A simulation-optimization approach for the management of the on-demand parcel delivery in sharing economy

the use of multiple delivery options and crowd drivers, reflecting the synchromodality in the urban context. We propose a multi-stage stochastic model, and we solve the problem by using a simulation-optimization strategy. It relies on a Monte Carlo simulation and a large neighborhood search (LNS) heuristic for optimization. We conduct a case study in the medium-sized city of Turin (Italy) to measure the potential impact of integrating cargo bikes and crowd drivers in parcel delivery. Experimental results show that combining crowd drivers and green carriers with the traditional van to manage the parcel delivery is beneficial in terms of economic and environmental cost-saving, while the operational efficiency decreases. Besides, the green carriers and crowd drivers are promising delivery options to deal with online customer requests in the context of stochastic and dynamic parcel delivery. The resulting set of policies are part of the outcomes of the Logistics and Mobility Plan 2019-2021 in the Piedmont region

Metaheuristics for the waste collection vehicle routing problem with time windows

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In this thesis there is a set of waste disposal facilities, a set of customers at which waste is collected and an unlimited number of homogeneous vehicles based at a single depot. Empty vehicles leave the depot and collect waste from customers, emptying themselves at the waste disposal facilities as and when necessary. Vehicles return to the depot empty. We take into consideration time windows associated with customers, disposal facilities and the depot. We also have a driver rest period. The problem is solved heuristically. A neighbour set is defined for each customer as the set of customers that are close, but with compatible time windows. This thesis uses six different procedures to obtain initial solutions for the problem. Then, the initial solutions from these procedures are improved in terms of the distance travelled using our phase 1 and phase 2 procedures, whereas we reduce the number of vehicles used using our vehicle reduction (VR) procedure. In a further attempt to improve the solutions three metaheuristic algorithms are presented, namely tabu search (TS), variable neighbourhood search (VNS) and variable neighbourhood tabu search (VNTS). Moreover, we present a modified disposal facility positioning (DFP), reverse order and change tracking procedures. Using all these procedures presented in the thesis, four solution procedures are reported for the two benchmark problem sets, namely waste collection vehicle routing problems with time windows (VRPTW) and multi-depot vehicle routing problem with inter-depot routes (MDVRPI). Our solutions for the waste collection VRPTW problems are compared with the solutions from Kim et al (2006), and our solutions for the MDVRPI problems are compared with Crevier et al (2007). Computational results for the waste collection VRPTW problems indicate that our algorithms produce better quality solutions than Kim et al (2006) in terms of both distance travelled and number of vehicles used. However for the MDVRPI problems, solutions from Crevier et al (2007) outperform our solutions.Ministry of Higher Education, Malaysi

Vehicle sharing and workforce scheduling to perform service tasks at customer sites

Most of the research done in the Vehicle Routing Problem (VRP) assumes that each driver is assigned to one and only one vehicle. However, in recent years, research in the VRP has increased its scope to further accommodate more restrictions and real-life features. In this line, vehicle sharing has grown in importance inside large companies with the aim of reducing vehicle emissions. The aim of this thesis is to study different situations where sharing vehicles brings an improvement. Our main study focuses on developing a framework that is capable of assigning different workers to a common vehicle, allowing them to share their journey. We introduce a mathematical programming model that combines the vehicle routing and the scheduling problem with time constraints that allows workers to share vehicles to perform their activities. To deal with bigger instances of the problem an algorithm capable of solving large scenarios needs to be implemented. A multi-phase algorithm is introduced, Phase 1 allows us to solve the non-sharing scheduling/routing problem whose aim is to find the best schedule for workers. Phase 2 will merge the allocated workers into common vehicles when possible, while Phase 3 is the improvement procedure of the algorithm. The algorithm is tested in three different settings; using workers as drivers, hiring dedicated drivers, and allowing workers to walk between jobs when possible. Results show that sharing vehicles is practicable under specific conditions, and it is able to reduce both the number of vehicles and the total distance, without affecting the performance of workers schedule

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Metaheuristics for the waste collection vehicle routing problem with time windows

