Optimal Route Planning for Truck–Drone Delivery Using Variable Neighborhood Tabu Search Algorithm

The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck–drones. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck–drone delivery route and improve the delivery efficiency.

A granular tabu search for the refrigerated vehicle routing problem with homogeneous fleet

The Refrigerated Capacitated Vehicle Routing Problem (RCVRP) considers a homogeneous fleet with a refrigerated system to decide the selection of routes to be performed according to customers' requirements. The aim is to keep the energy consumption of the routes as low as possible. We use a thermodynamic model to understand the unloading of products from trucks and the variables' efficiency, such as the temperature during the day influencing energy consumption. By considering various neighborhoods and a shaking procedure, this paper proposes a Granular Tabu Search scheme to solve the RCVRP. Computational tests using adapted benchmark instances from the literature demonstrate that the suggested method delivers high-quality solutions within short computing times, illustrating the refrigeration system's effect on routing decisions.

An enhanced Tabu search Cell formation Algorithm for Cellular Manufacturing System

The major benefit of using Cellular manufacturing systems (CMS) is the improvement in efficiency and reduction in the production time. In a CMS the part families and machine parts are identified to minimise the inter and intracellular movement and maximise the utilisation of machines within each cell. Many scholars have proposed methods for the evaluation of machine cell part layouts with single routes; this paper introduces a modified Hybrid Tabu Search Algorithm (HTSA) referred to as Hybrid Algorithm in this study for machine cell part layouts having multiple routes as well. The primary objective of this paper is to minimise the inter and intracellular movement using a hybrid algorithm. The paper presents a comparative analysis of the existing and the proposed algorithms, proving that the proposed hybrid algorithm is simple, easy to understand, and has a remarkable efficiency with a runtime of 5.6 seconds.

A hybrid approach of simulation and metaheuristic for the polyhedra packing problem

This document presents a simulation-based method for the polyhedra packing problem (PPP). This problem refers to packing a set of irregular polyhedra (convex and concave) into a cuboid with the objective of minimizing the cuboid’s volume, considering non-overlapping and containment constraints. The PPP has applications in additive manufacturing and packing situations where volume is at a premium. The proposed approach uses Unity® as the simulation environment and considers nine intensification and two diversification movements. The intensification movements induce the items within the cuboid to form packing patterns allowing the cuboid to decrease its size with the help of gravity-like accelerations. On the other hand, the diversification movements are classic transition operators such as removal and filling of pieces and enlargement of the container, which allow searching on different solution neighborhoods. All simulated movements were hybridized with a probabilistic tabu search. The proposed methodology (with and without the hybridization) was compared by benchmarking with all previous works solving the PPP with irregular items. Results show that satisfactory solutions were reached in a short time; even a few published results were improved.

Pengembangan Aplikasi Penentuan Rute Pengiriman Barang Berdasarkan Berat dan Time Windows Menggunakan Metode Nearest Neighbour dan Tabu Search

Salah satu contoh praktis dari CVRPTW adalah pengiriman barang. Faktor penting dalam pengiriman barang adalah biaya, kecepatan, pelayanan dan konsistensi. Agar faktor-faktor tersebut terpenuhi secara optimal harus diperhatikan muatan barang serta time windows. Muatan berpengaruh pada faktor pelayanan dan konsistensi, sehingga harus dipilih rute yang tepat dengan jarak terpendek serta ketepatan kapasitas barang. Time windows berpengaruh pada faktor kecepatan dan biaya pengiriman sehingga pengiriman barang harus dilakukan sesuai waktu yang ditentukan dan jam operasional perusahaan. Penelitian ini bertujuan menghasilkan rute pengiriman barang yang memperhatikan kapasitas muatan dan waktu tempuh pengiriman. Terdapat dua tahapan penyelesaian yaitu tahap clustering dan pencarian rute optimal. Tahap clustering menggunakan sudut polar dan tpencarian rute optimal menggunakan metode nearest neighbour serta tabu search. Hasil pengujian menunjukkan bahwa rute pengiriman yang dihasilkan oleh sistem dapat melakukan efisiensi jarak pengiriman sebesar 12,18%, waktu pengiriman sebesar 5,54%, muatan sebesar 1,27% dan efisiensi biaya sebesar 12,18%.

Tabu Search Algorithm Based on Lower Bound and Exact Algorithm Solutions for Minimizing the Makespan in Non-Identical Parallel Machines Scheduling

Recently, several heuristics have been interested in scheduling problems, especially those that are difficult to solve via traditional methods, and these are called NP-hard problems. As a result, many methods have been proposed to solve the difficult scheduling problems; among those, effective methods are the tabu search algorithm (TS), which is characterized by its high ability to adapt to problems of the large size scale and ease of implementation and gives solution closest to the optimum, but even though those difficult problems are common in many industries, there are only a few numbers of previous studies interested in the scheduling of jobs on unrelated parallel machines. In this paper, a developed TS algorithm based on lower bound (LB) and exact algorithm (EA) solutions is proposed with the objective of minimizing the total completion time (makespan) of jobs on nonidentical parallel machines. The given solution via EA was suggested to enhance and assess the solution obtained from TS. Moreover, the LB algorithm was developed to evaluate the quality of the solution that is supposed to be obtained by the developed TS algorithm and, in addition, to reduce the period for searching for the optimal solution. Two numerical examples from previous studies from the literature have been solved using the developed TS algorithm. Findings show that the developed TS algorithm proved its superiority and speed in giving it the best solution compared to those solutions previously obtained from the literature.

Decomposition approaches for parallel machine scheduling of step-deteriorating jobs to minimize total tardiness and energy consumption

In this paper, an identical parallel machine scheduling problem with step-deteriorating jobs is considered to minimize the weighted sum of tardiness cost and extra energy consumption cost. In particular, the actual processing time of a job is assumed to be a step function of its starting time and its deteriorating threshold. When the starting time of a job is later than its deteriorating threshold, the job faces two choices: (1) maintaining its status in holding equipment and being processed with a base processing time and (2) consuming an extra penalty time to finish its processing. The two work patterns need different amounts of energy consumption. To implement energy-efficient scheduling, the selection of the pre-processing patterns must be carefully considered. In this paper, a mixed integer linear programming (MILP) model is proposed to minimize the total tardiness cost and the extra energy cost. Decomposition approaches based on logic-based Benders decomposition (LBBD) are developed by reformulating the studied problem into a master problem and some independent sub-problems. The master problem is relaxed by only making assignment decisions. The sub-problems are to find optimal schedules in the job-to-machine assignments given by the master problem. Moreover, MILP and heuristic based on Tabu search are used to solve the sub-problems. To evaluate the performance of our methods, three groups of test instances were generated inspired by both real-world applications and benchmarks from the literature. The computational results demonstrate that the proposed decomposition approaches can compute competitive schedules for medium- and large-size problems in terms of solution quality. In particular, the LBBD with Tabu search performs the best among the suggested four methods.

Pembentukan Solusi Awal International Timetabling Competition 2021

Penjadawalan olahraga merupakan salah satu cabang dari optimasi di riset operasi. Penjadwalan olahraga memiliki berbagai macam batasan yang menantang para peneliti untuk menyelesaikannya. International Timetabling Competition (ITC) 2021 merupakan salah satu kompetisi optimasi yang menyediakan permasalahan penjadwalan olahraga. Permasalahan utama pada ITC 2021 yaitu menentukan jadwal waktu yang tepat untuk sebuah pertandingan. Sebuah jadwal dikatakan dapat digunakan (feasible) apabila tidak melanggar hard constraint yang ada. Pembentukan solusi awal yang feasible saat ini dapat dilakukan dengan algoritma constraint programming atau integer programming. Akan tetapi, kedua algoritma tersebut cukup rumit untuk diimplementasikan. Penelitian ini berfokus pada pembentukan solusi awal yang feasible dengan cara yang mudah untuk diimplementasikan. Cara yang digunakan yaitu dengan mengoptimasi pelanggaran hard constraint menggunakan algoritma Late Acceptance Hill Climbing (LAHC) dan Tabu Search dengan kerangka kerja Hyper-Heuristic yang melibatkan low level heuristic (LLH). Algoritma dijalankan maksimal dengan batasan waktu 6 jam untuk setiap data. Hasil dari optimasi pelanggaran hard constraint menggunakan algoritma LAHC dan tabu search dapat menghasilkan solusi awal yang feasible sebanyak 44.44% atau 24 dari 54 keseluruhan dataset.