Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2022.3.002
Weina Xu, G. Lin, Xide Zhu
The growing popularity of aggregation platforms has attracted widespread attention in the ride-hailing market in recent years. In order to obtain additional orders by charging commissions and slotting fees, many ride-hailing platforms choose to bundle with aggregation platforms. Unlike traditional reseller electronic channels, the bundle channels may affect pricing of platforms, service levels of drivers, market demands and they may further impact on profits. These different attitudes raise an interesting and key question about the influence of bundle channels in ride-hailing platforms. In this paper, we propose an analytical framework for pricing strategies of ride-hailing and aggregation platforms under bundle mode and analyze their pricing process from the perspective of Nash and Stackelberg games, where the platforms serve as leaders to determine optimal prices through Nash equilibrium and the drivers serve as followers to provide optimal service levels. Through sensitivity analysis of service levels and costs, we capture the distribution trends of profits between the platforms. Based on some numerical examples and results analysis, some interesting managerial insights on pricing of ride-hailing and aggregation platforms are gained.
{"title":"Nash-stackelberg game perspective on pricing strategies for ride-hailing and aggregation platforms under bundle mode","authors":"Weina Xu, G. Lin, Xide Zhu","doi":"10.5267/j.ijiec.2022.3.002","DOIUrl":"https://doi.org/10.5267/j.ijiec.2022.3.002","url":null,"abstract":"The growing popularity of aggregation platforms has attracted widespread attention in the ride-hailing market in recent years. In order to obtain additional orders by charging commissions and slotting fees, many ride-hailing platforms choose to bundle with aggregation platforms. Unlike traditional reseller electronic channels, the bundle channels may affect pricing of platforms, service levels of drivers, market demands and they may further impact on profits. These different attitudes raise an interesting and key question about the influence of bundle channels in ride-hailing platforms. In this paper, we propose an analytical framework for pricing strategies of ride-hailing and aggregation platforms under bundle mode and analyze their pricing process from the perspective of Nash and Stackelberg games, where the platforms serve as leaders to determine optimal prices through Nash equilibrium and the drivers serve as followers to provide optimal service levels. Through sensitivity analysis of service levels and costs, we capture the distribution trends of profits between the platforms. Based on some numerical examples and results analysis, some interesting managerial insights on pricing of ride-hailing and aggregation platforms are gained.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"13 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79946642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2022.1.002
D. Rodríguez, D. A. Martínez, J. Escobar
This paper proposes a matheuristic algorithm based on a column generation structure for the capacitated vehicle routing problem with three-dimensional loading constraints (3L–CVRP). In the column generation approach, the master problem is responsible for managing the selection of best-set routes. In contrast, the slave problem is responsible for solving a shorter restricted route problem (CSP, Constrained Shortest Path) for generating columns (feasible routes). The CSP is not necessarily solved to optimality. In addition, a greedy randomized adaptive search procedure (GRASP) algorithm is used to verify the packing constraints. The master problem begins with a set of feasible routes obtained through a multi-start randomized constructive algorithm (MSRCA) heuristic for the multi-container loading problem (3D–BPP, three-dimensional bin packing problem). The MSRCA consists of finding valid routes considering the customers' best packing (packing first-route second). The efficiency of the proposed approach has been validated by a set of benchmark instances from the literature. The results show the efficiency of the proposed approach and conclude that the slave problem is too complex and computationally expensive to solve through a MIP.
{"title":"A hybrid matheuristic approach for the vehicle routing problem with three-dimensional loading constraints","authors":"D. Rodríguez, D. A. Martínez, J. Escobar","doi":"10.5267/j.ijiec.2022.1.002","DOIUrl":"https://doi.org/10.5267/j.ijiec.2022.1.002","url":null,"abstract":"This paper proposes a matheuristic algorithm based on a column generation structure for the capacitated vehicle routing problem with three-dimensional loading constraints (3L–CVRP). In the column generation approach, the master problem is responsible for managing the selection of best-set routes. In contrast, the slave problem is responsible for solving a shorter restricted route problem (CSP, Constrained Shortest Path) for generating columns (feasible routes). The CSP is not necessarily solved to optimality. In addition, a greedy randomized adaptive search procedure (GRASP) algorithm is used to verify the packing constraints. The master problem begins with a set of feasible routes obtained through a multi-start randomized constructive algorithm (MSRCA) heuristic for the multi-container loading problem (3D–BPP, three-dimensional bin packing problem). The MSRCA consists of finding valid routes considering the customers' best packing (packing first-route second). The efficiency of the proposed approach has been validated by a set of benchmark instances from the literature. The results show the efficiency of the proposed approach and conclude that the slave problem is too complex and computationally expensive to solve through a MIP.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"46 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91227032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2021.10.001
Sergio Esteban Vega-Figueroa, Paula Andrea López-Becerra, E. López-Santana
This document addresses the problem of scheduling and routing a specific number of vehicles to visit a set of customers in specific time windows during a planning horizon. The vehicles have a homogeneous limited capacity and have their starting point and return in a warehouse or initial node, in addition, multiple variants of the classic VRP vehicle routing problem are considered, where computational complexity increases with the increase in the number of customers to visit, as a characteris-tic of an NP-hard problem. The solution method used consists of two connected phases, the first phase makes the allocation through a mixed-integer linear programming model, from which the visit program and its frequency in a determined plan-ning horizon are obtained. In the second phase, the customers are grouped through an unsupervised learning algorithm, the routing is carried out through an Ant Colony Optimization metaheuristic that includes local heu-ristics to make sure com-pliance with the restrictive factors. Finally, we test our algorithm by performance measures using instances of the literature and a comparative model, and we prove the effectiveness of the proposed algorithm.
