: R. A. Blecker y M. Setterfield (2019). Heterodox Macroeconomics: Models of Demand, Distribution and Growth. Cheltenham, Reino Unido, y Northampton, Massachusetts: Edward Elgar.