Sustainable Logistics and Transportation

Densely populated areas are often associated with high pollution of the air. In order to decrease the air quality (health) index and thereby improve the quality of life, we aim to minimize air pollution of a transportation network especially in those highly air-polluted areas. Even though some transportation providers have invested to “green up” their operations, some can or will not sufficiently trade off their profit for reducing CO₂ emissions. Therefore, governmental regulation on transportation routing is necessary. This chapter is concerned with a novel problem of optimizing a governmental regulation plan, by reducing the capacity of roads. The goal is to find a regulation plan that minimizes the air pollution in dense areas on the transportation network. As a result, transportation providers must reroute their trucks in order to disburden the highest polluted areas. We propose a mixed integer linear program to solve this problem and show the applicability and low computation times of our solution in computational experiments.

There has been a recent resurge of interest in vehicle routing problems, especially in the context of green vehicle routing. One popular and simplified model is that of the cumulative vehicle routing problem. In this chapter, we examine the motivation, the definition, and the mixed integer linear program for the cumulative VRP. We review some of the recent results on approximation algorithms for the cumulative VRP. A column generation-based procedure for solving the cumulative VRP is also described. We also review approximation algorithms for a stochastic version of the cumulative VRP.

In this chapter, several constructive algorithms developed for the cumulative vehicle routing problem with limited duration are used as an initial solution generator algorithm for various metaheuristics. Their performance on the solution quality obtained by solution-based and population-based metaheuristics is investigated. Data sets from the literature are used for the computational tests. The computational experiments show that the performance of simulated annealing is significantly affected by the initial solution generator. Although initial solution generators do not affect the performance of genetic algorithms as much as simulated annealing, choosing the best initial solution generator is still an important issue to obtain high-quality solutions in a proper computational time.

We consider a logistic distribution decision-making problem, in which a vehicle fleet must carry out a set of deliveries between pairs of nodes of the underlying transportation network. The goal is to maximize the number of deliveries that will be carried out, while also minimizing the number of vehicles utilized to this end. The optimization is lexicographic in the sense that the former objective exhibits higher priority than the latter one. For this problem, we develop an integer programming model formulation and an associated column generation-based solution methodology. The proposed methodology utilizes a master problem which tries to fulfill the maximum possible number of deliveries given a specific set of vehicle routes and a column generation subproblem which is used to generate cost-effective vehicle routes¹, for improving the master problem solution. We describe the steps of the proposed methodology, illustrating how it can be modified to accommodate interesting problem variations that often arise in practice. We also present extensive computational results demonstrating the computational performance of the proposed solution algorithm and illustrating how its behavior is influenced by key design parameters.

This chapter mainly discusses the mathematical programming models and methods used to design sustainable logistics networks (SLN) under epistemic uncertainty. Firstly, the relevant concepts and definitions are described and analyzed. Thereafter, a systemic review and analysis of the recent literature is provided to explore the most attractive research avenues in this area. A comprehensive description is given on environmental and social impact assessment methods in order to facilitate the quantification of environmental and social burden in the mathematical decision models. Two selected mathematical programming models for SLN design problem under uncertain data are provided and explained in detail to support quantitative decision-making in this area. Finally, a real industrial case study is described and investigated to show the applicability of the previously discussed mathematical programming methods.

A combination of transportation modes offers environmentally friendly alternatives to transport high volumes of freight over long distances. In order to reflect the advantages of each transportation mode, it is the challenge to deal with data uncertainty during the transportation planning phase. This chapter investigates the alternative ways of modeling the uncertainty by discussing them and their characteristics in terms of solution times, the quality, and the limitations. Moreover, several real-life case studies are provided to demonstrate potential environmental benefits by considering the principles of green logistics for single-mode and intermodal transportation.

As a result of the increase in environmental problems, green logistics has become an important subject in the supply chain literature. In this study, a multiproduct multi-vehicle inventory routing problem is modeled by considering the cost stemming from fuel consumption as an environmental objective. Demand and inventory holding costs are taken into account as uncertain parameters. A sample average approximation algorithm is used to solve the problem. The performance of the algorithm is evaluated in terms of optimality gap and computational time by using a data set from the literature. The computational experiments give promising results for further research.

The shipping industry carries over 90% of the world’s trade, and is hence a major contributor to CO₂ and other airborne emissions. As a global effort to reduce air pollution from ships, the implementation of the ECA (Emission Control Areas) regulations has given rise to the wide usage of cleaner fuels. This has led to an increased emphasis on the management and risk control of maritime bunker costs for many shipping companies. In this paper, we provide a novel view on the relationship between bunker risk management and CO₂ emissions. In particular, we investigate how different actions taken in bunker risk management, based on different risk aversions and fuel hedging strategies, impact a shipping company’s CO₂ emissions. We use a stochastic programming model and perform various comparison tests in a case study based on a major liner company. Our results show that a shipping company’s risk attitude on bunker costs has impacts on its CO₂ emissions. We also demonstrate that, by properly designing its hedging strategies, a shipping company can sometimes achieve noticeable CO₂ reduction with little financial sacrifice.

The concept of closed-loop supply chain (CLSC) has started to attract growing attention due to the consumer pressures, environmental awareness, and legislations. Managers in many companies have realized that a well-designed supply chain (SC) can improve the companies’ performance in the market. Thus, a lot of companies start to focus on CLSC issues including remanufacturing, refurbishing, recycling, and disposal of end-of-life products. The body of literature on CLSC management has been overwhelmingly dominated by noncooperative studies. In order to fill up this gap in the literature, we deal with an allied SC network in cooperative environment. With the implementation of allied SCs, companies not only maximize their profit but also minimize their various costs and become more flexible and efficient in the market. Following this motivation, we develop a decentralized multilevel CLSC model for allied SCs. At the first decision level, the plants in allied SCs are considered as the upper-level DMs of the Stackelberg game. At the second level, raw material suppliers, common suppliers, assembly centers, and common collection centers are considered as the lower-level DMs of the Stackelberg game. In order to tackle each decision-maker (DM)’s unique objectives, we propose a new fuzzy analytic hierarchy process (AHP)-based interactive fuzzy programming (IFP) approach. In the IFP approach, upper-level DMs determine the minimum satisfactory level for their own objectives, and by using this value, the lower DMs evaluate their own satisfactory level. A compromise solution can be derived until termination conditions are satisfied. The primary aim of this study is to design a decentralized CLSC network in cooperative environment and to propose a novel IFP approach. Finally, a numerical example is implemented and analyzed in order to demonstrate the efficiency of the proposed approach.