scholarly journals An Efficient Multi-Vehicle Routing Strategy for Goods Delivery Services

2021 ◽  
Author(s):  
Linh Nguyen
Keyword(s):  
Autonomous Vehicles ◽  
Optimization Problem ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Optimal Routing ◽  
Ground Vehicles ◽  
Routing Optimization ◽  
Routing Strategy ◽  
Delivery Services ◽  
Delivery Vehicles

<pre>The paper addresses the problem of efficiently planning routes for multiple ground vehicles used in goods delivery services. Given popularity of today's e-commerce, particularly under the COVID-19 pandemic conditions, goods delivery services have been booming than ever, dominated by small-scaled (electric) bikes and promised by autonomous vehicles. However, finding optimal routing paths for multiple delivery vehicles operating simultaneously in order to minimize transportation cost is a fundamental but challenging problem. In this paper, it is first proposed to exploit the mixed integer programming paradigm to model the delivery routing optimization problem (DROP) for multiple simultaneously-operating vehicles given their energy constraints. The routing optimization problem is then solved by the multi-chromosome genetic algorithm, where the number of delivery vehicles can be optimized. The proposed approach was evaluated in a real-world experiment in which goods were expected to be delivered from a depot to 26 suburb locations in Canberra, Australia. The obtained results demonstrate effectiveness of the proposed algorithm.</pre>

scholarly journals An Efficient Multi-Vehicle Routing Strategy for Goods Delivery Services

2021 ◽  
Author(s):  
Linh Nguyen
Keyword(s):  
Autonomous Vehicles ◽  
Optimization Problem ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Optimal Routing ◽  
Ground Vehicles ◽  
Routing Optimization ◽  
Routing Strategy ◽  
Delivery Services ◽  
Delivery Vehicles

<pre>The paper addresses the problem of efficiently planning routes for multiple ground vehicles used in goods delivery services. Given popularity of today's e-commerce, particularly under the COVID-19 pandemic conditions, goods delivery services have been booming than ever, dominated by small-scaled (electric) bikes and promised by autonomous vehicles. However, finding optimal routing paths for multiple delivery vehicles operating simultaneously in order to minimize transportation cost is a fundamental but challenging problem. In this paper, it is first proposed to exploit the mixed integer programming paradigm to model the delivery routing optimization problem (DROP) for multiple simultaneously-operating vehicles given their energy constraints. The routing optimization problem is then solved by the multi-chromosome genetic algorithm, where the number of delivery vehicles can be optimized. The proposed approach was evaluated in a real-world experiment in which goods were expected to be delivered from a depot to 26 suburb locations in Canberra, Australia. The obtained results demonstrate effectiveness of the proposed algorithm.</pre>

scholarly journals Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Farnaz Javadi Gargari ◽  
Mahjoube Sayad ◽  
Seyed Ali Posht Mashhadi ◽  
Abdolhossein Sadrnia ◽  
Arman Nedjati ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Model ◽  
Optimization Problem ◽  
Programming Model ◽  
Transportation Cost ◽  
Real Data ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Hub Location ◽  
Goal Programming Model

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model.

Algorithms for Localization and Routing of Unmanned Vehicles in GPS-Denied Environments

2018 ◽  
Author(s):  
Bingyu Wang ◽  
Sivakumar Rathinam ◽  
Rajnikant Sharma ◽  
Kaarthik Sundar
Keyword(s):  
Global Positioning System ◽  
Optimization Problem ◽  
Unmanned Vehicles ◽  
Routing Algorithms ◽  
Linear Program ◽  
Mixed Integer ◽  
Ground Vehicles ◽  
Placement Problem ◽  
Mixed Integer Linear Program ◽  
Global Positioning

A majority of the routing algorithms for unmanned aerial or ground vehicles rely on Global Positioning System (GPS) information for localization. However, disruption of GPS signals, by intention or otherwise, can render these algorithms ineffective. This article provides a way to address this issue by utilizing landmarks to aid localization in GPS-denied environments. Specifically, given a number of vehicles and a set of targets, we formulate a joint routing and landmark placement problem as a combinatorial optimization problem: to compute paths for the vehicles that traverse every target at least once, and to place landmarks to aid the vehicles in localization while each of them traverses its route, such that the sum of the traveling cost and the landmark placement cost is minimized. A mixed-integer linear program is presented, and a set of algorithms and heuristics are proposed for different approaches to address certain issues not covered by the linear program. The performance of each proposed algorithm is evaluated and compared through extensive computational and simulation results.

scholarly journals Joint optimization of computing ratio and access points’ density for mixed mobile edge/cloud computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Tianqi Jing ◽  
Shiwen He ◽  
Fei Yu ◽  
Yongming Huang ◽  
Luxi Yang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Coverage Probability ◽  
Optimization Problem ◽  
Mobile Cloud Computing ◽  
Low Complexity ◽  
Mixed Integer ◽  
Joint Optimization ◽  
Mobile Cloud ◽  
Convex Problem

AbstractCooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.

scholarly journals Optimization of inbound logistics cost in automotive industry

2018 ◽  
Vol 204 ◽  
pp. 02007
Author(s):  
Inaki Maulida Hakim ◽  
Rolina Oktapiani Zaqiah ◽  
Yuri M. Zagloel Teuku
Keyword(s):  
Automotive Industry ◽  
Raw Materials ◽  
Transportation Cost ◽  
Global Market ◽  
Mixed Integer ◽  
Integer Nonlinear Programming ◽  
Material Load ◽  
Optimization Methodology ◽  
Optimal Material ◽  
Inbound Logistics

The increasing growth of automotive industry in Indonesia has not been matched by the number of local suppliers and makes the automotive industry too dependent on imported raw materials. Along with the needs of import activities, it is also required a greater logistics activities. However, with high logistics costs, the manufacturer must increase efficiency to be able to compete in the global market. This can be accomplished by planning inbound logistics activities that control the movement of materials from suppliers to the manufacture. In this research, an optimization methodology, based on Mixed Integer Nonlinear Programming (MINLP) approach is developed and solved with branch and bound algorithm. The result of this research, which obtained the total cost of optimal inbound logistics include material cost, transportation cost, and administration cost. This model can also be used as a tool for the company in making decisions about the type and the number of container also with the total of the optimal material load in each container, therefore the optimal container space utilization value can be obtained.

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