Multiple depots vehicle routing based on the ant colony with the genetic algorithm

Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing, the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm together can improve the efficiency multiple depots vehicle routing.Peer Reviewe

A review of the Tabu Search Literature on Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is one of the most widely studied problems inrncombinatorial optimization. It has long been known to be NP-hard and hence research onrndeveloping algorithms for the TSP has focused on approximate methods in addition to exactrnmethods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. Inrnthis paper, we review the tabu search literature on the TSP, point out trends in it, and bringrnout some interesting research gaps in this literature.

RELOKASI SPPBE SEBAGAI GUDANG ANTARA DAN OPTIMISASI RUTE KENDARAAN UNTUK MENURUNKAN BIAYA DISTRIBUSI GAS LPG 3 KG DI KOTA BANDUNG (STUDI KASUS : PT PERTAMINA WILAYAH PEMASARAN KOTA BANDUNG TIMUR)

PT. Pertamina merupakan perusahaan milik negara yang bergerak dibidang energi, salah satu produknya adalah gas minyak bumi yang dicairkan atau biasa disebut lpg. Dalam pendistribusian gas lpg 3kg di kota Bandung, PT. Pertamina memiliki 6 SPPBE yang melayani 72 agen. Sistem distribusi yang dianut perusahaan saat ini adalah sistem distribusi terbuka yang artinya SPPBE dapat melayani agen mana saja, atau dengan kata lain SPPBE tidak memiliki agen tetap yang harus dilayaninya. Ke-6 SPPBE yang berada di kota Bandung ini berlokasi disatu wilayah yaitu Bandung Timur, hal ini menyebabkan jarak tempuh kendaraan menjadi besar. Disisi lain, SPPBE dalam melakukan kegiatan distribusi menggunakan satu kendaraan untuk setiap agen yang dilayaninya atau dengan kata lain muatan dalam kendaraan tidak dimaksimalkan. Lokasi SPPBE yang berjauhan dengan lokasi agen ini mengakibatkan besarnya jarak tempuh kendaraan serta kapasitas kendaraan yang tidak dimaksimalkan akan mengakibatkan tingginya biaya transportasi dan biaya distribusi yang harus dikeluarkan oleh perusahaan. Oleh karena itu, dalam penelitian ini diusulkan rancangan pemecahan masalah yang dapat menyelesaikan permasalahan yang dihadapi perusahaan. Dalam penelitian ini diusulkan 6 kelompok agen dengan pendekatan Fuzzy C-Means agar SPPBE memiliki kelompok layanan yang tetap. Relokasi SPPBE juga dilakukan dengan pendekatan P-Median Greedy Dropping Heuristic Algorithm sehingga dalam setiap kelompok memiliki SPPBE untuk mengalokasikan kebutuhan dalam kelompok layanannya. Dari relokasi SPPBE dilakukan penentuan rute terpendek yang dilalui kendaraan dengan menggunakan pendekatan Insertion Heuristic, yang kemudian rute tersebut dioptimisasi dengan pendekatan Tabu Search. Dari hasil perhitungan, hasil dari Tabu Search memberikan hasil yang lebih baik yaitu total jarak 350,4 km dengan total biaya dsitribusi Rp. 11.604.954,33 perhari yang harus dikeluarkan perusahaan, sedangkan pada kondisi eksisting sebesar Rp. 39.184.998, sehingga penghematannya adalah sebesar 70,38% dari biaya distribusi pada kondisi eksisting. Kata Kunci : Fuzzy C-Means, P-Median, Greedy Gropping Heuristic, Insertion Heuristic, Tabu Search Algorithm, Minimasi Biaya Distribusi

A solution method for a two-layer sustainable supply chain distribution model

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry׳s two-layer supply chain network. The DoE generates 6100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decision-maker is presented with a choice of several distribution routes on the demand side of the two-layer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO2 emissions and costs where it can be seen that the solution method outperforms existing solution methods

