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

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 Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

Sustainability ◽

10.3390/su11061596 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1596 ◽

Cited By ~ 9

Author(s):

Longlong Leng ◽

Yanwei Zhao ◽

Zheng Wang ◽

Jingling Zhang ◽

Wanliang Wang ◽

...

Keyword(s):

Fuel Consumption ◽

Carbon Emissions ◽

Time Windows ◽

Selection Strategy ◽

Low Carbon ◽

Location Routing Problem ◽

Routing Problem ◽

Hard Time ◽

Environmental Selection ◽

Location Routing

With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.

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An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16112064 ◽

2019 ◽

Vol 16 (11) ◽

pp. 2064 ◽

Cited By ~ 8

Author(s):

Longlong Leng ◽

Yanwei Zhao ◽

Jingling Zhang ◽

Chunmiao Zhang

Keyword(s):

Time Windows ◽

Mixed Integer ◽

Low Carbon ◽

Location Routing Problem ◽

Routing Problem ◽

Domain Specific ◽

Hard Time ◽

Selection Strategies ◽

Location Routing ◽

High Level

In this paper, we consider a variant of the location-routing problem (LRP), namely the the multiobjective regional low-carbon LRP (MORLCLRP). The MORLCLRP seeks to minimize service duration, client waiting time, and total costs, which includes carbon emission costs and total depot, vehicle, and travelling costs with respect to fuel consumption, and considers three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows. We formulated a multiobjective mixed integer programming formulations for the problem under study. Due to the complexity of the proposed problem, a general framework, named the multiobjective hyper-heuristic approach (MOHH), was applied for obtaining Pareto-optimal solutions. Aiming at improving the performance of the proposed approach, four selection strategies and three acceptance criteria were developed as the high-level heuristic (HLH), and three multiobjective evolutionary algorithms (MOEAs) were designed as the low-level heuristics (LLHs). The performance of the proposed approach was tested for a set of different instances and comparative analyses were also conducted against eight domain-tailored MOEAs. The results showed that the proposed algorithm produced a high-quality Pareto set for most instances. Additionally, extensive analyses were also carried out to empirically assess the effects of domain-specific parameters (i.e., fleet composition, client and depot distribution, and zones area) on key performance indicators (i.e., hypervolume, inverted generated distance, and ratio of nondominated individuals). Several management insights are provided by analyzing the Pareto solutions.

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

Algorithms ◽

10.3390/a12070129 ◽

2019 ◽

Vol 12 (7) ◽

pp. 129 ◽

Cited By ~ 3

Author(s):

Chunmiao Zhang ◽

Yanwei Zhao ◽

Longlong Leng

Keyword(s):

Customer Satisfaction ◽

Heuristic Algorithm ◽

Carbon Emissions ◽

Time Windows ◽

Low Carbon ◽

Location Routing Problem ◽

Routing Problem ◽

Location Routing ◽

Proposed Model ◽

The Impact

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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The Location-Routing Problem with Full Truckloads in Low-Carbon Supply Chain Network Designing

Mathematical Problems in Engineering ◽

10.1155/2018/6315631 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Cheng Chen ◽

Rongzu Qiu ◽

Xisheng Hu

Keyword(s):

Supply Chain ◽

Hybrid Approach ◽

Supply Chain Network ◽

Low Carbon ◽

Nsga Ii ◽

Location Routing Problem ◽

Routing Problem ◽

Total Cost ◽

Carbon Supply ◽

Location Routing

In recent years, low-carbon supply chain network design has been the focus of studies as the development of low-carbon economy. The location-routing problem with full truckloads (LRPFT) is investigated in this paper, which extends the existing studies on the LRP to full truckloads problem within the regional many-to-many raw material supply network. A mathematical model with dual objectives of minimizing total cost and environmental effects simultaneously is developed to determine the number and locations of facilities and optimize the flows among different kinds of nodes and routes of trucks as well. A novel multiobjective hybrid approach named NSGA-II-TS is proposed by combining a known multiobjective algorithm, NSGA-II, and a known heuristics, Tabu Search (TS). A chromosome presentation based on natural number and modified partially mapping crossover operator for the LRPFT are designed. Finally, the computational effectiveness of the hybrid approach is validated by the numerical results and a practical case study is applied to demonstrate the tradeoff between total cost and CO2 emission in the LRPFT.

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Location Routing Problem with Consideration of CO2 Emissions Cost: A Case Study

Jurnal Teknik Industri ◽

10.22219/jtiumm.vol21.no2.225-234 ◽

2020 ◽

Vol 21 (2) ◽

pp. 225-234

Author(s):

Ananda Noor Sholichah ◽

Y Yuniaristanto ◽

I Wayan Suletra

Keyword(s):

Co2 Emissions ◽

Time Windows ◽

Optimal Solution ◽

Mixed Integer ◽

Distribution Centers ◽

Location Routing Problem ◽

Routing Problem ◽

Location Routing ◽

Study Results

Location and routing are the main critical problems investigated in a logistic. Location-Routing Problem (LRP) involves determining the location of facilities and vehicle routes to supply customer's demands. Determination of depots as distribution centers is one of the problems in LRP. In LRP, carbon emissions need to be considered because these problems cause global warming and climate change. In this paper, a new mathematical model for LRP considering CO2 emissions minimization is proposed. This study developed a new Mixed Integer Linear Programming (MILP) model for LRP with time windows and considered the environmental impacts. Finally, a case study was conducted in the province of Central Java, Indonesia. In this case study, there are three depot candidates. The study results indicated that using this method in existing conditions and constraints provides a more optimal solution than the company's actual route. A sensitivity analysis was also carried out in this case study.

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