Customer Selection Research Articles

A GRASP-based Solution Construction Approach for the Multi-Vehicle Profitable Pickup and Delivery Problem

With the advancement of e-commerce and Internet shopping, the high competition between carriers has made many companies rethink their service mechanisms to customers, in order to ensure that they stay competitive in the market. Therefore, companies with limited resources focus on serving only customers who provide high profits at the lowest possible cost. The Multi-Vehicle Profitable Pickup and Delivery Problem (MVPPDP) is a vehicle routing problem and one variant of the Selective Pickup and Delivery Problem (SPDP) that is considered to plan the services for these types of companies. The MVPPDP aims to serve only the profitable customers, where the products are transformed from a selection of pickup customers to the corresponding delivery customers, within a given travel time limit. In this paper, we utilize the construction phase of the well-known Greedy Randomized Adaptive Search Procedure (GRASP) to build initial solutions for the MVPPDP. The performance of the proposed method is compared with two greedy construction heuristics that were previously used in the literature to build the initial solutions of the MVPPDP. The results proved the effectiveness of the proposed method, where eight new initial solutions are obtained for the problem. Our approach is especially beneficial for building a population of solutions that combine both diversity and quality, which can help to obtain good solutions in the improvement phase of the problem.

A multi-objective service composition recommendation method for individualized customer: Hybrid MPA-GSO-DNN model

With the development of manufacturing customization, unified manufacturing service recommendation is difficult to meet the customer’s individualized demand. To this end, the existing research hotspots focus on solving personalized service recommendation issues. However, the personalized recommendation for service composition is more complex compared with the existing single service recommendation. Especially in the case of less customer historical data, analyzing customer preference and recommending appropriate composite service is a difficult problem. Therefore, this paper proposes a hybrid MPA-GSO-DNN model based on manufacturing service to address the personalized recommendation problem for service composition. Firstly, a hybrid multi-objective preference analysis model and glowworm swarm optimization algorithm (MPA-GSO) is proposed to generate deep learning training set by analyzing customer preference and repetitively simulating the customer's selection process. The glowworm swarm optimization (GSO) algorithm is improved with dynamic step to solve the continuous multi-objective optimization in MPA-GSO. Secondly, a deep neural network (DNN) is structured to analyze candidate services and provide personalized recommendation. Finally, a case study is presented to demonstrate the performance and practicability of the proposed approach.

ChemSystemsChem: All Systems Go!

<i>ChemSystemsChem</i>: All Systems Go!

Assessing Patient-Reported Outcomes in People with Myelodysplastic Syndromes: Can a Customized Selection of Items from the EORTC Library Enhance the EORTC QLQ-C30?