Last-mile delivery in favelas: an explanatory study with Brazilian Companies

In urban logistics, the last-mile delivery from warehouse to the consumer's home has become more and more challenging with the continuous growth of urbanization, particularly in developing countries when addressing the logistical difficulties of distributing products in low-income population. This work presents an approach how companies distribute products within brazilian Favelas. Delivering products in these scenarios are not an easy task, high concentration of households without formal urbanization imposes hurdles to find and access to specific location added to the high number of cargo stolen, leads to lot of obstacles in this last mile operations. The company’s strategies are found by matching product type with Favela type in quadrants in the Conditions Decision Matrix. The results showed an emergent proposed model from data based on theory that helps to understand the last-mile delivery in Favelas having the risk as the moderator factor of logistics performance. The paper highlights that companies do not change information, practices neither synergies between their distribution models as well as do not relate to communities in, for example, social actions, in the vast majority of cases. It concludes by mapping the practical strategies applicable for the companies in the last-mil

Workload Equity in Vehicle Routing Problems: A Survey and Analysis

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap with an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures, and point out important connections between their properties and those of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e. composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions. We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis are valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research.Comment: Accepted Manuscrip

Redesign of Three-Echelon Multi-Commodity Distribution Network

This research studies the distribution network redesign of an actual electronics company. The problems are formulated based on multi-echelon capacitated Location Routing Problem (LRP) with two commodities: home products and service items. The objective function consists of three components: facility cost, closing cost of facility and transportation cost. We propose solution method based on clustering technique. The problem is decomposed into the Facility Location Allocation Problem (FLAP) and the Multi-Depot Vehicle Routing Problem (MDVRP). MDVRP is solved by clustering method and feed the results to the modified FLAP to allocate the demand nodes to facilities and configure all distribution networks, for the 2nd and 3rd echelon. The distribution is divided into five region zones. Previously, each region was operated independently but this research compares the solutions from solving each region independently and solving all five zones simultaneously. The results indicate that the proposed solution method can achieve computation time and total cost that are comparable to ones obtained from solving the problem to optimality. Exact approach can only solve small and medium problems, whereas the proposed solution method provides the acceptable solution of real-life largest problem in limit of computation time. Finally, we perform sensitivity analysis on the results

Profitable Vehicle Routing Problem with Multiple Trips: Modeling and Variable Neighborhood Descent Algorithm

Abstract In this paper, we tackle a new variant of the Veh icle Routing Problem (VRP) which comb ines two known variants namely the Profitable VRP and the VRP with Mult iple Trips. The resulting problem may be called the Profitable Vehicle Routing Problem with Multiple Trips. The main purpose is to cover and solve a more co mplex realistic situation of the distribution transportation. The profitability concept arises when only a subset of customers can be served due to the lack of means or for insufficiency of the offer. In this case, each customer is associated to an economical profit wh ich will be integrated to the objective function. The latter contains at hand the total collected profit minus the transportation costs. Each vehicle is allowed to perform several routes under a strict workday duration limit. This problem has a very practical interest especially for daily distribution schedules with limited vehicle fleets and short course transportation networks. We point out a new discursive approach for p rofits quantificat ion wh ich is mo re significant than those existing in the literature. We propose four equivalent mathematical formu lations for the problem which are tested and compared using CPLEX solver on small-size instances. Optimal solutions are identified. For large-size instance, two constructive heuristics are proposed and enhanced using Hill Climbing and Variable Neighborhood Descent algorithm based on a specific three-arrays-based coding structure. Finally, extensive co mputational experiments are performed including randomly generated instances and an extended and adapted benchmark fro m literature showing very satisfactory results

Coping with the Rise of E-commerce Generated Home Deliveries through Innovative Last-Mile Technologies and Strategies

Caltrans 65A0686 Task Order 066USDOT Grant 69A3551747114E-commerce can potentially make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities, particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include the use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection-points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic