ALGORITMA VARIABLE NEIGHBORHOOD DESCENT (VND) PADA VEHICLE ROUTING PROBLEM WITH SIMULTANEOUS DELIVERY AND PICKUP (VRPSDP) DAN IMPLEMENTASINYA

Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP) is a variant of Vehicle Routing Problem (VRP). VRPSDP has special constraints, namely requests and returns are carried out simultaneously. In this article we will use the Variable Neighborhood Descent (VND) algorithm to solve VRPSDP problems. There are two steps taken to use the VND algorithm on VRPSDP, namely finding an initial solution with the Insertion Heuristic algorithm and improving the position of the customer by using the neighborhood operator on the VND algorithm. The implementation of the VND algorithm on VRPSDP has been successfully made using the Borland Delphi 7.0 programming language. Inputs contained in the program are point position, distance between points, customer requests and returns and vehicle capacity. The output contained in the program in the form of routes that have been completed using an algorithm and output in the form of images of the final solution that has been obtained. Based on the results obtained, an example with 6 customers produces 3 routes with a total distance of 266 km, while an example with 10 customers produces 4 routes with a total distance of 100 km

A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection  Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company

An efficient meta-heuristic algorithm for solving capacitated vehicle routing problem

This work aims to develop an enhanced Perturbation based Variable Neighborhood Search with Adaptive Selection Mechanism (PVNS ASM) to solve the capacitated vehicle routing problem (CVRP). This approach combined Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on success rate of each perturbation scheme along the search. The ASM helped algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with existing heuristics in the literature has been performed on 21 CVRP benchmarks. The computational results proof that the developed method is competitive and very efficient in achieving high quality solution within reasonable computation time

The vehicle routing problem with simultaneous pickup and delivery and handling costs

In this paper we introduce the vehicle routing problem with simultaneous pickup and delivery and handling costs (VRPSPD-H). In the VRPSPD-H, a fleet of vehicles operates from a single depot to service all customers, which have both a delivery and a pickup demand such that all delivery items originate from and all pickup items go to the depot. The items on the vehicles are organized as a single linear stack where only the last loaded item is accessible. Handling operations are required if the delivery items are not the last loaded ones. We implement a heuristic handling policy approximating the optimal decisions for the handling sub-problem, and we propose two bounds on the optimal policy, resulting in two new myopic policies. We show that one of the myopic policies outperforms the other one in all configurations, and that it is competitive with the heuristic handling policy if many routes are required. We propose an adaptive large neighborhood search (ALNS) metaheuristic to solve our problem, in which we embed the handling policies. Computational results indicate that our metaheuristic finds optimal solutions on instances of up to 15 customers. We also compare our ALNS metaheuristic against best solutions on benchmark instances of two special cases, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) and the traveling salesman problem with pickups, deliveries and handling costs (TSPPD-H), and on two related problems, the vehicle routing problem with divisible pickup and delivery (VRPDPD) and the vehicle routing problem with mixed pickup and delivery (VRPMPD). We find or improve 39 out of 54 best known solutions (BKS) for the VRPSPD, 36 out of 54 BKS for the VRPDPD, 15 out of 21 BKS for the VRPMPD, and 69 out of 80 BKS for the TSPPD-H. Finally, we introduce and analyze solutions for the variations of the VRPDPD and VRPMPD with handling costs – the VRPDPD-H and the VRPMPD-H, respectively

Perancangan Sistem Optimasi Rute Distribusi Pengangkutan Sampah di Kabupaten Sidoarjo Menggunakan Algoritma Ant Colony Optimization (ACO)

Permasalahan sampah merupakan permasalahan umum yang terjadi di setiap kota dan sulit untuk dihindari, tak terkecuali Kabupaten Sidoarjo. Banyak faktor yang menjadi penyebab terjadinya permasalahan sampah di setiap kota/kabupaten, baik dari kurangnya kesadaran masyarakat dalam menjaga kebersihan lingkungan, juga kurang maksimalnya kinerja pemerintah, dalam hal ini Dinas Lingkungan Hidup dan Kebersihan setempat dalam melakukan pengelolaan sampah. Sehingga, pengangkutan sampah menjadi proses yang penting dalam melakukan pengelolaan sampah setempat Dengan luas wilayah Kabupaten Sidoarjo sebesar 714,24 km2 atau seluas dua kali luas wilayah Kota Surabaya, Dinas Lingkungan Hidup dan Kebersihan setempat harus melakukan pengangkutan sampah dengan jarak yang cukup jauh dari setiap kecamatan menuju Tempat Pembuangan Akhir (TPA) yang terletak di Jabon. Sehingga, diperlukannya suatu sistem yang dapat membantu pemerintah setempat dalam menentukan rute pengangkutan sampah yang mencakup seluruh titik Tempat Pembuangan Sementara (TPS) yang ada di setiap kecamatan. Oleh Karena itu, penelitian ini bertujuan untuk membentuk sebuah sistem optimasi rute distribusi pengangkutan sampah yang dapat membantu dinas kebersihan di pemerintahan setempat dalam menentukan rute yang optimal dalam pengangkutan sampah. Penelitian tugas akhir ini menggunakan Algoritma Ant Colony Optimization dalam menentukan pencarian rute di setiap TPS. Sedangkan aplikasi sistem yang memberikan visualisasi hasil rute yang direkomendasikan yang akan dikembangkan adalah aplikasi berbasis web dengan menggunakan bahasa pemrograman HTML5, CSS3, Javascript dan PHP. Dalam penelitian ini didapatkan rute baru dengan menggunakan algoritma ACO yang memangkas total jarak tempuh untuk 40 armada sebesar 160.97 km. ======================================================================================================================== Waste is a public issue that always happen in every city and hard to avoid. Sidoarjo also faces the same issue. Many factors that cause the waste issue such as lack of public hygiene awareness and the lack of government’s performance in waste management. So, waste transfering become an important process in the cycle of waste management. Sidoarjo District area is around 714,24 km2, twice of Surabaya. The government of Sidoarjo District should transfer the waste far from every sub-district to the final landfills in Jabon. Thereforethe government requires a system to determine the waste transfer routing that covers all landfills in every sub-district. This research aims to develop an optimization system of waste transfer route to help the government to determine the optimum route. We use Ant Colony Optimization Algorithm to determine the shortest route from every sub-district’s landfills. The visualisation application that show the recommended routes that will be developed is a web-based application using HTML5, CSS3, Javascript and PHP. The output in this research is new routes using the Ant Colony Optimization (ACO) algorithm that reduces mileage about 160.97 km