INTRODUCTIONAssessing patient-reported outcomes (PROs) is an essential part of understanding the ways in which a new drug might affect how a patient with cancer feels and functions within a clinical trial. Certain widely used PRO measures are now being supplemented with a selection of items from available item banks to ensure the measure is targeted to the specific context of use. Previous mixed methods research in higher risk myelodysplastic syndromes (HR MDS), chronic myelomonocytic leukemia (CMML), and low blast acute myeloid leukemia (LB AML) led to the development of a conceptual model of the symptoms and impacts for this population. It informed a PRO measurement strategy based on the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 items (QLQ-C30) along with an additional, customized selection of 10 supplemental items from the EORTC library to cover unaddressed concepts. Clinical features such as anemia cause fatigue and dyspnea, which may negatively impact patient functioning, thus further emphasizing the importance of the patient voice when evaluating the value of a new therapy to patients. The purpose of this research was to incorporate the patient voice and generate information on this measurement strategy in a sample of people with HR MDS/ CMML and LB AML.METHODSThis study was a one-time, cross-sectional patient-reported survey administered online. People living with HR MDS, non-proliferative CMML, and LB AML were recruited through the MDS Foundation, social media, and recruitment agencies. Patients completed the EORTC QLQ-C30 plus 10 supplemental items from the EORTC item library. In addition to PRO data, they were also asked to record demographics and disease-related information. Responses to the 40 PRO items were described and psychometric analyses based on Rasch measurement theory (RMT) were conducted on the original multi-item domains of the EORTC QLQ-C30. RMT analysis defines how a set of items should perform to generate reliable and valid measurements, which is important for the generalizability of the PRO instrument's psychometric evaluations to other contexts of use. RMT analyses were also performed on the following, enhanced domain measures which included original items and supplemental items: fatigue, dyspnea, physical functioning, and role functioning.RESULTSA total of 51 patients were recruited and participated in the online survey (HR MDS: 51%; CMML: 27%; LB AML: 10%). The mean age was 67.2 (SD: 11.8) and 49% of patients were female. The description of responses showed a possible floor effect for several EORTC QLQ-C30 items (i.e. a substantial percentage of responder reported the lowest value of the scale), whereas the responses to the supplemental items were well distributed across the response categories (Figure). This confirmed that the customized supplemental item selection was well targeted to the patient sample. Overall, the RMT psychometric examination of the original EORTC QLQ-C30 domain scores showed satisfactory ability to assess key outcomes for people with HR MDS/CMML and LB AML including fatigue and physical functioning. However, it highlighted some areas for possible improvement, due to gaps in the conceptual coverage of symptoms and impacts of HR MDS/CMML and LB AML. The enhanced fatigue, physical functioning and dyspnea measures that combined the original QLQ-C30 items with the supplemental items from the EORTC library showed improved measurement performances for these key domains for people with HR MDS/CMML and LB AML, typically in terms of conceptual coverage.CONCLUSIONSOur data shows how incorporating the patient voice early on in PRO measurement selection and a conceptually-driven approach to the development of customized item sets can lead to more fit-for-purpose PRO measures to be used in the context of clinical trials of patients with cancer. Specifically, while the QLQ-C30 may be a satisfactory PRO instrument for use in people with hematological stem cell disorders, it can be made more targeted and disease-specific by carefully utilizing mixed methods and selecting supplemental items from the EORTC item library. This patient-reported online survey provides early evidence, in a small sample of patients, on the appropriateness of both the original QLQ-C30 and enhanced item sets to assess PROs in HR MDS/CMML, and AML and should be further analyzed in future research. DisclosuresBell:Takeda Pharmaceuticals: Employment, Equity Ownership. Pompilus:Modus Outcomes: Employment; Takeda Pharmaceuticals: Research Funding. Christian:Modus Outcomes: Employment; Takeda Pharmaceuticals: Research Funding. Mazerolle:Modus Outcomes: Employment; Takeda Pharmaceuticals: Research Funding. Scipione:Takeda Pharmaceuticals: Employment. Bejar:Takeda: Research Funding; Celgene: Consultancy, Honoraria; Foundation Medicine: Consultancy; Modus Outcomes: Consultancy; Genoptix: Consultancy; AbbVie/Genentech: Consultancy, Honoraria; Astex/Otsuka: Consultancy, Honoraria. Galaznik:Takeda Pharmaceuticals International Co.: Employment. Fram:Takeda Pharmaceuticals: Consultancy; BeyondSpring Pharmaceuticals, Inc.: Consultancy. Faller:Takeda Pharmaceuticals: Employment, Equity Ownership. Cano:Modus Outcomes: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Takeda Pharmaceuticals: Research Funding. Regnault:Modus Outcomes: Employment, Membership on an entity's Board of Directors or advisory committees; Takeda Pharmaceuticals: Research Funding.

A hybrid multi-objective genetic local search algorithm for the prize-collecting vehicle routing problem

This paper studies the prize-collecting vehicle routing problem (PCVRP), which is a new variant of the vehicle routing problem. In the PCVRP, besides the vehicle assignment and visiting sequencing, customer selection also must be determined because the available vehicles are insufficient to visit all the customers. Two types of PCVRP are often encountered in the real world, namely the PCVRP in which the number of vehicles is predetermined (PCVRP-P) and that in which the number of vehicles is not predetermined (PCVRP-NP). In general, multiple optimization objectives are required to be considered in both the PCVRP-P and PCVRP-NP. Unlike the commonly used weighted-sum method, a Pareto-based evolutionary algorithm featuring a genetic algorithm combined with a local search strategy (denoted as HGLS) is proposed for solving the multi-objective PCVRP-P. Subsequently, based on the proposed HGLS, a decomposition strategy is designed to solve the multi-objective PCVRP-NP problem by decomposing it into multiple PCVRP-P problems. In order to determine the number of PCVRP-P problems in the decomposition strategy, a vehicle-number-range determination approach is proposed to obtain a tight upper bound and lower bound of the number of vehicles. The superiority of the proposed algorithm is demonstrated by conducting several experiments on the benchmark problems.

