Multi-Objective Optimization of Green Transportation Operations in Supply Chain Management

Supply chain is the integration of manufacturing process where raw materials are converted into final products, then delivered to customers. Supply chains consists of two basic integrated process that interact together: (1) production and inventory and (2) distribution and logistics. Maximizing competitiveness and profitability are of the main goals of a supply chain. Accounting only for economic impacts as variable and fixed costs does not serve the main goal of the supply chain. Therefore, considering customer satisfaction measures in distribution models is essential in supply chain management. Models that addressed the three objectives simultaneously handled one of the objectives as a constraint with a certain threshold in the problem, while others used weighted utility functions to address the problem objective in deterministic environment. This thesis focuses on the multi-objective Vehicle Routing Problem (VRP) in green environment. The proposed Green VRP (GVRP) deals with three different objectives simultaneously that considers economic, environmental, and social aspects. A new hybrid search algorithm to solve the capacitated VRP is presented and validated in Chapter 2. The developed algorithm combines the evolutionary genetic search with a new local search heuristic that considers both locations and demand quantities of the nodes to be visited in routing decisions, not just the distances travelled. The algorithm is then used to solve the multi-objective GVRP in Chapter 3. The objectives of the developed GVRP model are minimizing the total transportation operations cost, minimizing the fuel consumption, and maximizing customer satisfaction. Moreover, a new overlap index is developed to measure the amount of overlap between customers’ time windows that provides an indication of how tight/constrained the problem is. The model is then adapted to consider the uncertainty in travel times, service times, and unpredictable demands of customers in Chapter 4. Pareto fronts were obtained and trade-offs between the three objectives are presented in both deterministic and stochastic forms. Furthermore, analysis of the effects of changing vehicle capacity and customer time windows relaxation are presented