In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameter...
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In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameters, such as travel and service times, obtaining profit by protecting an asset, and upper bounds of time windows, are considered as stochastic parameters. Generating proper scenarios for uncertain parameters has a large impact on the accuracy of the obtained solutions. Therefore, artificial neural networks are employed to extract possible scenarios according to previous actual wildfire events. The problem cannot be solved by exact solvers for large instances, so two matheuristic algorithms are proposed in this study to solve the problem in a reasonable time. In the first algorithm, a set of feasible routes is generated based on a heuristic approach, then a route-based mathematical model is used to obtain the final solution. Also, another matheuristic algorithm based on adaptive large neighbourhood search (ALNS) is proposed. In this algorithm, routing decisions are determined using the ALNS algorithm while other decisions are achieved by solving an intermediate mathematical model. The numerical analysis confirms the efficiency of both proposed algorithms;however, the first algorithm performs more efficiently.
This paper provides a deeper insight into the train platforming problem (TPP). Many studies have focused on different versions of train scheduling and routing problems, and most of them assume that the platform track&...
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This paper provides a deeper insight into the train platforming problem (TPP). Many studies have focused on different versions of train scheduling and routing problems, and most of them assume that the platform track's capacity is one train. However, especially in busy and complex railway stations, most platform tracks are divided into two parts, allowing two trains to simultaneously share the same platform track for passenger boarding/alighting. This results in more efficient train assignment to the platform tracks. In addition, consideration of the track capacity makes the problem more difficult because directions of trains are problematic. Motivated by this challenge, we consider the TPP with two-train-capacity tracks. We first describe the problem in detail and then propose a mixed-integer programming model. The objective of the considered problem is to minimize the total weighted train delays, which are defined as the difference between the departure times calculated by the mathematical model (M1) and the scheduled departure times of the trains in the timetable. Because of the NP-hard nature of the problem, the proposed M1 may not find feasible solutions for large-size problems. Thus, a matheuristic algorithm (MA) is developed to solve large-size problems. We used randomly generated test problems to demonstrate the performance of the proposed M1 and MA. Experimental results showed that MA outperforms M1 in both solution quality and solution time. Additionally, a case study was conducted at the central station of Prague, Czechia.
Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensionin...
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Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensioning, resource assignment, scheduling, and routing. In this study, a two-stage stochastic mixed integer model is proposed that considers these decisions simultaneously. In the planning phase of a home health care system, the main uncertain parameters are travel and service times. Hence, the proposed model takes into account the uncertainty in travel and service times. Districting and staff dimensioning are defined as the first stage decisions, and assignment, scheduling, and routing are considered as the second stage decisions. A novel algorithm is developed for solving the proposed model. The algorithm consists of four phases and relies on a matheuristic-based method that calls on various mixed integer models. In addition, an algorithm based on the progressive hedging and Frank and Wolf algorithms is developed to reduce the computational time of the second phase of the proposed matheuristic algorithm. The efficiency and accuracy of the proposed algorithm are tested through several numerical experiments. The results prove the ability of the algorithm to solve large instances. (C) 2020 Elsevier B.V. All rights reserved.
The pollution traveling salesman problem (PTSP) and the energy minimization traveling salesman problem (EMTSP) generalize the well-known asymmetric traveling salesman problem by including environmental issues and the ...
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The pollution traveling salesman problem (PTSP) and the energy minimization traveling salesman problem (EMTSP) generalize the well-known asymmetric traveling salesman problem by including environmental issues and the goal of reducing carbon emissions. Both problems call for determining a Hamiltonian tour that, in the PTSP, minimizes a function of fuel consumption and driver cost (where the fuel consumption depends on the distance traveled, the vehicle speed, and the vehicle load), while, in the EMTSP, minimizes a function depending on the vehicle load and the traveled distances. For both PTSP and EMTSP, we propose a matheuristic algorithm that uses the solution of the linear programming relaxation of a mixed integer linear programming model for the considered problem to determine good initial feasible solutions, applies a multioperator genetic algorithm to improve these solutions, and refines the best solution found through an iterated local search procedure. In order to evaluate the performance of the proposed matheuristics, we compare them with exact and heuristic algorithms from the literature on benchmark instances of both problems.
Machine scheduling problems are one of the basic manufacturing problems. Thus, there are a lot of study in the literature. In most of these studies, the problem is considered as single objective. Although the single o...
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Machine scheduling problems are one of the basic manufacturing problems. Thus, there are a lot of study in the literature. In most of these studies, the problem is considered as single objective. Although the single objective approach makes it easier to solve problems theoretically, it is often not possible to provide realistic solutions because almost all real-life problems have multi-objective properties. It is aimed to close this gap in the literature by using the multi-objective programming method that emerged as a powerful solution approach. The objectives are minimizing the makespan and minimizing the total tardiness. A matheuristic algorithm is developed for solving the considered problem. The performance of the algorithm is compared with the results of the augmented e-constraint method. With the proposed matheuristic algorithm, both a solution time advantage is obtained and dominant solutions that could not be obtained with the augmented e-constraint method are reached.
Route balancing addresses fair workloads allocation among stakeholders to balance utilization of transportation resources and to guarantee equity among employees. Drop-and-pull transportation has been verified to be b...
