Optimization of In-Plant Production Supply with Black Hole Algorithm

Veres, Péter; Bányai, Tamás; Illés, Béla

doi:10.4028/www.scientific.net/ssp.261.503

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…Even so, Christopher & Holweg [2] argue that existing concepts dealing with volatility are not suitable in periods of emerging turbulence, instead proposing different means to achieve structural flexibility in order to deal with volatility more effectively. In addition, recent advances in heuristic optimization of supply chain coordination have made valuable new approaches available to SC managers who need to improve process efficiency in complex scenarios [10,11].…”

Section: Introductionmentioning

confidence: 99%

Unravelling the Complexity of Supply Chain Volatility Management

Nitsche

2018

Logistics

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Managing supply chain volatility (SCV) is often identified as one of the major challenges of modern supply chain management. While research has predominantly focused on describing the multidimensional areas of SCV and its negative impacts, clear guidelines on how to manage SCV for efficiency, and prioritize the areas on which to focus, are sparse. This study seeks to fill this gap in the research by: (1) assessing the relative impact of SCV sources, and (2) proposing means to deal with them. Based on an Analytical Hierarchy Process conducted with 17 SCM practitioners, the paper assesses the relative impact of sources of SCV, and further contextualizes them according to factors such as product lead time and production strategy, providing more fine-grained insights for SC managers seeking to manage SCV. Subsequently, the paper applies the Nominal Group Technique with the same group of practitioners in order to identify and condense strategies for dealing with the most impactful sources of SCV (intra-organizational misalignment, inaccurate forecasting, long lead times, erratic behavior of decision makers in the supply chain, erratic behavior of customers, and high level of competition), leading to a set of 44 SCV-management strategies.

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Section: Introductionmentioning

confidence: 99%

Unravelling the Complexity of Supply Chain Volatility Management

Nitsche

2018

Logistics

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“…Black hole optimization (BHO) can be used to solve binary [72,73], hybrid binary [74], discrete [75], and real problems [63]. The black hole algorithms can solve different NP-hard optimization problems related to electric power systems [76], scheduling in automotive industry [77], pattern synthesis ion radio science [78], earth sciences [79] logistics and supply chain [80], nondestructive testing of wiring networks [81], and electrochemical machining [82].…”

Section: Black Hole Algorithm-based Optimizationmentioning

confidence: 99%

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Bányai

2018

Energies

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Energy efficiency and environmental issues have been largely neglected in logistics. In a traditional supply chain, the objective of improving energy efficiency is targeted at the level of single parts of the value making chain. Industry 4.0 technologies make it possible to build hyperconnected logistic solutions, where the objective of decreasing energy consumption and economic footprint is targeted at the global level. The problems of energy efficiency are especially relevant in first mile and last mile delivery logistics, where deliveries are composed of individual orders and each order must be picked up and delivered at different locations. Within the frame of this paper, the author describes a real-time scheduling optimization model focusing on energy efficiency of the operation. After a systematic literature review, this paper introduces a mathematical model of last mile delivery problems including scheduling and assignment problems. The objective of the model is to determine the optimal assignment and scheduling for each order so as to minimize energy consumption, which allows to improve energy efficiency. Next, a black hole optimization-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase energy efficiency in last mile logistics.

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“…The design and operation of complex supply chain processes can be described as NPhard optimization problems [20]. These problems can be 3 Complexity solved using sophisticated models and methods based on metaheuristic algorithms, like black hole optimization [21]. Inventory management systems represent another important research field, where stochastic reliability measurement and design optimization are the main research directions [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

2018

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Supply chain management applies more and more Industry 4.0 innovations to increase their availability, elasticity, sustainability, and efficiency. In interconnected logistics networks, operations are integrated from suppliers through 3rd party logistics providers to customers. There are different delivery models depending on the time and cost. In the last few years, a wide range of customers is willing to pay an extra fee for the same delivery or instant delivery. This fact led to the increased importance of the optimized design and control of first mile/last mile (FMLM) delivery solutions. Cyberphysical system-based service innovations make it possible to enhance the productivity of FMLM delivery in the big data environment. The design and operation problems can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on heuristic and metaheuristic algorithms. This research proposes an integrated supply model of FMLM delivery. After a careful literature review, this paper introduces a mathematical model to formulate the problem of realtime smart scheduling of FMLM delivery. The integrated model includes the assignment of first mile and last mile delivery tasks to the available resources and the optimization of operations costs, while constraints like capacity, time window, and availability are taken into consideration. Next, a black hole optimization-(BHO-) based algorithm dealing with a multiobjective supply chain model is presented. The sensitivity of the enhanced algorithm is tested with benchmark functions. Numerical results with different datasets demonstrate the efficiency of the proposed model and validate the usage of Industry 4.0 inventions in FMLM delivery.

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Optimization of In-Plant Production Supply with Black Hole Algorithm

Cited by 14 publications

References 12 publications

Unravelling the Complexity of Supply Chain Volatility Management

Unravelling the Complexity of Supply Chain Volatility Management

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

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