Pub Date : 2021-04-01DOI: 10.1080/03155986.2021.1906057
Ludwig Dumetz, Jonathan Gaudreault, H. B. El-Haouzi, André Thomas, N. Lehoux, Philippe Marier
Abstract Various optimization tools have been used in industry to facilitate production planning at different levels of aggregation. Choosing the interoperability mechanisms of these systems, such as the planning frequencies, the information passed between them and the interpretation that other systems must make of them, has always been a challenge. This work focusses on production planning at the tactical and operational levels in North American sawmills, a commodity industry characterized by volatile prices and a divergent production process with coproduction. In this context, tactical planning produces aggregated plans, and information from these plans can be used as targets and/or constraints at the operational level (e.g., quantities to be produced/kept in stock per product and per period, sales targets, etc.). A simulation of this production system was therefore developed, encompassing the planning process and the market dynamic, to compare and evaluate the impact of different coordination approaches on business economic performance. Results showed that the type of information which should be shared from the tactical level to the operational level varies according to several factors, including the company’s order acceptance policy, price seasonality, and the presence or absence of overcapacity on the market. Highlights A simulation approach is used to evaluate coordination between tactical and operational planning The context of North American sawmills is the one investigated The production and the planning process as well as the market behavior is considered Results show that the information shared between the two levels impact the income The order acceptance policy chosen also has an influence on the revenue generated
{"title":"Tactical-operational coordination of a divergent production system with coproduction: the sawmilling challenge","authors":"Ludwig Dumetz, Jonathan Gaudreault, H. B. El-Haouzi, André Thomas, N. Lehoux, Philippe Marier","doi":"10.1080/03155986.2021.1906057","DOIUrl":"https://doi.org/10.1080/03155986.2021.1906057","url":null,"abstract":"Abstract Various optimization tools have been used in industry to facilitate production planning at different levels of aggregation. Choosing the interoperability mechanisms of these systems, such as the planning frequencies, the information passed between them and the interpretation that other systems must make of them, has always been a challenge. This work focusses on production planning at the tactical and operational levels in North American sawmills, a commodity industry characterized by volatile prices and a divergent production process with coproduction. In this context, tactical planning produces aggregated plans, and information from these plans can be used as targets and/or constraints at the operational level (e.g., quantities to be produced/kept in stock per product and per period, sales targets, etc.). A simulation of this production system was therefore developed, encompassing the planning process and the market dynamic, to compare and evaluate the impact of different coordination approaches on business economic performance. Results showed that the type of information which should be shared from the tactical level to the operational level varies according to several factors, including the company’s order acceptance policy, price seasonality, and the presence or absence of overcapacity on the market. Highlights A simulation approach is used to evaluate coordination between tactical and operational planning The context of North American sawmills is the one investigated The production and the planning process as well as the market behavior is considered Results show that the information shared between the two levels impact the income The order acceptance policy chosen also has an influence on the revenue generated","PeriodicalId":13645,"journal":{"name":"Infor","volume":"1 1","pages":"377 - 399"},"PeriodicalIF":1.3,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89985093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-01DOI: 10.1080/03155986.2021.1881360
Min Yang, Qingxian An, D. Hu, Liang Liang
Abstract With the rapid development of economy, China faces a considerable challenge from environmental problems especially with the waste of gas pollution emitted by China’s industry. To assess the environmental performance of China’s industry, this paper proposes a new generalized equilibrium efficiency frontier data envelopment analysis (GEEFDEA) approach that considers waste gases as undesirable outputs. It is assumed that the total amount of waste gases that the industry is permitted to emit is limited (or fixed), that is, increasing air pollution of one area should reduce that of other areas for offset. Compared with the prior evaluation approaches, the proposed approach is the only one that satisfies the following conditions simultaneously. 1) The approach considers the property of the “fixed sum” of undesirable outputs. 2) The approach avoids the case in prior approaches (e.g., fixed sum output and zero sum gains DEA approaches) that an infeasible solution may occur when accessing decision making unites (DMUs) with fixed-sum undesirable outputs. 3) The proposed approach inherits all advantages of the GEEFDEA approach such as achieving an equilibrium state by only one step and obtaining a complete ranking order. Finally, the proposed approach is applied to evaluate the performance in China’s industry from 2008 to 2013. Results show China’s industry has significant regional characteristics.
