Managing Uncertainty for an Integrated Fishery

This paper investigates ways to deal with the uncertainties in fishing trawler scheduling and production planning in a quota-based integrated commercial fishery. A commercial fishery faces uncertainty mainly from variation in catch rate, which may be due to weather, and other environmental factors....

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Main Author: MB Hasan
Format: Article
Language:English
Published: Operations Research Society of South Africa (ORSSA) 2012-06-01
Series:ORiON
Online Access:http://orion.journals.ac.za/pub/article/view/102
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spelling doaj-800429f0a8f94863a4ea8d266120c17e2020-11-24T21:42:55ZengOperations Research Society of South Africa (ORSSA)ORiON2224-00042012-06-01281375810.5784/28-1-102115Managing Uncertainty for an Integrated FisheryMB HasanThis paper investigates ways to deal with the uncertainties in fishing trawler scheduling and production planning in a quota-based integrated commercial fishery. A commercial fishery faces uncertainty mainly from variation in catch rate, which may be due to weather, and other environmental factors. The firm tries to manage this uncertainty through planning co-ordination of fishing trawler scheduling, catch quota, processing and labour allocation, and inventory control. <br />Scheduling must necessarily be done over some finite planning horizon, and the trawler schedule itself introduces man-made variability, which in turn induces inventory in the processing plant. This induced inventory must be managed, complicated by the inability to plan easily beyond the current planning horizon. We develop a surprisingly simple innovation in inventory, which we have not seen in other papers on production management, which of requiring beginning inventory to equal ending inventory. This tool gives management a way to calculate a profit-maximizing safety stock that counter-acts the man-made variability due to the trawler scheduling. We found that the variability of catch rate had virtually no effects on the profitability with inventory. We report numerical results for several planning horizon models, based on data for a major New Zealand fishery.http://orion.journals.ac.za/pub/article/view/102
collection DOAJ
language English
format Article
sources DOAJ
author MB Hasan
spellingShingle MB Hasan
Managing Uncertainty for an Integrated Fishery
ORiON
author_facet MB Hasan
author_sort MB Hasan
title Managing Uncertainty for an Integrated Fishery
title_short Managing Uncertainty for an Integrated Fishery
title_full Managing Uncertainty for an Integrated Fishery
title_fullStr Managing Uncertainty for an Integrated Fishery
title_full_unstemmed Managing Uncertainty for an Integrated Fishery
title_sort managing uncertainty for an integrated fishery
publisher Operations Research Society of South Africa (ORSSA)
series ORiON
issn 2224-0004
publishDate 2012-06-01
description This paper investigates ways to deal with the uncertainties in fishing trawler scheduling and production planning in a quota-based integrated commercial fishery. A commercial fishery faces uncertainty mainly from variation in catch rate, which may be due to weather, and other environmental factors. The firm tries to manage this uncertainty through planning co-ordination of fishing trawler scheduling, catch quota, processing and labour allocation, and inventory control. <br />Scheduling must necessarily be done over some finite planning horizon, and the trawler schedule itself introduces man-made variability, which in turn induces inventory in the processing plant. This induced inventory must be managed, complicated by the inability to plan easily beyond the current planning horizon. We develop a surprisingly simple innovation in inventory, which we have not seen in other papers on production management, which of requiring beginning inventory to equal ending inventory. This tool gives management a way to calculate a profit-maximizing safety stock that counter-acts the man-made variability due to the trawler scheduling. We found that the variability of catch rate had virtually no effects on the profitability with inventory. We report numerical results for several planning horizon models, based on data for a major New Zealand fishery.
url http://orion.journals.ac.za/pub/article/view/102
work_keys_str_mv AT mbhasan managinguncertaintyforanintegratedfishery
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