Vehicle routing with arrival time diversification

Unpredictable routes may be generated by varying the arrival time at each customer over successive visits. Inspired by a real-life case in cash distribution, this study presents an efficient solution approach for the vehicle routing problem with arrival time diversification by formulating it as a vehicle routing problem with multiple time windows in a rolling horizon framework. Because waiting times are not allowed, a novel algorithm is developed to efficiently determine whether routes or local search operations are time window feasible. To allow infeasible solutions during the heuristic search, four different penalty methods are proposed. The proposed algorithm and penalty methods are evaluated in a simple iterated granular tabu search that obtains new best-known solutions for all benchmark instances from the literature, decreasing average distance by 29% and reducing computation time by 93%. A case study is conducted to illustrate the practical relevance of the proposed model and to examine the trade-off between arrival time diversification and transportation cost

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: an application to fish aggregating devices

The paper addresses the synergies from combining a heuristic method with a predictive technique to solve the Dynamic Traveling Salesman Problem (DTSP). Particularly, we build a genetic algorithm that feeds on Newton's motion equation to show how route optimization can be improved when targets are constantly moving. Our empirical evidence stems from the recovery of fish aggregating devices (FADs) by tuna vessels. Based on historical real data provided by GPS buoys attached to the FADs, we first estimate their trajectories to feed a genetic algorithm that searches for the best route considering their future locations. Our solution, which we name Genetic Algorithm based on Trajectory Prediction (GATP), shows that the distance traveled is significantly shorter than implementing other commonly used methods.European Regional Development Fund | Ref. 10SEC300036PRMinisterio de Economía y Competitividad | Ref. ECO2013-45706

Improving police efficiency to meet demand issues

Demand modelling and simulation techniques are used in many industrial practices in order to be able to effectively manage the utilization of available resources. The current economic climate has intensified activity within this field with particular interest being paid to any potential cost savings and other financial benefits that may be obtained. Further the creation of a realistic representation of the demands present within a system can lead to a better understanding of system behaviour; this then may facilitate the identification of elements that are likely to allow improvement to system performance through their perturbation. Within this thesis a model is constructed for the demands upon front line Police officers that are used in response to high importance calls to service from the public. Tabu search and genetic algorithms are optimizing search techniques developed and applied across a wide variety of fields. They are particularly well suited to combinatorial problems in which the ordering or arrangement of system elements has an impact upon the quality of solution as assessed by some quantifying objective function. In this thesis both of these methods are applied to the staff resource allocation problem as posed by Leicestershire Police with the strengths and weaknesses of each evaluated. Customized diversification and intensification approaches are applied to the tabu search methodology in order to improve performance through tailoring it to the specific optimization problem considered. Both search algorithms are shown to be well suited to the target problem and each result in the generation of solutions of similar quality.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Iterative algorithm for lane reservation problem on transportation network

International audienceIn this paper, we study an NP-hard lane reservation problem on transportation network. By selecting lanes to be reserved on the existing transportation network under some special situations, the transportation tasks can be accomplished on the reserved lanes with satisfying the condition of time or safety. Lane reservation strategy is a flexible and economic method for traffic management. However, reserving lanes has impact on the normal traffic because the reserved lanes can only be passed by the special tasks. It should be well considered choosing reserved lanes to minimize the total traffic impact when applying the lane reservation strategy for the transportation tasks. In this paper, an integer linear program model is formulated for the considered problem and an optimal algorithm based on the cut-and-solve method is proposed. Some new techniques are developed for the cut-and-solve method to accelerate the convergence of the proposed algorithm. Numerical computation results of 125 randomly generated instances show that the proposed algorithm is much faster than a MIP solver of commercial software CPLEX 12.1 to find optimal solutions on average computing time

Matheuristics: using mathematics for heuristic design

Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptations to their abstract structure. The result can be metaheuristic hybrids having components derived from the mathematical model of the problems of interest, but the mathematical techniques themselves can define general heuristic solution frameworks. In this paper, we focus our attention on mathematical programming and its contributions to developing effective heuristics. We briefly describe the mathematical tools available and then some matheuristic approaches, reporting some representative examples from the literature. We also take the opportunity to provide some ideas for possible future development

Model-Based Heuristics for Combinatorial Optimization

Many problems arising in several and different areas of human knowledge share the characteristic of being intractable in real cases. The relevance of the solution of these problems, linked to their domain of action, has given birth to many frameworks of algorithms for solving them. Traditional solution paradigms are represented by exact and heuristic algorithms. In order to overcome limitations of both approaches and obtain better performances, tailored combinations of exact and heuristic methods have been studied, giving birth to a new paradigm for solving hard combinatorial optimization problems, constituted by model-based metaheuristics. In the present thesis, we deepen the issue of model-based metaheuristics, and present some methods, belonging to this class, applied to the solution of combinatorial optimization problems

Heuristics and metaheuristics for heavily constrained hybrid flowshop problems

Due to the current trends in business as the necessity to have a large catalogue of products, orders that increase in frequency but not in size, globalisation and a market that is increasingly competitive, the production sector faces an ever harder economical environment. All this raises the need for production scheduling with maximum efficiency and effectiveness. The first scientific publications on production scheduling appeared more than half a century ago. However, many authors have recognised a gap between the literature and the industrial problems. Most of the research concentrates on optimisation problems that are actually a very simplified version of reality. This allows for the use of sophisticated approaches and guarantees in many cases that optimal solutions are obtained. Yet, the exclusion of real-world restrictions harms the applicability of those methods. What the industry needs are systems for optimised production scheduling that adjust exactly to the conditions in the production plant and that generates good solutions in very little time. This is exactly the objective in this thesis, that is, to treat more realistic scheduling problems and to help closing the gap between the literature and practice. The considered scheduling problem is called the hybrid flowshop problem, which consists in a set of jobs that flow through a number of production stages. At each of the stages, one of the machines that belong to the stage is visited. A series of restriction is considered that include the possibility to skip stages, non-eligible machines, precedence constraints, positive and negative time lags and sequence dependent setup times. In the literature, such a large number of restrictions has not been considered simultaneously before. Briefly, in this thesis a very realistic production scheduling problem is studied. Various optimisation methods are presented for the described scheduling problem. A mixed integer programming model is proposed, in order to obtaiUrlings ., T. (2010). Heuristics and metaheuristics for heavily constrained hybrid flowshop problems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8439Palanci