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Source publication
A vehicle routing problem with time windows (VRPTW) is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functio...
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Context 1
... for large-sized problems, the gap between GA and SA is presented based on [100 × (Gbest-Meta − GMeta)/GMeta] in which, GMeta is the objective function of the obtained solution over meta-heuristic methods and Gbest-Meta is the objective function value of meta-heuristic methods that have better performance. Each meta-heuristic method runs for five times and the average gap is reported in Table 5. As can be inferred from Table 5, SA outperforms GA in terms of the solution quality in the all of the test problems. ...
Context 2
... meta-heuristic method runs for five times and the average gap is reported in Table 5. As can be inferred from Table 5, SA outperforms GA in terms of the solution quality in the all of the test problems. Based on the RMCGP method, the solution procedure of meta-heuristics algorithms includes two steps. ...
Context 3
... is calculated. The required computational times for the proposed meta-heuristics are reported in Table 5. The required computational time increased sharply as the test problem size becomes larger. ...
Context 4
... results demonstrate that GA and SA can obtain a near-optimum solution in a reasonable time especially in large-sized problems. As can be seen from Table 5, the required computational time of GA is more than SA. This difference is derived from the fact that GA has some additional mechanisms (e.g., selection mechanism and crossover), which is time consuming. ...
Citations
... Ketidakpastian ini dapat diantispasi dengan penggunaan Goal Programming, yang menggabungkan batasan dan fungsi tujuan (Güzel et al., 2022). Goal Programming sendiri dapat diterapkan pada permasalahan rute (Hu et al., 2018) (Yousefi et al., 2017)(Aghdaghi & Jolai, 2008 (Mamashli et al., 2021). Dari permasalahan diatas, maka tujuan penelitian ini adalah menyelesaikan masalah rute wisata di Yogyakarta dengan Goal Programming. ...
Ketika wisatawan ke suatu daerah wisata, maka besar kemungkinan wisatawan tersebut mengunjungain beberapa obyek wisata. Kunjungan pada beberapa lokasi tersebut menyebabkan memerlukan waktu dan biaya karena beberapa lokasi tersebut terkadang mempunyai jarak yang dapat berjauhan. Untuk itu diperlukan pemilihan rute yang optimal afar dapat menghemat waktu dan jarak tempuh. Selama ini terdapat beberapa metode optimasi yang dapat digunakan untuk masalah pemilihan rute, salah satu yang yaitu goal programming. Goal programing digunakan karena dapat mengakomodasi beberapa tujuan yang dalam kasus pemilihan rute ini adalah meminimalkan jarak dan waktu. Hasil solusi pemilihan rute dengan goal programming didapatkan perjalanan diawali dari Hotel di Kawasan Malioboro kemudian ke Kraton. Kemudian perjalanan dilanjutkan ke Candi Prambanan, setelah itu perjalanan dilanjutkan ke Wisata Obelix. Setelah itu kemudian tujuan lokasi wisata terakhir ke Merapi dan kemudian kembali ke Hotel di Kawasan malioboro. Rute yang terpilih telah melakukan penghematan sebesar 4 menit untuk waktu tempuh dan 7 Km untuk jarak tempuh
... Jiang et al. [38] studied a multi objective model of MDVRP by maximizing the total quantity of materials delivered to customers during a time horizon and minimizing the difference of materials received by multiple disaster points. Another research is done by Usefi et al. [39] by considering two objectives. The first objective is to minimize the fixed costs and the second one is to consider the priority of customer satisfaction by the gap between arrival time and ready time. ...
... Many approaches have been proposed to address this uncertainty, which are based on stochastic methods and are referred to as stochastic VRPs. The purpose of such problems is to achieve a set of near-optimal answers that optimize each of the uncertain parameters for a set of worst-case scenarios (Yousefi et al., 2017). ...
Purpose
This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.
Design/methodology/approach
The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.
Findings
According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.
Research limitations/implications
In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.
Practical implications
The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.
Originality/value
This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.
... In the second phase, the solution space is searched by applying self-adaptive operators and the variable neighborhood search algorithm. In this paper, the crossover operators are one point, two points, three points, and uniform operators; and mutation operators consist of the swap, insertion and reversion operators (Rabbani, Taheri, & Ravanbakhsh, 2016;Yousefi, Tavakkoli-Moghaddam, Oliaei, Mohammadi, & Mozaffari, 2017;Goodarzi, Tavakkoli-Moghaddam, & Amini, 2020). The structure of the self-adaptive crossover and mutation operators is presented in Fig. 4. Fig. 4 demonstrates the pseudo code of SDV. ...
