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In this paper, the authors introduced a real world new problem, the multi-trip vehicle routing problem with time windows and the possible use of a less-than-truckload carrier to satisfy customer demands. The purpose of this paper is to develop a heuristic algorithm to route the private trucks with time windows and to make a selection between truckload and less-than-truckload carriers by minimizing a total cost function.

Both mathematical model and heuristic algorithm are developed for routing the private trucks with time windows and for selecting of less-than-truckload carriers by minimizing the total cost function.

In all, 40 test problems were examined with the heuristics. Computational results show that the algorithm obtains the optimal or near-optimal solutions efficiently in terms of time and accuracy.

The research described in this paper differs from the previous one on fleet planning or vehicle routing, in that it modifies the Clarke and Wright method by shifting the performance measure from a distance to cost and also incorporates the fixed cost of different types of trucks into the model. In addition, the authors simultaneously consider the multiple trip vehicle routing problems with time windows and the selection of less-than-truckload carriers that is an integrated scenario of real-world application. To the best of the authors’ knowledge, this scenario has not been considered in the literature.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

This paper aims to address a pollution-routing problem with one general period of congestion (PRP-1GPC), where the start and finish times of this period can be set freely.

In this paper, three sets of decision variables are optimized, namely, travel speeds before and after congestion and departure times on given routes, aiming to minimize total cost including green-house gas emissions, fuel consumption and driver wages. A two-phase algorithm is introduced to solve this problem. First, an adaptive large neighborhood search heuristic is used where new removal and insertion operators are developed. Second, an analysis of optimal speed before congestion is presented, and a tailored speed-and-departure-time optimization algorithm considering congestion is proposed by obtaining the best node to be served first over the congested period.

The results show that the newly developed operator of congested service-time insertion with noise is generally used more than other insertion operators. Besides, compared to the baseline methods, the proposed algorithm equipped with the new operators provides better solutions in a short time both in PRP-1GPC instances and time-dependent pollution-routing problem instances.

This paper considers a more general situation of the pollution-routing problem that allows drivers to depart before the congestion. The PRP-1GPC is better solved by the proposed algorithm, which adds operators specifically designed from the new perspective of the traveling distance, traveling time and service time during the congestion period.

The purpose of the paper is to present an integrated model for the location of warehouse, the allocation of retailers to warehouses, and finding the number of vehicles to deliver the demand and the required vehicle routing in order to minimize total transportation costs, fixed and operating costs, and routing costs.

The model assumes that the number of plants has already been determined and answers the following questions: what is the number of warehouses to open? How warehouse are allocated to plants? How retailers are allocated to warehouses? Who are the retailers that will be visited and in what order? How many vehicles are required for each route? What are the total minimum costs?

The model was formulated as a mixed integer linear programming model and solved using Lagrange relaxation and sub‐gradient search for the location/allocation module and a traveling salesman heuristic for the routing module. The results for the randomly selected problems show that the deviation in objective function value ranges between 0.29 and 2.05 percent from the optimum value. Also, from the CPU time point of view, the performance was very good.

An attempt is made to integrate location, allocation, and routing decisions in the design of a supply chain network.

The purpose of this paper is to provide an effective solution method for the truck and trailer routing problem (TTRP) which is one of the important NP-hard combinatorial optimization problems owing to its multiple real-world applications. It is a generalization of the famous vehicle routing problem (VRP), involving a group of geographically scattered customers served by the vehicle fleet including trucks and trailers.

The meta-heuristic solution approach based on bat algorithm (BA) in which a local search procedure performed by five different neighborhood structures is developed. Moreover, a self-adaptive (SA) tuning strategy to preserve the swarm diversity is implemented. The effectiveness of the proposed SA-BA is investigated by an experiment conducted on 21 benchmark problems that are well known in the literature.

Computational results indicate that the proposed SA-BA algorithm is computationally efficient through comparison with other existing algorithms found from the literature according to solution quality. As for the actual computational time, the SA-BA algorithm outperforms others. However, the scaled computational time of the SA-BA algorithm underperforms the other algorithms.

In this work the authors show that the proposed SA-BA is effective as a method for the TTRP problem. To the authors’ knowledge, the BA has not been applied previously, as in this work, to solve the TTRP problem.

Drugs are strategic products with essential functions in human health. An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health. The vehicle-routing problem, focused on finding the lowest-cost routes with available vehicles and constraints, such as time constraints and road length, is an important aspect of this. In this paper, the vehicle routing problem (VRP) for a pharmaceutical company in Turkey is discussed.

A mixed-integer programming (MIP) model based on the vehicle routing problem with time windows (VRPTW) is presented, aiming to minimize the total route cost with certain constraints. As the model provides an optimum solution for small problem sizes with the GUROBI® solver, for large problem sizes, metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed. A real dataset was used to analyze the effectiveness of the metaheuristic algorithms. The proposed simulated annealing (SA) and adaptive large neighborhood search (ALNS) were evaluated and compared against GUROBI® and each other through a set of real problem instances.

The model is solved optimally for a small-sized dataset with exact algorithms; for solving a larger dataset, however, metaheuristic algorithms require significantly lesser time. For the problem addressed in this study, while the metaheuristic algorithms obtained the optimum solution in less than one minute, the solution in the GUROBI® solver was limited to one hour and three hours, and no solution could be obtained in this time interval.

The VRPTW problem presented in this paper is a real-life problem. The vehicle fleet owned by the factory cannot be transported between certain suppliers, which complicates the solution of the problem.

