دانلود رایگان ترجمه مقاله رویکرد ژنتیک برای بهینه سازی دو فازی زمانبندی زنجیره تامین پویا (نشریه الزویر 2010)

یک رویکرد ژنتیک برای بهینه سازی دو فازی برای زمانبندی زنجیره تامین دینامیک

A genetic approach to two-phase optimization of dynamic supply chain scheduling

Abstract

In today’s competitive environment, agility and leanness have become two crucial strategic concerns for many manufacturing firms in their efforts to broaden market share. Recently, the build-to-order (BTO) manufacturing strategy is becoming a popular operation strategy to achieve both in a mass-scale customization process. BTO system combines the characteristics of make-to-order strategy with a forecast driven make-to-stock strategy. As a means to improve customer responsiveness, customized products are assembled according to specific orders while standard components are pre-manufactured based on short-term forecasts. Planning of the two subsystems using a two-phase sequential approach offers both operational and modeling incentives. In this paper, we formulate a two-phase mixed integer linear programming (MILP) model for material procurement, components fabrication, product assembly and distribution scheduling of a BTO supply chain system. In the proposed approach, the entire problem is first decomposed into two subsystems and evaluated sequentially. The first phase deals with assembling and distribution scheduling of customizable products, while the second phase addresses fabrication and procurement planning of components and raw-materials. The objective of both models is to minimize the aggregate costs associated with each subsystem, while meeting customer service requirements. The search space for the first phase problem involves a complex landscape with too many candidate solutions. A genetic algorithm based solution procedure is proposed to solve the sub-problem efficiently.

1 Introduction

The build-to-order (BTO) manufacturing system is a pull system in which materials are pulled downstream of the supply chain driven by customer orders. It basically incorporates the characteristics of both lean and agile manufacturing strategies. Unlike the traditional make to stock supply chain, BTO strategy reduces the dependency of the system on demand forecasts, hence diminishing the requirement of high inventory buffers in the supply chain as pointed out in Gunasekaran and Ngai (2005). BTO systems combine the characteristics of both make-to-stock (forecast driven) and make-to-order (demand driven) strategies. Standard component parts and non-customizable subassemblies are acquired or build in-house based on short-term forecasts, while schedules for the few customizable parts and the final assembly are executed after detailed product specifications have been derived from booked customer orders, see Demirli and Yimer (2008).

Customization of products can only be achieved if there is some form of postponent strategy either in the assembly state, assembly area, delivery or at the design phase. As described by Li, Cheng, and Wang (2007), postponent refers to delaying some product differentiation or process as late as possible until the supply chain becomes cost effective. Customer’s input in BTO manufacturing environment would involve postponent in downstream decisions with some speculation on the upstream manufacturing and supplies, see Prasad, Tata, and Madan (2005). Manufacturing plants operating under BTO supply chain use one of the three form postponent strategies in their functions: finished goods, work-in-process parts and purchased items or raw-materials as shown in Krajewski, Wei, and Tang (2005). Sharma and LaPlaca (2005) study the long-term impact of adopting a BTO manufacturing system on the marketing function and identify the marketing strategies used by successful BTO companies. A BTO strategy positively affects market performance through its influence on the supply chain application knowledge downstream with customers, while a JIT strategy does the upstream application with suppliers, see Christensen, Germain, and Birou (2005).

If we consider the upholstered furniture business, it is characterized by a wide range of product styles and a diversified customer demand. A variety of basic frame styles, fabrics, colors and other special options would generate a wide range of custom-built products. Therefore, a lean production system along with an agile strategy must be implemented to keep the units moving through the plant and to the customer smoothly as shown in Lyons, Coronado-Mondragon, and Kehoe (2004). As a result, firms such as Pella, Herman Miller and Norwalk have shifted to a BTO manufacturing strategy and assemble different customized products, see Gunasekaran and Ngai (2005), Sharma and LaPlaca (2005), Yao and Carlson (2003). Agile manufacturing facility can cope with changes in customer requirements including price, quality, customization, and promised delivery dates as indicated by Christian and Zimmers (1999). In most cases, furniture products consume large amounts of space during production, storage and shipment. A lean production system is thus important to curb large space requirements. A lean furniture production system uses its skilled work force and flexible handling equipment to quickly move small batch of material units from one workstation to the next thereby minimizing WIP. To enhance both agility and leanness, constructing a recommended cluster of fabrics available in different styles and colors would help limit the degree of customization.