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Author ORCID Identifier

N/A

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Management

Year Degree Awarded

2018

Month Degree Awarded

September

First Advisor

Ahmed Ghoniem

Subject Categories

Business Administration, Management, and Operations

Abstract

Logistics constitutes a key function of modern-day supply chains and an indispensable prerequisite for the support and growth of conventional brick-and-mortar and online businesses. Whether for procurement or delivery purposes, manufacturers and service providers seek efficient and reliable logistical services. A 2014 Bloomberg survey reports that 73% of supply chain managers are experiencing a shift in their attitude towards transportation services; a function they now view as a key element of their business strategy. The advent of new mobile technologies and online platforms, the use of intermodal logistics, and the multiplication of customer-selected delivery options continue to prompt the development of large-scale complex transportation models. The scope of such models can address a single tier of the supply chain or lie at the interface of two tiers when this integration is necessary to reveal important managerial tradeoffs. Such problems require cutting-edge optimization techniques and powerful computing platforms. Given the scale and recurrence of logistical operations, data-driven optimized policies can achieve multi-million dollar savings in cost and significant improvement in service level. This dissertation develops, in its three essays, specialized algorithms for solving two integrated routing problems that have applications in bi-level transportation. Essay One proposes an exact branch-cut-and-price algorithm for the generalized vehicle routing problem (GVRP) which has applications in maritime transportation, survivable telecommunication network design, and health-care logistics. Decomposition techniques are used to reformulate the GVRP as a set-partitioning model which prompts the development of a column generation approach. A specialized dynamic programming algorithm is proposed for solving the pricing subproblem. The performance of the proposed algorithm is significantly improved by enforcing a set of rounded capacity valid inequalities. Computational results show that the proposed algorithm compares favorably against the state-of-the-art exact algorithm for the GVRP and closes 8 out of 9 previously open GVRP instances in the literature. Essay Two investigates a variant of the Vehicle Routing-Allocation Problem that arises in the distribution of pallets of goods by a food bank to a network of relatively distant nonprofit organizations. Vehicles are routed to selected intermediate delivery sites to which the nonprofit organizations travel to collect their demand. The logistical cost is shared and the objective is to minimize a weighted average of the food bank vehicle routing cost and the travel cost of the nonprofit organizations. We develop an efficient multi-start heuristic that iteratively constructs initial solutions to this problem and subsequently explores their neighborhoods via local improvement and perturbation schemes. In our experience, the proposed heuristic substantially outperforms alternative optimization-based heuristics in the literature in terms of the solution quality and computational efficiency and consistently yields solutions with an optimality gap of 0.5% on average. Essay Three develops an effective branch-and-price algorithm for the aforementioned food bank vehicle routing problem. The pricing subproblem is solved, exactly or heuristically, using a specialized labeling type dynamic programming (DP) algorithm. The computational efficacy of this DP approach stems primarily from the inclusion of preprocessing routines that enhance the label extension scheme by iteratively eliminating dominated (partial) solutions. The proposed exact DP algorithm, and five proposed heuristic variants, significantly reduce the computational time associated with the solution of the pricing subproblem (as opposed to solving the latter as a mixed-integer model with CPLEX). The resulting speedup enables the implementation of a branch-and-price algorithm that greatly outperforms the use of CPLEX over a test-bed of 60 problem instances.

DOI

https://doi.org/10.7275/12487013

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