Optimizing agents with genetic programming : an evaluation of hyper-heuristics in dynamic real-time logistics

Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents decentrally is to use the contract-net protocol (CNET) that uses auctions to allocate tasks among agents. To participate in an auction, agents require a method that estimates the value of a task. Typically this method is an optimization algorithm. Recently, hyper-heuristics has been proposed for automated design of heuristics. Two properties of automatically designed heuristics are particularly promising: 1) a generated heuristic computes quickly, it is expected therefore that hyper-heuristics heuristics perform especially well for urgent problems, and 2) by using simulationbased evaluation, hyper-heuristics can learn from the past and can therefore create a ‘rule of thumb’ that anticipates situations in the future. In the present paper we empirically evaluate whether hyper-heuristics, more specifically genetic programming (GP), can be used to improve agents decentrally coordinated via CNET. We compare several GP settings and compare the resulting heuristic with existing centralized and decentralized algorithms on a dynamic PDP dataset with varying levels of dynamism, urgency, and scale. The results indicate that the evolved heuristic always outperforms the optimization algorithm in the decentralized MAS and often outperforms the centralized optimization algorithm. Our paper shows that designing MASs using genetic programming is an effective way to obtain competitive performance compared to traditional operational research approaches. These results strengthen the relevance of decentralized agent based approaches in dynamic logistics

Informal paratransit in the Global South

This chapter synthesises current knowledge of informal paratransit services in cities of the Global South and discusses prevailing policy issues and emerging trends. The scope of the chapter is limited to unscheduled public transport and for-hire services operating in whole, or in part, within the informal economy. The chapter focuses on three regions of the Global South: Africa, Asia, and Latin America. It reviews current knowledge in relation to business models, regulatory regimes, and operating practices. While illustrating that the sector is heterogeneous across, and within, these regions, this review shows that informal paratransit services are usually operated by small businesses organised into associations that exert varying degrees of self-regulation. Service operations are seldom free of state regulation, but the extent can vary. Operating environments often have considerable infrastructure deficits, and driver employment conditions can be exploitative. Services are, in many cases, a response to gaps left by formal public transport undertakings. Prevailing business models, however, make operators demand-responsive, often providing the only service available to vulnerable groups. It is argued that important policy issues relate to integration with other public transport modes, service quality, and safety improvement. These challenges are compounded by poorly resourced regulatory authorities, often subjected to pervasive corruption. An important emerging trend identified takes the form of potentially disruptive technologies, most commonly in the form of ride-hailing apps. These platforms may have a significant impact on operating practices, and few cities have regulatory frameworks in anticipation of this change. Experience suggests that attempts to change business models and operating practices can be met with resistance. Policy intervention in this sector therefore requires careful analysis of local contexts and options

Computer Controlled Urban Transportation: A Survey of Concepts, Methods, and International Experiences

This book is concerned with the present and future traffic problems in the developing and developed world. It examines possible solutions to those problems based on technological innovations and implementing large-scale computerized traffic and transportation control systems. It discusses the basic concepts and methods for control and automation that have been proposed, developed, and implemented, and experience from real applications of these in different cities and nations

A probabilistic approach to pickup and delivery problems with time window uncertainty

In this paper we study a dynamic and stochastic pickup and delivery problem proposed recently by Srour, Agatz and Oppen. We demonstrate that the cost structure of the problem permits an effective solution method without generating multiple scenarios. Instead, our method is based on a careful analysis of the transfer probability from one customer to the other. Our computational results confirm the effectiveness of our approach on the data set of Srour et al

MODELING AND ANALYSIS OF AN AUTONOMOUS MOBILITY ON DEMAND SYSTEM

Ph.DDOCTOR OF PHILOSOPH

An Adaptive Tabu Search Heuristic for the Location Routing Pickup and Delivery Problem with Time Windows with a Theater Distribution Application

The time constrained pickup and delivery problem (PDPTW) is a problem of finding a set of routes for a fleet of vehicles in order to satisfy a set of transportation requests. Each request represents a user-specified pickup and delivery location. The PDPTW may be used to model many problems in logistics and public transportation. The location routing problem (LRP) is an extension of the vehicle routing problem where the solution identifies the optimal location of the depots and provides the vehicle schedules and distribution routes. This dissertation seeks to blend the PDPTW and LRP areas of research and formulate a location scheduling pickup and delivery problem with time windows (LPDPTW) in order to model the theater distribution problem and find excellent solutions. This research utilizes advanced tabu search techniques, including reactive tabu search and group theory applications, to develop a heuristic procedure for solving the LPDPTW. Tabu search is a metaheuristic that performs an intelligent search of the solution space. Group theory provides the structural foundation that supports the efficient search of the neighborhoods and movement through the solution space