A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different travel distances and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance. Due to the large scale and nonlinear constraints, the exact search showed is not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with the Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this bi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively. To evaluate the generality of the proposed algorithms, a set of benchmark instances with various features is extracted from real-life historical datasets. The experiment results show that GIHH significantly improves the quality of the final Pareto Solution Set, outperforming the state-of-the-art algorithms for similar problems. Its application on VRPTW also obtains promising results

Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching

In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical for most real-world scenarios primarily because of the uncertainties of dynamic events in both yard operations and seaside loading–unloading operations. These solutions are either unattractive or infeasible to execute. Herein, for more intelligent handling of these uncertainties and dynamics, a novel cooperative double-layer genetic programming hyper-heuristic (CD-GPHH) is proposed to tackle this challenging online optimisation problem. In this new CD-GPHH, a novel scenario genetic programming (GP) approach is added on top of a traditional GP method that chooses among different GP heuristics for different scenarios to facilitate optimised truck dispatching. In contrast to traditional arithmetic GP (AGP) and GP with logic operators (LGP) which only evolve on one population, our CD-GPHH method separates the scenario and the calculation into two populations, which improved the quality of solutions in multi-scenario problems while reducing the search space. Experimental results show that our CD-GPHH dominates AGP and LGP in solving a multi-scenario function fitting problem as well as a truck dispatching problem in container terminal

Hyper-heuristics for two complex vehicle routing problems: the urban transit routing problem, and a delivery and installation problem

Hyper-heuristics have emerged as general purpose search techniques that explore the space of low-level heuristics to improve a given solution under an iterative framework. They were introduced to raise the level of generality of search techniques representing self-configuring and automated reusable heuristic approaches for solving combinatorial problems. There are two classes of hyper-heuristics identified in the literatire: generation and selection hyper-heuristics. In this thesis, we focus on the class of selection hyperheuristics and their efficient design and application on complex routing problems. We specifically focus on two routing problems: the Urban Transit Network design Problem (UTRP), and a rich vehicle routing problem for the delivery and installation of equipment which was the subject of the VeRoLog solver challenge 2019. The urban transit routing problem (UTRP) aims to find efficient travelling routes for vehicles in public transportation systems. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of combinatorial problems whose optimal solution is hard to find with the complexity that arises from the large search space, and the multiple constraints imposed in constructing the solution. Furthermore, realistic benchmark data sets are lacking, making it difficult for researchers to compare their problem solving techniques with those of other researchers. We evaluate and compare the performance of a set of selection hyperheuristics on the UTRP, with the goal of minimising the passengers’ travel time and the operators’ costs. Each selection hyper-heuristic is empirically tested on a set of known benchmark instances and statistically compared against all the other hyper-heuristics to determine the best approach. A sequence-based selection method utilising a hidden markov model achieved the best performance between the tested selection methods, and better solutions than the current known best solutions are achieved on benchmark instances. Then, we propose a hyper-heuristic algorithm specifically designed to solve the UTRP with defined terminal nodes that determine the start and end points of bus journeys. The algorithm is applied to a novel set of benchmark instances with real world size and characteristics representing the extended urban area of Nottingham city. We compare the hyper-heuristic performance on the data set with the NSGAII algorithm and real world bus routes, and prove that better solutions are found by hyper-heuristics. Due to the clear gap in research between the application of optimisation algorithms in public routes network optimisation and the real world planning processes, we implemented a hyper-heuristic algorithm that interactively work with interface procedures to optimise the public transport lines in Visum transportation modelling software. We adopt Selection Hyper-heuristics for two optimisation problems and the optimisation objectives include the passengers’ average travel time and operators’ costs. The results demonstrate the successful implementation of the applied optimisation methods for multi-modal public transport networks. Finally we introduce a population based hyperheuristic algorithm and apply it on a complex vehicle routing problem consisting of two stages: a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) for the delivery of equipment, and the Service Technician Routing and Scheduling Problem (STRSP) for the installation of the delivered equipment. This problem was the subject of the VeRoLog solver challenge 2019. We apply the hyper-heuristic population-based algorithm on a small and large size data sets, and show that our approach performed better in terms of results and run time on small instances compared to the results of mathematical model implemented for this problem. We perform analysis of the new proposed algorithm and show that it finds better quality solutions compared to its constituent selection hyper-heuristics when applied individually. Finally we conclude the thesis with a summary of the work and future plans

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Last-mile logistics optimization in the on-demand economy

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