{"title":"Performance evaluation of China’s industry: a generalized equilibrium data envelopment analysis approach with fixed-sum undesirable output","authors":"Min Yang, Qingxian An, D. Hu, Liang Liang","doi":"10.1080/03155986.2021.1881360","DOIUrl":"https://doi.org/10.1080/03155986.2021.1881360","url":null,"abstract":"Abstract With the rapid development of economy, China faces a considerable challenge from environmental problems especially with the waste of gas pollution emitted by China’s industry. To assess the environmental performance of China’s industry, this paper proposes a new generalized equilibrium efficiency frontier data envelopment analysis (GEEFDEA) approach that considers waste gases as undesirable outputs. It is assumed that the total amount of waste gases that the industry is permitted to emit is limited (or fixed), that is, increasing air pollution of one area should reduce that of other areas for offset. Compared with the prior evaluation approaches, the proposed approach is the only one that satisfies the following conditions simultaneously. 1) The approach considers the property of the “fixed sum” of undesirable outputs. 2) The approach avoids the case in prior approaches (e.g., fixed sum output and zero sum gains DEA approaches) that an infeasible solution may occur when accessing decision making unites (DMUs) with fixed-sum undesirable outputs. 3) The proposed approach inherits all advantages of the GEEFDEA approach such as achieving an equilibrium state by only one step and obtaining a complete ranking order. Finally, the proposed approach is applied to evaluate the performance in China’s industry from 2008 to 2013. Results show China’s industry has significant regional characteristics.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"1 1","pages":"290 - 308"},"PeriodicalIF":1.3,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72819651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-15DOI: 10.1080/03155986.2021.1881359
V. Kayvanfar, M. R. A. Jokar, Majid Rafiee, S. Sheikh, Reza Iranzad
Abstract Hospitals are dealing with the daunting task of scheduling patients in operating rooms under a limited budget, time, and staff. This article finds the optimal schedule of surgeries by minimizing operating rooms’ idle times while maximizing the number of scheduled surgeries during the most effective and desirable time windows. Surgeries during ideal time windows are encouraged by assigning bonus weights in the objective function. Stated and implied benefits of this strategy include mitigating financial loss, complications, and death rate due to a reduction in surgery delays. We introduce a binary programming model for scheduling operating rooms and a mixed integer binary program for planning and scheduling both operating and recovery rooms for elected patients under deterministic conditions. We apply an open scheduling strategy for assigning operating rooms to surgeons and a Lagrangian relaxation method for finding promising solutions. We move hard constraints to the objective to reduce the complexity of the proposed NP-hard model. We incorporate a sub-gradient method that selects the best penalty vector. Finally, we benchmark the results to evaluate the efficiency of the proposed solutions.
{"title":"A new model for operating room scheduling with elective patient strategy","authors":"V. Kayvanfar, M. R. A. Jokar, Majid Rafiee, S. Sheikh, Reza Iranzad","doi":"10.1080/03155986.2021.1881359","DOIUrl":"https://doi.org/10.1080/03155986.2021.1881359","url":null,"abstract":"Abstract Hospitals are dealing with the daunting task of scheduling patients in operating rooms under a limited budget, time, and staff. This article finds the optimal schedule of surgeries by minimizing operating rooms’ idle times while maximizing the number of scheduled surgeries during the most effective and desirable time windows. Surgeries during ideal time windows are encouraged by assigning bonus weights in the objective function. Stated and implied benefits of this strategy include mitigating financial loss, complications, and death rate due to a reduction in surgery delays. We introduce a binary programming model for scheduling operating rooms and a mixed integer binary program for planning and scheduling both operating and recovery rooms for elected patients under deterministic conditions. We apply an open scheduling strategy for assigning operating rooms to surgeons and a Lagrangian relaxation method for finding promising solutions. We move hard constraints to the objective to reduce the complexity of the proposed NP-hard model. We incorporate a sub-gradient method that selects the best penalty vector. Finally, we benchmark the results to evaluate the efficiency of the proposed solutions.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"49 1","pages":"309 - 332"},"PeriodicalIF":1.3,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87204900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-21DOI: 10.1080/03155986.2020.1857629
M. Stålhane, Kamilla Hamre Bolstad, Manu Joshi, L. M. Hvattum
Abstract This paper studies the strategic problem of finding a cost optimal fleet of vessels to support maintenance operations at offshore wind farms. A dual-level stochastic model is formulated, taking into account both long-term strategic uncertainty and short-term operational uncertainty in a single optimization model. The model supports wind farm owners in making strategic decisions regarding the number, placement, charter length, and types of vessels to charter, to meet maintenance demands throughout the lifetime of a wind farm. To evaluate the quality of strategic fleet size and mix decisions, the model also considers the operational decisions of how to utilize the fleet to support maintenance operations. The model accounts for strategic uncertainties that have not been considered in previously developed optimization models for offshore wind, such as uncertainty related to long-term trends in electricity prices and subsidy levels, the stepwise development of wind farms, and technology development in the vessel industry. To solve the proposed stochastic programming model we have developed an ad hoc integer L-shaped method, with customized optimality cuts. The computational experiments show that the proposed method outperforms solving the deterministic equivalent using a commercial MIP solver.