A nurse home care scheduling system is described. The objective is to provide medical care at patients’ homes using the fewest number of nurses possible to deliver the required care. The heuristic scheduling system is easy to implement as a computerized adaptive system. As such, it is easy to use on a daily basis and easy to update as new data related to completed treatment and new requests are obtained. A case study illustrates the advantages of implementing such a system.

The purpose of this paper is to investigate the application of the dynamic vehicle routing problem for last mile distribution during disaster response. The authors explore a model that involves limited heterogeneous vehicles, multiple trips, locations with different accessibilities, uncertain demands, and anticipating new locations that are expected to build responsive last mile distribution systems.

The modified simulated annealing algorithm with variable neighborhood search for local search is used to solve the last mile distribution model based on the criterion of total travel time. A dynamic simulator that accommodates new requests from demand nodes and a sample average estimator was added to the framework to deal with the stochastic and dynamicity of the problem.

This study illustrates some practical complexities in last mile distribution during disaster response and shows the benefits of flexible vehicle routing by considering stochastic and dynamic situations.

This study only focuses day-to-day distribution on road/land transportation for distribution, and additional transportation modes need to be considered further.

The proposed model offers operational insights for government disaster agencies by highlighting the dynamic model concept for supporting relief distribution decisions. The result suggests that different characteristics and complexities of affected areas might require different distribution strategies.

This study modifies the concept of the truck and trailer routing problem to model locations with different accessibilities while anticipating the information gap for demand size and locations. The results show the importance of flexible distribution systems during a disaster for minimizing the disaster risks.

The purpose of this paper is to present a decision support system (DSS) for a Colombian public utility company in order to aid decision-making at the operational level regarding route planning and travel time. The aim is to provide a tool to assist technicians that perform interruption and reconnection of domiciliary services for about 2,000 customers a day.

The real-life problem is modeled as a Single Depot Vehicle Routing Problem with Time Windows (SDVRP-TW), which is a well-known optimization problem in Operations Research/Management Science. A two-stage approach integrated into decision-making software is provided. The first stage considers the clustering of customers generated by a combination of the sweep and the k-means algorithms, while the second phase plans the routing of technicians using the nearest-neighbor and the Or-opt heuristics. The proposed approach is tested using real data sets.

In comparison with the current route planning approach, the proposed method is able to obtain savings in total travel times, improving operational productivity by 22.2 percent.

Since the analysis is carried out based on mathematical modeling, assumptions about the relationships between variables and elements of the actual complex problem might be simplified. Although the proposed approach aids the route planning, decision makers make the final decisions.

The proposed DSS has a critical impact on actual operational practices at the company. Productivity and service level are improved, while reducing operational costs. The decision-making process itself will be improved so technicians and higher decision makers can focus on performing other tasks.

The real-life problem is modeled using mathematical programming and efficiently solved through a two-stage approach based on simple, quite intuitive, solution procedures that have not been implemented for such services. In addition, as actual data from the company is employed for experimental purposes, the solution approach is tested and its efficiency and efficacy are both validated in a realistic setting, hence providing realistic behavior for decision makers at the company.

presentar un sistema de soporte a las decisiones (Decision Support System, DSS) para una empresa colombiana de servicios públicos con el fin de apoyar el proceso de toma de decisiones a nivel operativo en lo relacionado con la planeación de rutas y el tiempo de servicio. El objetivo es suministrar una herramienta que ayude a los técnicos a desempeñar el servicio de corte y reconección de servicios domiciliarios para aproximadamente 2000 clientes por día.

el problema de una empresa real es modelado como un problema de enrutamiento de vehículos un único depósito y ventanas de tiempo (Single Depot Vehicle Routing Problem with Time Windows, SDVRP-TW). Éste es un problema de optimización muy conocido en Investigación de Operaciones / Ciencias de la Administración. Se presenta un enfoque de dos etapas integrado en un software de ayuda a la toma de decisiones. La primera etapa considera el agrupamiento de los clientes generado por una combinación de los algoritmos del barrido y el k-media, mientras que la segunda fase define el plan de rutas para los técnicos utilizando las heurísticas de vecino más cercano y Or-opt. El enfoque propuesto es validado empleando datos reales.

en comparación con el plan de rutas actualmente utilizado por la empresa, el método propuesto es capaz de obtener ahorros en el tiempo total de viaje incrementando la eficiencia operativa en un 22.2%.

puesto que el análisis se lleva a cabo a partir de un modelo matemático, los supuestos sobre las relaciones entre las variables y los elementos del sistema real complejo podrían simplificarse. Además, aunque el sistema propuesto realiza la planeación de rutas, la decisión final es tomada finalmente por las personas.

el DSS propuesto tiene un impacto crítico en la práctica operativa real de la empresa. La productividad y el nivel de servicio se mejoran, mientras se reducen los costos operativos. El proceso de toma de decisiones en sí mismo se verá mejorado pues los técnicos y los tomadores de decisiones pueden enfocarse en realizar otras tareas.

el problema real es modelado utilizando programación matemática y se resuelve de forma efectiva con un procedimiento de dos etapas sencillo y básicamente intuitivo que no ha sido puesto en marcha antes para tales empresas de servicios. Además, puesto que datos reales de la empresa son utilizados en la experimentación, el enfoque de solución es validado y su eficiencia y eficacia son comprobadas en un ambiente real, suministrando así un comportamiento real para los tomadores de decisiones en la empresa.