This paper presents a new bi-objective mathematical model to design a build-to-order supply chain (BTO-SC) network. In this paper, the demand of customers (dealers) is lead-time dependent and the time-sensitive parameter of the lead time is also dynamic and time-dependent. The objectives are to minimize the supply chain network cost and the number of lost demands, respectively. This paper integrates procurement, production and distribution problems to consider the trade-off between lead time, logistic cost, and the lost demands derived from lead time in the BTO-SC. Due to the NP-hard nature of the proposed model, a new hybrid meta-heuristic algorithm is presented. The proposed model is validated by GAMS software, and some test problems generated at random are solved to demonstrate the performance of the proposed meta-heuristic algorithm. To present the applicability of the proposed model, a real case study is presented and some sensitivity analyses are conducted to develop managerial insights.
... One of the methods commonly used is weighted goal programming (WGP), which has been previously applied in multi-objective location-routing problems (Zhao & Verter, 2015;Rabbani et al., 2017;Asefi et al., 2019;Yousefi et al., 2017). ...
... The existing various hydrocarbon products such as types of petrochemicals along with exporting these items and the neighboring countries make Hazmat transport is more important. Finally, the geographical location of Iran, as one of the most suitable transit routes for goods, makes Hazmat transport should be safer and more sustainable in the above country (Yousefi et al., 2017). ...
... By definition, risk represents the relationship between the hazards and the vulnerability factors for one or more components. The high risk component is also defined based on the chance of occurrence and its consequences (Yousefi et al., 2017). Transport risks, in transporting hazardous materials, include four main components of accident severity and frequency, affected population, environment and road infrastructures . ...
Due to existing risk on hazardous materials transportation, it is essential to avoid risk agglomeration over the specific edges which are frequently used on the intercity road network. Therefore, local and/or national authorities are dealing with distributing risk over the network while risk distribution may affect on the network accessibility. The aim of this study is to propose a procedure and develop mathematical models to distribute Hazmat transport risk, named risk equity, on the intercity road network and investigate the effects on the network accessibility. Accessibility is defined as dividing transport demand by distance, where the Min (Max) risk distribution technique is utilized for risk equity over the network. The effects have been investigated on a medium size of intercity road network in Guilan province, at the north of Iran. The proposed procedure and mathematical models have been run using experimental data including 46 nodes and 126 two-way edges including Hazmat Origin-Destination matrix. The results revealed that risk distribution technique has significant effects on network accessibility in which nodes’ accessibilities are statistically affected by risk equity models.
... The aim of the SVRP methodology is to find a near-best solution of the objective function responding to all possible data uncertainty. An alternative approach to handle the uncertain parameters is the robust optimization in which one can optimize against the worst scenario that can be generated from the source of uncertainty by using bi-objective function (Yousefi et al., 2017) and is immunized against this uncertainty (Sungur et al., 2008). In this context, the literature coats a large number of applications such as scheduling (Goren & Sabuncuoglu, 2008;Hazir et al., 2010), facility location (Minoux, 2010;Baron et al., 2011 ;Alumur et al., 2012 ;Gülpinar et al., 2013), inventory (Bienstock & Özbay, 2008;Ben-Tal et al.,2009), finance (Fabozzi et al., 2007;Gülpinar & Rustem, 2007;Pinar, 2007). ...
The main purpose of this paper is to study the vehicle routing problem with hard time windows where the main challenges is to include both sources of uncertainties, namely the travel and the service time that can arise due to multiple causes. We propose a new approach for the robust problem based on the implementation of an adaptive large neighborhood search algorithm and the use of efficient mechanisms to derive the best robust solution that responds to all uncertainties with reduced running times. The computational experiments are performed and improve the objective function of a set of instances with different levels of the uncertainty polytope to obtain the best robust solutions that protect from the violation of time windows for different scenarios.
... Multi-objective problems occur when simultaneous optimization more than one objective function is required. This type of optimization entails maximizing or minimizing of the objective functions; in fact, any maximization problem can be converted to a minimization problem [43]. Some equality and inequality constraints accompany the multi- objective problem. ...