{"title":"A dual-level stochastic fleet size and mix problem for offshore wind farm maintenance operations","authors":"M. Stålhane, Kamilla Hamre Bolstad, Manu Joshi, L. M. Hvattum","doi":"10.1080/03155986.2020.1857629","DOIUrl":"https://doi.org/10.1080/03155986.2020.1857629","url":null,"abstract":"Abstract This paper studies the strategic problem of finding a cost optimal fleet of vessels to support maintenance operations at offshore wind farms. A dual-level stochastic model is formulated, taking into account both long-term strategic uncertainty and short-term operational uncertainty in a single optimization model. The model supports wind farm owners in making strategic decisions regarding the number, placement, charter length, and types of vessels to charter, to meet maintenance demands throughout the lifetime of a wind farm. To evaluate the quality of strategic fleet size and mix decisions, the model also considers the operational decisions of how to utilize the fleet to support maintenance operations. The model accounts for strategic uncertainties that have not been considered in previously developed optimization models for offshore wind, such as uncertainty related to long-term trends in electricity prices and subsidy levels, the stepwise development of wind farms, and technology development in the vessel industry. To solve the proposed stochastic programming model we have developed an ad hoc integer L-shaped method, with customized optimality cuts. The computational experiments show that the proposed method outperforms solving the deterministic equivalent using a commercial MIP solver.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"1 1","pages":"257 - 289"},"PeriodicalIF":1.3,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88570529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-12DOI: 10.1080/03155986.2019.1627780
Khosro Soleimani-Chamkhorami, F. Lotfi, G. Jahanshahloo, M. Rostamy-Malkhalifeh
Abstract Data envelopment analysis (DEA) models are applied in commercial firms to evaluate their technical, cost and revenue efficiencies. The new models introduced here are based on inverse DEA for preserving cost/revenue efficiency. The commercial institution management seeks to minimize the input costs for a given level of outputs or maximize the revenue of outputs for a specified level of inputs. It is important to preserve cost/revenue efficiency when data are changed. In this method, we determine the cost efficiency score of decision-making units (DMUs). Then, for each one of them, the outputs are increased and the values of the required increment of inputs are obtained, while the cost efficiency of the unit under evaluation remains unchanged. For practical application, one of these introduced models is applied to a real data set of European and American banks for cost efficiency preservation.
{"title":"Preserving cost and revenue efficiency through inverse data envelopment analysis models","authors":"Khosro Soleimani-Chamkhorami, F. Lotfi, G. Jahanshahloo, M. Rostamy-Malkhalifeh","doi":"10.1080/03155986.2019.1627780","DOIUrl":"https://doi.org/10.1080/03155986.2019.1627780","url":null,"abstract":"Abstract Data envelopment analysis (DEA) models are applied in commercial firms to evaluate their technical, cost and revenue efficiencies. The new models introduced here are based on inverse DEA for preserving cost/revenue efficiency. The commercial institution management seeks to minimize the input costs for a given level of outputs or maximize the revenue of outputs for a specified level of inputs. It is important to preserve cost/revenue efficiency when data are changed. In this method, we determine the cost efficiency score of decision-making units (DMUs). Then, for each one of them, the outputs are increased and the values of the required increment of inputs are obtained, while the cost efficiency of the unit under evaluation remains unchanged. For practical application, one of these introduced models is applied to a real data set of European and American banks for cost efficiency preservation.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"43 2 1","pages":"561 - 578"},"PeriodicalIF":1.3,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80094719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1080/03155986.2021.2015825
Juyoung Wang, Mucahit Cevik, Merve Bodur
Abstract Multistage stochastic programming provides a modeling framework for sequential decision-making problems that involve uncertainty. One typically overlooked aspect of this methodology is how uncertainty is incorporated into modeling. Traditionally, statistical forecasting techniques with simple forms, e.g. (first-order) autoregressive time-series models, are used to extract scenarios to be added to optimization models to represent the uncertain future. However, often times, the performance of these forecasting models are not thoroughly assessed. Motivated by the advances in probabilistic forecasting, we incorporate a deep learning-based time-series forecasting method into multistage stochastic programming framework, and compare it with the cases where a traditional forecasting method is employed to model the uncertainty. We assess the impact of more accurate forecasts on the quality of two commonly used look-ahead policies, a deterministic one and a two-stage one, in a rolling-horizon framework on a practical problem. Our results illustrate that more accurate forecasts contribute substantially to the model performance, and enable obtaining high-quality solutions even from computationally cheap heuristics. They also show that the probabilistic forecasting capabilities of deep learning-based methods can be especially beneficial when used as a (conditional) sampling tool for scenario-based models, and to predict the worst-case scenario for risk-averse models.
