Pub Date : 2011-04-11DOI: 10.1109/CIPLS.2011.5953358
E. Ásgeirsson, Guðrún Sjöfn Axelsdóttir, H. Stefánsson
We look at the automation of a manual production scheduling process at a pharmaceutical company, by using mixed integer optimization and a simple greedy algorithm. The pharmaceutical company is a make to order producer with highly utilized resources and flexible production processes. We present the algorithms and analyze their performance using real data and compare the results with the manual approach currently used at the company. The results indicate that automated scheduling approaches can be used to improve the production scheduling process, while at the same time reducing the valuable time the human schedulers spend on preparing the production schedules.
{"title":"Automating a manual production scheduling process at a pharmaceutical company","authors":"E. Ásgeirsson, Guðrún Sjöfn Axelsdóttir, H. Stefánsson","doi":"10.1109/CIPLS.2011.5953358","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953358","url":null,"abstract":"We look at the automation of a manual production scheduling process at a pharmaceutical company, by using mixed integer optimization and a simple greedy algorithm. The pharmaceutical company is a make to order producer with highly utilized resources and flexible production processes. We present the algorithms and analyze their performance using real data and compare the results with the manual approach currently used at the company. The results indicate that automated scheduling approaches can be used to improve the production scheduling process, while at the same time reducing the valuable time the human schedulers spend on preparing the production schedules.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128106848","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953354
M. Chica, O. Cordón, S. Damas, J. Bautista
Time and space assembly line balancing considers realistic multiobjective versions of the classical assembly line balancing industrial problems, involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. The aim of this contribution is to present a new multiobjective memetic algorithm based on ant colony optimization for the 1/3 variant of this family of industrial problems. This variant involves the joint minimisation of the number and the area of the stations, given a fixed cycle time limit. The good behaviour of the proposal is shown in nine problem instances.
{"title":"A multiobjective memetic ant colony optimization algorithm for the 1/3 variant of the time and space assembly line balancing problem","authors":"M. Chica, O. Cordón, S. Damas, J. Bautista","doi":"10.1109/CIPLS.2011.5953354","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953354","url":null,"abstract":"Time and space assembly line balancing considers realistic multiobjective versions of the classical assembly line balancing industrial problems, involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. The aim of this contribution is to present a new multiobjective memetic algorithm based on ant colony optimization for the 1/3 variant of this family of industrial problems. This variant involves the joint minimisation of the number and the area of the stations, given a fixed cycle time limit. The good behaviour of the proposal is shown in nine problem instances.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125356518","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953353
J. Bautista, Alberto Cano, R. Companys, Imma Ribas
We present some results attained with two variants of the bounded dynamic programming algorithm to solve the Fm|block|Cmax problem using as experimental data the well-known Taillard instances. We have improved the best-known solutions for four of the Taillard's instances.
{"title":"A bounded dynamic programming algorithm for the blocking flow shop problem","authors":"J. Bautista, Alberto Cano, R. Companys, Imma Ribas","doi":"10.1109/CIPLS.2011.5953353","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953353","url":null,"abstract":"We present some results attained with two variants of the bounded dynamic programming algorithm to solve the Fm|block|Cmax problem using as experimental data the well-known Taillard instances. We have improved the best-known solutions for four of the Taillard's instances.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116784029","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953355
B. Pimentel, G. Mateus, F. A. Almeida
The strategic planning problem, when applied to a Global Mining Supply Chain, aims at developing the necessary capacity — through either incrementing capacity on existing assets (facilities or logistics channels), or establishing new capacity in the form of new assets — in order to satisfy increasing demand. Hence, throughout the planning horizon, decisions about which new assets to establish and where to increment capacity must be taken at minimal cost (or minimal risk) and in a timely manner. However, when demand varies non-monotonically, decisions about which assets to temporarily shut down in times of decreasing demand and which of those to reopen when market conditions improve must also be taken into account. In order to respond to the risky nature of commodity markets, we propose a multi-stage stochastic programming approach to deal with the capacity planning problem in a realistic Global Mining Supply Chain. A discrete probability scenario tree defines a large-scale integer program which is hard to solve even for modern optimization software and powerful workstations. An analysis of specific software configurations indicates a series of alternative solution approaches — from exact methods such as cutting planes to approximate methods such as local search — that can be further explored in order to develop more efficient algorithms.