Unified strategy for intensification and diversification balance in ACO metaheuristic

This intensification and diversification in Ant Colony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation).The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited either to the management of pheromone memory or to the adaptation of few parameters.This paper introduces the reactive ant colony optimization (RACO) strategy that sticks to the reactive way of automation using memory, diversity indication, and parameterization. The performance of RACO is evaluated on the travelling salesman and quadratic assignment problems from TSPLIB and QAPLIB, respectively.Results based on a comparison of relative percentage deviation revealed the superiority of RACO over other well-known metaheuristics algorithms.The output of this study can improve the quality of solutions as exemplified by RACO

Multi-objective vehicle routing and loading with time window constraints:a real-life application

Motivated by a real-life application, this research considers the multi-objective vehicle routing and loading problem with time window constraints which is a variant of the Capacitated Vehicle Routing Problem with Time Windows with one/two-dimensional loading constraints. The problem consists of routing a number of vehicles to serve a set of customers and determining the best way of loading the goods ordered by the customers onto the vehicles used for transportation. The three objectives pertaining to minimisation of total travel distance, number of routes to use and total number of mixed orders in the same pallet are, more often than not, conflicting. To achieve a solution with no preferential information known in advance from the decision maker, the problem is formulated as a Mixed Integer Linear Programming (MILP) model with one objective—minimising the total cost, where the three original objectives are incorporated as parts of the total cost function. A Generalised Variable Neighbourhood Search (GVNS) algorithm is designed as the search engine to relieve the computational burden inherent to the application of the MILP model. To evaluate the effectiveness of the GVNS algorithm, a real instance case study is generated and solved by both the GVNS algorithm and the software provided by our industrial partner. The results show that the suggested approach provides solutions with better overall values than those found by the software provided by our industrial partner

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

The dial-a-ride problem with electric vehicles and battery swapping stations

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for customers with special needs and/or disabilities. The DARP with Electric Vehicles and battery swapping stations (DARP-EV) concerns scheduling a fleet of EVs to serve a set of pre-specified transport requests during a certain planning horizon. In addition, EVs can be recharged by swapping their batteries with charged ones from any battery-swap stations. We propose three enhanced Evolutionary Variable Neighborhood Search (EVO-VNS) algorithms to solve the DARP-EV. Extensive computational experiments highlight the relevance of the problem and confirm the efficiency of the proposed EVO-VNS algorithms in producing high quality solutions

Eş zamanlı topla dağıt araç rotalama problemi için yeni bir çözüm önerisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Eş zamanlı topla dağıt araç rotalama problemi müşterilerin dağıtım ve toplama taleplerinin eş zamanlı olarak karşılandığı bir araç rotalama problemidir. Bu tez kapsamında bir ana depo üzerinden 76 müşteriye hizmet sağlayacak bir firmanın araç rotalama problemi ele alınmıştır. Minimum sayıda araç kullanımı ile gidilen mesafeyi en küçükleyecek araç rotalarının oluşturulması hedeflenmiştir. Problem çözümü için literatürde yer alan karışık tamsayılı matematiksel model kullanılmıştır ve sezgisel bir algoritma geliştirilmiştir. Farklı büyüklükteki veri setleri dikkate alınarak önerilen yöntemin etkinliği gösterilmiş ve regresyon analizi kullanılarak araç sayıları ve mesafeler arasındaki ilişki incelenmiştir.Pick up and delivery vehicle routing problem is that customers' demand are met using a vehicle with simultaneously pickup and delivery policies on each route. In this study, a vehicle routing problem consists of single depot and 76 customers is solved. The main objective is to create vehicle routes which minimize the distance travelled using the minimum number of vehicles. A Mixed Integer Linear Programming (MILP) from literature and a new heuristic algorithm are proposed to solve the problem. Effectives of new proposed algorithm is illustrated using different data set and a relationship among distances and number of vehicle is examined searched using a regression analysis