A collaborative scheduling GA for products-packages service within extended selling chains environment

Abstract

The theory of network coordination provides theoretical foundations to explain how companies can overcome organizational boundaries and constraints to jointly manage business processes across their selling chains. In particular, this work focuses on Collaborative Scheduling, a collaboration process whereby selling chain trading partners activate either on-line or off-line inter-firm coordination mechanisms to jointly plan production activities in order to deliver the final products to end customers each one of them, being the delivery date as close to the date desired as possible. The problem of collaborative scheduling is formally defined by means of a mathematical model. In the model, the defined objective function has the goal to minimize the total weighted tardiness of the package of products acquired by the clients to be delivered in a specific date. The delivery date of each Product-Package is conditioned by the latest date established by each supplier for each product that forms part of the same one. Besides, having different process times for each product and different penalties for each Product-Package, each supplier can offer a different mix of additional products with different due date. Due to the complexity of the problem a Genetic Algorithm has been the approach taken for its resolution. The GA elements and procedures are defined and the parameters are tuned. Although the major contribution of this work focuses on the algorithmic development of a proposal in the context of operations research that could help to solve the problem also is discussed the environment in which this occurs and that justifies our interest. In order to validate the proposed solutions diverse configurations are presented and the results obtained by means of the GA and some heuristics rules are compared.

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

“…Lam et al developed a scheduling Genetic Algorithm (GA) to minimize product design time [12]. Gomez-Gasquet et al developed a modified GA for collaborative scheduling of products-packages service [13]. Krishnamoorthy et al proposed a Lagrangean approach for solving the Personnel Task Scheduling Problem (PTSP) [14].…”

A Framework for Improving Planning-Scheduling Collaboration in Industrial Production Environment

“…Some papers dedicate to deliver partial elements. Sometimes papers describe some conceptual models or architectures [6], [22], others papers introduces methodologies or algorithms [7], [31], [26].…”