This thesis introduces and studies new classes of heterogeneous vehicle routing problems with or without location and pollution considerations. It develops powerful evolutionary and adaptive large neighborhood search based metaheuristics capable of solving a wide variety of such problems with suitable enhancements, and provides several important managerial insights. It is structured into five main chapters. After the introduction presented in Chapter 1, Chapter 2 classifies and reviews the relevant literature on heterogeneous vehicle routing problems, and presents a comparative analysis of the available metaheuristic algorithms for these problems. Chapter 3 describes a hybrid evolutionary algorithm for four variants of heterogeneous fleet vehicle routing problems with time windows. The algorithm successfully combines several metaheuristics and introduces a number of new advanced efficient procedures. Extensive computational experiments on benchmark instances show that the algorithm is highly competitive with state-of-the art methods for the three variants. New benchmark results on the fourth problem are also presented. In Chapter 4, the thesis introduces the eet size and mix location-routing problem with time windows (FSMLRPTW) which extends the classical location-routing problem by considering a heterogeneous fleet and time windows. The main objective of the FSMLRPTW is to minimize the sum of depot cost, vehicle fixed cost and routing cost. The thesis presents integer programming formulations for the FSMLRPTW, along with a family of valid inequalities and an algorithm based on adaptation of the hybrid evolutionary metaheuristic. The strengths of the formulations are evaluated with respect to their ability to yield optimal solutions. Extensive computational experiments on new benchmark instances show that the algorithm is highly effective. Chapter 5 introduces the fleet size and mix pollution-routing problem (FSMPRP) which extends the previously studied pollution-routing problem (PRP) by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and routing cost, where the latter can be defined with respect to the cost of fuel and CO2 emissions, and driver cost. An adaptation of the hybrid evolutionary algorithm is successfully applied to a large pool of realistic PRP and FSMPRP benchmark instances, where new best solutions are obtained for the former. Several analyses are conducted to shed light on the trade-offs between various performance indicators. The benefit of using a heterogeneous fleet over a homogeneous one is demonstrated. In Chapter 6, the thesis investigates the combined impact of depot location, fleet composition and routing decisions on vehicle emissions in urban freight distribution characterized by several speed limits, where goods need to be delivered from a depot to customers located in different speed zones. To solve the problem, an adaptive large neighborhood search algorithm is successfully applied to a large pool of new benchmark instances. Extensive analyses are conducted to quantify the effect of various problem parameters, such as depot cost and location, customer distribution and fleet composition on key performance indicators, including fuel consumption, emissions and operational costs. The results illustrate the benefits of locating depots located in suburban areas rather than in the city centre and of using a heterogeneous fleet over a homogeneous one. The conclusions, presented in Chapter 7, summarize the results of the thesis, provide limitations of this work, as well as future research directions.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:675181 |
| Date | January 2015 |
| Creators | Koc, Cagri |
| Contributors | Bektas, Tolga |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/384001/ |
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