In this thesis there is a set of waste disposal facilities, a set of customers at which waste is collected and an unlimited number of homogeneous vehicles based at a single depot. Empty vehicles leave the depot and collect waste from customers, emptying themselves at the waste disposal facilities as and when necessary. Vehicles return to the depot empty. We take into consideration time windows associated with customers, disposal facilities and the depot. We also have a driver rest period. The problem is solved heuristically. A neighbour set is defined for each customer as the set of customers that are close, but with compatible time windows. This thesis uses six different procedures to obtain initial solutions for the problem. Then, the initial solutions from these procedures are improved in terms of the distance travelled using our phase 1 and phase 2 procedures, whereas we reduce the number of vehicles used using our vehicle reduction (VR) procedure. In a further attempt to improve the solutions three metaheuristic algorithms are presented, namely tabu search (TS), variable neighbourhood search (VNS) and variable neighbourhood tabu search (VNTS). Moreover, we present a modified disposal facility positioning (DFP), reverse order and change tracking procedures. Using all these procedures presented in the thesis, four solution procedures are reported for the two benchmark problem sets, namely waste collection vehicle routing problems with time windows (VRPTW) and multi-depot vehicle routing problem with inter-depot routes (MDVRPI). Our solutions for the waste collection VRPTW problems are compared with the solutions from Kim et al (2006), and our solutions for the MDVRPI problems are compared with Crevier et al (2007). Computational results for the waste collection VRPTW problems indicate that our algorithms produce better quality solutions than Kim et al (2006) in terms of both distance travelled and number of vehicles used. However for the MDVRPI problems, solutions from Crevier et al (2007) outperform our solutions.EThOS - Electronic Theses Online ServiceMinistry of Higher Education, MalaysiaGBUnited Kingdo

GAPS : a hybridised framework applied to vehicle routing problems

In this thesis we consider two combinatorial optimisation problems; the Capacitated Vehicle Routing Problem (CVRP) and the Capacitated Arc Routing Problem (CARP). In the CVRP, the objective is to find a set of routes for a homogenous fleet of vehicles, which must service a set of customers from a central depot. In contrast, the CARP requires a set of routes for a fleet of vehicles to service a set of customers at the street level of an intercity network. After a comprehensive discussion of the existing exact and heuristic algorithmic techniques presented in the literature for these problems, computational experiments to provide a benchmark comparison of a subset of algorithmic implementations for these methods are presented for both the CVRP and CARP, run against a series of dataset instances from the literature. All dataset instances are re-catalogued using a standard format to overcome the difficulties of the different naming schemes and duplication of instances that exist between different sources. We then present a framework, which we shall call Genetic Algorithm with Perturbation Scheme (GAPS), to solve a number of combinatorial optimisation problems. The idea is to use a genetic algorithm as a container framework in conjunction with a perturbation or weight coding scheme. These schemes make alterations to the underlying input data within a problem instance, after which the changed data is fed into a standard problem specific heuristic and the solution obtained decoded to give a true solution cost using the original unaltered instance data. We first present GAPS in a generic context, using the Travelling Salesman Problem (TSP) as an example and then provide details of the specific application of GAPS to both the CVRP and CARP. Computational experiments on a large set of problem instances from the literature are presented and comparisons with the results achieved by the current state of the art algorithmic approaches for both problems are given, highlighting the robustness and effectiveness of the GAPS framework.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

GAPS: a hybridised framework applied to vehicle routing problems

In this thesis we consider two combinatorial optimisation problems; the Capacitated Vehicle Routing Problem (CVRP) and the Capacitated Arc Routing Problem (CARP). In the CVRP, the objective is to find a set of routes for a homogenous fleet of vehicles, which must service a set of customers from a central depot. In contrast, the CARP requires a set of routes for a fleet of vehicles to service a set of customers at the street level of an intercity network. After a comprehensive discussion of the existing exact and heuristic algorithmic techniques presented in the literature for these problems, computational experiments to provide a benchmark comparison of a subset of algorithmic implementations for these methods are presented for both the CVRP and CARP, run against a series of dataset instances from the literature. All dataset instances are re-catalogued using a standard format to overcome the difficulties of the different naming schemes and duplication of instances that exist between different sources. We then present a framework, which we shall call Genetic Algorithm with Perturbation Scheme (GAPS), to solve a number of combinatorial optimisation problems. The idea is to use a genetic algorithm as a container framework in conjunction with a perturbation or weight coding scheme. These schemes make alterations to the underlying input data within a problem instance, after which the changed data is fed into a standard problem specific heuristic and the solution obtained decoded to give a true solution cost using the original unaltered instance data. We first present GAPS in a generic context, using the Travelling Salesman Problem (TSP) as an example and then provide details of the specific application of GAPS to both the CVRP and CARP. Computational experiments on a large set of problem instances from the literature are presented and comparisons with the results achieved by the current state of the art algorithmic approaches for both problems are given, highlighting the robustness and effectiveness of the GAPS framewor

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing