EFFICIENT METAHEURISTIC ALGORITHMS FOR THE MULTI-STRIPE TRAVELLING SALESMAN PROBLEM

The Multi-stripe Travelling Salesman Problem (Ms-TSP) is an extension of the Travelling Salesman Problem (TSP). In the \textit{q}-stripe TSP with $q \geq 1$, the objective function sums the costs for travelling from one customer to each of the next \textit{q} customers along the tour. The resulting \textit{q}-stripe TSP generalizes the TSP and forms a special case of the Quadratic Assignment Problem. To solve medium and large size instances, a metaheuristic algorithm is proposed. The proposed algorithm has two main components, which are construction and improvement phases. The construction phase generates a solution using Greedy Randomized Adaptive Search Procedure (GRASP) while the optimization phase improves the solution with several variants of Variable Neighborhood Search, both coupled with a technique called Shaking Technique to escape from local optima. In addition, Adaptive Memory is integrated into our algorithms to balance between the diversification and intensification. To show the efficiency of our proposed metaheuristic algorithms, we extensively experiment on benchmark instances. The results indicate that the developed algorithms can produce efficient and effective solutions at a reasonable computation time

Metaheuristic for Solving the Delivery Man Problem with Drone

Delivery Man Problem with Drone (DMPD) is a variant of Delivery Man Problem (DMP). The objective of DMP is to minimize the sum of customers' waiting times. In DMP, there is only a truck to deliver materials to customers while the delivery is completed by collaboration between truck and drone in DMPD. Using a drone is useful when a truck cannot reach some customers in particular circumstances such as narrow roads or natural disasters. For NP-hard problems, metaheuristic is a natural approach to solve medium to large-sized instances. In this paper, a metaheuristic algorithm is proposed. Initially, a solution without drone is created. Then, it is an input of split procedure to convert DMP-solution into DMPD-solution. After that, it is improved by the combination of Variable Neighborhood Search (VNS) and Tabu Search (TS). To explore a new solution space, diversification is applied. The proposed algorithm balances diversification and intensification to prevent the search from local optima. The experimental simulations show that the proposed algorithm reaches good solutions fast, even for large instances

An Effective Metaheuristic for Multiple Traveling Repairman Problem with Distance Constraints

Multiple Traveling Repairman Problem with Distance Constraints (MTRPD) is an extension of the NP-hard Multiple Traveling Repairman Problem. In MTRPD, a fleet of identical vehicles is dispatched to serve a set of customers with the following constraints. First, each vehicle's travel distance is limited by a threshold. Second, each customer must be visited exactly once. Our goal is to find the visiting order that minimizes the sum of waiting times. To solve MTRPD we propose to combine the Insertion Heuristic (IH), Variable Neighborhood Search (VNS), and Tabu Search (TS) algorithms into an effective two-phase metaheuristic that includes a construction phase and an improvement phase. In the former phase, IH is used to create an initial solution. In the latter phase, we use VNS to generate various neighborhoods, while TS is employed to mainly prohibit from getting trapped into cycles. By doing so, our algorithm can support the search to escape local optima. In addition, we introduce a novel neighborhoods’ structure and a constant time operation which are efficient for calculating the cost of each neighboring solution. To show the efficiency of our proposed metaheuristic algorithm, we extensively experiment on benchmark instances. The results show that our algorithm can find the optimal solutions for all instances with up to 50 vertices in a fraction of seconds. Moreover, for instances from 60 to 80 vertices, almost all found solutions fall into the range of 0.9 %-1.1 % of the optimal solutions' lower bounds in a reasonable duration. For instances with a larger number of vertices, the algorithm reaches good-quality solutions fast. Moreover, in a comparison to the state-of-the-art metaheuristics, our proposed algorithm can find better solutions

Hybrid Variable Neighborhood Search for Solving School Bus-Driver Problem with Resource Constraints

The School Bus-Driver Problem with Resource Constraints (SBDP-RC) is an optimization problem with many practical applications. In the problem, the number of vehicles is prepared to pick a number of pupils, in which the total resource of all vehicles is less than a predefined value. The aim is to find a tour minimizing the sum of pupils’ waiting times. The problem is NP-hard in the general case. In many cases, reaching a feasible solution becomes an NP-hard problem. To solve the large-sized problem, a metaheuristic approach is a suitable approach. The first phase creates an initial solution by the construction heuristic based on Insertion Heuristic. After that, the post phase improves the solution by the General Variable Neighborhood Search (GVNS) with Random Neighborhood Search combined with Shaking Technique. The hybridization ensures the balance between exploitation and exploration. Therefore, the proposed algorithm can escape from local optimal solutions. The proposed metaheuristic algorithm is tested on a benchmark to show the efficiency of the algorithm. The results show that the algorithm receives good feasible solutions fast. Additionally, in many cases, better solutions can be found in comparison with the previous metaheuristic algorithms