{"title":"Stochastic capacity planning in a Global Mining Supply Chain","authors":"B. Pimentel, G. Mateus, F. A. Almeida","doi":"10.1109/CIPLS.2011.5953355","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953355","url":null,"abstract":"The strategic planning problem, when applied to a Global Mining Supply Chain, aims at developing the necessary capacity — through either incrementing capacity on existing assets (facilities or logistics channels), or establishing new capacity in the form of new assets — in order to satisfy increasing demand. Hence, throughout the planning horizon, decisions about which new assets to establish and where to increment capacity must be taken at minimal cost (or minimal risk) and in a timely manner. However, when demand varies non-monotonically, decisions about which assets to temporarily shut down in times of decreasing demand and which of those to reopen when market conditions improve must also be taken into account. In order to respond to the risky nature of commodity markets, we propose a multi-stage stochastic programming approach to deal with the capacity planning problem in a realistic Global Mining Supply Chain. A discrete probability scenario tree defines a large-scale integer program which is hard to solve even for modern optimization software and powerful workstations. An analysis of specific software configurations indicates a series of alternative solution approaches — from exact methods such as cutting planes to approximate methods such as local search — that can be further explored in order to develop more efficient algorithms.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125862331","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953352
V. Limère, Aditya Pradhan, Melih Çelik, M. Soldner
Small warehouses generally have different needs than large warehouses. They usually do not have warehouse management systems that are data intensive and involve high capital investment. Operational procedures are more nebulous and management control is less rigid. Because of the difference in operational approach, different measures are needed in order to enhance productivity. This paper describes the results and insights gained from a study of the inventory control and warehouse operations at an industrial distributor of maintenance and repair items. The accuracy of the inventory and the efficiency of order picking are studied and appropriate measures are proposed in order to improve operations. Improvements are in the areas of process organization, inventory accuracy, inventory control, and order picking. Implementations and results are reported. Major improvements include lowering inventory levels and more efficient order picking.
{"title":"Warehousing efficiency in a small warehouse","authors":"V. Limère, Aditya Pradhan, Melih Çelik, M. Soldner","doi":"10.1109/CIPLS.2011.5953352","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953352","url":null,"abstract":"Small warehouses generally have different needs than large warehouses. They usually do not have warehouse management systems that are data intensive and involve high capital investment. Operational procedures are more nebulous and management control is less rigid. Because of the difference in operational approach, different measures are needed in order to enhance productivity. This paper describes the results and insights gained from a study of the inventory control and warehouse operations at an industrial distributor of maintenance and repair items. The accuracy of the inventory and the efficiency of order picking are studied and appropriate measures are proposed in order to improve operations. Improvements are in the areas of process organization, inventory accuracy, inventory control, and order picking. Implementations and results are reported. Major improvements include lowering inventory levels and more efficient order picking.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114455789","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953359
Heitor Liberalino, C. Duhamel, A. Quilliot, S. Kedad-Sidhoum, P. Chrétienne
We consider the problem of scheduling both a production distributed on several sites and the transportation of items between those sites. By doing so, the objective is to synchronize the two components and to build a better overall solution. The production system on each site is modeled as a Capacitated Lot-Sizing Problem where stock both on resources and produced items is available. The inter-site items transportation is a simplified version of the Vehicle Routing Problem where time is discretized. We first propose a mixed integer linear programming formulation. Then we present two heuristics. The first one is based on production order propagation over the sites. Then, at each iteration, it computes a compatible transportation schedule and it tries to improve the solution by modifying the production on the sites. The second heuristic is an adaptation of the Relax and Fix strategy. Computational results are presented to evaluate the efficiency of the two heuristics.
{"title":"The integrated lot-sizing and vehicle routing problem","authors":"Heitor Liberalino, C. Duhamel, A. Quilliot, S. Kedad-Sidhoum, P. Chrétienne","doi":"10.1109/CIPLS.2011.5953359","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953359","url":null,"abstract":"We consider the problem of scheduling both a production distributed on several sites and the transportation of items between those sites. By doing so, the objective is to synchronize the two components and to build a better overall solution. The production system on each site is modeled as a Capacitated Lot-Sizing Problem where stock both on resources and produced items is available. The inter-site items transportation is a simplified version of the Vehicle Routing Problem where time is discretized. We first propose a mixed integer linear programming formulation. Then we present two heuristics. The first one is based on production order propagation over the sites. Then, at each iteration, it computes a compatible transportation schedule and it tries to improve the solution by modifying the production on the sites. The second heuristic is an adaptation of the Relax and Fix strategy. Computational results are presented to evaluate the efficiency of the two heuristics.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126845048","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953360
A. Baccouche, Selçuk Gören, A. Huyet, H. Pierreval
In certain design problems, the solution can have collective implications that are experienced by a number of different people with different responsibilities — a team of decision-makers. In such cases, the design problem should be addressed in a collective manner, so that everyone's considerations are taken into account. Unfortunately, even though there is a vast body of literature on simulation optimization, which is widely used to solve the design problems encountered in practice, the existing research generally concentrates on providing a single solution that is optimized according to one or more performance measures. In this paper, we consider the problem of determining the values of several decision variables of a design problem where several decision-makers are involved, who have different preferences for the final solution. The different designers' considerations may not be all known in advance or may not be included in the simulation model, but can only be examined once a candidate solution is proposed. To cope with such difficulties, we propose a two-stage approach. It is first necessary to find a set of different enough designs that can be considered efficient in terms of performance. The solutions can afterwards be passed on to the decision-makers and the most appropriate one can be decided on according to their preferences. We use the crowding clustering genetic algorithm (CCGA) to solve the first sub-problem, where the performances of the candidate designs are evaluated using simulation. We address the second sub-problem with a multiplicative variant of the popular analytic hierarchy process (AHP), which does not suffer from the dependence on irrelevant alternatives as the original version. We illustrate the benefits of the proposed two-stage approach on a supply chain design problem.
