Pub Date : 2020-07-08DOI: 10.1504/ejie.2020.10029435
M. Keerthana, N. Saranya, B. Sivakumar
This article analyses a stochastic inventory system with a service facility. This is an extended work of Yadavalli et al. (2008) by including the positive lead time. The customer arrives according to a renewal process and demanded item is delivered to the customer after performing an exponentially distributed service time. An (s, S) type ordering policy is adopted with exponentially distributed lead times. The stationary probability distribution for number of customers in the system and inventory level at arrival epoch and at arbitrary time point are derived. Some system performance measures in the steady state are computed and using these system performance measures the long-run expected cost rate is calculated. Since the long run expected cost rate is highly complex, the mixed integer distributed ant colony optimisation is used to obtain the optimal values. A sensitivity analysis to illustrate the effects of parameters and cost on the optimal values is also carried out in this work. [Received: 13 December 2018; Accepted: 10 October 2019]
{"title":"A stochastic queueing - inventory system with renewal demands and positive lead time","authors":"M. Keerthana, N. Saranya, B. Sivakumar","doi":"10.1504/ejie.2020.10029435","DOIUrl":"https://doi.org/10.1504/ejie.2020.10029435","url":null,"abstract":"This article analyses a stochastic inventory system with a service facility. This is an extended work of Yadavalli et al. (2008) by including the positive lead time. The customer arrives according to a renewal process and demanded item is delivered to the customer after performing an exponentially distributed service time. An (s, S) type ordering policy is adopted with exponentially distributed lead times. The stationary probability distribution for number of customers in the system and inventory level at arrival epoch and at arbitrary time point are derived. Some system performance measures in the steady state are computed and using these system performance measures the long-run expected cost rate is calculated. Since the long run expected cost rate is highly complex, the mixed integer distributed ant colony optimisation is used to obtain the optimal values. A sensitivity analysis to illustrate the effects of parameters and cost on the optimal values is also carried out in this work. [Received: 13 December 2018; Accepted: 10 October 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131314404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1504/ejie.2020.10029681
A. Abdi, S. Taghipour
Availability analysis of a fleet of assets requires modelling uncertainty sources that affect equipment reliability and maintainability. These uncertainties include complex, managerial causalities and risks which have been seldom examined in the asset management literature. The objective of this study is to measure the reliability, maintainability and availability of a fleet, considering the effect of common causal factors and extremely rare or previously unobserved events. We develop a fully probabilistic availability analysis model using hybrid Bayesian networks (BNs), to capture managerial, organisational and environmental causal factors that influence failure or repair rate, as well as those that affect both failure and repair rates simultaneously. The proposed methodology has been found more accurate in forecasting failure rate, repair rate, and average availability level of a fleet of assets, providing asset managers with an inference mechanism to not only measure the performance of the assets based on common causal factors, but also learn the actual level of such factors and thereby identify improvement areas. We have demonstrated the application of the model using a fleet of excavators located in Toronto, Ontario. The prediction accuracy of the proposed model is evaluated by use of a measure of prediction error. [Received: 19 March 2019; Accepted: 3 September 2019]
{"title":"A Bayesian Networks Approach to Fleet Availability Analysis Considering Managerial and Complex Causal Factors","authors":"A. Abdi, S. Taghipour","doi":"10.1504/ejie.2020.10029681","DOIUrl":"https://doi.org/10.1504/ejie.2020.10029681","url":null,"abstract":"Availability analysis of a fleet of assets requires modelling uncertainty sources that affect equipment reliability and maintainability. These uncertainties include complex, managerial causalities and risks which have been seldom examined in the asset management literature. The objective of this study is to measure the reliability, maintainability and availability of a fleet, considering the effect of common causal factors and extremely rare or previously unobserved events. We develop a fully probabilistic availability analysis model using hybrid Bayesian networks (BNs), to capture managerial, organisational and environmental causal factors that influence failure or repair rate, as well as those that affect both failure and repair rates simultaneously. The proposed methodology has been found more accurate in forecasting failure rate, repair rate, and average availability level of a fleet of assets, providing asset managers with an inference mechanism to not only measure the performance of the assets based on common causal factors, but also learn the actual level of such factors and thereby identify improvement areas. We have demonstrated the application of the model using a fleet of excavators located in Toronto, Ontario. The prediction accuracy of the proposed model is evaluated by use of a measure of prediction error. [Received: 19 March 2019; Accepted: 3 September 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117027333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1504/ejie.2020.10029777
