Comparative analysis of order batching and routing problem in the picking regarding classical HVRP (heterogeneous vehicle routing problem)

Este artículo tiene como objetivo comparar la conformación de lotes con ruteo, en la preparación de pedidos respecto al problema HVRP (Heterogeneous Vehicle Routing Problem) basándose en la utilización de una metodología de la revisión sistemática de la literatura. Del análisis comparativo se identifica la necesidad de realizar modificaciones radicales e incluir nuevos componentes al problema HVRP, para modelar la conformación de lotes con ruteo de mínimo tiempo, en la preparación de pedidos, considerando K equipos de manejo de materiales (EMM) heterogéneos, n productos, m posiciones de almacenamiento, la disponibilidad del inventario y demás restricciones asociadas a la operación.This paper aims to compare the order batching and routing problem(OBRP) regarding heterogeneous vehicle routing problem (HVRP), in order to identify whether there are any differences and similarities between these ones. The OBRP consist in generating product groups, which are collected from storage locations using material specific handling equipment. Each product group(or batch) is matched to a route, which states the sequences to pick the products in the shortest time possible. On the other hand, HVRP is a variant of the Vehicle Routing Problem(VRP), in which customers are served by a heterogeneous fleet of vehicles with various capacities, in order to delivery products in a distribution network at the lowest possible cost. Additionally, in the related literature were not identified HVRP papers that tackled order batching and routing problem (OBRP), but they were focused primarily in transportation and distribution process. Therefore, it was detected a gap in the state of the art. The comparation analysis was developed using a variation of the methodology called Systematic Literature Review (SLR) , which was based on analysis of papers. This methodology was implemented eight stages, the most important of which are as follows: i) formulating the research questions and evaluation criteria (stage 2), ii) inclusion and exclusion criteria (stage 3), iii) results of systematic review (stage 6), iv) comparative analysis between OBRP and HVRP based on set evaluation criteria (stage 7) and v) conclusions and research opportunities (stage 8). The main findings of this paper were as follow: First, order batching was not modeled in HVRP, hence relevance of this gap. Second, in order batching and routing problem is necessary to represent K heterogeneous MHE with different speed travels, load capacities and lift heights. In HVRP papers the heterogeneity is only caused by vehicles in different load capacities. Third, a constraint among n products, m storage locations and K heterogeneous EMH should be implemented to ensure the feasibility of solutions of OBRP. This constraint is raised, since any MHE are not able to pick some products from storage locations, due to theirs technical characteristics. In addition, none of HVRP papers represented this constraint. Fourth, setup time and handling time were not modeled in reviewed HVRP papers, since these times were not as significant in transportation and distribution routes. Therefore, these times should be included in HVRP to represent OBRP. Fifth, available of inventory were not considered in HVRP papers, since this condition was not important in the modeled process. It should be noted that this condition is critical in OBRP, since only can be picked products with available inventory in Distribution Centre (DC). Based on findings, it was detected a significant gap in the state of the art related to the formulation and solution a minimum time OBRP considering n products, m storage locations and K heterogeneous MHE and described constraint. Therefore, this approach, not only it will fill this gap, but also contribute to knowledge in OBRP. In addition, this paper it will be one of the first to analyze HVRP in warehouse and DC

Revisión del estado del arte del problema de ruteo abierto (OVRP)

En este documento se lleva a cabo una revisión bibliográfica del estado del arte del problema de ruteo abierto (OVRP; Open Vehicle Routing Problem). Se realiza la definición del problema, una clasificación de sus variantes y de los artículos e investigaciones publicadas en las bibliotecas virtuales: Scopus, Science Direct y Google Scholar acerca del tema. Además, se plantean los modelos de solución utilizados por los autores, las aplicaciones del estudio y las tendencias o futuras líneas de investigación. El OVRP es un problema de planificación de rutas de transporte, generalización del Problema del Agente Viajero muy conocido y ampliamente estudiado, tiene como característica diferenciadora que los vehículos una vez finalizadas las entregas correspondientes no están obligados a regresar al punto de partida o depósito. La revisión observa lo publicado hasta mayo del año 2017

Capacitated vehicle routing problem model for carriers

Background: The Capacitated Vehicle Routing Problem (CVRP) is one of the most important transportation problems in logistics and supply chain management. The standard CVRP considers a fleet of vehicles with homogeneous capacity that depart from a warehouse, collect products from (or deliver products to) a set of customer locations (points) and return to the same warehouse. However, the operation of carrier companies and third-party transportation providers may follow a different network flow for collection and delivery. This may lead to non-optimal route planning through the use of the standard CVRP. Objective: To propose a model for carrier companies to obtain optimal route planning. Method: A Capacitated Vehicle Routing Problem for Carriers (CVRPfC) model is used to consider the distribution scenario where a fleet of vehicles depart from a vehicle storage depot, collect products from a set of customer points and deliver them to a specific warehouse before returning to the vehicle storage depot. Validation of the model’s functionality was performed with adapted CVRP test problems from the Vehicle Routing Problem LIBrary. Following this, an assessment of the model’s economic impact was performed and validated with data from a real carrier (real instance) with the previously described distribution scenario. Results: The route planning obtained through the CVRPfC model accurately described the network flow of the real instance and significantly reduced its distribution costs. Conclusion: The CVRPfC model can thus improve the competitiveness of the carriers by providing better fares to their customers, reducing their distribution costs in the process

