Carlos Franco

A Column Generation Approach for Solving a Green Bi-objective Inventory Routing Problem

The aim of this paper is present a multi-objective algorithm embedded with column generation to solve a green bi-objective inventory routing problem. In contrast with the classic Inventory Routing Problem where the main objective is to minimize the total cost overall supply chain network, in the green logistics besides this objective a minimization of the \( CO_{2} \) emisions is included. For solving the bi-objective problem, we proposed the use of NISE (Noninferior Set Estimation) algorithm combined with column generation for reduce the amount of variables in the problem.

Solving the Interval Green Inventory Routing Problem Using Optimization and Genetic Algorithms

In this paper, we present a genetic algorithm embedded with mathematical optimization to solve a green inventory routing problem with interval fuel consumption. Using the idea of column generation in which only attractive routes are generated to the mathematical problem, we develop a genetic algorithm that allow us to determine speedily attractive routes that are connected to a mathematical model. We code our genetic algorithm using the idea of a integer number that represents all the feasible set of routes in which the maximum number allowed is the binary number that represents if a customer is visited or not. We approximate the fuel consumption as an interval number in which we want to minimize the overall fuel consumption of distribution. This is the first approximation made in the literature using this type of methodology so we cannot compare our approach with those used in the literature.

A Structured Review of Quantitative Models of the Pharmaceutical Supply Chain

The aim of this review is to identify and provide a structured overview of quantitative models in the pharmaceutical supply chain, a subject not exhaustively studied in the previous reviews on healthcare logistics related mostly to quantitative models in healthcare or logistics studies in hospitals. The models are classified into three categories of classification: network design, inventory models, and optimization of a pharmaceutical supply chain. A taxonomy for each category is shown describing the principal features of each echelon included in the review; this taxonomy allows the readers to identify easily a paper based on the actors of the pharmaceutical supply chain. The search process included research articles published in the databases between 1984 and November 2016. In total 46 studies were included. In the review process we found that in the three fields the most common source of uncertainty used is the demand in the 56% of the cases. Within the review process we can conclude that most of the articles in the literature are focused on the optimization of the pharmaceutical supply chain and inventory models but the field on supply chain network design is not deeply studied.

Criteria for decision-making in transportation logistics function

This paper presents the first phase of a methodology for making transportation decisions in the Supply Chain (SC), in the first part of this article are the importance of transactional costs in transportation and their decision levels. Then interrelationships and dependencies of the criteria in Transportation Logistics Function (TLF). Finally presents design of the Multicriteria Decision Network (MCDN), used to support decision-making by managers of goods and transportation companies for modes or carriers selection in the Supply Chain, together with theirs concepts for to standardize the criteria from the literature review.

A Two-Phase Method to Periodic Vehicle Routing Problem with Variable Service Frequency

This paper presents a method to solve the periodic vehicle routing problem with service frequency. The problem consists in finding a set of paths for a crew of vehicles to deliver products or services to a set of customers in a discrete planning horizon subject to constraints as vehicle capacity, distance-time constraints, time windows, and the variable demand that implies a not defined frequency. Our method solves iteratively two mixed integer programming models. The first one assigns customers to be visited on the planning horizon. The second finds paths to visit the customers for each period. However, in case of non-feasibility a set of rules modify the allocation and the process starts again until the solution is obtained. We present an example to illustrate the method.

A Hybrid Rule-Based and Fuzzy Logic Model to Diagnostic Financial Area for MSMEs

The importance of the micro, small and medium enterprises (MSMEs) in the regional and global economy causes the academic community to worry about its development and growth that is why several institutions publics and privates have advanced studies about problems of these organizations. We present a diagnostic model based on Rules which works together with the fuzzy logic. Our model allows to determine the possible diseases that suffer in financial matters as well as to determine their gravity and to offer alternative solutions.

A Fuzzy Linear Fractional Programming Approach to Design of Distribution Networks

This paper studies the distribution network design problem considering the uncertain information in the demand, capacities, costs and prices in a multi-product environment and multiple periods. We consider a fractional objective function that consist in maximize the ratio between total profit and total cost. We use a model that integrates a facility location problem with a distribution network problem with fuzzy constraints, technological coefficients, and costs. To solve the problem, we use a method that transform the fuzzy linear fractional programming model in an equivalent multi-objective linear fractional programming problem to calculate the upper, middle and lower bounds of the original problem.

Hybrid Simulation and GA for a Flexible Flow Shop Problem with Variable Processors and Re-entrant Flow

The problem of FFSP (Flexible Flow Shop Problem) has been sufficiently investigated due to its importance for production programming and control, although many of the solution methods have been based on GA (Genetic Algorithm) and simulation, these techniques have been used in deterministic environments and under specific conditions of the problem, that is, complying with restrictions given in the Graham notation. In this paper we describe an application of these techniques to solve a very particular case where manual work stations and equipment with different degrees of efficiency, technological restrictions, recirculation process are used. The nesting of the GA is used within a simulation process. It is showed that the method proposed in adjustment and efficiency is better compared with other heuristics, in addition to the benefits of using different techniques in series to solve problems of real manufacturing environments.

A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals

Managing resources in hospitals is one of the most challenging duties in healthcare. The complexity of supply chain management in hospitals is high due to different factors such as life cycle of medicines, demand uncertainty, variation of prices, monetary resources, space constraints, among others. The main important factor of the supply chain in hospitals is the welfare of patients which depends of the correct management and administration of medicines, in this way backorders or stockouts are not allowed. In this paper we propose a mathematical model to make real planning over a health care supply chain considering real factors face by decision makers. For testing results we have used real data considering different sources of uncertainty. We have choose 5 different types of medicines and run the optimization model to determine the optimal solution over a set of scenarios generated for modeling uncertainty. For testing the results, we have compare over a year planning the results obtained by our policy and the results obtained by the hospital, improving the results in terms of costs.

A Fuzzy Inference System to Scheduling Tasks in Queueing Systems

This paper studies the problem of scheduling customers or tasks in a queuing system. Generally the customers or a set of tasks in queuing system are attended according with different rules as round robin, equiprobable, shortest queue, among others. However, the condition of the system like the work in process, utilization and the length of queue is difficult to measure. We propose to use a fuzzy inference system in order to determine the status in the system depended of input variables like the length queue and the utilization. The experiment results shows an improvement in the performance measures compared with traditional scheduling policies.