Project Materials




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Chapter One:


1.1 Background

Logistics has become increasingly crucial in budgetary concerns for the government and its facilities, as well as revenue generating for private enterprises, since technology has advanced rapidly.

The ability for anyone on the earth to be globally connected has resulted in complex transportation networks that are extremely demanding and becoming increasingly vital. As a result, a successful logistics system can have a significant impact on organisations and related business processes.

Highlighting the importance of logistics in some sectors such as grocery delivery, online store delivery of goods, waste management, and intra-city public transportation, product prices can rise due to increases in distribution costs, whereas vehicle routing has the potential to save up to 30% (Hasle & Kloster, 2007). Therefore, vehicle routing becomes necessary.

Vehicle Routing Problem (VRP) is a type of optimisation problem in which the itineraries of a fleet of vehicles are optimised to service a specific set of clients (Cattaruzza et al., 2017). This condition accounts for a significant portion of the flow of cars for diverse logistical purposes in cities.

The framework is used to represent a wide range of difficulties in applications such as supply chain management, delivery services, public transportation, telecommunications, and production planning (Bocewicz et al., 2017).

The optimisation of vehicle routing can result in significant cost savings. The interest in VRP stems from its practical and economic significance. However, solving a VRP is not an easy process because it falls under the complexity class of choice problems (Kumar & Panneerselvam, 2012).

Subramanian et al. (2013) identified two variants of VRP:

1. Capacitated VRP(CVRP)

2. VRP for simultaneous pickup and delivery.

3. VRP for mixed pickup and delivery.

4. Multi-depot VRP with mixed pickup and delivery.

5. VRP with access time windows.

The goal of VRP can be to reduce delivery and vehicle expenses, optimise the number of drivers and vehicles, minimise delivery time, minimise overall route cost, and so on (Mahmoudi & Zhou, 2016). The optimisation problem can be designed to address any one or more of the listed objectives while taking into account their constraints.

Fuel and diesel transportation, the deployment of soldiers on the front lines, aeroplane flights, the delivery of food and beverages to restaurants, the delivery of cash to cash machines (ATMs), student and employee services

the delivery of items purchased online, transportation, and waste management (Kirci, 2016). This research will concentrate on the CVRP as it applies to waste management and the supply chain.

In transportation, a CVRP model is a technique that can handle the problem of incorrect logistic scheduling in the movement of people that requires a commercial transportation service to ensure that all clients are considered and picked up.

Also, throughout the supply chain, demand (payload of each client) and truck capacity are considered while organising payload drop offs and pick-ups.

In waste management, environment quality refers to how quickly an environment deteriorates in relation to human needs, particularly with regards to solid waste management. This leads to increased CO2 emissions and global warming (Budzianowski, 2016).

Waste management has been updated and improved over time utilising various technologies in response to the rise in solid waste caused by population growth (Moh & Manaf, 2014).

1.2 The Significance of Research

This research is motivated by a rising concern for obtaining quality solutions, particularly for routing models that take into account the number of vehicles and the cost of operation. According to the literature, applying a better strategy to this routing problem can result in a more optimised solution (Uchoa et al. 2017).

For this reason, the Firefly algorithm (FFA) will be used, which is associated with the CVRP’s features and qualities. The main characteristics of the FFA include the fact that the nodes (fireflies) are more versatile in their attractiveness, resulting in increased mobility and a more efficient exploration of the search space, i.e. the best route will be identified and exploited for vehicles to deliver to customers.

Also, the brightness of a node is related to its appeal, thus a less light Firefly will travel towards a brighter one along the shortest path. The goal function’s landscape structure determines the brightness of each Firefly.

Thus, taking fitness into account at each step of motion, the nodes (customers) move for each iteration in order to achieve a better result, dropping the previous result to be replaced, and this continues until the maximum iteration is reached.

1.3 Statement of Problem

When optimising route distance to conserve money and other resources, the first step is to establish the overall proximity of all other nodes by determining the ideal site for a depot, which affects route distance.

Secondly, determining which vehicle to allocate to which route while adhering to model capacity restrictions is a challenging task that might increase overall route costs.

Finally, a node-to-node travel path decides the next node for the vehicle to drive to; such a selection reduces the total distance travelled and, as a result, the route cost.

1.4 Aims and Objectives

The purpose of this study is to create an optimised routing strategy for route optimisation and depot location on a capacitated vehicle model utilising the Firefly algorithm (FFA).

The aims of this study are as follows:

1. Create a CVRP model and goal function that takes into account the random, optimised, centred, and eccentric (ROCE) depot placements.

2. Using the FFA, optimise the model generated in (1) and apply it to solid waste management and supply chain scenarios.

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