A Decision Support System for Fleet Sizing Problems in an Inter-Facility Material Handling Systems Using Queueing Networks

Material handling systems (MHSs) are an integral part of logistics functions in manufacturing and service organizations. Material handling equipment (MHE) is considered the pivotal actor of any given MHS. Organisations' focus on their core competency has placed third-party logistics service providers (3PL) to deliver supportive activities such as material handling, transportation, and storage using their expertise and economies of scale. Signing a leasing contract with a 3PL for the right fleet size of MHE that ensures the smooth transfer of materials between facilities is a challenging decision-making problem faced by manufacturing companies attempting to minimise transportation and handling costs.

This dissertation research aims to design a Decision Support System (DSS) framework for the logistics managers in determining the optimal fleet size of their MHS, in addition to assisting in strategic, managerial, and operational levels of decision-making processes considering the design and operation of their MHS. Moreover, this dissertation research considers the fleet sizing problem of trucks to be outsourced (or subcontracted) from a 3PL to be used for inter-facility material transfer operations under identified different circumstances.

In the context of optimising fleet size under different fleet structures, a closed queueing network (CQN) model is combined with mixed-integer nonlinear programming (MINLP) to formulate the problem. This study proposes an analytical approach utilising sequential quadratic programming (SQP) methodology coupled with the mean value analysis (MVA) algorithm to solve this NP-Hard problem. Additionally, a discrete event simulation (DES) model is developed to validate the optimisation of non-dominant solutions. The proposed method offers a novel and effective solution for fleet optimisation problems in complex supply chain systems. The thesis's final section presents a novel supply chain mapping approach to model the whole process, from acquiring raw materials to delivering products to customers. This approach leverages an open queueing network (OQN) to analyse the system's overall performance.

The above proposed analytical approaches and the simulation are implemented in a real case study relative to a steel manufacturing company. In this study, a fleet of trucks is needed to transport raw materials from storage yards to intermediate locations in accordance with daily demand and production schedules. The daily demands and production schedules vary according to the bill of materials and specific blending needed for different types of finished products. The transportation process entails several sub-processes and operates cyclically throughout each day until the corresponding raw materials demands are fully satisfied. The inherent variability and uncertainty associated with the functional characteristics of the raw materials and trucks make this fleet sizing problem extremely challenging and thus, resorting to the development of complex mathematical models to determine the network’s optimal performance measures.

The conclusion of the thesis suggests that queueing network models, along with the proposed analytical methodology, can serve as a decision-making tool in the analysis of MHS design and operation, particularly with regards to fleet sizing problems in inter-facility material transfer operations.