Metaheuristic for the Capacitated Multiple Traveling Repairman Problem

p-Median based formulations with backbone facility locations

Combining intensification and diversification strategies in VNS. An application to the Vertex Separation problem

The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is one of the variants of the Vehicle Routing Problem (VRP), which is the problem of determining the optimal route from the depot to the customer which is located spread out with different requests. In CVRPTW problem solving considers capacity and time constraints. Determining the optimal route can reduce costs and energy spent during the distribution process. The General Variable Neighborhood Search (GVNS) algorithm can be applied to the CVRPTW problem. GVNS is a VNS algorithm whose solution is improved using RVND. The GVNS algorithm begins with finding the initial solution, followed by randomization of the solution using perturbation, and then the repair procedure is carried out using RVND. Perturbation and improvements to the GVNS algorithm are performed repeatedly according to a predetermined maximum iteration. The GVNS algorithm has never been applied to CVRPTW. Therefore, in this research, the GVNS algorithm will be implemented on CVRPTW which then the results will be compared with the VNS algorithm. The GVNS algorithm for CVRPTW is implemented using the Borland Delphi 7.0 programming language. The program has been tested using 3 datasets, each consisting of 50 points. The experiment was carried out 10 times which then resulted in the optimal solution and compared with the solution from the VNS algorithm. Based on the solution obtained, the GVNS algorithm is better than the VNS algorithm on the three datasets that have been tested. The product in the form of this application can be used more practically to solve CVRPTW problems using the GVNS algorithm.

A Double-Adaptive General Variable Neighborhood Search algorithm for the solution of the Traveling Salesman Problem

A variable neighborhood search heuristic algorithm for production routing problems

General variable neighborhood search for home healthcare routing and scheduling problem with time windows and synchronized visits

In this work, we present an extension of the generalized covering salesman problem as distance constrained generalized m-covering salesman problem. Given a set of vertices including a depot, facilities, customer locations and demand associated with each customer location, the objective is to determine an optimal tour of each salesman such that the total covered demand is maximized and the tour length is minimized. A bi-objective mixed-integer programming model is formulated for the problem. The proposed model is solved using GUROBI 8.0 solver’s in-built hierarchical optimization approach. However, the computational complexity of the problem demands a specialized solution approach. To solve the problem efficiently, we propose two metaheuristics ant colony optimization (ACO) algorithm and greedy randomized adaptive search procedure (GRASP). Extensive computational experiments were performed using the benchmark instances from the literature. The results of computational experiments show the efficiency and effectiveness of the proposed metaheuristics algorithms. Although, the GRASP metaheuristics outperform the ACO algorithm in case of medium and large size instances.

A hybrid heuristic based on General Variable Neighborhood Search for the Single Vehicle Routing Problem with Deliveries and Selective Pickups

Production scheduling problems are typically combinational optimization problems named bases on the processing routes of jobs on different machines. In this paper, the flexible job shop scheduling problem aimed to minimize the maximum completion times of operations or makespan is considered. To solve such an NP-hard problem, variable neighborhood search (VNS) based on genetic algorithm is proposed to enhance the search ability and to balance the intensification and diversification. VNS algorithm has shown excellent capability of local search with systematic neighborhood search structures. External library is improved to save the optimal or near optimal solutions during the iterative process, and when the objective value of the optimal solutions are the same, the scheduling Gantt charts need to be considered. To evaluate the performance of our proposed algorithm, benchmark instances in different sizes are optimized. Consequently, the computational results and comparisons illustrate that the proposed algorithm is efficiency and effectiveness.

This paper presents new modifications of Variable Neighborhood Search approach for solving the file transfer scheduling problem. To obtain better solutions in a small neighborhood of a current solution, we implement two new local search procedures. As Gaussian Variable Neighborhood Search showed promising results when solving continuous optimization problems, its implementation in solving the discrete file transfer scheduling problem is also presented. In order to apply this continuous optimization method to solve the discrete problem, mapping of uncountable set of feasible solutions into a finite set is performed. Both local search modifications gave better results for the large size instances, as well as better average performance for medium and large size instances. One local search modification achieved significant acceleration of the algorithm. The numerical experiments showed that the results obtained by Gaussian modifications are comparable with the results obtained by standard VNS based algorithms, developed for combinatorial optimization. In some cases Gaussian modifications gave even better results.

