Strategies for an integrated distribution problem

The problems arising in the logistics of commercial distribution are complexand involve several players and decision levels. One important decision isrelated with the design of the routes to distribute the products, in anefficient and inexpensive way.This article explores three different distribution strategies: the firststrategy corresponds to the classical vehicle routing problem; the second isa master route strategy with daily adaptations and the third is a strategythat takes into account the cross-functional planning through amulti-objective model with two objectives. All strategies are analyzed ina multi-period scenario. A metaheuristic based on the Iteratetd Local Search,is used to solve the models related with each strategy. A computationalexperiment is performed to evaluate the three strategies with respect to thetwo objectives. The cross functional planning strategy leads to solutions thatput in practice the coordination between functional areas and better meetbusiness objectives.

Comparative genomics of chemosensory protein genes reveals rapid evolution and positive selection in ant-specific duplicates.

Gene duplications can have a major role in adaptation, and gene families underlying chemosensation are particularly interesting due to their essential role in chemical recognition of mates, predators and food resources. Social insects add yet another dimension to the study of chemosensory genomics, as the key components of their social life rely on chemical communication. Still, chemosensory gene families are little studied in social insects. Here we annotated chemosensory protein (CSP) genes from seven ant genomes and studied their evolution. The number of functional CSP genes ranges from 11 to 21 depending on species, and the estimated rates of gene birth and death indicate high turnover of genes. Ant CSP genes include seven conservative orthologous groups present in all the ants, and a group of genes that has expanded independently in different ant lineages. Interestingly, the expanded group of genes has a differing mode of evolution from the orthologous groups. The expanded group shows rapid evolution as indicated by a high dN/dS (nonsynonymous to synonymous changes) ratio, several sites under positive selection and many pseudogenes, whereas the genes in the seven orthologous groups evolve slowly under purifying selection and include only one pseudogene. These results show that adaptive changes have played a role in ant CSP evolution. The expanded group of ant-specific genes is phylogenetically close to a conservative orthologous group CSP7, which includes genes known to be involved in ant nestmate recognition, raising an interesting possibility that the expanded CSPs function in ant chemical communication.

On a problem of Alfréd Rényi

Alfréd Rényi, in a paper of 1962, A new approach to the theory ofEngel's series, proposed a problem related to the growth of theelements of an Engel's series. In this paper, we reformulate andsolve Rényi's problem for both, Engel's series and Pierceexpansions.

The P-median problem in a changing network: The case of Barcelona

In this paper a p--median--like model is formulated to address theissue of locating new facilities when there is uncertainty. Severalpossible future scenarios with respect to demand and/or the travel times/distanceparameters are presented. The planner will want a strategy of positioning thatwill do as ``well as possible'' over the future scenarios. This paper presents a discrete location model formulation to address this P--Medianproblem under uncertainty. The model is applied to the location of firestations in Barcelona.

Recent misconceptions about the "database search problem": a probabilistic analysis using Bayesian networks

This paper analyses and discusses arguments that emerge from a recent discussion about the proper assessment of the evidential value of correspondences observed between the characteristics of a crime stain and those of a sample from a suspect when (i) this latter individual is found as a result of a database search and (ii) remaining database members are excluded as potential sources (because of different analytical characteristics). Using a graphical probability approach (i.e., Bayesian networks), the paper here intends to clarify that there is no need to (i) introduce a correction factor equal to the size of the searched database (i.e., to reduce a likelihood ratio), nor to (ii) adopt a propositional level not directly related to the suspect matching the crime stain (i.e., a proposition of the kind 'some person in (outside) the database is the source of the crime stain' rather than 'the suspect (some other person) is the source of the crime stain'). The present research thus confirms existing literature on the topic that has repeatedly demonstrated that the latter two requirements (i) and (ii) should not be a cause of concern.

