Minimum Vehicle Fleet Size Under Time-Window Constraints at a Container Terminal

2005 ◽  
Vol 39 (2) ◽  
pp. 249-260 ◽  
Author(s):  
Iris F. A. Vis ◽  
René (M.) B. M. de Koster ◽  
Martin W. P. Savelsbergh
Keyword(s):  
Time Window ◽  
Container Terminal ◽  
Fleet Size ◽  
Vehicle Fleet ◽  
Time Window Constraints

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.

scholarly journals Randomized and Generated Instances Fitting with the Home Health Care Problem Subjected to Certain Constraints

2021 ◽  
Vol 11 (8) ◽  
pp. 3346
Author(s):  
Colin Huvent ◽  
Caroline Gagné ◽  
Aymen Sioud
Keyword(s):  
Health Care ◽  
Home Health Care ◽  
Hospital Cost ◽  
Time Window ◽  
Developed Countries ◽  
Home Health ◽  
Precedence Constraints ◽  
Social Organizations ◽  
Health Support ◽  
Time Window Constraints

Home Health Care (HHC) is a worldwide issue. It focuses on how medical and social organizations of different countries handle providing patients with health support at home. In most developed countries, reducing hospital cost constitutes a main objective. It is important to research the improvement of HHC logistics. This paper addressed the generation and development of a benchmark properly fitting different constraints of the HCC problem. Consequently, a generator was proposed dealing with all kinds of constraints such as time window constraints, workload constraints, synchronization, and precedence constraints. This generator allows researchers to validate and compare solving methods on a common dataset regardless of confidentiality issues. We validated our generator by firstly creating a common benchmark available for researchers and secondly by proposing a set of instances and a solving method based on an HHC problem found in the literature.

Parcel delivery cost minimization with time window constraints using trucks and drones

Networks ◽  
2021 ◽  
Author(s):  
Marc‐Antoine Coindreau ◽  
Olivier Gallay ◽  
Nicolas Zufferey
Keyword(s):  
Time Window ◽  
Cost Minimization ◽  
Delivery Cost ◽  
Time Window Constraints

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 Análisis comparativo de una metaheurística en base a algoritmo genético vs un método de ramificación y corte para un caso de entrega y recolección con restricciones de ventana de horario

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Fabian López
Keyword(s):  
Time Window ◽  
Time Frame ◽  
Branch And Cut ◽  
Pickup And Delivery ◽  
Np Hard ◽  
Practical Application ◽  
High Quality ◽  
Method Of Solution ◽  
Time Window Constraints ◽  
Computational Resources

Palabras claves: Algoritmos genéticos, logística de ruteo, metaheuristicas, secuenciaciónResumen. En la solución de problemas combinatorios, es importante evaluar el costo-beneficio entre la obtención de soluciones de alta calidad en detrimento de los recursos computacionales requeridos. El problema planteado es para el ruteo de un vehículo con entrega y recolección de producto y con restricciones de ventana de horario. En la práctica, dicho problema requiere ser atendido con instancias de gran escala (nodos ≥100). Existe un fuerte porcentaje de ventanas de horario activas (≥90%) y con factores de amplitud ≥75%. El problema es NP-hard y por tal motivo la aplicación de un método de solución exacta para resolverlo en la práctica, está limitado por el tiempo requerido para la actividad de ruteo. Se propone un algoritmo genético especializado, el cual ofrece soluciones de buena calidad (% de optimalidad aceptables) y en tiempos de ejecución computacional que hacen útil su aplicación en la práctica de la logística. Para comprobar la eficacia de la propuesta algorítmica se desarrolla un diseño experimental el cual hará uso de las soluciones óptimas obtenidas mediante un algoritmo de ramificación y corte sin límite de tiempo. Los resultados son favorables.Key words: Genetic algorithms, routing logistics, metaheuristics, schedulingAbstract. In an attempt to sovle the combinatorics problems, it is important to evaluate the costbenefit ratio between obtaining solutions of high quality and the loss of the computational resources required. The problem presented is for the routing of a vehicle with pickup and delivery of products with time window constraints. This problem requires instances of great scale (nodes≥100). A strong active time window percentage exists (≥90%) with factors of amplitude ≥75%. The problem is NP-hard and hence, the application of an exact method of solution, is limited by the time frame required for routing activity. A specialized genetic algorithm is proposed, which offers solutions of high precision and in computational times that makes its practical application useful. An experimental design is developed with good results that makes use of optimum solutions obtained by means of branch and cut algorithm without time limit.

A Computer Simulation Model for Determining Fleet Size and Composition

1977 ◽  
Vol 99 (1) ◽  
pp. 157-161
Author(s):  
G. C. Schultz ◽  
E. E. Enscore
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Search Algorithms ◽  
The Other ◽  
Hill Climbing ◽  
Computer Simulation Model ◽  
Fleet Size ◽  
Travel Costs ◽  
Total Vehicle ◽  
Vehicle Fleet

A heterogeneous vehicle fleet is one that is composed of several types of vehicles. The number of each type of vehicle in the fleet is called the fleet’s composition. The problem of determining the best fleet size and composition for an in-house heterogeneous company fleet having a known demand was solved in this paper. A computer model was developed which tied a fleet simulation model to two different search algorithms. One of the search algorithms is a complete factorial nonsequential search and the other is a combination of a partial factorial nonsequential search and a heuristic sequential hill-climbing search. The objective of both searches is to select the fleet size and composition which provides the lowest total vehicle travel costs to the company. Several examples were used to demonstrate the use of the model.

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