Minimizing total earliness and tardiness on a permutation flow shop using VNS and MIP

The aim of the work is to create a scenario modeling technique based on adaptation of metaheuristic multi-objective optimization algorithms to construct Pareto-optimal investment portfolios taking into account multiple restrictions on the company's portfolio. Investment portfolio is a tool for company's resources management. The process of portfolio construction through a combination of individual investment projects aims to optimize available resources utilization. Currently, there is no single approach to determining the optimal portfolio, since portfolio requirements depend on a particular company. Due to its simplicity in terms of implementation, approaches based on the ranking of projects according to individual technical and economic indicators are widely used. However, if a company needs to take into account several indicators, then this approach is not applicable. An effective solution to this problem is to create a technique that allows to find Pareto-optimal portfolios. In this work, the problem statement and analysis of existing solving approaches were carried out. Based on that, methodology of scenario modeling of a company's portfolio was created. The posed problem belongs to the class of NP-hard problems and for its efficient solving it is necessary to use metaheuristic algorithms. These algorithms include genetic algorithms, variable neighborhood search, bees algorithm, etc. Genetic algorithm NSGA-II is widely used for solving multi-objective optimization problems due to its high performance and ease of use. This algorithm was adapted to solve proposed problem. Based on it, a methodology was developed for the formation of Pareto-optimal investment portfolios, taking into account financial and infrastructural restrictions. Testing of the methodology was carried out on a dataset of synthetic projects simulating real investment projects. Various combinations of this projects simulated investment portfolios. The optimization objectives were maximization of net present value (NPV), maximization of cumulative oil production and minimization of total investments. In order to control the quality of the results obtained using genetic algorithm, the problem was also solved by using variable neighborhood search. However, the quality of the results obtained using genetic algorithm was higher. Analysis of the results allows us to consider the use of multi-objective optimization as a tool for forming a pool of investment portfolios, which provides flexible company management. In the proposed work, it is shown that the problem of scenario-based modeling of company's investment portfolio can be considered as a multi-objective optimization problem. Based on the existing metaheuristic algorithms, a Pareto-optimal investment portfolio search methodology has been developed. Proposed methodology can effectively solve the problem of managing company resources.

This article addresses a challenging industrial problem known as the unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times. In UPMSP, we have a set of machines and a group of jobs. The goal is to find the optimal way to schedule jobs for execution by one of the several available machines. UPMSP has been classified as an NP-hard optimisation problem and, thus, cannot be solved by exact methods. Meta-heuristic algorithms are commonly used to find sub-optimal solutions. However, large-scale UPMSP instances pose a significant challenge to meta-heuristic algorithms. To effectively solve a large-scale UPMSP, this article introduces a two-stage evolutionary variable neighbourhood search (EVNS) methodology. The proposed EVNS integrates a variable neighbourhood search algorithm and an evolutionary descent framework in an adaptive manner. The proposed evolutionary framework is employed in the first stage. It uses a mix of crossover and mutation operators to generate diverse solutions. In the second stage, we propose an adaptive variable neighbourhood search to exploit the area around the solutions generated in the first stage. A dynamic strategy is developed to determine the switching time between these two stages. To guide the search towards promising areas, a diversity-based fitness function is proposed to explore different locations in the search landscape. We demonstrate the competitiveness of the proposed EVNS by presenting the computational results and comparisons on the 1640 UPMSP benchmark instances, which have been commonly used in the literature. The experiment results show that our EVNS obtains better results than the compared algorithms on several UPMSP instances.

Optimization of CAR T-cell therapies supply chains

Travelling Salesman Problem with profits (TSPP) is a special case of the general Travelling Salesman Problem, all nodes must not be visited, but profit is collected from visited nodes. It is a well-known NP-hard combinatorial optimization problem in the literature. Because of the problem's complexity, exact methods cannot find the global optimum solution to this problem, so many heuristic and metaheuristic solution techniques are studied to find a feasible solution in a reasonable time. In this research, two different metaheuristic algorithms, Simulated Annealing with Many-moves and Variable Neighborhood Search, are proposed to solve the TSPP. Proposed algorithms are tested with three different problem instances and compared in terms of the efficiency of algorithms.