Dispersed application of Battery Energy Storages (BESs) can have many benefits in terms of voltage regulation and energy management in Active Distribution Networks (ADNs). The batteries are high-cost technologies, and they must be installed and managed optimally to benefit from their innumerable advantages. In addition, each battery technology has specific economic and technical attributes which can be appropriate or inappropriate in a particular condition and utilization. The purpose of this paper is that the optimal size and location of various battery technologies are specified in the distribution network to minimize total cost and maximize reliability index considering the uncertainty of load demand as well as the output power of the wind and solar. Also, multi-objective particle swarm optimization (MOPSO) algorithm is used to minimize two objective functions. Monte Carlo Simulation (MCS) is used to model the uncertainties of economic and technical characteristics of photovoltaic, wind power and load demand. The suggested planning scheme is tested on the modified IEEE 33-bus system. Finally, the different types of the battery technologies in one, five, ten, fifteen and twenty-year period are compared to present the optimum type.
... The research on vehicle routing problem (VRP) began in 1959. Based on the unremitting efforts of experts at home and abroad in this field, a series of intelligent algorithms have been introduced into the field of research, such as the tabu search algorithm, simulated annealing algorithm, ant colony algorithm, genetic algorithm, artificial neural network, and particle swarm optimization algorithm [1][2][3][4][5][6][7][8][9]. However, there are few articles on intelligent algorithms to explore the optimal decision-making problem of spare parts supply paths with time windows. ...
... Equation (9) indicates that the vehicle must meet the time window requirements of the visiting customers during the driving process. ...
... There is more than one way to generate initial population. First way is to generate it randomly (e.g., [24], [35], [37], [39], [41], [42], [44]- [46], [49], [54], [59], [61], [66], [68], [72], [75], [76]). Second way is to use heuristics for generating the initial population, such as the nearest addition method (NAM), the sweep algorithm (SWA), the savings algorithm (SA), the Clarke and Wright heuristic (C&W), the Push Forward Insertion Heuristic (PFIH), the Nearest Neighbor Heuristic (NNH) and Insertion heuristic (IH) , etc, (e.g., [26], [27], [40], [47], [48], [55]- [58], [60], [62]- [64], [66], [67], [70]). ...
... First way is to generate it randomly (e.g., [24], [35], [37], [39], [41], [42], [44]- [46], [49], [54], [59], [61], [66], [68], [72], [75], [76]). Second way is to use heuristics for generating the initial population, such as the nearest addition method (NAM), the sweep algorithm (SWA), the savings algorithm (SA), the Clarke and Wright heuristic (C&W), the Push Forward Insertion Heuristic (PFIH), the Nearest Neighbor Heuristic (NNH) and Insertion heuristic (IH) , etc, (e.g., [26], [27], [40], [47], [48], [55]- [58], [60], [62]- [64], [66], [67], [70]). Other way is to a combination of heuristic and randomly (e.g., [36], [38], [43], [50], [51], [53], [65], [69], [71], [73]). ...
... In the literature, ten types of crossover operator methods are found: One-Point Crossover (1PX) (e.g., [43], [44], [46], [59]), Two-Point Crossover (2PX) (e.g., [35], [44], [49]), Order Crossover (OX) (e.g., [26], [36], [37], [43], [48], [50], [54], [56], [57], [60], [62], [71], [76]), Partially Mapped Crossover (PMX) (e.g., [40]- [43], [45], [46], [52], [67]), Cyclical Crossover (CX) (e.g., [43], [75]), Route Based Crossover (RBX) (e.g., [61], [70], [72], [76]), Sequence-Based Crossover (SBX) (e.g., [76]), Single Parent Crossover (SPO) (e.g., [36]), Genetic Vehicle Representation Crossover (GVR) (e.g., [69]) and Best Cost-Best Route Crossover (BCBRC) (e.g., [38], [53], [64]- [66]). ...
The capacitated vehicle routing problem (CVRP) is an NP-hard problem. Therefore, metaheuristics are often more suitable for practical applications. In this paper, a genetic algorithm (GA) is proposed to solve the problem. The performance of the proposed algorithm is tested on different sets of benchmark instances. The computational results indicate that the algorithm has a satisfactory performance in solving the problem.
