scholarly journals A Synchronous Optimization Model for Multiship Shuttle Tanker Fleet Design and Scheduling Considering Hard Time Window Constraint

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Zhenfeng Jiang ◽  
Dongxu Chen ◽  
Zhongzhen Yang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Routing Problem ◽  
Hard Time ◽  
Proposed Model ◽  
Total Transportation Cost ◽  
Time Window Constraints

A Synchronous Optimization for Multiship Shuttle Tanker Fleet Design and Scheduling is solved in the context of development of floating production storage and offloading device (FPSO). In this paper, the shuttle tanker fleet scheduling problem is considered as a vehicle routing problem with hard time window constraints. A mixed integer programming model aiming at minimizing total transportation cost is proposed to model this problem. To solve this model, we propose an exact algorithm based on the column generation and perform numerical experiments. The experiment results show that the proposed model and algorithm can effectively solve the problem.

scholarly journals Model of Flexible Periodic Vehicle Routing Problem-Service Choice Considering Inventory Status

2021 ◽  
Vol 22 (1) ◽  
pp. 125-137
Author(s):  
Muhammad Alde Rizal ◽  
Ifa Saidatuningtyas
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Time Window ◽  
Programming Model ◽  
Mixed Integer ◽  
Routing Problem ◽  
Inventory Costs ◽  
Delivery Times ◽  
Periodic Vehicle Routing Problem ◽  
Time Window Constraints

Vehicle routing problems and inventory problems need to be integrated in order to improve performance. This research discusses the determination of vehicle routes for product delivery with periodic delivery times that are released at any time depending on the inventory status. A mixed-integer linear programming model in determining periodic flexible visiting vehicles' route considering inventory is proposed to solve this problem. This model also accommodates time window constraints, retailer warehouse capacity. The search for solutions was carried out using the branch-and-bound method with the help of Lingo 18.0. The mathematical model testing result saves shipping costs and inventory costs. In addition, the developing mathematical model offers the flexibility of visiting depending on the inventory status of the consumer. The sensitivity analysis of the model results in the vehicle capacity influence the total cost and routes formed.

scholarly journals Vehicle Routing and Scheduling Problem for a multi-period, multi-perishable product system with time window: A Case study

2017 ◽  
Vol 5 (2) ◽  
pp. 45 ◽  
Author(s):  
Alireza Rashidi Komijan ◽  
Danial Delavari
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Programming Model ◽  
Transportation Costs ◽  
Mixed Integer ◽  
Pickup And Delivery ◽  
Routing Problem ◽  
Routing And Scheduling ◽  
Perishable Product ◽  
Proposed Model

<div data-canvas-width="542.172"><p>The well-known Vehicle Routing Problem (VRP) is to find proper sequence of routes in order to minimize transportation costs. In this paper, a mixed-integer programming model is presented for a food distributer company and the model outputs are to determine the optimal routes and amount of pickup and delivery. In the objective function, the costs of transportation, holding, tardiness and earliness are considered simultaneously. The proposed model with respect to real conditions is multi-period and has two different time periods: one for dispatching vehicles to customers and suppliers and the other for receiving  customers’ orders. Time window and split pickup and delivery are considered for perishable products. The proposed model is  nonlinear and will be linearized using exact techniques. At the end, model is solved using GAMS and the sensitivity analysis  is performed. The results indicate that the trend of changes in holding and transportation costs in compared to tardiness and  earliness costs are closed together and are not so sensitive to demand changes.</p></div>

scholarly journals The Optimization of Transportation Costs in Logistics Enterprises with Time-Window Constraints

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Qingyou Yan ◽  
Qian Zhang
Keyword(s):  
Arrival Time ◽  
Time Window ◽  
Transportation Cost ◽  
Transportation Costs ◽  
Routing Problem ◽  
Fleet Size ◽  
Inventory Costs ◽  
Penalty Costs ◽  
Total Transportation Cost ◽  
Time Window Constraints

This paper presents a model for solving a multiobjective vehicle routing problem with soft time-window constraints that specify the earliest and latest arrival times of customers. If a customer is serviced before the earliest specified arrival time, extra inventory costs are incurred. If the customer is serviced after the latest arrival time, penalty costs must be paid. Both the total transportation cost and the required fleet size are minimized in this model, which also accounts for the given capacity limitations of each vehicle. The total transportation cost consists of direct transportation costs, extra inventory costs, and penalty costs. This multiobjective optimization is solved by using a modified genetic algorithm approach. The output of the algorithm is a set of optimal solutions that represent the trade-off between total transportation cost and the fleet size required to service customers. The influential impact of these two factors is analyzed through the use of a case study.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

Insights to optimise marketing decisions on pig-grower farms

2019 ◽  
Vol 59 (6) ◽  
pp. 1126 ◽  
Author(s):  
S. V. Rodríguez-Sanchez ◽  
L. M. Pla-Aragones ◽  
R. De Castro
Keyword(s):  
Weight Control ◽  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Management Policy ◽  
Mixed Integer ◽  
Factors Affecting ◽  
Pig Production ◽  
Marketing Policy ◽  
Feeding Program