Measuring Factors That Influence Customers' Selection on Wealth Management Services in Taiwan

The aim of this study is to investigate the key factors that influence customers' selection of wealth management (WM) services in Taiwan. We used a face-to-face interview to execute a two-stage questionnaire survey and employed an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to measure factors influencing customers' selection of WM services. The results of EFA suggest that factors consisting of brand image, perceived value, and financial consultants' service quality, might influence customers' selection of WM services. CFA results further provide evidence that those factors significantly account for customers' overall satisfaction. The moderating analysis result however reveals that married and single groups have different views on selecting a WM services. The single customers are more likely to rely on brand images in the choice of WM services, whereas the married customers are more likely to place the emphasis on consultants' service quality. This study provides a chance for WM service providers to understand the key factors that impact customer's selection decision, so providers can address those key factors in their marketing strategy. Therefore, they can compete in this intensive market in better shape. In sum, consultants' service quality is the basic requirement for customers while perceived value plays the important role for financial institutions to excel in the WM markets.

Analysing customer based airline brand equity: Perspective from Taiwan

This research develops and examines a customer-based Airline brand equity (CBABE) model and examines the mediating effects of Airline brand reputation on the relationship among CBABE and Airline brand trust. By means of structural equation modelling (SEM) analysis, the four dimensions of CBABE– Airline flight service quality, Airline brand affect, Airline brand awareness and Airline brand association– are recognized to have positive effects on Airline brand reputation. Besides, it is established that Airline brand affect, customer self-congruence, Airline brand awareness and Airline brand reputation has a positive effect on Airline brand trust. The data collected was analysed using 7 – Likert scale instrument and this research provides insight into effective strategies that can be used to increase Airline brand trust and protect Airline brand reputation for an Airline. Based on the outcomes obtained from the relationship between CBABE, Airline brand reputation and Airline brand trust, the study confirms substantial difference among the groups deliberating to the customer purpose of air travel that is associated to CBABE dimensions. The findings provide enriched understanding of intricate psychological process involved in air travel customer selection criteria for an Airline or Airline industry.

ANALYSIS OF THE NETWORK WITH MULTIPLE CLASSES OF POSITIVE CUSTOMERS AND SIGNALS AT A NON-STATIONARY REGIME

The object of research is G-network with positive customers and signals of multiple classes. The present paper describes an analysis of this network at a non-stationary regime, also provided a description of method for finding non-stationary state probabilities.At the beginning of the article, a description of the network with positive customers and signals is given. A signal when entering the system destroys a positive customer of its type or moves the customer of its type to another system. Streams of positive customers and signals arriving to each of the network systems are independent. Selection of positive customers of all classes for service – randomly. For non-stationary state probabilities of the network, the system of Kolmogorov difference-differential equations (DDE) has been derived. It is solved by a modified method of successive approximations, combined with the method of series. The convergence of successive approximations with time has been proved to the stationary distribution of probabilities, the form of which is indicated in the article, and the sequence of approximations converges to the unique solution of the DDE system. Any successive approximation is representable in the form of a convergent power series with an infinite radius of convergence, the coefficients of which satisfy recurrence relations, which is convenient for computer calculations.The obtained results can be applied for modeling behavior of computer viruses and attack in computer systems and networks, for example, as model impact of some file viruses on server resources.

Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain

In the supply chain of Fast-moving consumer goods, logistics costs are a main part of the expenses. In low levels of these chains, we usually face with a Vehicle Routing Problem. In practice, due to the high cost of service in many cases, some customers are not chosen for serving. Restrictions associated with the investment in many cases makes impossible serve to some potential clients. In this problem, design a supply chain network, including a location-allocation problem in the warehouse, Multiple Depot Vehicle Routing Problem at the level of distribution and customer selection at the retail level in several periods of time is considered. In this issue, in addition to certain methods that can be used in small sizes, Meta-heuristic algorithms to solve large-scale model have been used. With the aim of improving performance, if not improve in a few diversifications, algorithms are temporarily enhanced. In GA this change leads to improve 1.2 percent and 1.6 percent about the mean of solutions and the best solution and in SA algorithm, this change is led to improving by 2.1% and 2.4% in average solutions and the best solutions. Finally, this approach about a real problem in Sepahan Oil Company is employed.

Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K Means

This research was conducted to study data mining using K Means algorithm which is implemented in the selection of potential customers in MC Laundry. Data collected through observation and interviews conducted to the owner of the company concerned. Further data is analyzed to determine the data needed in the process of determining potential customers. From experiments conducted using K Means algorithm and using Tanagra Software can provide accurate results in classifying potential customers.&#x0D; Keywords: Data Mining, K-Means Algorithm, Tanagra Software

Radiometric Evaluation of SNPP VIIRS Band M11 via Sub-Kilometer Intercomparison with Aqua MODIS Band 7 over Snowy Scenes

A refined intersensor comparison study is carried out to evaluate the radiometric stability of the 2257 nm channel (M11) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. This study is initiated as part of the examination into the performance of key shortwave infrared (SWIR) bands for SNPP VIIRS ocean color data processing and applications, with Band M11 playing key role over turbid and inland waters. The evaluation utilizes simultaneous nadir overpasses (SNOs) to compare SNPP VIIRS Band M11 against Band 7 of the MODerate-resolution Imaging Spectroradiometer (MODIS) in the Aqua satellite over concurrently observed scenes. The standard result of the radiance comparison is a seemingly uncontrolled and inconsistent time series unsuitable for further analyses, in great contrast to other matching band-pairs whose radiometric comparisons are typically stable around 1.0 within 1% variation. The mismatching relative spectral response (RSR) between the two respective bands, with SNPP VIIRS M11 at 2225 to 2275 nm and Aqua MODIS B7 at 2125 to 2175 nm, is demonstrated to be the cause of the large variation because of the different dependence of the spectral responses of the two bands over identical scenes. A consistent radiometric comparison time series, however, can be extracted from SNO events that occur over snowy surfaces. A customized selection and analysis procedure successfully identifies the snowy scenes within the SNO events and builds a stable comparison time series. Particularly instrumental for the success of the comparison is the use of the half-kilometer spatial resolution data of Aqua MODIS B7 that significantly enhances the statistics. The final refined time series of Aqua MODIS B7 radiance over the SNPP VIIRS M11 radiance is stable at around 0.39 within 2.5% showing no evidence of drift. The radiometric ratio near 0.39 suggests the strong presence of medium-grained snow of a mixed-snow condition in those SNO scenes leading to successful comparison. The multi-year stability indicates the correctness of the on-orbit RSB calibration of SNPP VIIRS M11 whose result does not suffer from long-term drift.

Abstract P4-06-09: Addition of a remote genetic counselor to the breast specialist's team improves clinical decision-making

Abstract Background: There is a shortage of trained genetic counselors (GC) and often long wait times for appointments, resulting in other specialists frequently ordering genetic testing. However, non-genetic specialists, including breast surgeons, find it difficult to stay current in genetics due to rapid advances in gene discovery, expanded panel offerings, and frequent changes to professional guidelines. We tested a novel model for hereditary cancer risk assessment where breast surgeons had “on demand” access to a remote laboratory-based genetic counselor for peer to peer consultation. In this study we sought to determine the impact this model has on breast surgeons' routinely ordering genetic testing including test identification, ordering patterns, and medical management. Methods: An IRB approved multi-center prospective study involved 14 community-based breast cancer surgeons experienced with hereditary cancer risk assessment without a genetic counselor as part of their practice. Cases were all discussed with a remote Invitae GC to determine testing eligibility and selection. Physicians had the option to utilize remote GCs to discuss results or to refer to traditional genetic counseling services. Pre and post-test surveys were completed for each patient by the testing physician. To protect patient privacy, a unique case ID was used to link patient test data with identifying data. Results: A total of 192 patients were evaluated with median age of 52. Risk assessment via BRCAPRO and the Hughes Risk model were performed on 98% of patients by the physicians. 65% of patients met NCCN guidelines for testing. Pathogenic mutations were found in 14% of patients. Breast surgeons changed their test selection 21% of the time after discussion with a GC. They called to discuss results in 47% of cases and medical management changes were incorporated in 15% of these cases based on discussion with a remote GC. Conclusions: Remote GC provider support assisted physicians in facilitating customized test selection, aided in navigating challenging counseling cases, and impacted clinical management. This service may serve as a viable, effective model for 'on demand' genetic counseling support and may be a novel opportunity to expand genetic testing in a breast surgery setting. Citation Format: Rosen B, O'Leary E, Shan Y, Pat W, Peter B. Addition of a remote genetic counselor to the breast specialist's team improves clinical decision-making [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-06-09.