{"title":"On the impact of deep learning-based time-series forecasts on multistage stochastic programming policies","authors":"Juyoung Wang, Mucahit Cevik, Merve Bodur","doi":"10.1080/03155986.2021.2015825","DOIUrl":"https://doi.org/10.1080/03155986.2021.2015825","url":null,"abstract":"Abstract Multistage stochastic programming provides a modeling framework for sequential decision-making problems that involve uncertainty. One typically overlooked aspect of this methodology is how uncertainty is incorporated into modeling. Traditionally, statistical forecasting techniques with simple forms, e.g. (first-order) autoregressive time-series models, are used to extract scenarios to be added to optimization models to represent the uncertain future. However, often times, the performance of these forecasting models are not thoroughly assessed. Motivated by the advances in probabilistic forecasting, we incorporate a deep learning-based time-series forecasting method into multistage stochastic programming framework, and compare it with the cases where a traditional forecasting method is employed to model the uncertainty. We assess the impact of more accurate forecasts on the quality of two commonly used look-ahead policies, a deterministic one and a two-stage one, in a rolling-horizon framework on a practical problem. Our results illustrate that more accurate forecasts contribute substantially to the model performance, and enable obtaining high-quality solutions even from computationally cheap heuristics. They also show that the probabilistic forecasting capabilities of deep learning-based methods can be especially beneficial when used as a (conditional) sampling tool for scenario-based models, and to predict the worst-case scenario for risk-averse models.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"79 1","pages":"133 - 164"},"PeriodicalIF":1.3,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80320917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-28DOI: 10.1080/03155986.2020.1811613
Jinrong Liu, Qi Xu, Guoqing Zhang
Abstract Retailers are increasingly offering customers a seamless shopping experience through all available shopping channels. However, when should an online retailer open an offline showroom or a physical store to implement an omni-channel strategy? In this study, we establish profit models in four scenarios and obtain the conditions under which an online retailer can be profitable in different channels. The results show that, after opening a showroom or store, demand in the online channel decreases and total demand increases. Furthermore, the choice of opening a showroom or a physical store depends on the online return rate and the unit inventory holding costs of the online and offline channels. In addition, if the perceived experience service is not too high, the total profit after opening a showroom or store increases with the experience service and the shopping cost of the online channel. Furthermore, the growth rate with a showroom is higher than that with a store.
{"title":"Omni-channel strategy for an online retailer: considering experience service and shopping costs","authors":"Jinrong Liu, Qi Xu, Guoqing Zhang","doi":"10.1080/03155986.2020.1811613","DOIUrl":"https://doi.org/10.1080/03155986.2020.1811613","url":null,"abstract":"Abstract Retailers are increasingly offering customers a seamless shopping experience through all available shopping channels. However, when should an online retailer open an offline showroom or a physical store to implement an omni-channel strategy? In this study, we establish profit models in four scenarios and obtain the conditions under which an online retailer can be profitable in different channels. The results show that, after opening a showroom or store, demand in the online channel decreases and total demand increases. Furthermore, the choice of opening a showroom or a physical store depends on the online return rate and the unit inventory holding costs of the online and offline channels. In addition, if the perceived experience service is not too high, the total profit after opening a showroom or store increases with the experience service and the shopping cost of the online channel. Furthermore, the growth rate with a showroom is higher than that with a store.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"30 1","pages":"232 - 256"},"PeriodicalIF":1.3,"publicationDate":"2020-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79279516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-25DOI: 10.1080/03155986.2020.1807243
D. Dutta
Abstract This paper concerns the total weighted tardiness on single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only small size problems can be solved efficiently due to its NP-hardness. Therefore, 4 (four) heuristic methods are developed to solve real size applications including the size of 1000 jobs. The applied heuristics are: genetic, genetic with solution combination, kangaroo and genetic-kangaroo hybrid algorithms. Solutions denote that developed heuristics are efficient for the proposed model. Research of this topic shows that, no study exists on the total weighted tardiness problem with learning effect and unequal release dates together asserted in this paper.