Optimal Routing for Heterogeneous Fixed Fleets of Multicompartment Vehicles

We present a metaheuristic called the reactive guided tabu search (RGTS) to solve the heterogeneous fleet multicompartment vehicle routing problem (MCVRP), where a single vehicle is required for cotransporting multiple customer orders. MCVRP is commonly found in delivery of fashion apparel, petroleum distribution, food distribution, and waste collection. In searching the optimum solution of MCVRP, we need to handle a large amount of local optima in the solution spaces. To overcome this problem, we design three guiding mechanisms in which the search history is used to guide the search. The three mechanisms are experimentally demonstrated to be more efficient than the ones which only apply the known distance information. Armed with the guiding mechanisms and the well-known reactive mechanism, the RGTS can produce remarkable solutions in a reasonable computation time

Optimasi Distribusi Bahan Bakar Minyak ke SPBU Menggunakan Optimasi Metaheuristik

Penyaluran Bahan Bakar Minyak (BBM) harus efisien dan ekonomis baik dari segi waktu maupun biaya. Kendala yang biasa dihadapi dalam penyaluran bahan bakar dari tempat satu ke tempat lainnya yakni letak geografis yang cukup jauh. Biaya distribusi merupakan salah satu komponen dalam perumusan harga keekonomian yang ditetapkan oleh Badan Pengatur Hilir Minyak dan Gas Bumi. Permasalahan untuk mendapatkan rute distribusi BBM menjadi semakin rumit. Vehicle Routing Problem (VRP) dikembangkan untuk mempelajari bagaimana mendapatkan jalur distribusi BBM terpendek. Metode optimasi metaheuristik yang digunakan dalam penyelesaian VRP dan terbukti mampu menemukan solusi dengan baik dan cepat serta efisien dalam menyelesaikan masalah rute terpendek. Penelitian ini akan menyelesaikan VRP dengan metode optimasi metaheuristik Genetic Algorithm dan Simulated Annealing. Metode simulated annealing mendapatkan hasil rute terpendek dengan waktu tempuh 1019 menit, sedangkan metode genetic algorithm mendapatkan rute terpendek 1208 menit. Waktu pencarian rute yang dibutuhkan oleh metode simulated annealing sekitar 0,442 detik, sedangkan metode genetic algorithm selama 2,03 detik. Dengan demikian pada penelitiann ini metode simulated annealing lebih baik daripada genetic algorithm baik dari segi hasil optimasi rute terpendek dan waktu pencarian rute. ================================================================================================ Distribution of fuel must be efficient and economical both in terms of time and cost. Constraints that are usually faced in the distribution of fuel from one place to another, which is quite a geographical location. Distribution costs are one component in the formulation of economic prices set by BPH-Migas. The problem of getting fuel distribution routes is becoming increasingly complex. Vehicle Routing Problem (VRP) was developed to learn how to get the shortest distribution path. Metaheuristic optimization method used in VRP completion and proven to be able to find a solution well and fast, and efficiently in solving the shortest route problem. This study will complete VRP with metaheuristic optimization method Genetic Algorithm and Simulated Annealing. The simulated annealing method gets the shortest route with a travel time of 1019 minutes, while the genetic algorithm method gets the shortest route 1208 minutes. The route search time needed by the simulated annealing method is around 0.442 seconds, while the genetic algorithm method is 2.03 seconds. Thus in this study the simulated annealing method is better than genetic algorithm both in terms of the optimization results of the shortest route and route search time

The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory

In this paper we consider a real life Vehicle Routing Problem inspired by the gas delivery industry in the United Kingdom. The problem is characterized by heterogeneous vehicle fleet, demand-dependent service times, maximum allowable overtime and a special light load requirement. A mathematical formulation of the problem is developed and optimal solutions for small sized instances are found. A new learning-based Population Variable Neighbourhood Search algorithm is designed to address this real life logistic problem. To the best of our knowledge Adaptive Memory has not been hybridized with a classical iterative memoryless method. In this paper we devise and analyse empirically a new and effective hybridization search that considers both memory extraction and exploitation. In terms of practical implications, we show that on a daily basis up to 8% cost savings on average can be achieved when overtime and light load requirements are considered in the decision making process. Moreover, accommodating for allowable overtime has shown to yield 12% better average utilization of the driver's working hours and 12.5% better average utilization of the vehicle load, without a significant increase in running costs. We also further discuss some managerial insights and trade-offs