{"title":"Hybrid algorithm for the solution of the periodic vehicle routing problem with variable service frequency","authors":"Sergio Esteban Vega-Figueroa, Paula Andrea López-Becerra, E. López-Santana","doi":"10.5267/j.ijiec.2021.10.001","DOIUrl":"https://doi.org/10.5267/j.ijiec.2021.10.001","url":null,"abstract":"This document addresses the problem of scheduling and routing a specific number of vehicles to visit a set of customers in specific time windows during a planning horizon. The vehicles have a homogeneous limited capacity and have their starting point and return in a warehouse or initial node, in addition, multiple variants of the classic VRP vehicle routing problem are considered, where computational complexity increases with the increase in the number of customers to visit, as a characteris-tic of an NP-hard problem. The solution method used consists of two connected phases, the first phase makes the allocation through a mixed-integer linear programming model, from which the visit program and its frequency in a determined plan-ning horizon are obtained. In the second phase, the customers are grouped through an unsupervised learning algorithm, the routing is carried out through an Ant Colony Optimization metaheuristic that includes local heu-ristics to make sure com-pliance with the restrictive factors. Finally, we test our algorithm by performance measures using instances of the literature and a comparative model, and we prove the effectiveness of the proposed algorithm.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"60 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83894067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2022.5.002
F. Mohammadipour, M. Amiri, I. R. Vanani, J. B. Soofi
This paper considers simultaneously two areas of facility location and assortment planning in a competitive environment. In fact, a chain store that has competitors in the market locates a new facility. As there are different products in the market that can substitute with each other, it is intended to determine the best product assortment as well. An integer nonlinear programming problem is proposed to model the mentioned subject. For solving the model, the problem is reformulated as a mixed integer linear programming one. Therefore, a MIP solver software can be used for solving the small- and medium-size problems. For large-scale problems, a firefly algorithm is designed for obtaining a satisfactory solution. By using the proposed model, it is numerically shown that, in addition to the optimal location, it is also necessary to determine simultaneously the best product assortment for the new store. Actually, comparison results reveal that the location significantly affects the assortment scenarios for the new store. In other words, the selection of new store locations may lead to loss of large profit if the assortment planning is neglected.
{"title":"A model for location-assortment problem in a competitive environmen","authors":"F. Mohammadipour, M. Amiri, I. R. Vanani, J. B. Soofi","doi":"10.5267/j.ijiec.2022.5.002","DOIUrl":"https://doi.org/10.5267/j.ijiec.2022.5.002","url":null,"abstract":"This paper considers simultaneously two areas of facility location and assortment planning in a competitive environment. In fact, a chain store that has competitors in the market locates a new facility. As there are different products in the market that can substitute with each other, it is intended to determine the best product assortment as well. An integer nonlinear programming problem is proposed to model the mentioned subject. For solving the model, the problem is reformulated as a mixed integer linear programming one. Therefore, a MIP solver software can be used for solving the small- and medium-size problems. For large-scale problems, a firefly algorithm is designed for obtaining a satisfactory solution. By using the proposed model, it is numerically shown that, in addition to the optimal location, it is also necessary to determine simultaneously the best product assortment for the new store. Actually, comparison results reveal that the location significantly affects the assortment scenarios for the new store. In other words, the selection of new store locations may lead to loss of large profit if the assortment planning is neglected.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"16 2-3","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72472311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2022.1.004
Liang-Tu Chen, Guo-Ciang Wu, Cher‐Hung Tseng, Ren-Zong Kuo
This research proposes a dynamic decision-making framework for a hybrid production system that incorporates manufacturing and remanufacturing procedures into a closed-loop supply chain network with merchandise substitution and shortages within traditional markets (TM) and electronic markets (EM). In particular, we develop models of profit maximization and equilibrium analysis by using calculus with dynamic programming under four business schemes, including a manufacturing-only model within TM/EM and a hybrid remanufacturing model within TM/EM. Dynamic decision-making planning was taken for brand-new and like-new decayed merchandise in hybrid production systems. The results demonstrate that solutions generated within EMs surpass those within TMs in terms of maximizing profits. Further, the hybrid remanufacturing model did not surpass the manufacturing-only model under a general setting, but had better performance under certain conditions, including intense competition, a smaller remanufacturing cost, a larger brand-new merchandise market size, and a smaller like-new merchandise market size.
