An integer linear programming approach for a sustainable vehicle allocation problem with the assumption of vertical logistics collaboration

With the spread of production and consumption around wider locations and the development of international trade, the importance of logistics operations is increasing for businesses. Accordingly, businesses need to plan their related logistics operations effectively and efficiently to provide a competitive advantage by meeting demands of customers faster and economically. Furthermore, increasing awareness of environmental and social problems reveal the necessity of taking into account the sustainability pillars (economic, environmental and social) while planning logistics operations. Besides, collaboration with customers during operation planning increases both the efficiency of transportation activities and customer satisfaction, thus contributing to competitive advantage and profitability of businesses. Additionally, improving vehicle utilization through the logistics collaboration approach enables businesses to carry out logistics operations that contribute to the performance of the three pillars of sustainability by providing less fuel consumption and emissions and more profit margins, and increasing customer/employee satisfaction. This thesis aims to propose a decision support model to businesses operating in the logistics sector. In this direction, an integer linear programming model is proposed for the sustainable vehicle allocation problem, which is considered under the assumption of vertical logistics collaboration. The use of the proposed mathematical model aims to maximize profit, and to examine the effects of vertical logistics collaboration assumption on the performance of transportation operations and the three pillars of sustainability. The model takes into account homogeneous vehicle fleet and deterministic customer demands. The accuracy and applicability of the model are demonstrated by a case study and subsequent numerical analyses that employ a hypothetical data set that is constructed as close to the size of an entire logistics network as possible. Numerical analyses reveal the positive impact of vertical logistics collaboration on system and sustainability performance.