Probabilistic maximal covering location-allocation models with constrained waiting time or queue length for congested systems

When dealing with the design of service networks, such as healthand EMS services, banking or distributed ticket selling services, thelocation of service centers has a strong influence on the congestion ateach of them, and consequently, on the quality of service. In this paper,several models are presented to consider service congestion. The firstmodel addresses the issue of the location of the least number of single--servercenters such that all the population is served within a standard distance,and nobody stands in line for a time longer than a given time--limit, or withmore than a predetermined number of other clients. We then formulateseveral maximal coverage models, with one or more servers per service center.A new heuristic is developed to solve the models and tested in a 30--nodesnetwork.

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.

The struggle against creative accounting: Is "true and fair view" part of the problem or part of the solution?

Creative accounting is a growing issue of interest in Spain. In this article we argue that the concept true and fair view can limit or promote the use of creative accounting depending upon its interpretation. We review the range of meanings that true and fair view can take at an international level and compare the experience of the United Kingdom with the Australian one by analysing the use of true and fair view to limit creative accounting. Finally, we suggest lines of action to be considered by the Spanish accounting standards-setting institutions.

Consumer choice in competitive location models: Formulations and heuristics

A new direction of research in Competitive Location theory incorporatestheories of Consumer Choice Behavior in its models. Following thisdirection, this paper studies the importance of consumer behavior withrespect to distance or transportation costs in the optimality oflocations obtained by traditional Competitive Location models. To dothis, it considers different ways of defining a key parameter in thebasic Maximum Capture model (MAXCAP). This parameter will reflectvarious ways of taking into account distance based on several ConsumerChoice Behavior theories. The optimal locations and the deviation indemand captured when the optimal locations of the other models are usedinstead of the true ones, are computed for each model. A metaheuristicbased on GRASP and Tabu search procedure is presented to solve all themodels. Computational experience and an application to 55-node networkare also presented.

Inventory-routing model, for a multi-period problem with stochastic and deterministic demand

The need for integration in the supply chain management leads us to considerthe coordination of two logistic planning functions: transportation andinventory. The coordination of these activities can be an extremely importantsource of competitive advantage in the supply chain management. The battle forcost reduction can pass through the equilibrium of transportation versusinventory managing costs. In this work, we study the specific case of aninventory-routing problem for a week planning period with different types ofdemand. A heuristic methodology, based on the Iterated Local Search, isproposed to solve the Multi-Period Inventory Routing Problem with stochasticand deterministic demand.

The donor problem

Donors often rely on local intermediaries to deliver benefits to target beneficiaries. Each selected recipient observes if the intermediary under-delivers to them, so they serve as natural monitors. However, they may withhold complaints when feeling unentitled or grateful to the intermediary for selecting them. Furthermore, the intermediary may distort selection (e.g. by picking richer recipients who feel less entitled) to reduce complaints. We design an experimental game representing the donor s problem. In one treatment, the intermediary selects recipients. In the other, selection is random - as by an uninformed donor. In our data, random selection dominates delegation of the selection task to the intermediary. Selection distortions are similar, but intermediaries embezzle more when they have selection power and (correctly) expect fewer complaints.

Location models for ceding market share and shrinking services

New location models are presented here for exploring the reduction of facilities in aregion. The first of these models considers firms ceding market share to competitorsunder situations of financial exigency. The goal of this model is to cede the leastmarket share, i.e., retain as much of the customer base as possible while sheddingcostly outlets. The second model considers a firm essentially without competition thatmust shrink it services for economic reasons. This firm is assumed to close outlets sothat the degradation of service is limited. An example is offered within a competitiveenvironment to demonstrate the usefulness of this modeling approach.

Location models for airline hubs behaving as M/D/c queues

Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value $\alpha$. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.

A simple optimised search heuristic for the job-shop scheduling problem

This paper presents a simple Optimised Search Heuristic for the Job Shop Scheduling problem that combines a GRASP heuristic with a branch-and-bound algorithm. The proposed method is compared with similar approaches and leads to better results in terms of solution quality and computing times.

Hierarchical location-allocation models for congested systems

In this paper we address the issue of locating hierarchical facilities in the presence of congestion. Two hierarchical models are presented, where lower level servers attend requests first, and then, some of the served customers are referred to higher level servers. In the first model, the objective is to find the minimum number of servers and theirlocations that will cover a given region with a distance or time standard. The second model is cast as a Maximal Covering Location formulation. A heuristic procedure is then presented together with computational experience. Finally, some extensions of these models that address other types of spatial configurations are offered.

Location of multiple server common service centers or public facilities for minimizing general congestion and travel cost functions

We propose a model and solution methods, for locating a fixed number ofmultiple-server, congestible common service centers or congestible publicfacilities. Locations are chosen so to minimize consumers congestion (orqueuing) and travel costs, considering that all the demand must be served.Customers choose the facilities to which they travel in order to receiveservice at minimum travel and congestion cost. As a proxy for thiscriterion, total travel and waiting costs are minimized. The travel costis a general function of the origin and destination of the demand, whilethe congestion cost is a general function of the number of customers inqueue at the facilities.

Metaheuristics for the bus-driver scheduling problem

We present new metaheuristics for solving real crew scheduling problemsin a public transportation bus company. Since the crews of thesecompanies are drivers, we will designate the problem by the bus-driverscheduling problem. Crew scheduling problems are well known and severalmathematical programming based techniques have been proposed to solvethem, in particular using the set-covering formulation. However, inpractice, there exists the need for improvement in terms of computationalefficiency and capacity of solving large-scale instances. Moreover, thereal bus-driver scheduling problems that we consider can present variantaspects of the set covering, as for example a different objectivefunction, implying that alternative solutions methods have to bedeveloped. We propose metaheuristics based on the following approaches:GRASP (greedy randomized adaptive search procedure), tabu search andgenetic algorithms. These metaheuristics also present some innovationfeatures based on and genetic algorithms. These metaheuristics alsopresent some innovation features based on the structure of the crewscheduling problem, that guide the search efficiently and able them tofind good solutions. Some of these new features can also be applied inthe development of heuristics to other combinatorial optimizationproblems. A summary of computational results with real-data problems ispresented.

Adaptive dynamics and the implementation problem with complete information

This paper studies the equilibrating process of several implementationmechanisms using naive adaptive dynamics. We show that the dynamics convergeand are stable, for the canonical mechanism of implementation in Nash equilibrium.In this way we cast some doubt on the criticism of ``complexity'' commonlyused against this mechanism. For mechanisms that use more refined equilibrium concepts,the dynamics converge but are not stable. Some papers in the literatureon implementation with refined equilibrium concepts have claimed that themechanisms they propose are ``simple'' and implement ``everything'' (incontrast with the canonical mechanism). The fact that some of these ``simple''mechanisms have unstable equilibria suggests that these statements shouldbe interpreted with some caution.

The geophysical electromagnetic prospecting in the spatial location of the travertine deposits of the Banyoles Depression (Girona)

The results of the application of the geophysical electromagnetic prospection methods in the resolution of the problems of the spatial location of the travertine quaternary formations of the Banyoles depression are presented

Dispersal probability distributions and the wave-front speed problem

The speed and width of front solutions to reaction-dispersal models are analyzed both analytically and numerically. We perform our analysis for Laplace and Gaussian distribution kernels, both for delayed and nondelayed models. The results are discussed in terms of the characteristic parameters of the models