Metaheuristic Algorithms for Supply Chain Management Problems

2013 ◽  
pp. 110-135 ◽  
Author(s):  
Ata Allah Taleizadeh ◽  
Leopoldo Eduardo Cárdenas-Barrón
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Optimization Problems ◽  
Harmony Search ◽  
Metaheuristic Algorithms ◽  
Lot Size ◽  
Business Economics ◽  
Chain Management ◽  
Single Vendor ◽  
Partial Backordering

Recently, metaheuristic algorithms (MHAs) have gained noteworthy attention for their abilities to solve difficult optimization problems in engineering, business, economics, finance, and other fields. This chapter introduces some applications of MHAs in supply chain management (SCM) problems. For example, consider a multi-product multi-constraint SCM problem in which demands for each product are not deterministic, the lead-time varies linearly with regard to the lot-size and partial backordering of shortages are assumed. Thus, since the main goal is to determine the re-order point, the order quantity and number of shipments under the total cost of the whole chain is minimized. In this chapter, the authors concentrate on MHAs such as harmony search (HS), particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA), and simulated annealing (SA) for solving the following four supply chain models: single-vendor single-buyer (SBSV), multi-buyers single-vendor (MBSV), multi-buyers multi-vendors (MBMV) and multi-objective multi-buyers multi-vendors (MOMBMV). These models typically are in any supply chain. For illustrative purposes, a numerical example is solved in each model.

Metaheuristic Algorithms for Supply Chain Management Problems

2012 ◽  
pp. 1814-1837 ◽  
Author(s):  
Ata Allah Taleizadeh ◽  
Leopoldo Eduardo Cárdenas-Barrón
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Optimization Problems ◽  
Harmony Search ◽  
Metaheuristic Algorithms ◽  
Lot Size ◽  
Business Economics ◽  
Chain Management ◽  
Single Vendor ◽  
Partial Backordering

Recently, metaheuristic algorithms (MHAs) have gained noteworthy attention for their abilities to solve difficult optimization problems in engineering, business, economics, finance, and other fields. This chapter introduces some applications of MHAs in supply chain management (SCM) problems. For example, consider a multi-product multi-constraint SCM problem in which demands for each product are not deterministic, the lead-time varies linearly with regard to the lot-size and partial backordering of shortages are assumed. Thus, since the main goal is to determine the re-order point, the order quantity and number of shipments under the total cost of the whole chain is minimized. In this chapter, the authors concentrate on MHAs such as harmony search (HS), particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA), and simulated annealing (SA) for solving the following four supply chain models: single-vendor single-buyer (SBSV), multi-buyers single-vendor (MBSV), multi-buyers multi-vendors (MBMV) and multi-objective multi-buyers multi-vendors (MOMBMV). These models typically are in any supply chain. For illustrative purposes, a numerical example is solved in each model.

scholarly journals Rolling Supply Chain Scheduling considering Suppliers, Production, and Delivery Lot-Size

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Rong-Hwa Huang ◽  
Tung-Han Yu ◽  
Chen-Yun Lee
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Simulated Data ◽  
Planning Horizon ◽  
Scheduling Problems ◽  
Supply Chain Scheduling ◽  
Lot Size ◽  
Delivery Cost ◽  
Chain Management ◽  
Rolling Planning

Supply chain management and integration play a key factor in contemporary manufacturing concept. Companies seek to integrate itself within a cooperative and mutual benefiting supply chain. Supply chain scheduling, as an important aspect of supply chain management, highly emphasizes on minimizing stock costs and delivery costs. Most previous researches on supply chain scheduling problems assume make-to-order production, which includes delivery cost in lot-size. This practice simplifies the complexity of the problem. Instead, this research discusses make-to-contract production, where the supply chain has a rolling planning horizon that changes according to contracts. Within a planning horizon, two types of interval are defined. The first is frozen interval, in which the manufacturing decision cannot be changed. The second is free interval, where schedules can be adjusted depending on new contracts. This research aims to build a robust rolling supply management schedule to satisfy customers’ needs, by considering supplier, production, and delivery lot-size simultaneously. The objective is to effectively decide a combination of supplier, production, and delivery lot-size that minimizes total cost consisting of supplier cost, finish good stock cost, and delivery cost. Based on the concept, this study designs a problem-solving process that combines the methods of rolling planning horizon and genetic algorithm. Delivery size (DS), finish good stock (FS), and early delivery cost (ED) are the three methods applied; each will provide a guideline to produce a feasible solution. By further considering the fluctuations in practical needs and performing an overall evaluation, a robust and optimal supply chain scheduling plan can be decided, including the optimal lot-sizes of supplier, production, and delivery. In the effectiveness test which considers 3 types of customer demands and 11 types of company cost structures, the simulated data test results suggest that the proposed methods in this study have excellent performance.