Hadis Derikvand, Seyed Mohammad Hajimolana, A. Jabbarzadeh, S. Najafi
Providing blood units in a blood supply chain should be effective, appropriate and well-organised since it directly affects the health of individuals, and if not provided promptly, can even lead to the death of patients. This study presents a robust stochastic bi-objective programming model for an inventory-distribution problem in a blood supply chain, the first objective of which attempts to minimise the total number of shortages and wastages and the second objective maximises the connection between two different types of hospitals. The blood supply chain under investigation includes one blood centre, type-1 and type-2 hospitals and patients. Mathematical approximations are employed to remove the nonlinear terms, and a hybrid solution approach, combining the e-constraint and the Lagrangian relaxation method, is applied to solve the proposed bi-objective model. Finally, the model is implemented and analysed using the data inspired by a real case study in Iran to show its potential applicability [Received: 24 September 2018; Revised: 15 June 2019; Revised: 1 September 2019; Accepted: 1 September 2019]
{"title":"A robust stochastic bi-objective model for blood inventory-distribution management in a blood supply chain","authors":"Hadis Derikvand, Seyed Mohammad Hajimolana, A. Jabbarzadeh, S. Najafi","doi":"10.1504/ejie.2020.10029777","DOIUrl":"https://doi.org/10.1504/ejie.2020.10029777","url":null,"abstract":"Providing blood units in a blood supply chain should be effective, appropriate and well-organised since it directly affects the health of individuals, and if not provided promptly, can even lead to the death of patients. This study presents a robust stochastic bi-objective programming model for an inventory-distribution problem in a blood supply chain, the first objective of which attempts to minimise the total number of shortages and wastages and the second objective maximises the connection between two different types of hospitals. The blood supply chain under investigation includes one blood centre, type-1 and type-2 hospitals and patients. Mathematical approximations are employed to remove the nonlinear terms, and a hybrid solution approach, combining the e-constraint and the Lagrangian relaxation method, is applied to solve the proposed bi-objective model. Finally, the model is implemented and analysed using the data inspired by a real case study in Iran to show its potential applicability [Received: 24 September 2018; Revised: 15 June 2019; Revised: 1 September 2019; Accepted: 1 September 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132776840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1504/ejie.2020.10029776
M. Kabak, Eren Özceylan, Mehmet Erbaş, Cihan Çetinkaya
After the internal disturbance in Syria in 2011, many Syrian refugees migrated to Turkey progressively, and the Turkish Government provided humanitarian aid to people in Syria. These incidents caused a huge amount of density on current border gates. Also, increasing potential terrorist attacks and growing frontier infringements also create a need for a new border gate on Turkey's Syria frontier. Thus, a four-step hybrid solution approach is developed for this problem. This approach starts with determination of selection criteria; then, the spatial database of these criteria is created by using a geographical information system. In the third step, the DEMATEL technique is applied to assign importance levels to the criteria. Lastly, MULTIMOORA technique is used to rank the potential sites. The results indicate that, recommended potential sites are more suitable than current border gates. This paper can serve as a scientific-base while selecting the optimal site for border gates. [Received: 8 February 2019; Revised: 1 July 2019; Accepted: 7 August 2019]