{"title":"A dynamic decision-making framework for a hybrid production system for decayed merchandise with shortages in traditional and electronic markets","authors":"Liang-Tu Chen, Guo-Ciang Wu, Cher‐Hung Tseng, Ren-Zong Kuo","doi":"10.5267/j.ijiec.2022.1.004","DOIUrl":"https://doi.org/10.5267/j.ijiec.2022.1.004","url":null,"abstract":"This research proposes a dynamic decision-making framework for a hybrid production system that incorporates manufacturing and remanufacturing procedures into a closed-loop supply chain network with merchandise substitution and shortages within traditional markets (TM) and electronic markets (EM). In particular, we develop models of profit maximization and equilibrium analysis by using calculus with dynamic programming under four business schemes, including a manufacturing-only model within TM/EM and a hybrid remanufacturing model within TM/EM. Dynamic decision-making planning was taken for brand-new and like-new decayed merchandise in hybrid production systems. The results demonstrate that solutions generated within EMs surpass those within TMs in terms of maximizing profits. Further, the hybrid remanufacturing model did not surpass the manufacturing-only model under a general setting, but had better performance under certain conditions, including intense competition, a smaller remanufacturing cost, a larger brand-new merchandise market size, and a smaller like-new merchandise market size.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"28 18 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87481362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2021.10.003
M. S. Nagano, M. Takano, João Vítor Silva Robazzi
In this paper it is presented an improvement of the branch and bound algorithm for the permutation flow shop problem with blocking-in-process and setup times with the objective of minimizing the total flow time and tardiness, which is known to be NP-Hard when there are two or more machines involved. With that objective in mind, a new machine-based lower bound that exploits some structural properties of the problem. A database with 27 classes of problems, varying in number of jobs (n) and number of machines (m) was used to perform the computational experiments. Results show that the algorithm can deal with most of the problems with less than 20 jobs in less than one hour. Thus, the method proposed in this work can solve the scheduling of many applications in manufacturing environments with limited buffers and separated setup times.
{"title":"A branch and bound method in a permutation flow shop with blocking and setup times","authors":"M. S. Nagano, M. Takano, João Vítor Silva Robazzi","doi":"10.5267/j.ijiec.2021.10.003","DOIUrl":"https://doi.org/10.5267/j.ijiec.2021.10.003","url":null,"abstract":"In this paper it is presented an improvement of the branch and bound algorithm for the permutation flow shop problem with blocking-in-process and setup times with the objective of minimizing the total flow time and tardiness, which is known to be NP-Hard when there are two or more machines involved. With that objective in mind, a new machine-based lower bound that exploits some structural properties of the problem. A database with 27 classes of problems, varying in number of jobs (n) and number of machines (m) was used to perform the computational experiments. Results show that the algorithm can deal with most of the problems with less than 20 jobs in less than one hour. Thus, the method proposed in this work can solve the scheduling of many applications in manufacturing environments with limited buffers and separated setup times.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"113 2 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87751374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2021.10.002
A. Prakash, U. Balakrishna, Jayanth Kumar Thenepalle
An assignment problem (AP) usually deals with how a set of persons/tasks can be assigned to a set of tasks/persons on a one-to-one basis in an optimal manner. It has been observed that balancing among the persons and jobs in several real-world situations is very hard, thus such scenarios can be seen as unbalanced assignment models (UAP) being a lack of workforce. The solution techniques presented in the literature for solving UAP’s depend on the assumption to allocate some of the tasks to fictitious persons; those tasks assigned to dummy persons are ignored at the end. However, some situations in which it is inevitable to assign more tasks to a single person. This paper addresses a practical variant of UAP called k-cardinality unbalanced assignment problem (k-UAP), in which only of persons are asked to perform jobs and all the persons should perform at least one and at most jobs. The k-UAP aims to determine the optimal assignment between persons and jobs. To tackle this problem optimally, an enumerative Lexi-search algorithm (LSA) is proposed. A comparative study is carried out to measure the efficiency of the proposed algorithm. The computational results indicate that the suggested LSA is having the great capability of solving the smaller and moderate instances optimally.