An efficient two-phase metaheuristic algorithm for the time dependent traveling Salesman problem

The Time Dependent Traveling Salesman Problem (TDTSP) is a class of NP-hard combinatorial optimization problems which has many practical applications. To the best of our knowledge, developing metaheuristic algorithm for the problem has not been studied much before, even though it is a natural and general extension of the Minimum Latency Problem (MLP) or Traveling Salesman Problem (TSP). In this paper, we propose an effective two-phase metaheuristic which combines the Insertion Heuristic (IH), Variable Neighborhood Search (VNS) and the tabu search (TS) to solve the problem. In a construction phase, the IH is used to create an initial solution that is good enough. In an improvement phase, the VNS is employed to generate diverse and various neighborhoods, while the main attribute of tabu search is to prohibit our algorithm from getting trapped into cycles, and to guide the search to escape local optima. Moreover, we introduce a novel neighborhoods’ structure in VNS and present a O(1) operation for calculating the cost of each neighboring solution in a special case of TDTSP where the TDTSP becomes the MLP. Extensive computational experiments on 355 benchmark instances show that our algorithm can find the optimal solutions for small instances with up to 100 vertices in a reasonable amount of time. For larger instances, our algorithm obtains the new best solutions in comparison with the state-of-the-art algorithm solutions

Multifactorial Evolutionary Algorithm for Simultaneous Solution of TSP and TRP

We study two problems called the Traveling Repairman Problem (TRP) and Traveling Salesman Problem (TSP). The TRP wants to minimize the total time for all customers that have to wait before being served, while the TSP aims to minimize the total time to visit all customers. In this sense, the TRP takes a customer-oriented view, whereas the TSP is server-oriented. In the literature, there exist numerous algorithms that are developed for two problems. However, these algorithms are designed to solve each problem independently. Recently, Multifactorial Evolutionary Algorithm (MFEA) has been a variant of Evolutionary Algorithm (EA) aiming to solve multiple optimization tasks simultaneously. The MFEA framework has yet to be fully exploited, but the realm has recently attracted much interest from the research community. This paper proposed a new approach using the MFEA framework to solve these two problems simultaneously. The MFEA has two tasks simultaneously: the first is solving the TRP problem, and the second is solving the TSP. Experiment results show the efficiency of the proposed MFEA: 1. for small instances, the algorithm reaches the optimal solutions of both problems; 2. for large instances, our solutions are better than those of the previous MFEA algorithms

ILC Reference Design Report Volume 1 - Executive Summary

The International Linear Collider (ILC) is a 200-500 GeV center-of-mass high-luminosity linear electron-positron collider, based on 1.3 GHz superconducting radio-frequency (SCRF) accelerating cavities. The ILC has a total footprint of about 31 km and is designed for a peak luminosity of 2x10^34 cm^-2s^-1. This report is the Executive Summary (Volume I) of the four volume Reference Design Report. It gives an overview of the physics at the ILC, the accelerator design and value estimate, the detector concepts, and the next steps towards project realization.The International Linear Collider (ILC) is a 200-500 GeV center-of-mass high-luminosity linear electron-positron collider, based on 1.3 GHz superconducting radio-frequency (SCRF) accelerating cavities. The ILC has a total footprint of about 31 km and is designed for a peak luminosity of 2x10^34 cm^-2s^-1. This report is the Executive Summary (Volume I) of the four volume Reference Design Report. It gives an overview of the physics at the ILC, the accelerator design and value estimate, the detector concepts, and the next steps towards project realization

ILC Reference Design Report Volume 4 - Detectors

This report, Volume IV of the International Linear Collider Reference Design Report, describes the detectors which will record and measure the charged and neutral particles produced in the ILC's high energy e+e- collisions. The physics of the ILC, and the environment of the machine-detector interface, pose new challenges for detector design. Several conceptual designs for the detector promise the needed performance, and ongoing detector R&D is addressing the outstanding technological issues. Two such detectors, operating in push-pull mode, perfectly instrument the ILC interaction region, and access the full potential of ILC physics.This report, Volume IV of the International Linear Collider Reference Design Report, describes the detectors which will record and measure the charged and neutral particles produced in the ILC's high energy e+e- collisions. The physics of the ILC, and the environment of the machine-detector interface, pose new challenges for detector design. Several conceptual designs for the detector promise the needed performance, and ongoing detector R&D is addressing the outstanding technological issues. Two such detectors, operating in push-pull mode, perfectly instrument the ILC interaction region, and access the full potential of ILC physics