scholarly journals Multi-objective optimization model for planning metro-based underground logistics system network: Nanjing case study

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Xiliang Sun ◽  
Wanjie Hu ◽  
Xiaolong Xue ◽  
Jianjun Dong
Keyword(s):  
Programming Model ◽  
Freight Transport ◽  
Optimization Techniques ◽  
Economic Feasibility ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Logistics System ◽  
Transit System ◽  
Multi Objective ◽  
Good Ability

<p style='text-indent:20px;'>Utilizing rail transit system for collaborative passenger-and-freight transport is a sustainable option to conquer urban congestion. This study proposes effective modeling and optimization techniques for planning a city-wide metro-based underground logistics system (M-ULS) network. Firstly, a novel metro prototype integrating retrofitted underground stations and newly-built capsule pipelines is designed to support automated inbound delivery from urban logistics gateways to in-city destinations. Based on four indicators (i.e. unity of freight flows, regional accessibility, environmental cost-saving, and order priority), an entropy-based fuzzy TOPSIS evaluation model is proposed to select appropriate origin-destination flows for underground freight transport. Then, a mixed integer programming model, with a well-matched solution framework combining multi-objective PSO algorithm and A* algorithm, are developed to optimize the location-allocation-routing (LAR) decisions of M-ULS network. Finally, real-world simulation based on Nanjing metro case is conducted for validation. The best facility configurations and flow assignments of the three-tier M-ULS network are reported in details. Results confirm that the proposed algorithm has good ability in providing high-quality Pareto-optimal LAR decisions. Moreover, the Nanjing M-ULS project shows strong economic feasibility while bringing millions of Yuan of annual external benefit to the society and environment.</p>

scholarly journals Capacitated Multi-objective Disassembly Scheduling with Fuzzy Processing Time viaAFruit Fly Optimization Algorithm

2021 ◽  
Author(s):  
Gang Yuan ◽  
Yinsheng Yang ◽  
Guangdong Tian ◽  
Amir Mohammad Fathollahi-Fard
Keyword(s):  
Optimization Algorithm ◽  
Cycle Time ◽  
Processing Time ◽  
Programming Model ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Fruit Fly Optimization Algorithm ◽  
Scheduling Problems ◽  
Fruit Fly Optimization ◽  
Multi Objective

Abstract This work proposes a capacitated fuzzy disassembly scheduling model with cycle time and environmental cost, which has broad applications in remanufacturing and many other production systems. Disassembly scheduling is not always given accurately as a time quota in a production system, particularly in the obsolete products remanufacturing process. It is meaningful to study a novel model and algorithm based on uncertainty processing time to solve uncertainty disassembly scheduling problems. Therefore, a mixed-integer mathematical programming model is proposed to minimize the cycle time and environmental cost, whilst a metaheuristic approach based on a fruit fly optimization algorithm is developed to find a fuzzy disassembly scheduling scheme. To estimate the effectiveness of the proposed method, the proposed algorithm is tested with different size cases of products disassembly scheduling. Furthermore, experiments are conducted to compare with other multi-objective optimization algorithms. The computational results demonstrate the proposed algorithm outperforms other algorithms on computational efficiency and applicability performance. Finally, a case study is described to illustrate the proposed method. The main finding of this current work is to provide a new idea to solve the problem of disassembly scheduling in an uncertain environmental practically and efficiently.

scholarly journals Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

2019 ◽  
Vol 11 (11) ◽  
pp. 3127 ◽  
Author(s):  
Tarik Chargui ◽  
Abdelghani Bekrar ◽  
Mohamed Reghioui ◽  
Damien Trentesaux
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Sustainable Logistics ◽  
Cross Docking ◽  
Physical Internet

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

scholarly journals Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution

2020 ◽  
Vol 10 (12) ◽  
pp. 4362 ◽  
Author(s):  
Junsu Kim ◽  
Hongbin Moon ◽  
Hosang Jung
Keyword(s):  
Urban Areas ◽  
Programming Model ◽  
Computation Time ◽  
Intelligent Transportation ◽  
Mixed Integer ◽  
Operation Planning ◽  
Logistics System ◽  
Proposed Model ◽  
Optimality Gap ◽  
Good Operation

In general, the demand for delivery cannot be fulfilled efficiently due to the excessive traffic in dense urban areas. Therefore, many innovative concepts for intelligent transportation of freight have recently been developed. One of these concepts relies on drone-based parcel delivery using rooftops of city buildings. To apply drone logistics system in cities, the operation design should be adequately prepared. In this regard, a mixed integer programming model for drone operation planning and a heuristic based on block stacking are newly proposed to provide solutions. Additionally, numerical experiments with three different problem sizes are conducted to check the feasibility of the proposed model and to assess the performance of the proposed heuristic. The experimental results show that the proposed model seems to be viable and that the developed heuristic provides very good operation plans in terms of the optimality gap and the computation time.

scholarly journals A Location-Procurement-Distribution Model Combined with Data Envelopment Analysis Model under a Multi-period Environment and Gradual Budget Injection for Humanitarian Relief Supply Chains

2021 ◽  
Author(s):  
Leyla Fazli
Keyword(s):  
Data Envelopment Analysis ◽  
Programming Model ◽  
Mixed Integer ◽  
Distribution Model ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Humanitarian Relief ◽  
Analysis Model ◽  
Multi Objective ◽  
Proposed Model

