A simulated annealing heuristic for the capacitated location routing problem

Yu, Vincent F.; Lin, Shih-Wei; Lee, Wenyih; Ting, Ching‐Jung

doi:10.1016/j.cie.2009.10.007

Cited by 248 publications

(129 citation statements)

References 38 publications

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“…A presentation of these different solution approaches to distribution system models is provided in Table 1. Marinakis and Marinaki (2008b) Hybrid particle swarm optimisation; multiple phase neighbourhood search -greedy randomized adaptive search procedure Yang and Zi-Xia (2009) Sequential and iterative procedure using particle swarm optimisation Liu et al (2012) Multi-objective particle swarm optimisation combined with grey relational analysis and entropy weight Gendreau et al (1994) Tabu search heuristic with a generalised insertion procedure Tuzun and Burke (1999) Two-phase Tabu search algorithm coded in C Chiang and Russell (2004) Set partitioning approach and tabu search algorithm Melechovský et al (2005) p-median approach to find an initial feasible solution and a meta-heuristic integrating variable neighbourhood search and Tabu search to improve the solution Albareda-Sambola et al (2005) Tabu search metaheuristic solution with CPLEX 6.5 solver Lin and Kwok (2006) A combined Tabu search and simulated annealing metaheuristics Caballero et al (2007) Multi-objective combinatorial optimisation based on tabu search Russell et al (2008) Reactive Tabu search method based metaheuristics approach Schwardt and Fischer (2009) A neural network approach based on a self-organising map Lin et al (2002) Metaheuristics approach based on threshold accepting and simulated annealing Wu et al (2002) Simulated annealing Yu et al (2010) Simulated annealing Stenger et al (2012) Simulated annealing Prins et al (2006a) Greedy randomised adaptive search procedure Duhamel et al (2010) Greedy randomised adaptive search procedure Nguyen et al (2012) Greedy randomised adaptive search procedure Ghodsi and Amiri (2010) Variable neighbourhood search algorithm Derbel et al (2011) Variable neighbourhood search algorithm Bell and McMullen (2004) Ant colony optimisation Bin et al (2009) Ant colony optimisation Ting and Chen (2012) Ant colony optimisation Hwang (2002) Genetic algorithm Prins et al (2006b) Genetic algorithm Zhou and Liu (2007) Genetic algorithm Marinak...…”

Section: Overview Of the Related Methodsmentioning

confidence: 99%

“…The total costs include routing costs, fixed costs of the vehicle, fixed costs and operating costs of the facility (Karaoglan et al 2012). Distribution systems are modelled as NP-hard combinatorial optimisation problems (Nagy and Salhi 2007;Marinakis and Marinaki 2008;Yu et al 2010). The nature of NP-hard problems is such that the computational effort required for solution attainment grows exponentially with increasing problem size (Erdoğan and Miller-Hooks 2012).…”

Section: Literature Surveymentioning

confidence: 99%

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A solution method for a two-layer sustainable supply chain distribution model

Validi

Bhattacharya

Byrne

2015

Computers & Operations Research

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This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry's two-layer supply chain network. The DoE generates 6,100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decisionmaker is presented with a choice of several distribution routes on the demand side of the twolayer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO 2 emissions and costs where it can be seen that the solution method outperforms existing solution methods.

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Section: Overview Of the Related Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

A solution method for a two-layer sustainable supply chain distribution model

Validi

Bhattacharya

Byrne

2015

Computers & Operations Research

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“…A short up to date synopsis of optimisation models in distribution systems is presented in Table 1, but for a more detailed historical survey of the varying distribution system techniques and their origins the interested reader is referred to Madsen (1983), Min et al (1998), Kenyon and Morton (2001), and Nagy and Salhi (2007). Lin et al (2002), Yu et al (2010), Stenger et al (2012). Greedy randomised adaptive search optimisation Prins et al (2006), Duhamel et al (2010), Nguyen et al (2012) Variable neighbourhood search optimisation Melechovský et al (2005), Ghodsi and Amiri (2010), Derbel et al (2011) Genetic algorithms Zhou and Liu (2007), , Jin et al (2010), Karaoglan and Altiparmak (2010) Branch and cut optimisation Belenguer et al (2011), Karaoglan et al (2011) Mixed-integer programming; Integer linear programming Alumur and Kara (2007), Diabat and Simchi-Levi (2009);Laporte et al (1989); Ambrosino and Scutella (2005) …”

Section: Distribution Systems and The Food Supply Chainmentioning

confidence: 99%

A case analysis of a sustainable food supply chain distribution system—A multi-objective approach

Validi

Bhattacharya

Byrne

2014

International Journal of Production Economics

240

111

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Abstract:Sustainable supply chain management is a topical area which is continuing to grow and evolve. Within supply chains, downstream distribution from producers to customers plays a significant role in the environmental performance of production supply chains. With consumer consciousness growing in the area of sustainable food supply, food distribution needs to embrace and adapt to improve its environmental performance, while still remaining economically competitive. With a particular focus on the dairy industry, a robust solution approach is presented for the design of a capacitated distribution network for a two-layer supply chain involved in the distribution of milk in Ireland. In particular the green multiobjective optimisation model minimises CO2 emissions from transportation and total costs in the distribution chain. These distribution channels are analysed to ensure the non-dominated solutions are distributed along the Pareto fronts. A multi-attribute decision-making approach, TOPSIS, has been used to rank the realistic feasible transportation routes resulting from the trade-offs between total costs and CO 2 emissions. The refined realistic solution space allows the decision-makers to geographically locate the sustainable transportation routes. In addition to geographical mapping the decision maker is also presented with a number of alternative analysed scenarios which forcibly open closed distribution routes to build resiliency into the solution approach. In terms of model performance, three separate GA based optimisers have been evaluated and reported upon. In the case presented NSGA-II was found to outperform its counterparts of MOGA-II and HYBRID.

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“…However, SA is a highly effective stochastic method based on local search to solve combinatorial optimization problem (Yu et al 2010;Yen et al 2004). This paper proposes a GA-SA hybrid framework where GA explores better solutions by means of its strong global search ability and SA refines these solutions further in order to find the best solution.…”

Section: An Integrated Ga-sa/cpm/markov Algorithmmentioning

confidence: 99%

GA–SA/CPM/Markov based dynamic risk-management planning for virtual enterprises

et al. 2014

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A virtual enterprise (VE) is always in an environment with unpredictable change and dynamic markets. Therefore, a VE is more susceptible to risks. Minimizing risk in the operation of a project and ensuring success are major issues to concern in VEs. This paper develops a novel three-level dynamic risk-management planning model for VEs focusing on project organization mode and dynamic features of risk with the objective to maximize the completion probability under the constraints of cost, due date, and quality. The first level adopts non-linear integer programming techniques, the second level is about risk evaluation for the whole project based on network analysis, and the third level is on a Markov process based single process risk evaluation. An algorithm of integrated genetic algorithm plus simulated annealing/critical path method/Markov (GA-SA/CPM/Markov) is then designed to solve the problem and

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A simulated annealing heuristic for the capacitated location routing problem

Cited by 248 publications

References 38 publications

A solution method for a two-layer sustainable supply chain distribution model

A solution method for a two-layer sustainable supply chain distribution model

A case analysis of a sustainable food supply chain distribution system—A multi-objective approach

GA–SA/CPM/Markov based dynamic risk-management planning for virtual enterprises

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