Using an Evolutionary Algorithm to Discover Low CO2 Tours within a Travelling Salesman Problem

Urquhart, Neil; Scott, Cathy; Hart, Emma

doi:10.1007/978-3-642-12242-2_43

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…This section introduces the fuel consumption, carbon emission model, namely, the comprehensive modal emission model (CMEM). At present, the CMEM model has been applied to PRP and its variants [5,7,8,[11][12][13]34,36]. The CMEM model is shown in Equation (1):…”

Section: Fuel Consumption/carbon Emission Modelmentioning

confidence: 99%

A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem

2019

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This paper proposes a low-carbon location routing problem (LCLRP) model with simultaneous delivery and pick up, time windows, and heterogeneous fleets to reduce the logistics cost and carbon emissions and improve customer satisfaction. The correctness of the model is tested by a simple example of CPLEX (optimization software for mathematical programming). To solve this problem, a hyper-heuristic algorithm is designed based on a secondary exponential smoothing strategy and adaptive receiving mechanism. The algorithm can achieve fast convergence and is highly robust. This case study analyzes the impact of depot distribution and cost, heterogeneous fleets (HF), and customer distribution and time windows on logistics costs, carbon emissions, and customer satisfaction. The experimental results show that the proposed model can reduce logistics costs by 1.72%, carbon emissions by 11.23%, and vehicle travel distance by 9.69%, and show that the proposed model has guiding significance for reducing logistics costs.

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Section: Fuel Consumption/carbon Emission Modelmentioning

confidence: 99%

A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem

2019

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“…Instantaneous fuel consumption and emission rates are put at a more detailed level characterized as time-dependent integral function. Among this classification, the most frequently used models are instantaneous fuel consumption model (IFCM) (Urquhart et al) [21], four-mode elemental fuel consumption model (FMEFCM) (Demir et al) [13], Comprehensive Modal Emission Model (CMEM) (Koc et al) [5,22], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (18) and (19) make sure that the load of vehicle in each edge must be less than its capacity and lager than 0. Constraints (20) and (21) guarantee that the total load of the vehicles leaving/arriving a depot is equal to the total delivery/pickup demand of all clients assigned to it. Constraint (22) is the equitation to calculate arrive time of a vehicle.…”

Section: Proposed Formulationmentioning

confidence: 99%

Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows

Leng

Zhao

Wang

et al. 2018

Mathematical Problems in Engineering

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In this paper, we consider a variant of the location-routing problem (LRP), namely, the regional low-carbon LRP with reality constraint conditions (RLCLRPRCC), which is characterized by clients and depots that located in nested zones with different speed limits. The RLCLRPRCC aims at reducing the logistics total cost and carbon emission and improving clients satisfactory by replacing the travel distance/time with fuel consumption and carbon emission costs under considering heterogeneous fleet, simultaneous pickup and delivery, and hard time windows. Aiming at this project, a novel approach is proposed: hyperheuristic (HH), which manipulates the space, consisted of a fixed pool of simple operators such as “shift” and “swap” for directly modifying the space of solutions. In proposed framework of HH, a kind of shared mechanism-based self-adaptive selection strategy and self-adaptive acceptance criterion are developed to improve its performance, accelerate convergence, and improve algorithm accuracy. The results show that the proposed HH effectively solves LRP/LRPSPD/RLCLRPRCC within reasonable computing time and the proposed mathematical model can reduce 2.6% logistics total cost, 27.6% carbon emission/fuel consumption, and 13.6% travel distance. Additionally, several managerial insights are presented for logistics enterprises to plan and design the distribution network by extensively analyzing the effects of various problem parameters such as depot cost and location, clients’ distribution, heterogeneous vehicles, and time windows allowance, on the key performance indicators, including fuel consumption, carbon emissions, operational costs, travel distance, and time.

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“…A solution (good or bad) in the TSP is therefore a permutation of the set of cities in the order they must be visited. 14,15 For a TSP with n cities, (n-1)!/2 possible solutions exist, meaning the complexity of the problem and the number of possible solutions grows rapidly as the number of cities increases (n! possible solutions exist if equivalent solutions are considered unique where the starting cities and the direction in which the cities are traversed are different).…”

Section: The Fitness Functionmentioning

confidence: 99%

Naturally selecting solutions

2013

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For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the postgenomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

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Using an Evolutionary Algorithm to Discover Low CO2 Tours within a Travelling Salesman Problem

Cited by 11 publications

References 7 publications

A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem

A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem

Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows

Naturally selecting solutions

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