Swarm intelligence applied in green logistics: A literature review

https://doi.org/10.1016/j.engappai.2014.09.007Get rights and content

Abstract

Green logistics (GL) is gaining increasing attention among academic researchers and industrial practitioners, due to the escalating deterioration of the environment. Various green activities and operations aiming at improving the performance of GL have been applied synthetically, and most of the activities can be modeled as combinatorial optimization (CO) problems. Exact approaches tend to be incapable of solving the CO problems, especially with the increasing complexity. Thus, meta-heuristic approaches are widely adopted, which can generate a satisfactory solution within an acceptable time. Swarm intelligence (SI) is an innovative branch of meta-heuristics derived from imitating the behavioral pattern of natural insects. The distributed control mechanism and simple interactive rules can manage the swarm of insects effectively and efficiently. There are some pilot studies in applying SI into GL, which indicates that the integration of GL and SI could be a promising choice and of great potential. This research reviews the application of SI in GL through a comprehensive and extensive investigation and analysis of extant literature, which includes 115 publications in the last twenty years. The integration of GL and SI is analyzed from the perspective of both the problem context and the methodology. The categories of GL and SI are classified systematically. The CO problems of GL are further studied with SI algorithms, and innovative and universal guidance for algorithm customization in resolving CO problems emerges as well. Further potential research issues and opportunities of GL and SI are also identified in this research.

Introduction

Global warming, environment deterioration, and government regulations arouse the awareness of academic researchers and industrial practitioners for considering the “green” strategies in logistics industry (Murphy, 2000). Green Logistics (GL) concerns not only the provision of green products or services to customers, but also the overall logistics flow of items from cradle to grave, together with reverse logistics. Various green activities and operations have been implemented, such as production scheduling and network construction. In order to improve the performance of GL, individual logistics parties not only need to implement green activities and operations by themselves, but also the cooperation and collaboration among different logistics parties (Zhou et al., 2000). The performance of GL cannot be measured simply in an economic way, but in a sustainable way taking account of environmental and societal factors as well, which are also the objectives of GL (Björklund et al., 2012, Hervani et al., 2005). GL can be understood as the combination of traditional logistics and reverse logistics (RL). Traditional logistics comprises the flow from the raw materials to finished products, while RL is a rather new research field, which involves the concept of recycling used products in order to reduce waste and to increase an industry׳s performance and resulting profits. RL is of great importance, as it not only complements integrated logistics research, but also improves the performance of GL significantly in terms of all the economic, environmental and societal objectives (Lee and Lam, 2012). RL also consists of numerous activities and operations, such as returned products collection, examination, pre-processing, recycling, remanufacturing or disposal (Rogers and Tibben-Lembke, 2001).

Most of the activities and operations in GL can be formulated as combinatorial optimization (CO) models with multiple objectives, constraints and decision variables. Exact methods, such as Linear Programming (LP) and Branch-and-Bound (B&B), are becoming less popular for solving CO problems, as they are either unable to solve complicated CO problems with large numbers of variables or it takes long time to find the solution for CO problems (Laporte, 1992). By contrast, meta-heuristic approaches are becoming increasingly popular as these approaches are approximate approaches, which suggest that they could find satisfactory solutions within an acceptable time instead of finding the optimal solution. Intuitively speaking, meta-heuristic approaches can be classified into two categories: the single-solution based approaches and the population based approaches (Blum and Roli, 2003). The single-solution based approaches, also named the trajectory methods (Consoli and Darby-Dowman, 2007), such as Tabu Search (TS), Simulated Annealing (SA) and various local search methods, in that only one candidate solution exists during the whole search process. However, the population based approach indicates that the search process starts with a population of candidate choices, and the whole population further evolves. The advantages and disadvantages of both the single-solution based approaches and the population based approaches can be found in the literature (Glover and Kochenberger, 2003, Jones et al., 2002). Two important examples of the population based approach are Evolutionary Algorithms (EAs) and Swarm intelligence (SI). The most typical example of EAs is the Genetic Algorithm (GA), which was proposed by Holland in 1975 and simulates the Darwin evolution concept (Holland, 1975).

SI approaches were originally inspired by the collective behavior of natural species, such as ant colony optimization (ACO) from ants, Particle Swarm Optimization (PSO) from birds and the Artificial Bee Colony (ABC) from bees (Bonabeau et al., 1999). SI is a relatively new branch of meta-heuristics comparing with EAs and other single-solution based approaches. SI approaches use approximate and non-deterministic strategies to effectively and efficiently explore and exploit the search space in order to find near-optimal solutions (Blum and Li, 2008, Blum and Merkle, 2008). SI has three fundamental and essential properties, namely decentralization, self-organization and collective behavior, which are necessary and sufficient to acquire SI behaviors. Decentralization means that no central control mechanism exists. The behaviors of individuals are determined by themselves. And the self-organization of individual relies upon four fundamental properties, i.e. positive feedback, negative feedback, fluctuations and multiple interactions (Jeanne, 1986). The interaction between two individuals or environment follows simple rules. The result from interaction would either impel or restrain the behavior of certain individual as positive feedback or negative feedback. The decision of certain individual might be affected by some random factors, which leads to fluctuations. Collective behavior refers that in a swarm, the individual behavior may act randomly, however the aggregation of individual behavior turns to be globally intelligent. In other words, SI indicates that a number of cognitive individuals accumulate their knowledge through the interaction with other individuals or the environment, determine their behaviors solely and finally achieve the target. The characteristics and details of each SI approach are presented in Section 4.