{"title":"An exact algorithm for constrained k-cardinality unbalanced assignment problem","authors":"A. Prakash, U. Balakrishna, Jayanth Kumar Thenepalle","doi":"10.5267/j.ijiec.2021.10.002","DOIUrl":"https://doi.org/10.5267/j.ijiec.2021.10.002","url":null,"abstract":"An assignment problem (AP) usually deals with how a set of persons/tasks can be assigned to a set of tasks/persons on a one-to-one basis in an optimal manner. It has been observed that balancing among the persons and jobs in several real-world situations is very hard, thus such scenarios can be seen as unbalanced assignment models (UAP) being a lack of workforce. The solution techniques presented in the literature for solving UAP’s depend on the assumption to allocate some of the tasks to fictitious persons; those tasks assigned to dummy persons are ignored at the end. However, some situations in which it is inevitable to assign more tasks to a single person. This paper addresses a practical variant of UAP called k-cardinality unbalanced assignment problem (k-UAP), in which only of persons are asked to perform jobs and all the persons should perform at least one and at most jobs. The k-UAP aims to determine the optimal assignment between persons and jobs. To tackle this problem optimally, an enumerative Lexi-search algorithm (LSA) is proposed. A comparative study is carried out to measure the efficiency of the proposed algorithm. The computational results indicate that the suggested LSA is having the great capability of solving the smaller and moderate instances optimally.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"33 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83736628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2022.3.001
P. Esmaeili, A. Makui, S. Seyedhosseini, R. Ghousi
Social dilemmas describe conflict situations between immediate self-interest and longer-term collective interests. In these situations, it is better that all players work together to attain a common goal, but individuals may threaten the best payoff of the group by free-riding. Human behavior in a pandemic is one example of a social dilemma but wait-and-see games and relying on herd immunity to get a free ride generates a threat of continuing the pandemic. This study aims to use probabilistic incentives given by a third party as a mechanism to inhibit free-riding behavior by promoting cooperation in the volunteer dilemma game. For more realistic human behavior simulation, we use an agent-based model of network topology. When the parameters of the problem change gradually, an abrupt jump in the cooperation rate may happen and lead to a significant shift in the outcome. Catastrophe theory is a valuable approach to survey these nonlinear changes. This study tries to give some managerial insights to the decision-makers to find the minimum level of necessary effort in which the cooperation dominates the defection.
{"title":"The effect of probabilistic incentives to promote cooperation during the pandemics using simulation of multi-agent evolutionary game","authors":"P. Esmaeili, A. Makui, S. Seyedhosseini, R. Ghousi","doi":"10.5267/j.ijiec.2022.3.001","DOIUrl":"https://doi.org/10.5267/j.ijiec.2022.3.001","url":null,"abstract":"Social dilemmas describe conflict situations between immediate self-interest and longer-term collective interests. In these situations, it is better that all players work together to attain a common goal, but individuals may threaten the best payoff of the group by free-riding. Human behavior in a pandemic is one example of a social dilemma but wait-and-see games and relying on herd immunity to get a free ride generates a threat of continuing the pandemic. This study aims to use probabilistic incentives given by a third party as a mechanism to inhibit free-riding behavior by promoting cooperation in the volunteer dilemma game. For more realistic human behavior simulation, we use an agent-based model of network topology. When the parameters of the problem change gradually, an abrupt jump in the cooperation rate may happen and lead to a significant shift in the outcome. Catastrophe theory is a valuable approach to survey these nonlinear changes. This study tries to give some managerial insights to the decision-makers to find the minimum level of necessary effort in which the cooperation dominates the defection.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"90 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78713468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2022.8.001
Zi Wang, Daofang Chang, Xingyu Man
In this paper, we present a two-dimensional irregular bin packing problem (2DIBPP) that takes into account the slit distance and allows the pieces to rotate freely. The target is to arrange a specified collection of pieces with irregular shapes into a minimal number of bins. Firstly, we develop a mathematical model for the 2DIBPP that considers slit distance and free rotation of the pieces, and an equidistant edge expansion approach is then proposed to handle the slit distance. Secondly, a two-stage method is implemented to get a finite collection of promising rotation angles, effectively decreasing the search neighbourhood. Thirdly, we decompose the 2DIBPP into two sub-problems: piece assignment and packing. The Partial Bin Packing (PBP) strategy is employed in the allocation stage, and we adopt an overlap minimization method to pack the pieces into an individual bin. Finally, we use a local search (LS) algorithm to advance the quality of the solutions by adjusting the piece assignment across bins. Experimental evidence exhibits that our approach is competitive in most instances of the literature, with four better results in five benchmark instances.