{"title":"A multi-criteria spatial analysis using GIS to evaluate potential sites for a new border gate on Turkey's Syria frontier","authors":"M. Kabak, Eren Özceylan, Mehmet Erbaş, Cihan Çetinkaya","doi":"10.1504/ejie.2020.10029776","DOIUrl":"https://doi.org/10.1504/ejie.2020.10029776","url":null,"abstract":"After the internal disturbance in Syria in 2011, many Syrian refugees migrated to Turkey progressively, and the Turkish Government provided humanitarian aid to people in Syria. These incidents caused a huge amount of density on current border gates. Also, increasing potential terrorist attacks and growing frontier infringements also create a need for a new border gate on Turkey's Syria frontier. Thus, a four-step hybrid solution approach is developed for this problem. This approach starts with determination of selection criteria; then, the spatial database of these criteria is created by using a geographical information system. In the third step, the DEMATEL technique is applied to assign importance levels to the criteria. Lastly, MULTIMOORA technique is used to rank the potential sites. The results indicate that, recommended potential sites are more suitable than current border gates. This paper can serve as a scientific-base while selecting the optimal site for border gates. [Received: 8 February 2019; Revised: 1 July 2019; Accepted: 7 August 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126593640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1504/ejie.2020.10029682
Ivan Kristianto Singgih, Byung-In Kim
This research discusses an electric vehicle (EV) relocation problem, wherein multiple types of EVs are transported using heterogeneous trucks. The initial position, battery level of the EVs, and the required number of EVs and empty parking slots at each station are provided as inputs. Relocations are performed during the night, while no EVs are used. Before the end of the relocation planning horizon, each EV must be charged to a certain battery level. The charging process can only be performed when the EV is not being transported. The objectives are to minimise the total transportation costs, the total truck fixed costs, and the total unsatisfied empty parking slot requirements while ensuring that all EV demands are satisfied. A mixed-integer linear programming (MILP) model and construction and improvement heuristic approaches are proposed. The results of the computational experiments indicate that the proposed approaches perform well. [Received: 25 February 2019; Accepted: 26 August 2019]
{"title":"Multi-type electric vehicle relocation problem considering required battery-charging time","authors":"Ivan Kristianto Singgih, Byung-In Kim","doi":"10.1504/ejie.2020.10029682","DOIUrl":"https://doi.org/10.1504/ejie.2020.10029682","url":null,"abstract":"This research discusses an electric vehicle (EV) relocation problem, wherein multiple types of EVs are transported using heterogeneous trucks. The initial position, battery level of the EVs, and the required number of EVs and empty parking slots at each station are provided as inputs. Relocations are performed during the night, while no EVs are used. Before the end of the relocation planning horizon, each EV must be charged to a certain battery level. The charging process can only be performed when the EV is not being transported. The objectives are to minimise the total transportation costs, the total truck fixed costs, and the total unsatisfied empty parking slot requirements while ensuring that all EV demands are satisfied. A mixed-integer linear programming (MILP) model and construction and improvement heuristic approaches are proposed. The results of the computational experiments indicate that the proposed approaches perform well. [Received: 25 February 2019; Accepted: 26 August 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126623289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1504/ejie.2020.10029775
S. Pashapour, A. Azadeh, A. Bozorgi-Amiri, A. Keramati, S. Ghaderi
Economic resilience is defined as a tool capable of reducing the losses caused by disasters. It can be defined in two major concepts. Static economic resilience is the effective allocation of available resources and dynamic economic resilience refers to accelerating the recovery process through the repair and rebuilding of the capital stock. In this research, the performance of a petrochemical plant in the face of crisis is investigated. For this, a bi-objective mathematical model that considers cost and resilience capability as objective functions is developed to choose the best portfolio of static and dynamic plans. To solve the mathematical model, a weighted augmented e-constraint method and a multi-stage possibilistic stochastic programming (MSPSP) approach are employed. The numerical results showed that the proposed approach is effective in optimising the performance of a petrochemical plant in facing crisis situations and in choosing the best portfolio of economic resilience plans. [Received: 11 January 2019; Revised: 9 July 2019; Revised: 13 August 2019; Accepted: 13 August 2019]
