Cooperative bacterial foraging optimization method for multi-objective multi-echelon supply chain optimization problem

Niu, Ben; Tan, Lijing; Liu, Jing; Liu, Jia; Yi, Wenjie; Wang, Hong

doi:10.1016/j.swevo.2019.05.003

Cited by 24 publications

(11 citation statements)

References 34 publications

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“…Except above three categories, BFO can also be improved by exploring the boundary treatments to deal with the infeasible solutions, 17,36 chemotaxis‐communication strategies to speed up the convergence performance, 37,38 new reproduction or E‐D strategies to preserve the global optimal, 39,40 and so forth. Though great efforts have been made to modify the BFO, it is still a challenging task in the BFO research to keep a good balance among the simple algorithmic structure, efficient execution, fast convergence, and excellent exploration.…”

Section: Bacterial Foraging Optimizationmentioning

confidence: 99%

Simplified bacterial foraging optimization with quorum sensing for global optimization

et al. 2021

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Bacterial foraging optimization (BFO) has been exploited for function optimization, owing to its innovative ideas gleaned from the microbiological system. This paper first discusses its three crucial limitations: high computational cost, difficulty in parameter settings, and premature convergence. To alleviate the above problems, simplified BFO with quorum sensing (QS) is proposed. First, a novel computational framework is provided to reduce the computational complexity, leading to a simplified version. Second, the concept of “QS,” bacterial reciprocal behavior, is integrated into the simplified version by utilizing a new position updating equation coupled with a dynamic communication topology. Each bacterium adjusts its search trajectory based on both biased random walk and promising search directions provided by its communicatees. The communicatees are selected via a dynamic communication topology, where a rank‐based communication strategy and two information mutation schemes are used for global exploration of the search space. Finally, a parameter automation strategy is introduced to promote the exploitation of promising regions. Further, the effectiveness and efficiency of the proposed algorithm are empirically confirmed on 30 benchmark functions, by comparing it with the four variants of BFO and four other advanced algorithms.

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Section: Bacterial Foraging Optimizationmentioning

confidence: 99%

Simplified bacterial foraging optimization with quorum sensing for global optimization

et al. 2021

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“…Mardan (2019) planned an accelerated Bender's decomposition algorithm for a bi-objective green CLSC network design problem. Niu et al (2019) devised a cooperative bacterial foraging optimization method for a multi-objective multi-echelon supply chain optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving a new bi-objective multi-echelon supply chain problem with a Jackson open-network issue under uncertainty

2021

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In today's world, changes in the economic and industrial sectors are taking place more quickly than in the past. Based on today's competitive market, considering the needs and desires of the customer is very important. Thus, designing the supply chain network to minimize the costs and improve the system efficiency, the service facility, and attention to the timely service delivery to the customer should be considered. Also, deployment costs to determine the number and position of facilities are regarded as another strategic issue. Therefore, to become closer to the real-world application, by considering the Jackson network model (queuing theory), the present study aims to design a closedloop supply chain (CLSC) network to reduce the waiting time in the queue. Two objectives, namely minimizing the cost and waiting time incurred in queues formed in producers and distributors, are investigated. Queuing mathematical expressions are used both in dealing with objectives and constraints. To develop a model, which is close to real by considering uncertainty, the parameters, including demand rate and shipping cost of products, were considered as a trapezoidal fuzzy number. Also, we crisp this fuzzy uncertainty using both Jimenez and chance-constrained programming approaches. Due to the suggested model's complexity and nonlinearity, we linearized the mathematical model as much as possible, then two meta-heuristic algorithms, namely multiobjective particle swarm optimization (MOPSO) and non-dominated sorted genetic algorithm (NSGA-II), are used to solve the model. First, the structural parameters of these algorithms are tuned by the Taguchi method. Then, numerous numerical test problems ranging from small to large scales are used to compare these algorithms considering Pareto front measuring indexes and various hypothesis tests.

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“…One the biggest problem in the PFSC there is an imbalance between supply and demand, with periods of high and low supply being generated, while the demand does not respond in the same way, which affects the balance in the flows (Orjuela Castro, Diaz & Bernal, 2017). Specifically, in supply chains with seasonal fruits, this imbalance is common, which forces the reconfiguration of the chain, this affects different strategic decisions such as selection of suppliers, location of facilities, among others (Nayeri, Paydar, Asadi-Gangraj & Emami, 2020), however, to ensure that the reconfiguration of the chain is sustainable, it is required to meet multiple objectives at the same time (Niu, Tan, Liu, Liu, Yi & Wang, 2019). Many times, these objectives are in conflict and compete for limited resources, which complicates the problem (Trisna, Marimin, Arkeman & Sunarti, 2016).…”

Section: Introductionmentioning

confidence: 99%

Logistics network configuration for seasonal perishable food supply chains

Castro

Orejuela-Cabrera

Adarme-Jaimes

2021

JIEM

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Purpose: The location of actors and production, transformation, commercialization and distribution facilities generate the configuration of the perishable food supply chain (PFSC). The logistics network design (LND) lead to different supply, storage and distribution environments and result in flows, these logistics operations have an impact on the performance of the chain of fresh agricultural products. Specifically, in the PFSC with seasonality, a temporary imbalance between supply and demand is generated, which affects the balance in the flows and affects the losses, costs and response capacity. This research study that problem.Design/methodology/approach: A multi-echelon, multi-objective and multi-product model is established that determines the configuration of the logistics network of the PFSC with seasonally and allows actors to approach efficiency frontiers, which contribute to their permanence in the markets. The model was applied to the case of the perishable fruit supply chain, in a developing country, Colombia. The population information was taken from government surveys and studies, which allowed parameterizing the model.Findings: The application of model permitted to answer the next questions: How does improve the performance from PFSC through logistics network design? And, How the existence of smallholdings in food production and from retail merchants is preserved? In the perishable food supply chain.Social implications: Where the perishable food supply chain actors are located in non-efficient borders and their sustainability may be affected by competition factors, the stakeholders should focus on increasing food availability and reducing losses to improve access and therefore food security.Originality/value: A new model for the logistics network configurations of seasonal perishable fruit supply chain was development, the optimizing from five objectives with two sub-models is obtained, one for surplus and the other for the deficit moments. The model includes real conditions, such as, fruit loss due T0 and RH by different thermal floors and the means used for the transport of fruits in mountain areas at countries in route of development. The impact over the small producers, retailers and consumers in the times of scarcity or surplus was analyzed.

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Cooperative bacterial foraging optimization method for multi-objective multi-echelon supply chain optimization problem

Cited by 24 publications

References 34 publications

Simplified bacterial foraging optimization with quorum sensing for global optimization

Simplified bacterial foraging optimization with quorum sensing for global optimization

Solving a new bi-objective multi-echelon supply chain problem with a Jackson open-network issue under uncertainty

Logistics network configuration for seasonal perishable food supply chains

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