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.

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.

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.

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

Note on the blocking flow shop problem

We present some results attained with different algorithms for the Fm|block|Cmax problem using as experimental data the well-known Taillard instances.