The vehicle routing problem

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems DVRP, with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are 1 type of problem, 2 logistical context, 3 transportation mode, 4 objective function, 5 fleet size, 6 time constraints, 7 vehicle capacity constraints, 8 the ability to reject customers, 9 the nature of the dynamic element, 10 the nature of the stochasticity if any, and 11 the solution method. We comment on technological vis-i-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 671, 3-31 2016

/pdf/dynamic-vehicle-routing-problems-three-decades-and-counting-3phqfejokj.pdf

Dynamic vehicle routing problems: Three decades and counting

https://pure.tue.nl/ws/files/3887204/760250.pdf

A review of recent research on green road freight transportation

https://www.repository.cam.ac.uk/bitstream/1810/280260/1/Geissdoerfer%20et%20al.%202018_Author%27s%20version.pdf

Business models and supply chains for the circular economy

Evolution of sustainability in supply chain management: A literature review

Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.

Survey of Green Vehicle Routing Problem: Past and future trends

To propose a GA-based optimization model for designing green transportation schemes.To examine the economic and environmental value based on a cost analysis in depth.To provide a guidance of implementing green transportation for the logistics service providers. Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO2 emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost.

A Genetic Algorithm-based optimization model for supporting green transportation operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.

A decision support system for optimizing dynamic courier routing operations

In today's volatile markets, increasingly unpredictable customer demand is exerting great challenges to responsive replenishment. The complexity of responsive replenishment is higher when the business is global in which demand in both domestic and overseas markets has to be catered for. The emergence of cloud computing has eased the difficulties as it allows nearly real-time access to a universal platform for information sharing between franchisors and franchisees, creating huge opportunities for understanding global market needs for responsive replenishment. Considering the existence of uncertainties due to the fluctuating demands, fuzzy logic is useful in providing decision support for replenishment in uncertain environments. This paper presents a cloud-based responsive replenishment system to manage operation data of a franchise business using cloud computing, and for analysis using fuzzy logic in order to provide franchisors with the required inventory levels. To the best of our knowledge, this is the first study that applies cloud computing and artificial intelligence techniques in franchising. A pilot run of the system is conducted in an education company, which is considered to be a good representation of an industry operating with a franchise model. The results show that the system allows franchisors to formulate effective responsive replenishment strategies.

A cloud-based responsive replenishment system in a franchise business model using a fuzzy logic approach

City logistics is facing the challenging problem of providing a quick-response and on-time delivery service in congested urban areas with frequent traffic jams. The dynamically changing traffic conditions make the predetermined best transportation plans suboptimal and consequently cause increased logistics cost and even greater air pollution. To help the driver determine time-optimal routing solutions in order to avoid congestion according to the real-time traffic flow, a Real-time Mobile Intelligent Routing System is designed and deployed on drivers' Smartphones to help in routing decision making. Data mining techniques are employed to discover the routing patterns from the past cases of routing plans so as to generate case-based routing plans for the drivers. A metaheuristic is used to undertake the optimization of a real-time optimal routing plan based on real-time traffic information. A case study and computational experiments demonstrate the effectiveness of the proposed methods in significantly reducing the traveling time.

Canhong Lin

Papers

Survey of Green Vehicle Routing Problem: Past and future trends

A Genetic Algorithm-based optimization model for supporting green transportation operations

A decision support system for optimizing dynamic courier routing operations

A cloud-based responsive replenishment system in a franchise business model using a fuzzy logic approach

A data mining and optimization-based real-time mobile intelligent routing system for city logistics