{"title":"A resource allocation model to choose the best portfolio of economic resilience plans: a possibilistic stochastic programming model","authors":"S. Pashapour, A. Azadeh, A. Bozorgi-Amiri, A. Keramati, S. Ghaderi","doi":"10.1504/ejie.2020.10029775","DOIUrl":"https://doi.org/10.1504/ejie.2020.10029775","url":null,"abstract":"Economic resilience is defined as a tool capable of reducing the losses caused by disasters. It can be defined in two major concepts. Static economic resilience is the effective allocation of available resources and dynamic economic resilience refers to accelerating the recovery process through the repair and rebuilding of the capital stock. In this research, the performance of a petrochemical plant in the face of crisis is investigated. For this, a bi-objective mathematical model that considers cost and resilience capability as objective functions is developed to choose the best portfolio of static and dynamic plans. To solve the mathematical model, a weighted augmented e-constraint method and a multi-stage possibilistic stochastic programming (MSPSP) approach are employed. The numerical results showed that the proposed approach is effective in optimising the performance of a petrochemical plant in facing crisis situations and in choosing the best portfolio of economic resilience plans. [Received: 11 January 2019; Revised: 9 July 2019; Revised: 13 August 2019; Accepted: 13 August 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133599927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-27DOI: 10.1504/ejie.2020.10027218
N. Nahas
This paper presents an integrated optimisation model to simultaneously solve buffer allocation, equipment selection and line balancing problems in unreliable production line systems. The considered unreliable serial production line consists of m workstations and m − 1 intermediate buffers. The objective is to maximise the system throughput level. A decomposition method is used to estimate the production line throughput. The decision variables in the formulated optimal design problem are buffer levels, types of equipment and the sets of tasks assigned to the workstations. An efficient algorithm, based on the nonlinear threshold accepting algorithm (NLTA) is proposed to solve this problem. The efficiency of the proposed approach is compared to existing algorithms and first tested on a simple assembly line balancing type-2 problem (SALB-2). Here the objective is to minimise the cycle time with a fixed number of workstations. In the second numerical experiment, the integrated model is solved using the NLTA, and its performance is compared to that of the great deluge algorithm (GDA) through several numerical examples. [Received: 9 June 2018; Revised: 15 September 2018; Revised: 18 April 2019; Accepted: 2 August 2019]
{"title":"Buffer allocation, equipment selection and line balancing optimisation in unreliable production lines","authors":"N. Nahas","doi":"10.1504/ejie.2020.10027218","DOIUrl":"https://doi.org/10.1504/ejie.2020.10027218","url":null,"abstract":"This paper presents an integrated optimisation model to simultaneously solve buffer allocation, equipment selection and line balancing problems in unreliable production line systems. The considered unreliable serial production line consists of m workstations and m − 1 intermediate buffers. The objective is to maximise the system throughput level. A decomposition method is used to estimate the production line throughput. The decision variables in the formulated optimal design problem are buffer levels, types of equipment and the sets of tasks assigned to the workstations. An efficient algorithm, based on the nonlinear threshold accepting algorithm (NLTA) is proposed to solve this problem. The efficiency of the proposed approach is compared to existing algorithms and first tested on a simple assembly line balancing type-2 problem (SALB-2). Here the objective is to minimise the cycle time with a fixed number of workstations. In the second numerical experiment, the integrated model is solved using the NLTA, and its performance is compared to that of the great deluge algorithm (GDA) through several numerical examples. [Received: 9 June 2018; Revised: 15 September 2018; Revised: 18 April 2019; Accepted: 2 August 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126632671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-27DOI: 10.1504/ejie.2020.10027213
A. Lorenc, M. Szkoda, A. Szarata, I. Jacyna-Gołda
The paper presents an algorithm for calculating the approximate picking time. In order to perform a simulation, the PickupSimulo software was developed. For this purpose, the PHP language and MySQL relational databases were used. The PickupSimulo makes it possible to define the warehouse topology, solve the product allocation problem (PAP) based on pre-defined criteria and calculate an approximate time of the picking process for that product layout. The warehouse under analysis enables the stocking of over 22,000 pallets. Two variants were analysed. In the first one, the product weight does not matter, whilst in the other the picker must make sure the lightest products are placed at the top of the logistic unit. Such an approach reduces the risk of damaging light products by heavy ones. The research results show that the presented method enables a reduction of the total warehouse costs by 10–16%. [Received: 18 August 2017; Revised: 20 May 2018; Revised: 18 December 2018; Accepted: 20 May 2019].