Modern pig production in a vertically integrated company is a highly specialised and industrialised activity, requiring increasingly complex and critical decision-making. The present paper focuses on the decisions made on the pig-grower farms operating on an all-in–all-out management policy at the last stage of pig production. Based on a mixed-integer linear-programming model, an assessment for specific parameters to support marketing decisions on farms without individual weight control is made. The analysis of several key factors affecting the optimal marketing policy, such as transportation cost, when and how many pigs to deliver to the abattoir and weight homogeneity of the batch, served to gain insight into marketing decisions. The results confirmed that not just the feeding program, but also the grading price system, transportation and batch homogeneity have an enormous impact on the optimal marketing policy of fattening farms in a vertically integrated company. In addition, within the range of conditions considered, a time window of 4 weeks was deemed as optimal for delivering animals to the abattoir and the subsequent revenue was 15% higher than with traditional marketing rules.

scholarly journals Container Swap Trailer Transportation Routing Problem Based on Genetic Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hua-wei Ma ◽  
Lei Tao ◽  
Xiao-xuan Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Programming Model ◽  
Routing Problem ◽  
Adjustment Policy ◽  
Organization Mode ◽  
Adaptive Adjustment ◽  
Transportation Routing ◽  
Time Window Constraints ◽  
Self Adaptive

In swap trailer transportation routing problems, trucks and trailers conduct swap operations at special positions called trailer points. The parallelization of stevedoring and transportation can be achieved by means of these trailer points. This logistics organization mode can be more effective than the others. In this paper, an integer programming model with capacity and time-window constraints was established. A repairing strategy is embedded in the genetic algorithm (GA) to solve the model. The repairing strategy is executed after the crossover and mutation operation to eliminate the illegal routes. Furthermore, a parameter self-adaptive adjustment policy is designed to improve the convergence. Then numerical experiments are implemented based on the generated datasets; the performance and robustness of the algorithm parameter self-adaptive adjustment policy are discussed. Finally, the results show that the improved algorithm performs better than elementary GA.

scholarly journals Matheuristics and Column Generation for a Basic Technician Routing Problem

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 313
Author(s):  
Nicolas Dupin ◽  
Rémi Parize ◽  
El-Ghazali Talbi
Keyword(s):  
Machine Learning ◽  
Column Generation ◽  
Time Window ◽  
Time Windows ◽  
Machine Learning Techniques ◽  
Mixed Integer ◽  
Routing Problems ◽  
Routing Problem ◽  
Feasible Solutions ◽  
Time Window Constraints

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

scholarly journals Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Guangcan Xu ◽  
Qiguang Lyu
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Programming Model ◽  
Distribution Network ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Routing Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Vehicle Routing Optimization

In recent years, emergency events have affected urban distribution with increasing frequency. For example, the 2019 novel coronavirus has caused a considerable impact on the supply guarantee of important urban production and living materials, such as petrol and daily necessities. On this basis, this study establishes a dual-objective mixed-integer linear programming model to formulate and solve the cooperative multidepot petrol emergency distribution vehicle routing optimization problem with multicompartment vehicle sharing and time window coordination. As a method to solve the model, genetic variation of multiobjective particle swarm optimization algorithm is considered. The effectiveness of the proposed method is analyzed and verified by first using a small-scale example and then investigating a regional multidepot petrol distribution network in Chongqing, China. Cooperation between petrol depots in the distribution network, customer clustering, multicompartment vehicle sharing, time window coordination, and vehicle routing optimization under partial road blocking conditions can significantly reduce the total operation cost and shorten the total delivery time. Meanwhile, usage of distribution trucks is optimized in the distribution network, that is, usage of single- and double-compartment trucks is reduced while that of three-compartment trucks is increased. This approach provides theoretical support for relevant government departments to improve the guarantee capability of important materials in emergencies and for relevant enterprises to improve the efficiency of emergency distribution.

scholarly journals Multidepot UAV Routing Problem with Weapon Configuration and Time Window

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Tianren Zhou ◽  
Jiaming Zhang ◽  
Jianmai Shi ◽  
Zhong Liu ◽  
Jincai Huang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Mixed Integer Linear Programming ◽  
Time Window ◽  
Programming Model ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Routing Problem ◽  
Routing Strategy ◽  
Increasing Trend

In recent wars, there is an increasing trend that unmanned aerial vehicles (UAVs) are utilized to conduct military attacking missions. In this paper, we investigate a novel multidepot UAV routing problem with consideration of weapon configuration in the UAV and the attacking time window of the target. A mixed-integer linear programming model is developed to jointly optimize three kinds of decisions: the weapon configuration strategy in the UAV, the routing strategy of target, and the allocation strategy of weapons to targets. An adaptive large neighborhood search (ALNS) algorithm is proposed for solving the problem, which is tested by randomly generated instances covering the small, medium, and large sizes. Experimental results confirm the effectiveness and robustness of the proposed ALNS algorithm.

Sign in / Sign up

Export Citation Format

Share Document