Impact of information sharing in hierarchical decision-making framework in manufacturing supply chains

Abstract

This paper presents a comprehensive framework for the analysis of the impact of information sharing in hierarchical decision-making in manufacturing supply chains. In this framework, the process plan selection and real-time resource allocation problems are formulated as hierarchical optimization problems, where problems at each level in the hierarchy are solved by separate multi-objective genetic algorithms. The considered multi-objective genetic algorithms generate near optimal solutions for NP-hard problems with less computational complexity. In this work, a four-level hierarchical decision structure is considered, where the decision levels are defined as enterprise level, shop level, cell level, and equipment level. Using this framework, the sources of information affecting the achievement of best possible decisions are then identified at each of these levels, and the extent of their effects from sharing them are analyzed in terms of the axis, degree and the content of information. The generality and validity of the proposed approach have been successfully tested for diverse manufacturing systems generated from a designed experiment.

IoT-based framework for performance measurement

“…Having a glance in the literature in this area, one can find some researchers such as Puigjaner and Laínez (2008) which proposed a multi-stage stochastic MILP model in order to analyze different scenarios in a real-time environment. Celik et al (2012) developed a solution method using the genetic algorithm to solve a multi-period real-time model. Wong (2012) developed a decision support system (DSS) using fuzzy logic, based on expert judgments to select 3PL.…”

A multi objective SCOR-Based Decision Alignment for Supply Chain Performance Management

“…Celik et al developed a solution method using the Genetic Algorithm (GA) to solve a multi-period real-time model [23]. High usability and exciting philosophy of Goal Programing (GP) for practitioners and researchers in handling decision-making problems with multi-objective structures made it very applicable [24,25].…”

“…Constraint (22) ensures that the supplied raw material n will be delivered to manufacturer j from supplier i if and only if the corresponding supplier is established. Finally, Constraints (23) and (24) [42] is one of the most commonly used multi-objective algorithms among researchers. In this research, an e ort is made to apply NSGAII to the considered multiechelon SC problem.…”

An efficient and scalable ubiquitous storage scheme for delay-sensitive IT applications

“…People have strict requirements on such ubiquitous services, especially on access time, location independence and system scalability. Undesired performance degradation will impact the user experience and accordingly damage the value chain of the storage industry (Celik et al 2010;Park et al 2009). …”