A distributed decision making system for integrated optimization of production scheduling and distribution for aluminum production line

Nishi, Tatsushi; Konishi, Masami; Ago, Masatoshi

doi:10.1016/j.compchemeng.2006.10.006

Cited by 43 publications

(15 citation statements)

References 20 publications

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“…This is done until all hard constraints are satisfied and penalties are preferably at a minimum. Details on the exact working of mathematical decomposition can be found in works by Nishi et al (2007), Walther et al (2008) and Lu et al (2012).…”

Section: Review Of Earlier Taxonomies and Classification Attemptsmentioning

confidence: 99%

“…The remaining three studied systems employed mathematical decomposition through Lagrangean relaxation of constraints. The first of these by Nishi et al (2007) was developed to determine the production scheduling and distribution planning for a single stage production system with parallel distributed production units. The novelty is in their use of quadratic penalty terms in the objective function.…”

Section: Analysis Of Literaturementioning

confidence: 99%

See 1 more Smart Citation

A Decentralized Production and Distribution Planning Model in an Uncertain Environment

Hegeman

Peidro

Alemany

et al. 2014

Supply Chain Management Under Fuzziness

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Distributed Decision Making (DDM) is a discipline of decision theory in which decision making power is distributed among several decision making units. Supply Chain planning problems usually involve multiple decision makers, making DDM highly suitable for realistic modelling. Furthermore, due to the complexity and dynamism of supply chain environments, accounting for uncertainty is important when modelling a supply chain planning problem. This chapter contributes to existing knowledge on the one hand with a brief literature review of DDM systems developed in the recent past. On the other hand, it contributes a proposed DDM coordination mechanism for a supply chain planning problem with two distributed decision makers, in a multi-echelon context, with multiple product levels. The DDM system's performance is evaluated under demand uncertainty by applying a fuzzy approach. Computational results show that the proposed distributed model closely approximated the optimal solutions generated by the centralised model, strengthening the evidence for DDM's applicability to real problems. Finally, the fuzzy approach is shown to be a useful tool for decision makers in evaluating risk in their supply chain planning decisions.

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Section: Review Of Earlier Taxonomies and Classification Attemptsmentioning

confidence: 99%

Section: Analysis Of Literaturementioning

confidence: 99%

A Decentralized Production and Distribution Planning Model in an Uncertain Environment

Hegeman

Peidro

Alemany

et al. 2014

Supply Chain Management Under Fuzziness

View full text Add to dashboard Cite

show abstract

“…The study contained di erent transportation times and costs to solve the values of decision variables with the objective of minimizing cost. Similar studies include Nishi et al [9] who used Lagrangian decomposition to solve the multi-stage supply network problems of a single plant with multiple products. Kanyalkar and Adil [10] incorporated production capabilities and limited inventory space, and applied the heuristic method to maximize target pro ts.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Green Supply Chain Network Designs for Transportation Mode Selection

Gong

Chen

2017

Scientia Iranica

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Abstract. This research considers both cost and environmental protection to design a multi-objective optimization model. With multi-period customer demands, the model can solve a multi-plant resource allocation and production planning problem by focusing the decisions on supplier selection, facility selection, production batches, transportation mode selection, and distribution of the materials and commodities of a green supply network. In this paper, four transportation modes, namely, road, rail, air, and sea, have their corresponding transportation time, costs, and CO 2 emissions. Based on multiple planning periods, this research calculates the minimal total cost and total CO2 emissions based on production and transportation capacity. Using numerical analyses, the results show that, when the budget is su cient, only production capabilities with = 1:5 and 2.0 are bene cial for improving environmental protection; carbon dioxide emissions of both production capacities are not signi cantly di erent. Furthermore, when the production batch size increases, total cost increases. Regarding transportation capacity, the results show that, when the budget is su cient, increasing transportation quantity limits will be slightly bene cial for improving environmental protection.

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“…In SCM, the companies are not considered as independent entities, but interacting entities which need to coordinate and integrate their process along the SC. Thus, the uncertainties related to external and internal processes constitute a challenge for the coordinated and integrated processes of SCM (Nishi, 2007). Jung et al (2008) developed a decentralized supply chain planning framework based on minimal-information sharing between the manufacturer and a third party logistics provider.…”

Section: Literature Of Past Workmentioning

confidence: 99%

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Khalili‐Damghani¹,

Ghasemi²

2016

Industrial Engineering and Management Systems

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Complex and uncertain issues in supply chain result in integrated decision making processes in supply chains. So decentralized (distributed) decision making (DDM) approach is considered as a crucial stage in supply chain planning. In this paper, an uncertain DDM through coordination mechanism is addressed for a multi-product supply chain planning problem. The main concern of this study is comparison of DDM approach with centralized decision making (CDM) approach while some parameters of decision making are assumed to be uncertain. The uncertain DDM problem is modeled through fuzzy mathematical programming in which products' demands are assumed to be uncertain and modeled using fuzzy sets. Moreover, a CDM approach is customized and developed in presence of fuzzy parameters. Both approaches are solved using three fuzzy mathematical optimization methods. Hence, the contribution of this paper can be summarized as follows: 1) proposing a DDM approach for a multi-product supply chain planning problem; 2) Introducing a coordination mechanism in the proposed DDM approach in order to utilize the benefits of a CDM approach while using DDM approach; 3) Modeling the aforementioned problem through fuzzy mathematical programming; 4) Comparing the performance of proposed DDM and a customized uncertain CDM approach on multiproduct supply chain planning; 5) Applying three fuzzy mathematical optimization methods in order to address and compare the performance of both DDM and CDM approaches. The results of these fuzzy optimization methods are compared. Computational results illustrate that the proposed DDM approach closely approximates the optimal solutions generated by the CDM approach while the manufacturer's and retailers' decisions are optimized through a coordination mechanism making lasting relationship. .

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A distributed decision making system for integrated optimization of production scheduling and distribution for aluminum production line

Cited by 43 publications

References 20 publications

A Decentralized Production and Distribution Planning Model in an Uncertain Environment

A Decentralized Production and Distribution Planning Model in an Uncertain Environment

Multi-Objective Optimization of Green Supply Chain Network Designs for Transportation Mode Selection

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

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