{"title":"An approach based on simulation optimization and AHP to support collaborative design: With an application to supply chains","authors":"A. Baccouche, Selçuk Gören, A. Huyet, H. Pierreval","doi":"10.1109/CIPLS.2011.5953360","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953360","url":null,"abstract":"In certain design problems, the solution can have collective implications that are experienced by a number of different people with different responsibilities — a team of decision-makers. In such cases, the design problem should be addressed in a collective manner, so that everyone's considerations are taken into account. Unfortunately, even though there is a vast body of literature on simulation optimization, which is widely used to solve the design problems encountered in practice, the existing research generally concentrates on providing a single solution that is optimized according to one or more performance measures. In this paper, we consider the problem of determining the values of several decision variables of a design problem where several decision-makers are involved, who have different preferences for the final solution. The different designers' considerations may not be all known in advance or may not be included in the simulation model, but can only be examined once a candidate solution is proposed. To cope with such difficulties, we propose a two-stage approach. It is first necessary to find a set of different enough designs that can be considered efficient in terms of performance. The solutions can afterwards be passed on to the decision-makers and the most appropriate one can be decided on according to their preferences. We use the crowding clustering genetic algorithm (CCGA) to solve the first sub-problem, where the performances of the candidate designs are evaluated using simulation. We address the second sub-problem with a multiplicative variant of the popular analytic hierarchy process (AHP), which does not suffer from the dependence on irrelevant alternatives as the original version. We illustrate the benefits of the proposed two-stage approach on a supply chain design problem.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126895260","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953357
Yuhan Guo, G. Goncalves, T. Hsu
Rising vehicle numbers and increased use of private cars have caused significant traffic congestion, noise and energy waste. Public transport cannot always be set up in the non-urban areas. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles has emerged to be a viable possibility for reducing private car usage around the world. In this paper, we present a guided genetic algorithm (GGA) for long-term car pooling problem. Computational results are given to show that this approach is competitive with some of the most powerful heuristics.
{"title":"A guided genetic algorithm for solving the long-term car pooling problem","authors":"Yuhan Guo, G. Goncalves, T. Hsu","doi":"10.1109/CIPLS.2011.5953357","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953357","url":null,"abstract":"Rising vehicle numbers and increased use of private cars have caused significant traffic congestion, noise and energy waste. Public transport cannot always be set up in the non-urban areas. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles has emerged to be a viable possibility for reducing private car usage around the world. In this paper, we present a guided genetic algorithm (GGA) for long-term car pooling problem. Computational results are given to show that this approach is competitive with some of the most powerful heuristics.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131015306","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 : 2011-04-11DOI: 10.1109/CIPLS.2011.5953356
Luis Francisco López-Castro, J. Montoya-Torres
This paper presents our work-in-progress on the study of a practical variant of the vehicle routing problem in which customers are to be serviced within given service time windows. In comparison with time windows models traditionally studied in the VRP literature in which hard time windows are to be respected when servicing a customer, our problem allows the violation of such time windows by considering fuzzy time windows. A hybrid genetic algorithm with fuzzy membership functions is proposed to solve the multi-objective problem of minimizing total travel distance of vehicles and maximizing customer service level. Preliminary results on a set of experiments carried out using well-known instances from literature are also presented.
{"title":"Vehicle routing with fuzzy time windows using a genetic algorithm","authors":"Luis Francisco López-Castro, J. Montoya-Torres","doi":"10.1109/CIPLS.2011.5953356","DOIUrl":"https://doi.org/10.1109/CIPLS.2011.5953356","url":null,"abstract":"This paper presents our work-in-progress on the study of a practical variant of the vehicle routing problem in which customers are to be serviced within given service time windows. In comparison with time windows models traditionally studied in the VRP literature in which hard time windows are to be respected when servicing a customer, our problem allows the violation of such time windows by considering fuzzy time windows. A hybrid genetic algorithm with fuzzy membership functions is proposed to solve the multi-objective problem of minimizing total travel distance of vehicles and maximizing customer service level. Preliminary results on a set of experiments carried out using well-known instances from literature are also presented.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121682411","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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