{"title":"RETRACTED ARTICLE: Approach towards problem solving on single machine scheduling with unequal release dates and learning effect","authors":"D. Dutta","doi":"10.1080/03155986.2020.1807243","DOIUrl":"https://doi.org/10.1080/03155986.2020.1807243","url":null,"abstract":"Abstract This paper concerns the total weighted tardiness on single machine scheduling problem with the concept of learning effect and unequal release dates. A mathematical model is proposed with binary variables and only small size problems can be solved efficiently due to its NP-hardness. Therefore, 4 (four) heuristic methods are developed to solve real size applications including the size of 1000 jobs. The applied heuristics are: genetic, genetic with solution combination, kangaroo and genetic-kangaroo hybrid algorithms. Solutions denote that developed heuristics are efficient for the proposed model. Research of this topic shows that, no study exists on the total weighted tardiness problem with learning effect and unequal release dates together asserted in this paper.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"93 1","pages":"109 - 132"},"PeriodicalIF":1.3,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73903460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-13DOI: 10.1080/03155986.2020.1803720
Reza Nemati-Lafmejani, Hamed Davari-Ardakani
Abstract In real-world environments, selecting the right contractor is an important issue which considerably influences completion time, total cost and quality of performing the project. This paper deals with the multi-mode resource constrained project scheduling problem (MRCPSP) and contractor selection (CS) in an integrated manner. In fact, each activity is assigned to a contractor, an execution mode is selected for each activity, and the start/finish times of activities are determined. This paper presents a bi-objective optimization model to deal with MRCPSP-CS, aiming to minimize the total cost and completion time of the project, simultaneously. Then, four multi-objective decision making (MODM) techniques are used to solve the proposed model. Since none of MODM techniques dominates other ones, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to assess the performance of MODM techniques, confirming that MCGP-U outranks other ones. Finally, the augmented -Constraint method is used to solve some test problems, and perform sensitivity analysis on the number of contractors. Sensitivity analyses show that by increasing the number of available contractors, the Pareto front is significantly improved, and the Number of Pareto-optimal Solutions (NPS) increases. This helps decision maker(s) make appropriate decisions in a more flexible manner.
在现实环境中，选择合适的承包商是一个重要的问题，它对工程的完工时间、总成本和执行质量有很大的影响。本文将多模式资源约束下的项目调度问题(MRCPSP)与承包商选择问题(CS)进行了综合研究。实际上，将每个活动分配给一个承包商，为每个活动选择执行模式，并确定活动的开始/结束时间。本文提出了一个处理MRCPSP-CS的双目标优化模型，旨在同时使项目的总成本和完工时间最小化。然后，采用四种多目标决策(MODM)技术对模型进行求解。由于没有一种MODM技术优于其他技术，因此使用TOPSIS (technical for Order of Preference by Similarity to Ideal Solution)来评估MODM技术的性能，证实MCGP-U优于其他技术。最后，利用增广约束方法解决了一些测试问题，并对承包商数量进行了敏感性分析。敏感性分析表明，随着承包者数量的增加，Pareto前沿得到显著改善，Pareto最优解(NPS)数量增加。这有助于决策者以更灵活的方式做出适当的决策。
{"title":"Multi-mode resource constrained project scheduling problem along with contractor selection","authors":"Reza Nemati-Lafmejani, Hamed Davari-Ardakani","doi":"10.1080/03155986.2020.1803720","DOIUrl":"https://doi.org/10.1080/03155986.2020.1803720","url":null,"abstract":"Abstract In real-world environments, selecting the right contractor is an important issue which considerably influences completion time, total cost and quality of performing the project. This paper deals with the multi-mode resource constrained project scheduling problem (MRCPSP) and contractor selection (CS) in an integrated manner. In fact, each activity is assigned to a contractor, an execution mode is selected for each activity, and the start/finish times of activities are determined. This paper presents a bi-objective optimization model to deal with MRCPSP-CS, aiming to minimize the total cost and completion time of the project, simultaneously. Then, four multi-objective decision making (MODM) techniques are used to solve the proposed model. Since none of MODM techniques dominates other ones, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to assess the performance of MODM techniques, confirming that MCGP-U outranks other ones. Finally, the augmented -Constraint method is used to solve some test problems, and perform sensitivity analysis on the number of contractors. Sensitivity analyses show that by increasing the number of available contractors, the Pareto front is significantly improved, and the Number of Pareto-optimal Solutions (NPS) increases. This helps decision maker(s) make appropriate decisions in a more flexible manner.","PeriodicalId":13645,"journal":{"name":"Infor","volume":"150 1","pages":"212 - 231"},"PeriodicalIF":1.3,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86140787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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