A house price index defined in the potential outcomes framework

Current methods for constructing house price indices are based on comparisons of sale prices of residential properties sold two or more times and on regression of the sale prices on the attributes of the properties and of their locations. The two methods have well recognised deficiencies, selection bias and model assumptions, respectively. We introduce a new method based on propensity score matching. The average house prices for two periods are compared by selecting pairs of properties, one sold in each period, that are as similar on a set of available attributes (covariates) as is feasible to arrange. The uncertainty associated with such matching is addressed by multiple imputation, framing the problem as involving missing values. The method is applied to aregister of transactions ofresidential properties in New Zealand and compared with the established alternatives.

Locating emergency services with different priorities: The priority queuing covering location problem

Previous covering models for emergency service consider all the calls to be of the sameimportance and impose the same waiting time constraints independently of the service's priority.This type of constraint is clearly inappropriate in many contexts. For example, in urban medicalemergency services, calls that involve danger to human life deserve higher priority over calls formore routine incidents. A realistic model in such a context should allow prioritizing the calls forservice.In this paper a covering model which considers different priority levels is formulated andsolved. The model heritages its formulation from previous research on Maximum CoverageModels and incorporates results from Queuing Theory, in particular Priority Queuing. Theadditional complexity incorporated in the model justifies the use of a heuristic procedure.

Adverse selection, credit and efficiency: The case of the missing market

We analyze a standard environment of adverse selection in credit markets. In our environment,entrepreneurs who are privately informed about the quality of their projects needto borrow in order to invest. Conventional wisdom says that, in this class of economies, thecompetitive equilibrium is typically inefficient.We show that this conventional wisdom rests on one implicit assumption: entrepreneurscan only access monitored lending. If a new set of markets is added to provide entrepreneurswith additional funds, efficiency can be attained in equilibrium. An important characteristic ofthese additional markets is that lending in them must be unmonitored, in the sense that it doesnot condition total borrowing or investment by entrepreneurs. This makes it possible to attainefficiency by pooling all entrepreneurs in the new markets while separating them in the marketsfor monitored loans.

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.

Hiring and firing costs, adverse selection and long-term unemployment

In this paper, we present a matching model with adverse selection that explains why flows into and out of unemployment are much lower in Europe compared to North America, while employment-to-employment flows are similar in the two continents. In the model,firms use discretion in terms of whom to fire and, thus, low quality workers are more likely to be dismissed than high quality workers. Moreover, as hiring and firing costs increase, firms find it more costly to hire a bad worker and, thus, they prefer to hire out of the pool of employed job seekers rather than out of the pool of the unemployed, who are more likely to turn out to be 'lemons'. We use microdata for Spain and the U.S. and find that the ratio of the job finding probability of the unemployed to the job finding probability of employed job seekers was smaller in Spain than in the U.S. Furthermore, using U.S. data, we find that the discrimination of the unemployed increased over the 1980's in those states that raised firing costs by introducing exceptions to the employment-at-will doctrine.

Improved estimation of the covariance matrix of stock returns with an application to portofolio selection

This paper proposes to estimate the covariance matrix of stock returnsby an optimally weighted average of two existing estimators: the samplecovariance matrix and single-index covariance matrix. This method isgenerally known as shrinkage, and it is standard in decision theory andin empirical Bayesian statistics. Our shrinkage estimator can be seenas a way to account for extra-market covariance without having to specifyan arbitrary multi-factor structure. For NYSE and AMEX stock returns from1972 to 1995, it can be used to select portfolios with significantly lowerout-of-sample variance than a set of existing estimators, includingmulti-factor models.

Selection of artificial hosts for oviposition by wild Anastrepha obliqua (Macquart) (Diptera, Tephritidae): influence of adult food and effect of experience

Several factors influence the selection of oviposition substrates by insects. The aim of the present work was to find answers to the following questions related to the oviposition behavior of Anastrepha obliqua. Can carbohydrates (glucose or sucrose) present in the adult diet have influence on the female preference for an oviposition substrate with similar composition? Can the previous experience with a host containing one of mentioned carbohydrates interfere in further selection of oviposition substrates? The results showed that the kind of carbohydrate present in the adult diet did not affect the female preference for an artificial oviposition substrate, neither when it was presented by itself nor in combination with brewer's yeast. The effect of experience in the oviposition behavior was observed when there was a previous contact with artificial oviposition substrates containing yeast and sucrose. The data are discussed in terms of the behavioral plasticity presented by this species in relation to feeding and oviposition behaviors.

