Decision making under uncertain and dependent system rates in service systems

Ekin, Tahir; Aktekin, Tevfik

doi:10.1016/j.ejor.2020.09.005

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…The authors also conducted numerical experiments to demonstrate the trade-offs of quality level and materials cost. Other works (Brahmi et al, 2020;Nogueira et al, 2020;Tolooie et al, 2020;Astuty et al, 2021;Ekin and Aktekin, 2021;Frank et al, 2021;Martins et al, 2021;Ongcunaruk et al, 2021;Pando et al, 2021) considered various features of adjustable manufacturing rates and discrete shipping plans to optimize fabrication-inventory systems and supply-chain operations. This study proposes a two-stage delayed differentiation model for a multiproduct manufacturer-retailer coordinated supply chain featuring the adjustable-rate for making common parts and a multi-shipment policy for transporting finished goods.…”

Section: Introductionmentioning

confidence: 99%

Multiproduct manufacturer-retailer coordinated supply chain with adjustable rate for common parts, delayed differentiation, and multi-shipment

Lin¹,

Chiu²,

Wu³

et al. 2022

10.5267/j.uscm

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Operating in today’s turbulent and competitive world marketplaces, manufacturers must find the best production scheme and delivery policy to meet timely client’s multiproduct requirements and minimize the total manufacturing-shipment expenses. This study proposes a two-stage delayed differentiation model for a multiproduct manufacturer-retailer coordinated supply chain featuring the adjustable-rate for making common parts and a multi-shipment policy for transporting finished goods. The aim is to help present-day manufacturers achieve their operational goals mentioned above. The mathematical techniques help us build a specific model to explicitly represent the problem and derive its overall operating expense. Then, the convexity of the total expense is verified by Hessian matrix equations. The differential calculus helps derive the cost-minimized fabrication-shipment decision. This study offers an example to demonstrate the applicability and capabilities of our proposed model numerically. The following crucial information has been made available to the managers to facilitate their operating decision makings: (1) the problem’s best fabrication-shipment policy; (2) the collective influence of various common part’s completion rates and values on the problem’s total expenses and optimal fabrication-shipment policy; (3) the impact of various adjustable-rates in stage one on utilization and stage one’s uptime; (4) the details of cost contributors to the problem; and (5) the collective impacts of critical features on the problem’s performance.

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