Helena Ramalhinho

An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints

Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the products. In this paper, we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a heterogeneous fleet of vehicles that are allowed to perform multiple trips. The problem also includes docking constraints in which some vehicles are unable to serve some particular customers, and a realistic objective function with vehicles’ fixed and distance-based costs and a cost per customer visited. We design a trajectory search heuristic called GILS-VND that combines Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) procedures. This method obtains competitive solutions and improves the company solutions leading to significant savings in transportation costs.

Biased randomization of heuristics using skewed probability distributions: A survey and some applications

Abstract Randomized heuristics are widely used to solve large scale combinatorial optimization problems. Among the plethora of randomized heuristics, this paper reviews those that contain biased-randomized procedures (BRPs). A BRP is a procedure to select the next constructive ‘movement’ from a list of candidates in which their elements have different probabilities based on some criteria (e.g., ranking, priority rule, heuristic value, etc.). The main idea behind biased randomization is the introduction of a slight modification in the greedy constructive behavior that provides a certain degree of randomness while maintaining the logic behind the heuristic. BRPs can be categorized into two main groups according to how choice probabilities are computed: (i) BRPs using an empirical bias function; and (ii) BRPs using a skewed theoretical probability distribution. This paper analyzes the second group and illustrates, throughout a series of numerical experiments, how these BRPs can benefit from parallel computing in order to significantly outperform heuristics and even simple metaheuristic approaches, thus providing reasonably good solutions in ‘real time’ to different problems in the areas of transportation, logistics, and scheduling.

On the improvement of blood sample collection at clinical laboratories.

BackgroundBlood samples are usually collected daily from different collection points, such hospitals and health centers, and transported to a core laboratory for testing. This paper presents a project to improve the collection routes of two of the largest clinical laboratories in Spain. These routes must be designed in a cost-efficient manner while satisfying two important constraints: (i) two-hour time windows between collection and delivery, and (ii) vehicle capacity.MethodsA heuristic method based on a genetic algorithm has been designed to solve the problem of blood sample collection. The user enters the following information for each collection point: postal address, average collecting time, and average demand (in thermal containers). After implementing the algorithm using C programming, this is run and, in few seconds, it obtains optimal (or near-optimal) collection routes that specify the collection sequence for each vehicle. Different scenarios using various types of vehicles have been considered. Unless new collection points are added or problem parameters are changed substantially, routes need to be designed only once.ResultsThe two laboratories in this study previously planned routes manually for 43 and 74 collection points, respectively. These routes were covered by an external carrier company. With the implementation of this algorithm, the number of routes could be reduced from ten to seven in one laboratory and from twelve to nine in the other, which represents significant annual savings in transportation costs.ConclusionsThe algorithm presented can be easily implemented in other laboratories that face this type of problem, and it is particularly interesting and useful as the number of collection points increases. The method designs blood collection routes with reduced costs that meet the time and capacity constraints of the problem.

A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs

Our approach of PFSP includes risk of machine failures due to lack of breaks.We model the problem as a non-smooth optimization problem.We propose the use of a biased-randomized algorithm to solve it.We combine Iterated Local Search with biased randomization of classical heuristics.Our approach outperforms other approaches just based on the makespan. This paper analyzes a realistic variant of the Permutation Flow-Shop Problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a Biased-Randomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the well-known NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of non-smooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failure-risk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other state-of-the-art metaheuristic approaches, which have been conveniently adapted to consider failure-risk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar non-smooth objective functions.

Designing e-commerce supply chains: a stochastic facility–location approach

e-Commerce activity has been increasing during recent years, and this trend is expected to continue in the near future. e-Commerce practices are subject to uncertainty conditions and high variability in customers’ demands. Considering these characteristics, we propose two facility–location models that represent alternative distribution policies in e-commerce (one based on outsourcing and another based on in-house distribution). These models take into account stochastic demands as well as more than one regular supplier per customer. Two methodologies are then introduced to solve these stochastic versions of the well-known capacitated facility–location problem. The first is a two-stage stochastic-programming approach that uses an exact solver. However, we show that this approach is not appropriate for tackle large-scale instances due to the computational effort required. Accordingly, we also introduce a “simheuristic” approach that is able to deal with large-scale instances in short computing times. An extensive set of benchmark instances contribute to illustrate the efficiency of our approach, as well as its potential utility in modern e-commerce practices.

Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign

Cross-selling campaigns seek to offer the right products to the set of customers with the goal of maximizing expected profit, while, at the same time, respecting the purchasing constraints set by investors. In this context, a bi-objective version of this NP-Hard problem is approached in this paper, aiming at maximizing both the promotion campaign total profit and the risk-adjusted return, which is estimated with the reward-to-variability ratio known as Sharpe ratio. Given the combinatorial nature of the problem and the large volume of data, heuristic methods are the most common used techniques. A Greedy Randomized Neighborhood Structure is also designed, including the characteristics of a neighborhood exploration strategy together with a Greedy Randomized Constructive technique, which is embedded in a multi-objective local search metaheuristic. The latter combines the power of neighborhood exploration by using a Pareto Local Search with Variable Neighborhood Search. Sets of non-dominated solutions obtained by the proposed method are described and analyzed for a number of problem instances. HighlightsUse of profit variability measure in connection to the client response.Consider the Sharpe ratio index for calculating risk-adjusted profit in targeted offers.Introduction of a bi-objective direct marketing promotional campaign.Adaptation, description and use of a general Pareto local search.

Operations research and simulation in master's degrees: a case study regarding different universities in Spain

This paper presents several experiences regarding Operations Research (OR) and Simulation education activities in three master programs, each of them offered at a different university. The paper discusses the importance of teaching these contents in most managerial and engineering masters. After a brief overview of existing related work, the paper provides some recommendations-based on our own teaching experiences-that instructors should keep in mind when designing OR/Simulation courses, either in traditional face-to-face as well as in pure online learning models. The case studies exposed here include students from business management, computer science, and aeronautical management degrees, respectively. For each type of student, different OR/Simulation tools are employed in the courses, ranging from easy-to-use optimization and simulation software to simulation-based algorithms developed from scratch using a programming language.

Using simheuristics to promote horizontal collaboration in stochastic city logistics

This paper analyzes the role of horizontal collaboration (HC) concepts in urban freight transportation under uncertainty scenarios. The paper employs different stochastic variants of the well-known vehicle routing problem (VRP) in order to contrast a non-collaborative scenario with a collaborative one. This comparison allows us to illustrate the benefits of using HC strategies in realistic urban environments characterized by uncertainty in factors such as customers’ demands or traveling times. In order to deal with these stochastic variants of the VRP, a simheuristic algorithm is proposed. Our approach integrates Monte Carlo simulation inside a metaheuristic framework. Some computational experiments contribute to quantify the potential gains that can be obtained by the use of HC practices in modern city logistics.

Teaching distribution planning: a problem-based learning approach

Purpose – The purpose of this paper is to present a problem-based learning (PBL) activity that uses a decision support system (DSS) to teach one of the most fundamental topics in distribution planning: vehicle routing. Design/methodology/approach – The authors describe their teaching experience in a logistics and supply chain management (LSCM) course. In the PBL activity proposed, students need to solve a typical vehicle routing case with no previous theoretical background taught. The paper is written as a teaching guide for other instructors, detailing how the activity may be carried out in class. Findings – The PBL activity involved students from the very beginning, challenging them to solve a rather complicated problem. Its acceptance was very positive according to the student feedback survey conducted after the activity. Only when struggling with the difficulties of the case proposed, did students really appreciate the potential value of a DSS for making better decisions. Moreover, this activity raise...

A VNS approach for book marketing campaigns generated with quasi-bicliques probabilities

Abstract This paper focuses on Book Marketing Campaigns, where the benefit of offering each book is calculated based on a bipartite graph (biclique). A quasi Biclique problem is assessed for obtaining the probabilities of success of a given client buy a given book, considering it had received another book as free offer. The remaining optimization decision problem can be solved following the Targeted Offers Problem in Direct Marketing Campaigns. The main objective is to maximize the feedback of customers purchases, offering books to the set of customers with the highest probability of buying others ones from its biclique and, at the same time, minimizing campaign operational costs. Given the combinatorial nature of the problem and the large volume of data, which can involve real cases with up to one million customers, metaheuristics procedures have been used as an efficient way for solving it. Here, a hybrid trajectory search based algorithm, namely GGVNS, which combines the Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search, is used. The strategy for generating the quasi Biclique problem is described and a new instance generator for the TOPDMC is introduced. Computational results regarding the GGVNS algorithm shows it is able to find useful and profitable sets of clients.