Ever since the introduction of SI, various SI algorithms have been proposed and applied to solve the CO problems in multifarious disciplines, among which the domain of GL could be a promising research area due to its inherent characteristics and features. However, given that there have been many researches of solving GL problems using SI algorithms. Most of them are individually separated, either solving an independent GL problem or adopting a single SI algorithm. In this regard, a comprehensive and extensive literature review of the integration of GL and SI from both the problem context and the methodology perspective is needed urgently. In this research, the state-of-the-art applications of the SI algorithms in the GL background are fully investigated and analyzed, and can help researchers to obtain an intuitive and profound understanding of current research situations. In addition, considering the implementation of various SI algorithms and their variants, this research also provides innovative and universal principles of algorithm selection, improvement and even customization through detailed algorithm analysis and comparison, which can offer practical guidance when solving CO problems using SI related algorithms.

After a brief introduction, the research methodology, i.e. the process of literature review, is described in Section 2. The classification schemes of GL and SI are presented in 3 GL classification scheme, 4 Classification of swarm intelligence respectively. Section 5 discusses the integration of GL and SI and the guiding principles of choosing and optimizing algorithms for specific problems is presented Finally, Section 6 concludes the work and suggests research opportunities and directions for further work.

Section snippets

Research methodology

The objective of this research is to identify major works on interdisciplinary research in GL and SI, and thereafter, to classify and integrate them so as to discover gaps, critical issues and opportunities for further study and research. The literature review is a valid approach and necessary step in exploring new research directions and forms an integral part of the related research, which also helps to scrutinize the conceptual aspects and guides the research toward new theoretical

GL classification scheme

The concept of GL can be well understood from different perspectives. For instance, from the logistics parties point of view, all logistics parties including suppliers, manufacturers, distribution centers and customers are involved in GL; from the standpoint of logistics flows, GL can be viewed as the combination of forward logistics and reverse logistics. The activities and operations in GL, associated with one or more parties, work collaboratively and cooperatively to construct an integrated

Classification of swarm intelligence

The concept of SI was originally introduced by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems (Beni and Wang, 1993), while Bonabeau et al. (1999) redefined it in 1999 as “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies”. Most of the SI approaches are inspired by the collective behavior of natural species, such as ants foraging, birds flocking and bees

Results discussion and implication

In this section, we analyze the results of reviewed publications first with the predefined schemes for both GL and SI. Practical implications and potential research opportunities can be derived from the results. After the results analysis, we provide a mapping scheme between problem types and swarm intelligent algorithms, which can help researchers to get an intuitive and intrinsic understanding of the problems and methods. The useful principles of selection, improvement, hybridization and even

Conclusions and further work

Because of the deterioration of environment and the consumption of the finite and diminishing energy sources, green logistics is gaining increasing attention substantially. Economic performance is no longer the only objective in logistics; two other aspects, environmental and societal performance, are becoming more important than ever before for the purpose of sustainable development. The operational level activities in logistics are the fundamental for any upper management strategies, thus the

Acknowledgments

This work is supported by the Hong Kong Polytechnic University. Our gratitude is also extended to the research committee and the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University for support in this project (#4-RTY0).

References (194)

  • M. Dorigo et al.

    Ant colonies for the traveling salesman problem

    BioSystems

    (1997)
  • T. Efendigil et al.

    A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness

    Comput. Ind. Eng.

    (2008)
  • B. Eksioglu et al.

    The vehicle routing problem: A taxonomic review

    Comput. Ind. Eng.

    (2009)
  • M. Fleischmann et al.

    Quantitative models for reverse logistics: a review

    Eur. J. Oper. Res.

    (1997)
  • G. Fuellerer et al.

    Ant colony optimization for the two-dimensional loading vehicle routing problem

    Comput. Oper. Res.

    (2009)
  • Y. Gajpal et al.

    An ant colony system (ACS) for vehicle routing problem with simultaneous delivery and pickup

    Comput. Oper. Res.

    (2009)
  • Y. Gajpal et al.

    Multi-ant colony system (MACS) for a vehicle routing problem with backhauls

    Eur. J. Oper. Res.

    (2009)
  • Y. Gajpal et al.