Assessing lifetime profitability of customers with purchasing cycles

PurposeThe purpose of this paper is to propose a method to help firms assess lifetime profitability of customers whose buying behaviors are characterized by purchasing cycles, which are determined by both intrinsic purchasing cycles and cumulative effects of firms’ marketing solicitations.Design/methodology/approachThis paper first proposes a probability model to predict customers’ responses to firms’ marketing solicitations in which a customer’s inter-purchase times are assumed to follow a Poisson distribution, whose parameters vary across customers and follow a gamma distribution. The paper then proposes a customer profitability scoring model that uses customers’ responses as an input to assess their lifetime profitability at a given point of time.FindingsThe paper illustrates the proposed method using individual-level purchasing data of 529 customers from a catalog firm. The paper shows that the proposed model outperforms the benchmark model in terms of both explaining and predicting customers’ purchases. The paper also demonstrates significant profit consequences to the firm if incorrect methods are used instead of the proposed method.Practical implicationsThe proposed method can help firms select or eliminate customers based on their lifetime profitability so that firms can focus their marketing efforts in a more targeted manner to increase total profits.Originality/valueThe proposed Gamma-Poisson probability model and the profitability scoring method are easy to implement due to the attractive conjugacy property. It is valuable for firms’ customer relationship management applications from the standpoint of making customer selection and inventory management decisions.

A new stratified random sample customer selection for load research study in distribution networks

Distribution network decision makers need accurate and reliable information about load characteristics to plan, estimate and control the system properly. Load information is generally extracted from the collected load data of selected sample customers and, therefore, a proper sample customer selection is the pillar of any load research study. In this regard, this paper presents a new stratified sampling technique which includes three stages of sample size determination and customer stratification, sample size assignment to determined strata (subgroups), and sample customer selection. The sample size assignment to determined strata is done by considering the compatibility between load research objectives and sampling design and preparing the way to use the data and information provided by previous load research studies. Furthermore, sample customers are selected by considering the energy consumption (kWh) range of customers, their activity classification, and their locations in distribution feeders. The numerical results from a real data of an electric power distribution system in Esfahan-Iran verify the efficiency of the proposed technique when compared to the conventional method.

Variable Selection of Customers for Churn Analysis in Telecommunication Industry

The telecommunication industry considers customer relationship management as a significant issue for organizational adaptation. Mobile service providers have enforced CRM with the objective to reduce the number of customers that churn. The objective of this article is to detect high impact factors leading to customer churn in the mobile industry over the present-day market situation in Delhi-NCR by using a questionnaire survey and examine their importance. The study is done to understand usage patterns of customers using mobile data services. The data collected was analyzed using descriptive statistics to identify the most common issues to identify attributes of selecting a service provider, cellular usage, and service quality. Thus, the authors have selected possible variables for modeling the decision tree to build a churn prediction model. A renewed customer service, after analyzing this experience, could predict those customers who are at risk of switching to a different provider.

When Salespeople Manage Customer Relationships: Multidimensional Incentives and Private Information

Incentivized salespeople are often responsible for CRM activities that focus not only on customer acquisition, but also maintenance activities that increase customer lifetime value. While incentives can induce effort on the incentivized tasks, a salesperson’s private information about customers may induce moral hazard by salespeople seeking to gain short-term compensation at the expense of the firm’s long run profitability. We investigate the dynamics in the effort--moral hazard tradeoff in response to multidimensional (acquisition and maintenance) performance incentives in the presence of private information. Using unique panel data on customer loan acquisition and repayments linked to salespeople from a microfinance bank, we detect evidence of salesperson private information. Acquisition incentives induce salesperson moral hazard leading to adverse customer selection, but maintenance incentives moderate it as salespeople recognize the negative effects of acquiring low quality customers on future payoffs. Critically, without the moderating effect of maintenance incentives, the adverse selection effect of acquisition incentives overwhelms the sales enhancing effort effects, clarifying the importance of multidimensional incentives in CRM-type settings. Reducing private information (through job transfers) hurts customer maintenance, but has greater impact on productivity by moderating adverse selection at acquisition. The paper also contributes to the recent literature on detecting and disentangling customer adverse selection and customer moral hazard (defaults) with a new identification strategy that exploits the time varying effects of salesperson incentives.

Dependence of a Loan Portfolio Structure on a Cut-Off Level in a Scoring Model

The study aims to validate a mathematical model of influence of applications’ selection process on a loan portfolio structure expected by the end of a planned period. When predicting risks and profitability of a loan portfolio, many authors use a mathematical model of a loan portfolio in the form of a Markov chain with discrete time. This model usually does not consider the process of attracting new customers. The present paper proposes a more complete model for changing a loan portfolio structure in the form of a Markov chain taking into account a procedure of attracting new customers and selecting them based on the credit rating. The main advantage of this scheme is that it allows taking into consideration the change in a cut-off level when using a scoring model of customer selection. This provides an opportunity to predict dynamics of the volume and structure of a loan portfolio depending on the selected cut-off level under sufficiently stable economic conditions

Identification and mapping of ts (tender spines), a gene involved in soft spine development in Cucumis sativus.