{"title":"Optimization of two-dimensional irregular bin packing problem considering slit distance and free rotation of pieces","authors":"Zi Wang, Daofang Chang, Xingyu Man","doi":"10.5267/j.ijiec.2022.8.001","DOIUrl":"https://doi.org/10.5267/j.ijiec.2022.8.001","url":null,"abstract":"In this paper, we present a two-dimensional irregular bin packing problem (2DIBPP) that takes into account the slit distance and allows the pieces to rotate freely. The target is to arrange a specified collection of pieces with irregular shapes into a minimal number of bins. Firstly, we develop a mathematical model for the 2DIBPP that considers slit distance and free rotation of the pieces, and an equidistant edge expansion approach is then proposed to handle the slit distance. Secondly, a two-stage method is implemented to get a finite collection of promising rotation angles, effectively decreasing the search neighbourhood. Thirdly, we decompose the 2DIBPP into two sub-problems: piece assignment and packing. The Partial Bin Packing (PBP) strategy is employed in the allocation stage, and we adopt an overlap minimization method to pack the pieces into an individual bin. Finally, we use a local search (LS) algorithm to advance the quality of the solutions by adjusting the piece assignment across bins. Experimental evidence exhibits that our approach is competitive in most instances of the literature, with four better results in five benchmark instances.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"152 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85375068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.ijiec.2022.5.003
Ö. Yılmaz, A. A. Altun, Murat Köklü
Artificial neural networks (ANNs) are one of the artificial intelligence techniques used in real-world problems and applications encountered in almost all industries such as education, health, chemistry, food, informatics, logistics, transportation. ANN is widely used in many techniques such as optimization, modelling, classification and forecasting, and many empirical studies have been carried out in areas such as planning, inventory management, maintenance, quality control, econometrics, supply chain management and logistics related to ANN. The most important and just as hard stage of ANNs is the learning process. This process is about finding optimal values in the search space for different datasets. In this process, the values generated by training algorithms are used as network parameters and are directly effective in the success of the neural network (NN). In classical training techniques, problems such as local optimum and slow convergence are encountered. Meta-heuristic algorithms for the training of ANNs in the face of this negative situation have been used in many studies as an alternative. In this study, a new hybrid algorithm namely MVOSANN is suggested for the training of ANNs, using Simulated annealing (SA) and Multi-verse optimizer (MVO) algorithms. The suggested MVOSANN algorithm has been experimented on 12 prevalently classification datasets. The productivity of MVOSANN has been compared with 12 well-recognized and current meta-heuristic algorithms. Experimental results show that MVOSANN produces very successful and competitive results.
{"title":"Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA","authors":"Ö. Yılmaz, A. A. Altun, Murat Köklü","doi":"10.5267/j.ijiec.2022.5.003","DOIUrl":"https://doi.org/10.5267/j.ijiec.2022.5.003","url":null,"abstract":"Artificial neural networks (ANNs) are one of the artificial intelligence techniques used in real-world problems and applications encountered in almost all industries such as education, health, chemistry, food, informatics, logistics, transportation. ANN is widely used in many techniques such as optimization, modelling, classification and forecasting, and many empirical studies have been carried out in areas such as planning, inventory management, maintenance, quality control, econometrics, supply chain management and logistics related to ANN. The most important and just as hard stage of ANNs is the learning process. This process is about finding optimal values in the search space for different datasets. In this process, the values generated by training algorithms are used as network parameters and are directly effective in the success of the neural network (NN). In classical training techniques, problems such as local optimum and slow convergence are encountered. Meta-heuristic algorithms for the training of ANNs in the face of this negative situation have been used in many studies as an alternative. In this study, a new hybrid algorithm namely MVOSANN is suggested for the training of ANNs, using Simulated annealing (SA) and Multi-verse optimizer (MVO) algorithms. The suggested MVOSANN algorithm has been experimented on 12 prevalently classification datasets. The productivity of MVOSANN has been compared with 12 well-recognized and current meta-heuristic algorithms. Experimental results show that MVOSANN produces very successful and competitive results.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87903969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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