    An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops

    Int. J. Prod. Econ.

    (2006)
  • A.H. Gandomi et al.

    Krill herd: a new bio-inspired optimization algorithm

    Commun. Nonlinear Sci. Numer. Simul.

    (2012)
  • F.P. Goksal et al.

    A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

    Comput. Ind. Eng.

    (2013)
  • Y. Hani et al.

    Ant colony optimization for solving an industrial layout problem

    Eur. J. Oper. Res.

    (2007)
  • S.-H. Huang et al.

    A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty

    Transp. Res. Part E: Logist. Transp. Rev.

    (2010)
  • D.F. Jones et al.

    Multi-objective meta-heuristics: an overview of the current state-of-the-art

    Eur. J. Oper. Res.

    (2002)
  • R.S. Kadadevaramath et al.

    Application of particle swarm intelligence algorithms in supply chain network architecture optimization

    Expert Syst. Appl.

    (2012)
  • C.B. Kalayci et al.

    Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem

    Expert Syst. Appl.

    (2013)
  • J.-P. Kenné et al.

    Production planning of a hybrid manufacturing–remanufacturing system under uncertainty within a closed-loop supply chain

    Int. J. Prod. Econ.

    (2012)
  • H.J. Ko et al.

    A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs

    Comput. Oper. Res.

    (2007)
  • G. Laporte

    The vehicle routing problem: An overview of exact and approximate algorithms

    Eur. J. Oper. Res.

    (1992)
  • B. Latha Shankar et al.

    A bi-objective optimization of supply chain design and distribution operations using non-dominated sorting algorithm: a case study

    Expert Syst Appl.

    (2013)
  • C.-Y. Lee et al.

    An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem

    Appl. Intell.

    (2010)
  • C.K.M. Lee et al.

    Managing reverse logistics to enhance sustainability of industrial marketing

    Ind. Market. Manag.

    (2012)
  • X. Li et al.

    A discrete artificial bee colony algorithm with composite mutation strategies for permutation flow shop scheduling problem

    Sci. Iran.

    (2012)
  • C.-J. Liao et al.

    An ant colony optimization algorithm for setup coordination in a two-stage production system

    Appl. Soft Comput.

    (2011)
  • B.M.T. Lin et al.

    Development of new features of ant colony optimization for flowshop scheduling

    Int. J. Prod. Econ.

    (2008)
  • Y.-F. Liu et al.

    A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem

    Appl. Soft Comput.

    (2013)
  • Abbass, H.A., 2001. MBO: Marriage in honey bees optimization—A haplometrosis polygynous swarming approach. In:...
  • E.B.E.I. Adiba et al.

    A Hybrid Ant Colony System for Green capacitated vehicle routing problem in sustainbale transport

    J. Theor. Appl. Inf. Technol.

    (2013)
  • G. Agarwal et al.

    A PSO-based optimum consumer incentive policy for WEEE incorporating reliability of components

    Int. J. Prod.Res.

    (2012)
  • S. Agrawal et al.

    A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem

    Int. J. Prod. Res.

    (2007)
  • B. Akay et al.

    Artificial bee colony algorithm for large-scale problems and engineering design optimization

    J. Intell. Manuf.

    (2012)
  • A. Askarzadeh et al.

    A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer

    Int. J. Energy Res.

    (2013)
  • J.E. Bell et al.

    Swarm intelligence: application of the ant colony optimization algorithm to logistics-oriented vehicle routing problems

    J. Bus. Logist.

    (2010)
  • G. Beni et al.

    Swarm Intelligence in Cellular Robotic Systems, Robots and Biological Systems: Towards a New Bionics?

    (1993)
  • M. Björklund et al.

    Performance measurements in the greening of supply chains

    Supply Chain Manag.

    (2012)
  • C. Blum et al.

    Swarm intelligence in optimization

  • C. Blum et al.

    Swarm intelligence: introduction and applications

    (2008)
  • C. Blum et al.

    Metaheuristics in combinatorial optimization: Overview and conceptual comparison

    ACM Comput. Surveys (CSUR)

    (2003)
  • E. Bonabeau et al.

    Swarm intelligence: from natural to artificial systems

    (1999)
  • J. Branke et al.

    Solving the probabilistic TSP with ant colony optimization

    J. Math. Model. Algor.

    (2005)
  • P.M. Brewerton et al.

    Organizational research methods: A guide for students and researchers

    (2001)
  • Cited by (110)

    • MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach

      2022, Computers and Security
      Citation Excerpt :

      Meta-heuristic algorithms are used to solve complex optimization challenges and NP-hard problems. These algorithms are suboptimal algorithms, and they find adequate solutions in an acceptable time, instead of finding exact solutions (Zhang et al., 2015). Intrusion detection datasets contain a lot of irrelevant and redundant features.

    View all citing articles on Scopus
    View full text