Nature-Inspired Intelligence in Supply Chain Management

2011 ◽  
pp. 1-31
Author(s):  
Vassilios Vassiliadis ◽  
Giorgos Dounias
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Optimization Problems ◽  
Real Life ◽  
Ant Colonies ◽  
Chain Management ◽  
Complex Optimization ◽  
Nature Inspired Intelligence ◽  
Optimum Solution

Supply chain management is a vital process for the competitiveness and profitability of companies. Supply chain consists of a large and complex network of components such as suppliers, warehouses, customers etc. which are connected in almost every possible way. Companies’ main aim is to optimize the components of these complex networks to their benefit. This constitutes a challenging optimization problem and often, traditional mathematical approaches fail to overcome complexity and to converge to the optimum solution. More robust methods are required sometimes in order to yield to the optimal. The field of artificial intelligence offers a great variety of meta-heuristic techniques which specialize in solving such complex optimization problems, either accurately, or by obtaining a practically useful approximation, even if real time constraints are imposed. The aim of this chapter is to present a survey of the available literature, regarding the use of nature-inspired methodologies in supply chain management problems. Nature-inspired intelligence is a specific branch of artificial intelligence. Its unique characteristic is the algorithmic imitation of real life systems such as ant colonies, flock of birds etc. in order to solve complex problems.

Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions

2019 ◽  
Vol 24 (1) ◽  
pp. 107-123 ◽  
Author(s):  
Gunjan Soni ◽  
Vipul Jain ◽  
Felix T.S. Chan ◽  
Ben Niu ◽  
Surya Prakash
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Network Design ◽  
Optimization Problems ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Supply Chain Optimization ◽  
Chain Management ◽  
Content Type

Purpose It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM. Design/methodology/approach A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms. Findings The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers. Originality/value The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

Gestão da Cadeia de Suprimentos e Sustentabilidade: um estudo bibliométrico da produção científica na base Web of Science

2013 ◽  
Vol 4 (1) ◽  
pp. 155-170 ◽  
Author(s):  
Adilson Carlos da Rocha ◽  
Luciana Barbieri da Rosa ◽  
Caroline Rossetto Camargo ◽  
João Fernando Zamberlan ◽  
Clandia Maffini Gomes
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Web Of Science ◽  
Cleaner Production ◽  
Business Economics ◽  
Chain Management ◽  
Web Of Knowledge

Este artigo analisou as características das publicações relacionadas aos temas Gestão da Cadeia de Suprimentos e Sustentabilidade. A pesquisa foi realizada na base de dados Web of Science da ISI Web of Knowledge, procurando identificar as principais áreas temáticas, autores, tipos de documentos, título das fontes, ano das publicações, instituições, idiomas e países destas publicações, assim como a identificação dos “hot topics” relacionados aos tópicos “Supply Chain Management and Sustainability” e relacionou as publicações mais citadas com os autores que mais publicam na temática pesquisada. A análise dos dados teve por base os cálculos dos índices h-b e m de Banks (2006). De acordo com os resultados deste estudo, o número de publicações cresceu de forma significativa no período analisado, concentrando-se nos Estados Unidos, com 99% das publicações escritas no idioma inglês tendo como principal fonte o periódico Journal of Cleaner Production, e como principais temas: Business Economics (Economia) e Engineering (Engenharia). Dentre os 20 tópicos combinados com o tema pesquisado, Management (Gestão) e Performance (Desempenho) se destacaram como “hot topics”.Constatou-se ainda que a publicação com o maior número de citações, sendo uma referencia na temática pesquisada não pertence aos autores que mais publicam sobre o mesmo tema.

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