A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

[EN] This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the vehicles are not required to return to the depot after completing a service. In this new problem, the demands of customers are fulfilled by a heterogeneous fixed fleet of vehicles having various capacities, fixed costs and variable costs. This problem is an important variant of the open vehicle routing problem (OVRP) and can cover more practical situations in transportation and logistics. Since this problem belongs to NP-hard Problems, An approach based on column generation (CG) is applied to solve the HFFOVRP. A tight integer programming model is presented and the linear programming relaxation of which is solved by the CG technique. Since there have been no existing benchmarks, this study generated 19 test problems and the results of the proposed CG algorithm is compared to the results of exact algorithm. Computational experience confirms that the proposed algorithm can provide better solutions within a comparatively shorter period of time.Yousefikhoshbakht, M.; Dolatnejad, A. (2017). A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem. International Journal of Production Management and Engineering. 5(2):55-71. doi:10.4995/ijpme.2017.5916SWORD557152Aleman, R. E., & Hill, R. R. (2010). A tabu search with vocabulary building approach for the vehicle routing problem with split demands. International Journal of Metaheuristics, 1(1), 55. doi:10.1504/ijmheur.2010.033123Anbuudayasankar, S. P., Ganesh, K., Lenny Koh, S. C., & Ducq, Y. (2012). Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls. Expert Systems with Applications, 39(3), 2296-2305. doi:10.1016/j.eswa.2011.08.009Brandão, J. (2009). A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. 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The capacitated vehicle routing problem with stochastic demands and time windows. Computers & Operations Research, 38(12), 1775-1783. doi:10.1016/j.cor.2011.02.007Li, X., Leung, S. C. H., & Tian, P. (2012). A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem. Expert Systems with Applications, 39(1), 365-374. doi:10.1016/j.eswa.2011.07.025Li, X., Tian, P., & Aneja, Y. P. (2010). An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 46(6), 1111-1127. doi:10.1016/j.tre.2010.02.004Penna, P. H. V., Subramanian, A., & Ochi, L. S. (2011). An Iterated Local Search heuristic for the Heterogeneous Fleet Vehicle Routing Problem. Journal of Heuristics, 19(2), 201-232. doi:10.1007/s10732-011-9186-ySaadati Eskandari, Z., YousefiKhoshbakht, M. (2012). Solving the Vehicle Routing Problem by an Effective Reactive Bone Route Algorithm, Transportation Research Journal, 1(2), 51-69.Subramanian, A., Drummond, L. M. A., Bentes, C., Ochi, L. S., & Farias, R. (2010). A parallel heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Computers & Operations Research, 37(11), 1899-1911. doi:10.1016/j.cor.2009.10.011Syslo, M., Deo, N., Kowalik, J. (1983). Discrete Optimization Algorithms with Pascal Programs, Prentice Hall.Taillard, E. D. (1999). A heuristic column generation method for the heterogeneous fleet VRP, RAIRO Operations Research, 33, 1-14. https://doi.org/10.1051/ro:1999101Tarantilis, C. D., & Kiranoudis, C. T. (2007). A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector. European Journal of Operational Research, 179(3), 806-822. doi:10.1016/j.ejor.2005.03.059Wang, H.-F., & Chen, Y.-Y. (2012). A genetic algorithm for the simultaneous delivery and pickup problems with time window. Computers & Industrial Engineering, 62(1), 84-95. doi:10.1016/j.cie.2011.08.018Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm. International Journal of Production Research, 52(9), 2565-2575. doi:10.1080/00207543.2013.855337Yousefikhoshbakht, M., & Khorram, E. (2012). Solving the vehicle routing problem by a hybrid meta-heuristic algorithm. Journal of Industrial Engineering International, 8(1). doi:10.1186/2251-712x-8-1

Diseño y validación de una metodología para dimensionamiento de capacidades de flotas de transporte basada en programación dinámica

Esta investigación reseña la etapa de diseño y validación de una metodología de solución al problema de dimensionamiento, mezcla y ruteo de flotas de transporte (Fleet Size and Mix Vehicle Routing Problem) dinámico multiobjetivo. La metodología diseñada consta de tres fases, la primera se fundamenta en un algoritmo genético que modela el problema de asignación de vehículos a la flota de transporte basado en un problema de programación dinámica, la segunda fase evalúa la flota asignada con base a los objetivos de maximización de utilidades y minimización del costo de las externalidad de la flota a partir de un algoritmo de ruteo basado en programación dinámica y por último una tercera fase evalúa las soluciones a través de una simulación de las condiciones operacionales de la flota. La validación de la metodología es realizada a partir de la aplicación como soporte al proceso de diseño de embarcaciones arrojando embarcaciones de similar capacidad con menores costos de producción y una reducción de los costos de las externalidades del transporteMaestríaMagister en Ingeniería Industria