Drivers and sources of supply flexibility: An exploratory study

Purpose - There has been much research on manufacturing flexibility, but supply chain flexibility is still an under-investigated area. This paper focuses on supply flexibility, the aspects of flexibility related to the upstream supply chain. Our purpose is to investigate why and how firms increase supply flexibility.Methodology/Approach An exploratory multiple case study was conducted. We analyzed seven Spanish manufacturers from different sectors (automotive, apparel, electronics and electrical equipment).Findings - The results show that there are some major reasons why firms need supply flexibility (manufacturing schedule fluctuations, JIT purchasing, manufacturing slack capacity, low level of parts commonality, demand volatility, demand seasonality and forecast accuracy), and that companies increase this type of flexibility by implementing two main strategies: to increase suppliers responsiveness capability and flexible sourcing . The results also suggest that the supply flexibility strategy selected depends on two factors: the supplier searching and switching costs and the type of uncertainty (mix, volume or delivery).Research limitations - This paper has some limitations common to all case studies, such as the subjectivity of the analysis, and the questionable generalizability of results (since the sample of firms is not statistically significant).Implications - Our study contributes to the existing literature by empirically investigating which are the main reasons for companies needing to increase supply flexibility, how they increase this flexibility, and suggesting some factors that could influence the selection of a particular supply flexibility strategy.

Driver scheduling problem modelling

The Drivers Scheduling Problem (DSP) consists of selecting a set of duties for vehicle drivers, for example buses, trains, plane or boat drivers or pilots, for the transportation of passengers or goods. This is a complex problem because it involves several constraints related to labour and company rules and can also present different evaluation criteria and objectives. Being able to develop an adequate model for this problem that can represent the real problem as close as possible is an important research area.The main objective of this research work is to present new mathematical models to the DSP problem that represent all the complexity of the drivers scheduling problem, and also demonstrate that the solutions of these models can be easily implemented in real situations. This issue has been recognized by several authors and as important problem in Public Transportation. The most well-known and general formulation for the DSP is a Set Partition/Set Covering Model (SPP/SCP). However, to a large extend these models simplify some of the specific business aspects and issues of real problems. This makes it difficult to use these models as automatic planning systems because the schedules obtained must be modified manually to be implemented in real situations. Based on extensive passenger transportation experience in bus companies in Portugal, we propose new alternative models to formulate the DSP problem. These models are also based on Set Partitioning/Covering Models; however, they take into account the bus operator issues and the perspective opinions and environment of the user.We follow the steps of the Operations Research Methodology which consist of: Identify the Problem; Understand the System; Formulate a Mathematical Model; Verify the Model; Select the Best Alternative; Present the Results of theAnalysis and Implement and Evaluate. All the processes are done with close participation and involvement of the final users from different transportation companies. The planner s opinion and main criticisms are used to improve the proposed model in a continuous enrichment process. The final objective is to have a model that can be incorporated into an information system to be used as an automatic tool to produce driver schedules. Therefore, the criteria for evaluating the models is the capacity to generate real and useful schedules that can be implemented without many manual adjustments or modifications. We have considered the following as measures of the quality of the model: simplicity, solution quality and applicability. We tested the alternative models with a set of real data obtained from several different transportation companies and analyzed the optimal schedules obtained with respect to the applicability of the solution to the real situation. To do this, the schedules were analyzed by the planners to determine their quality and applicability. The main result of this work is the proposition of new mathematical models for the DSP that better represent the realities of the passenger transportation operators and lead to better schedules that can be implemented directly in real situations.