scholarly journals A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2325
Author(s):  
Cong Wang ◽  
Zhongxiu Peng ◽  
Xijun Xu
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Programming Model ◽  
Programming Approach ◽  
Low Carbon ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Level Model ◽  
The Impact

To identify the impact of low-carbon policies on the location-routing problem (LRP) with cargo splitting (LRPCS), this paper first constructs the bi-level programming model of LRPCS. On this basis, the bi-level programming models of LRPCS under four low-carbon policies are constructed, respectively. The upper-level model takes the engineering construction department as the decision-maker to decide on the distribution center’s location. The lower-level model takes the logistics and distribution department as the decision-maker to make decisions on the vehicle distribution route’s scheme. Secondly, the hybrid algorithm of Ant Colony Optimization and Tabu Search (ACO-TS) is designed, and an example is introduced to verify the model’s and algorithm’s effectiveness. Finally, multiple sets of experiments are designed to explore the impact of various low-carbon policies on the decision-making of the LRPCS. The experimental results show that the influence of the carbon tax policy is the greatest, the carbon trading and carbon offset policy have a certain impact on the decision-making of the LRPCS, and the influence of the emission cap policy is the least. Based on this, we provide the relevant low-carbon policies advice and management implications.

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

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 129 ◽  
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.

Development of metaheuristics to solve a transportation inventory location routing problem considering lost sale for perishable goods

2019 ◽  
Vol 14 (1) ◽  
pp. 175-198 ◽  
Author(s):  
S.M.T. Fatemi Ghomi ◽  
B. Asgarian
Keyword(s):  
Mathematical Model ◽  
Distribution Systems ◽  
Hybrid Approach ◽  
Rational Approach ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Content Type ◽  
Location Routing ◽  
Perishable Goods ◽  
The Impact

PurposeFinding a rational approach to maintain a freshness of foods and perishable goods and saving their intrinsic attributes during a distribution of these products is one of the main issues for distribution and logistics companies. This paper aims to provide a framework for distribution of perishable goods which can be applied for real life situations.Design/methodology/approachThis paper proposes a novel mathematical model for transportation inventory location routing problem. In addition, the paper addresses the impact of perishable goods age on the demand of final customers. The model is optimally solved for small- and medium-scale problems. Moreover, regarding to NP-hard nature of the proposed model, two simple and one hybrid metaheuristic algorithms are developed to cope with the complexity of problem in large scale problems.FindingsNumerical examples with different scenarios and sensitivity analysis are conducted to investigate the performance of proposed algorithms and impacts of important parameters on optimal solutions. The results show the acceptable performance of proposed algorithms.Originality/valueThe authors formulate a novel mathematical model which can be applicable in perishable goods distribution systems In this regard, the authors consider lost sale which is proportional to age of products. A new hybrid approach is applied to tackle the problem and the results show the rational performance of the algorithm.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Longlong Leng ◽  
Yanwei Zhao ◽  
Zheng Wang ◽  
Hongwei Wang ◽  
Jingling Zhang
Keyword(s):  
Fuel Consumption ◽  
Carbon Emission ◽  
Time Windows ◽  
Travel Distance ◽  
Low Carbon ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Total Cost ◽  
Location Routing ◽  
Self Adaptive

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.

scholarly journals A Location-Inventory-Routing Problem in Forward and Reverse Logistics Network Design

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Qunli Yuchi ◽  
Zhengwen He ◽  
Zhen Yang ◽  
Nengmin Wang
Keyword(s):  
Network Design ◽  
Programming Model ◽  
Optimal Solution ◽  
Nonlinear Integer Programming ◽  
Inventory Routing ◽  
Distribution Centers ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Reverse Logistic

We study a new problem of location-inventory-routing in forward and reverse logistic (LIRP-FRL) network design, which simultaneously integrates the location decisions of distribution centers (DCs), the inventory policies of opened DCs, and the vehicle routing decision in serving customers, in which new goods are produced and damaged goods are repaired by a manufacturer and then returned to the market to satisfy customers’ demands as new ones. Our objective is to minimize the total costs of manufacturing and remanufacturing goods, building DCs, shipping goods (new or recovered) between the manufacturer and opened DCs, and distributing new or recovered goods to customers and ordering and storage costs of goods. A nonlinear integer programming model is proposed to formulate the LIRP-FRL. A new tabu search (NTS) algorithm is developed to achieve near optimal solution of the problem. Numerical experiments on the benchmark instances of a simplified version of the LIRP-FRL, the capacitated location routing problem, and the randomly generated LIRP-FRL instances demonstrate the effectiveness and efficiency of the proposed NTS algorithm in problem resolution.

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

2019 ◽  
Vol 16 (11) ◽  
pp. 2064 ◽  
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.

scholarly journals Formulation and a heuristic approach for the Orienteering Location-Routing Problem

2020 ◽  
Author(s):  
Ali Nadizadeh
Keyword(s):  
Programming Model ◽  
Standard Test ◽  
Test Problems ◽  
Military Operations ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Practical Applications ◽  
Location Routing ◽  
Commercial Solver ◽  
Short Time

In this paper, a new version of the location-routing problem (LRP), named orienteering location-routing problem (OLRP) is investigated. The problem is composed of two-well known problems: team orienteering problem (TOP) and LRP. There are some challenging practical applications in logistics, tourism, military operations, and other fields, which can be modeled by OLRP. The problem is to consider the location and routing with a special objective function . In the OLRP, a set of nodes with specific scores is given, and some stations among candidate stations should be established. Moreover, there are some routes limited in length, which start from a station, visit some nodes and then return to the same station. Maximizing the sum of the collected scores is the goal of OLRP. To model the problem, an integer linear programming model is proposed. Against a commercial solver, a heuristic GRASP is developed for solving the standard test problems. Most test problems are found difficult to solve optimally with commercial software while the GRASP can find the best known or close to the best-known solution in a short time .

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