Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction

The main body of competition has changed from the competition between enterprises to the competition between supply chains. The cooperation of the chain-to-chain attracts large attention from entrepreneurs and scholars in an increasingly competitive environment. Thus, this paper studies a chain-to-chain system with price competition consisting of one manufacturer and one retailer in each supply chain under carbon cap-and-trade regulation. Considering vertical-horizontal cooperation, the six different models are established by introducing the structural cooperation and the contractual cooperation (adopting the wholesale price contract) of supply chains. Based on the Stackelbeg game and Nash game, the optimal solutions of six models are obtained. The results show that the decisions, profits, and social welfare of the supply chains are affected by the structural cooperation than the contractual cooperation. The asymmetric competition structure changes the bargaining power of the supply chains. The relationship of supply chains' pricing only is affected by the cooperation structures. The relationship of the cap setting, profits, and social welfare are affected by the cooperation structures and the main decision parameters.

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“…As revenue sharing contract, cost sharing contract and wholesale price contract etc. are widely adopted in academic researchers [13,17,18,21,35].…”

Section: Introductionmentioning

confidence: 99%

Withdrawn: Optimal cap setting and pricing in supply chains under vertical-horizontal cooperation and cap-and-trade regulation

Shang

Qin

et al. 2021

RAIRO-Oper. Res.

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“…To model the uncertainty, the cost of energy loss was treated as a fuzzy variable assuming that it follows a trapezoidal membership function. In order to make a variable fuzzy, the crisp input is converted to the fuzzy number [46][47][48][49]. Then fuzzy numbers are evaluated using logical rules.…”

Section: Formulation Of Objective Function and Constraintsmentioning

confidence: 99%

Power Distribution Network Expansion and Location Optimization of Additional Facilities: A Case Study

et al. 2021

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Planning the power distribution network is critical and challenging; the main challenges include the multiple costs involved, selecting the appropriate locations of different nodes of the network at minimal cost, and minimizing the cost of energy loss for both the primary and secondary networks. Literature on the power distribution network presents different approaches, however, lacks to address the several issues of the complex power distribution networks and many aspects are yet to be explored; for example, the uncertain cost of energy loss. This study intends to address the gaps in the literature by proposing a four-phased approach. In doing so, first, an integer linear programming model is formulated with the objective of cost minimization. Secondly, fuzzy variables are used to tackle the parameters with uncertainty; cost of energy loss. In the third phase, a fine-tuned genetic algorithm (FT-GA) that uses the Taguchi Orthogonal Array is introduced to solve the mathematical model. It is worth mentioning that during the design of the experiment, the input parameters are crossover rate, elite count, and population size. In the last phase, a pragmatic approach is adopted and a Pakistan-based case study is used to validate the proposed model and its implication in real-life scenarios. The results exhibit that our proposed approach outperforms traditional methods like the genetic algorithm (GA) and inter-point methods in terms of fitness function value, number of generations, and computational time. This research contributes at both theoretical and managerial levels and may help decision-makers to design networks more efficiently and cost-effectively in Pakistan, Asia, and beyond.

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“…Further, this study incorporates the budget constraints for the vendor and the space constraint for the buyer while considering the variable production rate and the customers' service level. [4] Production NA Tajbakhsh [32] Inventory NA Glock [6] Production NA Pasandideh et al [37] SCM NA Jamshidi et al [38] SCM NA Sarkar et al [33] Production Continuous Bazan et al [22] SCM NA Gholami-Qadikolaei et al [28] Inventory NA Shin et al [30] SCM NA Sarkar et al [39] SCM Discrete Wangsa [16] SCM NA AlDurgam et al [7] SCM NA Malik and Sarkar [29] Inventory Continuous Aljazzar et al [25] SCM NA Sarkar et al [8] SCM NA Malik and Sarkar [40] SCM Discrete Marchi et al [26] SCM NA Sarkar and Chung [3] SCM Discrete This Model SCM Discrete SCM: Supply chain management; NA: Not applicable to the study.…”

Section: Introductionmentioning

confidence: 99%

“…In supply chain models, researchers considered a fixed setup cost for the vendor. However, the setup cost can be reduced by the initial investment to raise the technology of production systems ( [3,40]). For setup cost reductions, a discrete investment function is considered in this study.…”

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confidence: 99%

A Constrained Production System Involving Production Flexibility and Carbon Emissions

Malik

Kim

2020

Mathematics

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The proposed study presents an economic lot size and production rate model for a single vendor and a single buyer setup. This model involves greenhouse gas (GHG) emissions from industrial sources. The carbon emissions in this model are considered as two types: direct emissions and indirect emissions. The production rate affects carbon emissions generation in production, i.e., generally, higher production rates result in more emissions, which is governable in many real-life cases. The production rate also impacts the process reliability and quality. Faster production deteriorates the production system quickly, leading to machine failure and defective items. Such reliability and quality problems increase energy consumptions and supply chain (SC) costs. This paper formulates a vendor-buyer SC model that tackles these issues. It considers two decision-making policies: integrated or centralized as well as decentralized, where the aim is to obtain the optimal values of the decision variables that give the minimum total SC cost. It includes the costs of setup, holding inventory, carbon emissions, order processing, production, reworking, and inspection processes. The decision variables are the production rate, lead time, order quantity, the number of shipments, and the investments for setup cost reduction. In the later sections, this paper compares the numerical outcomes of the two centralized and decentralized policies. It also provides sensitivity analysis and useful insights on the economic and environmental execution of the SC.

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Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction

Cited by 29 publications

References 62 publications

Withdrawn: Optimal cap setting and pricing in supply chains under vertical-horizontal cooperation and cap-and-trade regulation

Withdrawn: Optimal cap setting and pricing in supply chains under vertical-horizontal cooperation and cap-and-trade regulation

Power Distribution Network Expansion and Location Optimization of Additional Facilities: A Case Study

A Constrained Production System Involving Production Flexibility and Carbon Emissions

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