Abstract Humanmade or natural catastrophes such as droughts, floods, earthquakes, storms, coups, economic and political crises, wars, and so forth impact various areas of the world annually. Furthermore, the lack of adequate preparations and proper coping against them causes nations to suffer heavy losses and casualties, which are sometimes irrecoverable. Consequently, as an essential activity in crisis management, humanitarian relief logistics has been of particular importance and has taken a good deal of notice at the international level during recent years. Aid facilities location and the storage of necessary commodities before a disaster and the proper distribution of relief commodities among demand points following a disaster are critical logistical strategies to improve performance and reduce latency when responding to a given disaster. In this regard, this study presents a stochastic multi-objective mixed-integer non-linear programming model in a two-level network that includes warehouses and affected areas. The model aims at minimizing total social costs, which include the expense of founding warehouses, the expense of procuring commodities, and deprivation cost, as well as maximizing fulfilled demands and warehouses utility. In this study, several pre-disaster periods, a limited budget for establishing warehouses and procuring relief commodities with their gradual injection into the system, the time value of money, various criteria for evaluating warehouses, the risk of disruption in warehouses and transportation networks, and heterogeneous warehouses are considered. The maximization of warehouses utility is done according to a data envelopment analysis model. Moreover, a multi-objective fuzzy programming model called the weighted max-min model is applied to solve the proposed model. Ultimately, the outcomes of the evaluation and validation of the proposed model show its appropriate and efficient performance.

scholarly journals A Multi-Objective Mathematical Model for Real-Time Employee Assignment in the Service Industry

2021 ◽  
Author(s):  
Gercek Budak ◽  
Xin Chen
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Customer Service ◽  
Service Industry ◽  
Programming Model ◽  
Manufacturing Sector ◽  
Mixed Integer ◽  
Multi Objective ◽  
Service Rates ◽  
The Mathematical Model

Abstract The American economy has shifted toward services since the 1980s. The service industry is an important part of economy and is growing quickly in the last three decades. It is more human-capital intensive than the manufacturing sector and there is a shortage of highly-skilled workforce. One solution to this problem is to improve the efficiency through optimization. Because demand in the service industry changes constantly, it is a great challenge to determine the number of employees and their tasks to improve customer service while reducing cost. This article develops a multi-objective mixed-integer linear programming model to dynamically assign employees to different workstations in real time. A case study of the model is solved in less than one second and its pareto optimal solutions determine the number of employees who are assigned to each workstation and the expected customer service times. The mathematical model is robust and provides optimal employee assignment and service rates for workstations in many situations.

A Multi-Objective Model for Taiwan Notebook Computer Distribution Problem

2006 ◽  
pp. 171-182
Author(s):  
Ling-Lang Tang ◽  
Yei-Chun Kuo ◽  
E. Stanley Lee
Keyword(s):  
Customer Service ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Performance Criteria ◽  
Mixed Integer ◽  
Analytic Hierarchy ◽  
Multi Objective ◽  
Notebook Computer ◽  
Objective Model ◽  
Hierarchy Process

A multi-objective model of global distribution for the Taiwan notebook computer industry is proposed. The proposed two-stage approach involves a mixed integer linear programming model and the fuzzy analytic hierarchy process (AHP) approach. The analytic method provides quantitative assessment of the relationships between manufacturers and customer service. To show the effectiveness of the proposed approach, a Taiwan notebook computer model is solved. The results of this multi-objective model show some dynamic characteristics among various performance criteria of the outbound logistics.

Research on Logistics Distribution Route Based on Multi-objective Sorting Genetic Algorithm

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040020
Author(s):  
Jun Zhao ◽  
Hui Xiang ◽  
Jinbao Li ◽  
Jie Liu ◽  
Luyao Guo
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Optimal Path ◽  
Reference Value ◽  
Theoretical Research ◽  
Logistics System ◽  
Logistics Distribution ◽  
Multi Objective ◽  
Elite Strategy ◽  
Low Dimensional

With the continuous development of society, the social division of labor is further improved, and social production tends to be highly specialized and industrialized. Moreover, enterprise production is increasingly internationalized, and sales are gradually expanding. Therefore, the multi-objective sequencing in logistics distribution is incorporated into the path optimization of the logistics system, and a multi-objective bi-level programming model of time and cost is established. What is more, considering the limitations of the traditional algorithm in solving multi-objective problems, the low-dimensional multi-objective problem is selected, and according to the actual situation, the inheritance strategy of genetic factors is adopted to solve the more targeted rapid dominating sorting genetic problem. Besides, the specific conditions and characteristics of the model determine the encoding method, which is brought into the operation of the cross-mutation law and the interruption of individual populations, so that the building foundation of the model is improved. Based on the further theoretical research on the distribution efficiency of logistics system, the corresponding mathematical model is constructed by using the planning method, and the single cost target is transformed into the time and cost double objective, and the improved fast non dominated sorting genetic algorithm with elite strategy is used to solve the problem, which has certain theoretical innovation. Through simulation, the optimal or near optimal path of distribution vehicles in a certain area is given, which has certain practicality and reference value for the optimization of actual logistics distribution path.

Regional Power Planning Optimization Model and Scenario Analysis Considering the Environmental Cost

2013 ◽  
Vol 805-806 ◽  
pp. 1122-1128
Author(s):  
Zong Wu Wang ◽  
Guo He Huang ◽  
Xiao Kun Li
Keyword(s):  
Linear Programming ◽  
Scenario Analysis ◽  
Pollution Control ◽  
Optimization Model ◽  
Programming Model ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Regional Power ◽  
Planning Optimization ◽  
Decision Alternatives

In this study, a regional power planning optimization model (RPPOM) is developed considering the environmental cost and the restriction of resource and environment, based on interval linear programming and mixed integer linear programming. Model is applied to a case study on the power planning in Henan province, and scenario analysis is conducted. Interval solutions associated with scenario of pollution control have been obtained. They can be used for generating decision alternatives and helping decision makers identify desired power policies for power planning to meet the growth in electricity demand considering the constraints of resources and environment with a minimized system cost. Scenario analysis of environmental pollution control at different levels can also be tackled.

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