Public consciousness on environmental sustainability and industry impact on environment have been increasing for the last few decades. As a result, more and more companies are taking the pollution factor into account when planning their logistic activities. Therefore, a logistics process, known as Green Logistics, that considers the environmental, ecological, and social effects has recently received increasing attentions from governments and organizations. In this research, two vehicle routing problems with environmental considerations, namely the Hybrid Vehicle Routing Problem (HVRP) and the Heterogeneous Fleet Pollution Routing Problem (HFPRP) are studied.

HVRP is an extension of the Green Vehicle Routing Problem (G-VRP), which considers vehicles that use a hybrid power source, known as the Plug-in Hybrid Electric Vehicle (PHEV). This study formulates a mathematical model to minimize the total travel cost of PHEVs. Moreover, the model considers the utilization of electric and fuel power. Pollution Routing Problem (PRP) is an extension of the Vehicle Routing Problem with Time Windows (VRPTW). It determines a set of optimal routes and vehicle speed on each route segment for a fleet of vehicles serving a set of customers within specific time windows. In practice, many vehicle routing problems involve a fleet of heterogeneous vehicles with different capacities and travel costs. Emission levels vary by the vehicle type due to differences in curb weight and capacity. Therefore, the Heterogeneous Fleet Pollution Routing Problem is proposed as an extension of PRP. In this study, a mathematical model for HFPRP with the objective of minimizing the total costs consisting of fuel cost, greenhouse gas (GHG) emissions, and vehicle variable cost is formulated.

Two versions of the Simulated Annealing heuristics for solving HVRP and HFPRP are developed. The first one is the basic version, denoted as SA. The second version employs a restart strategy, denoted as SA_RS. The proposed algorithms are first tested on benchmark instances of the Capacitated Vehicle Routing Problem (CVRP). The result indicates that both algorithms perform well on solving CVRP. Then, the proposed SA and SA_RS are used to solve HVRP. The numerical experiment presents that vehicle type and the number of electric charging stations have an impact on the total travel cost. The proposed SA and SA_RS heuristics are then tested on benchmark instances of PRP. The result shows that SA and SA_RS perform as good as the state-of-the-art algorithms on solving PRP benchmark instances. Then, SA and SA_RS are used to solve HFPRP and the results are compared to those obtained by CPLEX. The comparative result shows that SA and SA_RS effectively solve HFPRP.

Public consciousness on environmental sustainability and industry impact on environment have been increasing for the last few decades. As a result, more and more companies are taking the pollution factor into account when planning their logistic activities. Therefore, a logistics process, known as Green Logistics, that considers the environmental, ecological, and social effects has recently received increasing attentions from governments and organizations. In this research, two vehicle routing problems with environmental considerations, namely the Hybrid Vehicle Routing Problem (HVRP) and the Heterogeneous Fleet Pollution Routing Problem (HFPRP) are studied.

HVRP is an extension of the Green Vehicle Routing Problem (G-VRP), which considers vehicles that use a hybrid power source, known as the Plug-in Hybrid Electric Vehicle (PHEV). This study formulates a mathematical model to minimize the total travel cost of PHEVs. Moreover, the model considers the utilization of electric and fuel power. Pollution Routing Problem (PRP) is an extension of the Vehicle Routing Problem with Time Windows (VRPTW). It determines a set of optimal routes and vehicle speed on each route segment for a fleet of vehicles serving a set of customers within specific time windows. In practice, many vehicle routing problems involve a fleet of heterogeneous vehicles with different capacities and travel costs. Emission levels vary by the vehicle type due to differences in curb weight and capacity. Therefore, the Heterogeneous Fleet Pollution Routing Problem is proposed as an extension of PRP. In this study, a mathematical model for HFPRP with the objective of minimizing the total costs consisting of fuel cost, greenhouse gas (GHG) emissions, and vehicle variable cost is formulated.

Two versions of the Simulated Annealing heuristics for solving HVRP and HFPRP are developed. The first one is the basic version, denoted as SA. The second version employs a restart strategy, denoted as SA_RS. The proposed algorithms are first tested on benchmark instances of the Capacitated Vehicle Routing Problem (CVRP). The result indicates that both algorithms perform well on solving CVRP. Then, the proposed SA and SA_RS are used to solve HVRP. The numerical experiment presents that vehicle type and the number of electric charging stations have an impact on the total travel cost. The proposed SA and SA_RS heuristics are then tested on benchmark instances of PRP. The result shows that SA and SA_RS perform as good as the state-of-the-art algorithms on solving PRP benchmark instances. Then, SA and SA_RS are used to solve HFPRP and the results are compared to those obtained by CPLEX. The comparative result shows that SA and SA_RS effectively solve HFPRP.

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