Using map-based cloning of ts gene, we identified a new sort of gene involved in the initiation of multicellular tender spine in cucumber. The cucumber (Cucumis sativus L.) fruit contains spines on the surface, which is an extremely valuable quality trait affecting the selection of customers. In this study, we elaborated cucumber line NC072 with wild type (WT) hard fruit spines and its spontaneous mutant NC073, possessing tender and soft spines on fruits. The mutant trait was named as tender spines (ts), which is controlled by a single recessive nuclear gene. We identified the gene ts by map-based cloning with an F2 segregating population of 721 individuals generated from NC073 and WT line SA419-2. It was located between two markers Indel6239679 and Indel6349344, 109.7kb physical distance on chromosome 1 containing fifteen putative genes. With sequencing and quantitative reverse transcription-polymerase chain reaction analysis, the Csa1G056960 gene was considered as the most possible candidate gene of ts. In the mutant, Csa1G056960 has a nucleotide change in the 5' splicing site of the second intron, which causes different splicing to delete the second exon, resulting in a N-terminal deletion in the predicted amino acid sequence. The gene encodes a C-type lectin receptor-like tyrosine-protein kinase which would play an important role in the formation of cucumber fruit. This is firstly reported of a receptor kinase gene regulating the development of multicellular spines/trichomes in plants. The ts allele could accelerate the molecular breeding of cucumber soft spines.

Symposium review: Possibilities in an age of genomics: The future of selection indices

Selective breeding has been practiced since domestication, but early breeders commonly selected on appearance (e.g., coat color) rather than performance traits (e.g., milk yield). A breeding index converts information about several traits into a single number used for selection and to predict an animal's own performance. Calculation of selection indices is straightforward when phenotype and pedigree data are available. Prediction of economic values 3 to 10 yr in the future, when the offspring of matings planned using the index will be lactating, is more challenging. The first USDA selection index included only milk and fat yield, whereas the latest version of the lifetime net merit index includes 13 traits and composites (weighted averages of other additional traits). Selection indices are revised to reflect improved knowledge of biology, new sources of data, and changing economic conditions. Single-trait selection often suffers from antagonistic correlations with traits not in the selection objective. Multiple-trait selection avoids those problems at the cost of less-than-maximal progress for individual traits. How many and which traits to include is not simple to determine because traits are not independent. Many countries use indices that reflect the needs of different producers in different environments. Although the emphasis placed on trait groups differs, most indices include yield, fertility, health, and type traits. Addition of milk composition, feed intake, and other traits is possible, but they are more costly to collect and many are not yet directly rewarded in the marketplace, such as with incentives from milk processing plants. As the number of traits grows, custom selection indices can more closely match genotypes to the environments in which they will perform. Traditional selection required recording lots of cows across many farms, but genomic selection favors collecting more detailed information from cooperating farms. A similar strategy may be useful in less developed countries. Recording important new traits on a fraction of cows can quickly benefit the whole population through genomics.

Investigating the environmental and economic impact of loading conditions and repositioning strategies for pallet pooling providers

Pallets are fundamental assets critical to worldwide supply chain logistics. This research develops models for closed-loop pallet pooling providers to understand the environmental and economic impact of customer characteristics and design options. First, an analytical model is developed to quantify the effects of repair facility location and pallet service conditions on a pallet pooling system's economic and environmental performance. Next, a simulation model is developed to investigate two common operational policies, crossdocking and take-back, and to quantify the impact of pallet handling and loading conditions and customer network structures on several key performance indicators. Results indicate that pallet handling and loading conditions are the most important factors determining the cost and carbon equivalent emission of a pallet pooling operation. Better pallet handling and appropriate loading increase the percentage of pallets that can be repositioned with little or no repair. This increases the radius within which a closed-loop pallet pooling system is feasible. Under random handling/loading conditions and distances, a crossdocking approach satisfies demand with 28% fewer pallets than a take-back policy. This is due to a quicker reissue time under a crossdocking approach. However, associated costs and emissions of the two policies are nearly identical due to the increased transportation costs associated with crossdocking. The models and insights proposed in this work can help support decision making by pallet pooling providers to determine operational regions and customer selection, among other network design trade-offs.