Resource allocation by genetic algorithm with fuzzy inference

Wang, Kung‐Jeng; Lin, Yi-Jay

doi:10.1016/j.eswa.2006.08.013

Cited by 16 publications

(7 citation statements)

References 12 publications

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“…Some scholars thus have developed a variety of heuristic and soft-computing methods (e.g., Wang & Lin, 2007;Wang, Lin, Chien, & Chen, 2009). Among them, using GA to solve the location allocation problem is one promising approach.…”

Section: Literature Surveymentioning

confidence: 99%

Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution

Wang

Makond

Liu³

2011

Expert Systems with Applications

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Section: Literature Surveymentioning

confidence: 99%

Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution

Wang

Makond

Liu³

2011

Expert Systems with Applications

Self Cite

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“…A fuzzy classifier model has been proposed by Morandin et al (2008) for generating the best production sequence for the decision-maker. Wang and Lin (2007) developed an intelligent resource allocation model using genetic algorithm and fuzzy inference, which can minimise the lateness of orders with specific due dates. actual situation status.…”

Section: Literature Reviewmentioning

confidence: 99%

An order-picking operations system for managing the batching activities in a warehouse

Lam

Choy

et al. 2013

International Journal of Systems Science

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Nowadays, customer orders with high product variety in small quantities are often received and requested for timely delivery. However, the order-picking process is a labour-intensive and costly activity to handle those small orders separately. In such cases, small orders are often grouped into batches so that two or more orders can be served at once to increase the picking efficiency and thus reduce the travel distance. In this paper, an order-picking operations system (OPOS) is proposed to assist the formulation of an order-picking plan and batch-handling sequence. The study integrates a mathematical model and fuzzy logic technique to divide the receiving orders into batches and prioritise the batch-handling sequence for picking, respectively. Through the proposed system, the order-picking process can be managed as batches with common picking locations to minimise the travel distance, and the batch-picking sequence can be determined as well. To demonstrate the use of the system, a case study in a third-party logistics warehouse is presented, and the result shows that both the order-picking activity and labour utilisation can be better organised. IntroductionTo survive in this highly competitive business environment, logistics service providers (LSPs) have changed their strategic goals to achieve shorter order cycle times, lower costs and better customer service. Instead of keeping a large amount of stock for long-term storage, the warehouse of LSPs has explored its functions to handle orders that contain fast-moving items with large product variety (Chow, Choy, Lee, and Lau 2006). Thus, customers are allowed to place the delivery orders based on actual sales demand. Therefore, delivery orders with small quantities of goods are frequently received. Planning of warehouse order-picking activities effectively becomes complex, so that the order can be picked and delivered at a designated time.With the goal to allocate minimum warehouse resources and satisfy customer demands within a short period of time, the idea of order batching becomes important to increase the picking efficiency. The orders are consolidated into batches before the actual picking process, that is, items of two or more orders are grouped in a single picking trip. The picker can then travel and pick the same types of items at once for several orders to increase the efficiency. In general, the total travel time and distance can be reduced by the order-batching strategy if orders with a similar item storage location are handled at the same time (Ho and Tseng 2011). However, the consideration on assigning appropriate amount of labour resources to pick the

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“…In reality, it is a common practice for organizations to use one or more of the following (technical) systems and concepts to support their KM efforts (Binney, 2001;Wenger, 2001;Mazilescu, 2009b): Knowledge Maps, Taxonomies, Enterprise search engine, e-collaboration tools, Information repositories, Expert Systems, Data Mining / Knowledge Discovery systems, Case-based Reasoning / Question-Answering tools (for Helpdesk and/or Contact Centers), E-Learning and/or Learning Management Systems (LMS), Enterprise Information Portal, Intellectual Capital (IC) measurement tools. Expert systems are examples of relevant knowledge-based methodologies (as Knowledge Capture Systems) that have much to contribute to KMSs, because they manipulate knowledge in order to implement various tasks (Tsui, 2002;Wang & Lin, 2007;Schwartz, 2006;Omar, 2008). KMSs based on Agent Technology try to provide computers the ability to perform various intelligent tasks, for which their human users resort to their own knowledge and to collective intelligence.…”

Section: Related Workmentioning

confidence: 99%

A Knowledge Management System Embedded in the New Semantic Technologies

Mazilescu¹

2012

New Research on Knowledge Management Technology

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Resource allocation by genetic algorithm with fuzzy inference

Cited by 16 publications

References 12 publications

Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution

Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution

An order-picking operations system for managing the batching activities in a warehouse

A Knowledge Management System Embedded in the New Semantic Technologies

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