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Route balancing addresses fair workloads allocation among stakeholders to balance utilization of transportation resources and to guarantee equity among employees. Drop-and-pull transportation has been verified to be beneficial in container drayage and has gradually received attentions from the scientific literature. A container drayage problem that simultaneously considers the drop-and-pull mode and the issue of route balancing is investigated. In the problem, apart from the economic objectives, balanced distribution of workloads from social perspective is especially pursued. A mixed-integer programming model is formulated and a matheuristic algorithm is developed based on embedding the model in a heuristic framework to solve the problem. A division approach is designed to transform the original problem into sub-problems to ease the solving pressure. A fix-and- optimize method is adopted to further reduce the computational burdens from substantial decision variables. The proposed algorithm is tested on solving different application scenarios as single- and multi-trailer drop-and-pull problems. Experimental results indicate that the algorithm can provide much better solutions in shorter computational time when compared to standard approaches. Some managerial insights and sensitivity analyses concerning the route balancing are also presented.
In this paper, we propose an adaptive large neighborhood search-based matheuristic algorithm to solve a multi-product many-to-many maritime inventory routing problem. The problem addresses a short sea inventory routin...
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In this paper, we propose an adaptive large neighborhood search-based matheuristic algorithm to solve a multi-product many-to-many maritime inventory routing problem. The problem addresses a short sea inventory routing problem that aims to find the best route and distribution plan for multiple products with a heterogeneous fleet of vessels through a network including several producers and customers. Each port can be visited a given number of times during the planning horizon, and the stock level for each product should lie within the predefined bound limits. The problem was introduced by Hemmati et al. (Eur J Oper Res 252:775-788, 2016). They developed a mixed integer programming formulation and proposed a matheuristic algorithm to solve the problem. Although their proposed algorithm worked well in terms of running time, it suffers from disregarding a part of the solution space. In this study, we propose a new matheuristic algorithm to find better solutions by exploring the entire solution space for the same problem. In our solution methodology, we split the variables into routing and non-routing variables. Then in an iterative process, we determine the values of the routing variables with an adaptive large neighborhood search algorithm, and we pass them as input to a penalized model which is a relaxed and modified version of the mathematical model introduced in Hemmati et al. (2016). The information from solving the penalized model, including the values of the non-routing variables, is then passed to the adaptive large neighborhood search algorithm for the next iteration. Several problem-dependent operators are defined. The operators use the information they get from the penalized model and focus on decreasing the penalty values. Computational results show up to 26% improvement in the quality of the solutions for the group of instances with a large feasible solution space. We get the optimal value for the remaining instances matched with the reported results.
This paper addresses the orienteering problem with service time dependent profits (OPSTP), in which the profit collected at each vertex is characterized by a nonlinear function of service time, and the objective is to...
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This paper addresses the orienteering problem with service time dependent profits (OPSTP), in which the profit collected at each vertex is characterized by a nonlinear function of service time, and the objective is to maximize the total profits by determining a subset of the vertices to be visited and assigning appropriate service time to each of them within a given time budget. To solve this problem, a mixed integer nonlinear programming model is formulated, and a two-phase matheuristic algorithm that consists of a tabu search method and a nonlinear programming is implemented. Extensive computational experiments are conducted on both randomly generated instances and instances that are adapted from the TSPLIB. The results show that our proposed matheuristic algorithm could be quite effective in finding good-quality solutions. (C) 2018 Elsevier B.V. All rights reserved.
This paper presents a new combinatorial optimization problem that can be used to model the deployment of broadband telecommunications systems in which optical fiber cables are installed between a central office and a ...
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This paper presents a new combinatorial optimization problem that can be used to model the deployment of broadband telecommunications systems in which optical fiber cables are installed between a central office and a number of end-customers. In this capacitated network design problem the installation of optical fiber cables with sufficient capacity is required to carry the traffic from the central office to the end-customers at minimum cost. In the situation motivating this research the network does not necessarily need to connect all customers (or at least not with the best available technology). Instead, some nodes are potential customers. The aim is to select the customers to be connected to the central server and to choose the cable capacities to establish these connections. The telecom company takes the strategic decision of fixing a percentage of customers that should be served, and aims for minimizing the total cost of the network providing this minimum service. For that reason the underlying problem is called the Prize-Collecting Local Access Network Design problem (PC-LAN). We propose a branch-and-cut approach for solving small instances. For large instances of practical importance, our approach turns into a mixed integer programming (MIP) based heuristic procedure which combines the cutting-plane algorithm with a multi-start heuristic algorithm. The multi-start heuristic algorithm starts with fractional values of the LP-solutions and creates feasible solutions that are later improved using a local improvement strategy. Computational experiments are conducted on small instances from the literature. In addition, we introduce a new benchmark set of real-world instances with up to 86,000 nodes, 116,000 edges and 1500 potential customers. Using our MIP-based approach we are able to solve most of the small instances to proven optimality. For more difficult instances, we are not only able to provide high-quality feasible solutions, but also to provide certificate
This article develops a hub-and-spoke architecture for a parcel delivery system using a network that includes a distribution centre and several cross-dock facilities. Several real-world assumptions, including an elect...
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This article develops a hub-and-spoke architecture for a parcel delivery system using a network that includes a distribution centre and several cross-dock facilities. Several real-world assumptions, including an electric truck fleet, mobile charging station, third-party logistics, capacity constraints, last-mile deliveries and customer dissatisfaction, are incorporated in this problem. Besides, because of the high complexity of the problem, a novel matheuristic algorithm based on the combination of the fix-and-relax algorithm (FARA) and genetic algorithm (GA), namely Mb-FARGA, is developed to solve large-sized instances. A comprehensive computational analysis is accomplished to validate the proposed mathematical models and evaluate the performance of the Mb-FARGA method. According to the results, the Mb-FARGA method demonstrates a very favourable performance. Moreover, the results show that considering a distribution centre with appropriate capacity in the cross-docking system is an excellent strategy to enhance customer satisfaction, improve the system's performance and reduce capacity shortages.
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