{"title":"Evaluation of the effectiveness of methods and criteria for product classification in the warehouse","authors":"A. Lorenc, M. Szkoda, A. Szarata, I. Jacyna-Gołda","doi":"10.1504/ejie.2020.10027213","DOIUrl":"https://doi.org/10.1504/ejie.2020.10027213","url":null,"abstract":"The paper presents an algorithm for calculating the approximate picking time. In order to perform a simulation, the PickupSimulo software was developed. For this purpose, the PHP language and MySQL relational databases were used. The PickupSimulo makes it possible to define the warehouse topology, solve the product allocation problem (PAP) based on pre-defined criteria and calculate an approximate time of the picking process for that product layout. The warehouse under analysis enables the stocking of over 22,000 pallets. Two variants were analysed. In the first one, the product weight does not matter, whilst in the other the picker must make sure the lightest products are placed at the top of the logistic unit. Such an approach reduces the risk of damaging light products by heavy ones. The research results show that the presented method enables a reduction of the total warehouse costs by 10–16%. [Received: 18 August 2017; Revised: 20 May 2018; Revised: 18 December 2018; Accepted: 20 May 2019].","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127417331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-27DOI: 10.1504/ejie.2020.10027216
Hojat Nabovati, H. Haleh, B. Vahdani
In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem, considering the transferring of jobs between different machines using a limited number of multi-mode automated guided vehicles. Since the model is strictly NP-hard, and because objectives contradict each other, a meta-heuristic algorithm called 'multi-objective invasive weeds optimisation algorithm' with a new chromosome structure which guarantees the feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms namely 'non-dominated sorting genetic algorithm' and 'multi-objective particle swarm optimisation algorithm' are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimisation algorithm. A certain method was applied to select the algorithm with the best performance. The result of ranking the algorithms indicated that the developed multi-objective invasive weeds optimisation algorithm had the best performance in terms of solving the mentioned problems. [Received: 7 January 2017; Revised: 30 December 2017; Revised: 17 August 2018; Revised: 22 January 2019; Accepted: 26 July 2019]
{"title":"Multi-objective invasive weeds optimisation algorithm for solving simultaneous scheduling of machines and multi-mode automated guided vehicles","authors":"Hojat Nabovati, H. Haleh, B. Vahdani","doi":"10.1504/ejie.2020.10027216","DOIUrl":"https://doi.org/10.1504/ejie.2020.10027216","url":null,"abstract":"In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem, considering the transferring of jobs between different machines using a limited number of multi-mode automated guided vehicles. Since the model is strictly NP-hard, and because objectives contradict each other, a meta-heuristic algorithm called 'multi-objective invasive weeds optimisation algorithm' with a new chromosome structure which guarantees the feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms namely 'non-dominated sorting genetic algorithm' and 'multi-objective particle swarm optimisation algorithm' are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimisation algorithm. A certain method was applied to select the algorithm with the best performance. The result of ranking the algorithms indicated that the developed multi-objective invasive weeds optimisation algorithm had the best performance in terms of solving the mentioned problems. [Received: 7 January 2017; Revised: 30 December 2017; Revised: 17 August 2018; Revised: 22 January 2019; Accepted: 26 July 2019]","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-22DOI: 10.22541/au.158240027.70198817
Zhen Zhang, Songtao Zhang, M. Yue
This paper focuses on the cooperation mechanism between two retailers. To reduce the average processing cost, the supplier usually sets a threshold for trade credit to stimulate retailers' orders. Retailers can enjoy permissible delay in payments only when their order quantities are more than or equal to the given threshold. However, considering the diversity of retailers, the motivation effect of the threshold may be limited. To resolve the problem, the supplier can additionally provide retailers with a joint ordering policy under which two retailers can make delayed payments as long as their total order quantity meets the required threshold. Thus, the two retailers should decide whether to place a joint order or not and determine their respective order quantities simultaneously. We provide a mutually acceptable order-allocation scheme for retailers, and determine the optimal payment methods for them. In addition, an optimal threshold is identified for the supplier to maximize the total order quantity of retailers. Based on this, some managerial insights are obtained. A numerical experiment is performed to illustrate the validity of the model.
{"title":"Joint ordering policy for a conditional trade credit model with two retailers","authors":"Zhen Zhang, Songtao Zhang, M. Yue","doi":"10.22541/au.158240027.70198817","DOIUrl":"https://doi.org/10.22541/au.158240027.70198817","url":null,"abstract":"This paper focuses on the cooperation mechanism between two retailers. To reduce the average processing cost, the supplier usually sets a threshold for trade credit to stimulate retailers' orders. Retailers can enjoy permissible delay in payments only when their order quantities are more than or equal to the given threshold. However, considering the diversity of retailers, the motivation effect of the threshold may be limited. To resolve the problem, the supplier can additionally provide retailers with a joint ordering policy under which two retailers can make delayed payments as long as their total order quantity meets the required threshold. Thus, the two retailers should decide whether to place a joint order or not and determine their respective order quantities simultaneously. We provide a mutually acceptable order-allocation scheme for retailers, and determine the optimal payment methods for them. In addition, an optimal threshold is identified for the supplier to maximize the total order quantity of retailers. Based on this, some managerial insights are obtained. A numerical experiment is performed to illustrate the validity of the model.","PeriodicalId":314867,"journal":{"name":"European J. of Industrial Engineering","volume":"33 7-8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116470662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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