Integer programming formulations of discrete hub location problems

We reviewed the use of genetic algorithms on the MDVRP (multi depot vehicle routing problem).Survey was made on every operator and setting of genetic algorithm for this problem.We tested different genetic operators and compared the results.We compared the genetic algorithms to other metaheuristic algorithms on MDVRP based on the results on standard benchmarks. This article presents a survey of genetic algorithms that are designed for solving multi depot vehicle routing problem. In this context, most of the articles focus on different genetic approaches, methods and operators, commonly used in practical applications to solve this well-known and researched problem. Besides providing an up-to-date overview of the research in the field, the results of a thorough experiment are presented and discussed, which evaluated the efficiency of different existing genetic methods on standard benchmark problems in detail. In this manner, the insights into strengths and weaknesses of specific methods, operators and settings are presented, which should help researchers and practitioners to optimize their solutions in further studies done with the similar type of the problem in mind. Finally, genetic algorithm based solutions are compared with other existing approaches, both exact and heuristic, for solving this same problem.

A survey of genetic algorithms for solving multi depot vehicle routing problem

In this study, the optimal locations of a specific type of charging facilities for electric vehicles (EVs), wireless power transfer facilities, are investigated. A mathematical model has been developed to address this problem. The objective of the model is to locate a given number of wireless charging facilities for EVs out of a set of candidate facility locations for capturing the maximum traffic flow on a network. The interaction between traffic flow patterns and the location of the charging facilities is incorporated explicitly by applying the stochastic user equilibrium principle to describe electric vehicle drivers’ routing choice behavior. Firstly, the problem is formulated into a mixed-integer nonlinear program, secondly a solution method is developed to obtain the global optimal solution of the linearized program. Numerical experiments are presented to demonstrate the model validity.

Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium

In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p-median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p-median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p-median and the maximal covering location models, our results suggest that (1) the p-median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases.

Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China

To reduce greenhouse gas emissions in transportation sector, battery electric vehicle (BEV) is a better choice towards the ultimate goal of zero-emission. However, the shortened range, extended recharging time and insufficient charging facilities hinder the wide adoption of BEV. Recently, a wireless power transfer technology, which can provide dynamic recharging when vehicles are moving on roadway, has the potential to solve these problems. The dynamic recharging facilities, if widely applied on road network, can allow travelers to drive in unlimited range without stopping to recharge. This paper aims to study the complex charging facilities location problem, assuming the wireless charging is technologically mature and a new type of wireless recharging BEV is available to be selected by consumers in the future other than the traditional BEV requiring fixed and static charging stations. The objective is to assist the government planners on optimally locating multiple types of BEV recharging facilities to satisfy the need of different BEV types within a given budget to minimize the public social cost. Road users’ ownership choice among multiple types BEV and BEV drivers’ routing choice behavior are both explicitly considered. A tri-level programming is then developed to model the presented problem. The formulated model is first treated as a black-box optimization, and then solved by an efficient surface response approximation model based solution algorithm.

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Locating multiple types of charging facilities for battery electric vehicles

Survey: Covering problems in facility location: A review

Finding the optimal location for industrial facilities has always been of a crucial importance and priority from geographers’ and economists’ view point. Although economics play the main role in presenting location theories by resorting to its theories, geographers, with their focus on locative and spatial changes leading to natural phenomenon, have also had a share in complimenting these location theories and finding optimal points to locate industrial activities location. From this point of view, materials, market and other manufacturing factors have not been concentrated in one point, and their spatial separation necessitates traveling distances which, in turn, brings about costs. That is why optimal industrial location is also a part of geographical studies.

Warehouse Location Problem

A supply chain (SC) is composed of different facilities and also their definite roles with the aim of delivering goods or services to the customer in an effective way. Obviously the physical structure of each SC has an important role in its performance. In this chapter, the authors aim to describe SC network (SCN) design definition, classification and related issues to this subject, models and solution methods. At the end, the conclusion is provided.

Nooshin Heidari

Papers

Survey: Covering problems in facility location: A review

Warehouse Location Problem

Strategic Issues of Supply Chain Design and Management