Developing sustainable supply chains in regional Australia considering demand uncertainty, government subsidies and carbon tax regulation

There is a tremendous opportunity to implement sustainable supply chain management practices in terms of logistics, operations, and transport network in regional Australia. Unfortunately, this opportunity has not been investigated and there is a lack of academic studies in this body of knowledge. This thesis is made up by three related, but independent models designed to efficiently distribute products from a regional hub to other part of the country. This research aims to develop efficient and sustainable supply chain practices to deliver regional Australian products across the country and overseas. As the airports of most Australian capital cities are over-crowded while many regional airports are under-utilised, the first model examines the ways to promote the use of regional airports. Australia is a significant food producer and the agricultural products are primarily produced in regional areas. In the other two models, we focus on the distribution of perishable products from regional Australia. The first model presented in Chapter 2 outlines how different government subsidy schemes can be used to influence airfreight distributions that favour the use of regional airports and promote regional economic development. The model simultaneously considers time-window and release-time constraints as well as the heterogeneous fleet for ground distribution where fuel consumption is subject to load, travel distance, speed and vehicle characteristics. A real-world case study in the state of Queensland, Australia is used to demonstrate the application of the model. The results suggest that the regional airport's advantages can be promoted with suitable subsidy programs and the logistics costs can be reduced by using the regional airport from the industry’s perspective. The second model presented in Chapter 3 examines the impacts of carbon emissions arising from the storage and transportation of perishable products on logistical decisions in the cold supply chain considering carbon tax regulation and uncertain demand. The problem is formulated as a two-stage stochastic programming model where Monte Carlo approach is used to generate scenarios. The aim of the model is to determine optimal replenishment policies and transportation schedules to minimise both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The proposed model was implemented in a real-world case study in the state of Queensland, Australia to demonstrate the application of the model. The results highlight that a higher emissions price does not always contribute to the efficiency of the cold supply chain system. The third model presented in Chapter 4 investigates the impacts of two different transport modes - road and rail - on the efficiency and sustainability of transport network to deliver meat and livestock from regional Queensland to large cities and seaports. The model is formulated as a mixed-integer linear programming model that considers road traffic congestions, animal welfare, quality of meat products and environmental impacts from fuel consumption of different transport modes. The aim of the model is to determine an optimal network configuration where each leg of journey is conducted by the most reliable, sustainable and efficient transport mode. The results indicate that it would be possible to significantly decrease total cost if a road-rail intermodal network is used. Considering animal welfare, product quality and traffic congestion can have a significant effect on the decisions related to transport mode selection

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Optimal delivery and replenishment policies for perishable products considering lost sale cost: an efficient hybrid algorithm

In today’s competitive global market, improving the quality of customer service and waste reduction are two important factors to maximise the profits of a system. These goals can be achieved throughout well-managed logistical operations such as proper inventory management and timely delivery of perishable items to customers. This paper presents an integrated mathematical model that schedules replenishment of a perishable product to determine delivery routes, truck loads and inventory levels, where the vehicle arrival time has a direct impact on the quantity of the product delivered to customers. Given the NP-hard nature of the problem, finding optimum or near optimum solutions in polynomial time is challenging, therefore a hybrid algorithm based on a linear programming model and a simulated annealing algorithm is developed to solve the problem efficiently. The proposed method achieves a solution near-optimal solution in an efficient computational time. Finally, an analysis is carried out to verify the effectiveness of the algorithm

Reliability estimation using an integrated support vector regression – variable neighborhood search model

As failure and reliability predictions play a significant role in production systems they have caught the attention of researchers. In this study, Support Vector Regression (SVR), which is known as a powerful neural network method, is developed as a way of forecasting reliability. Generally, SVR is applied in many research environments, and the results illustrate that SVR is a successful method in solving non-linear regression problems. However, SVR parameters tuning is a vital task for performing an accurate reliability estimation. We propose variable neighborhood search (VNS) for continuous space, including some simple but efficient shaking and local search as its main operators, to tune the SVR parameters and create a novel SVR-VNS hybrid system to improve the reliability of estimation accuracy. The proposed method is validated with a benchmark from the former literature and compared with conventional techniques, namely RBF (Gaussian), AR (autoregressive), MLP (logistic), MLP (Gaussian), and SVMG (SVM with genetic algorithm). The experimental results indicate that the proposed model has a superior performance for prediction reliability than other techniques

Promoting Australian regional airports with subsidy schemes: A vehicle routing problem perspective

The major metropolitan airports in Australia have become increasingly congested, resulting in substantial increases in operational costs and waiting time at these airports. This paper assesses the effectiveness of subsidy programs in shifting airfreight from metropolitan airports to regional airports assuming the vehicle routing problem approach is used to optimises the downstream (i.e., road) logistics. We analyse the freight distribution network structure and logistics decisions under two subsidy scenarios. We develop a mixed integer linear programming model incorporating the time-window and release-time constraints. A case study in Australia is used to illustrate the application of the proposed framework. The results show that introducing subsidies can effectively reduce the total costs from the prospective of industries involved in the airfreight distribution. The subsidy program under a non-linear subsidy provides a better performance from the economic and delivery time perspectives. However, if the primary goal is to reduce the volume of cargo traffic at the metropolitan airport, a linear subsidy program is preferred

Sustainable cold supply chain management under demand uncertainty and carbon tax regulation

Increasing awareness of sustainability in supply chain management has prompted organizations and individuals to consider environmental impacts when managing supply chains. The issues concerning environmental impacts are significant in cold supply chains due to substantial carbon emissions from storage and distribution of temperature-sensitive product. This paper investigates the impact of carbon emissions arising from storage and transportation in the cold supply chain in the presence of carbon tax regulation, and under uncertain demand. A two-stage stochastic programming model is developed to determine optimal replenishment policies and transporta- tion schedules to minimize both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The performance and robustness of the matheuristic algo- rithm are analyzed using test instances in various sizes. A real-world case study in Queensland, Australia is used to demonstrate the application of the model. The results highlight that higher emissions price does not always contribute to the efficiency of the cold supply chain system. Furthermore, the analyses indicate that using heterogeneous fleet including light duty and medium duty vehicles can lead to further cost saving and emissions reduction