Elsevier

Omega

Volume 28, Issue 5, 1 October 2000, Pages 581-598
Omega

A multi-objective approach to simultaneous strategic and operational planning in supply chain design

https://doi.org/10.1016/S0305-0483(99)00080-8Get rights and content

Abstract

In this research, an integrated multi-objective supply chain (SC) model is developed for use in simultaneous strategic and operational SC planning. Multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (volume or delivery). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single-measure approaches. Moreover, this model incorporates production, delivery, and demand uncertainty, and provides a multi-objective performance vector for the entire SC network. The model developed here will aid in the: (1) design of efficient, effective, and flexible supply chain systems and (2) evaluation of competing SC networks.

Introduction

A supply chain is a set of facilities, supplies, customers, products and methods of controlling inventory, purchasing, and distribution. The chain links suppliers and customers, beginning with the production of raw material by a supplier, and ending with the consumption of a product by the customer. In a supply chain, the flow of goods between a supplier and a customer passes through several echelons, and each echelon may consist of many facilities.

The problems of supply–production, production–distribution, and inventory–distribution systems have been studied for many years. Most of these studies focus only on a single component of the overall supply–production–distribution system, such as procurement, production, transportation, or scheduling, although limited progress has been made towards integrating these components in a single supply chain.

Supply chain management (SCM) is a subject of increasing interest to academics, and practitioners. SCM can be divided into two levels: strategic and operational. Models have been developed for optimizing supply chain operations at these two levels. The primary objective of strategic optimization models is to determine the most cost-effective location of facilities (plants and distribution centers), flow of goods throughout the supply chain (SC), and assignment of customers to distribution centers (DCs). These types of models do not seek to determine required inventory levels, and customer service levels. The main purpose of the optimization at the operational level is to determine the safety stock for each product at each location, the size and frequency of the product batches that are replenished or assembled, the replenishment transport and production lead times, and the customer service levels.

Uncertainty is one of the most challenging but important problems in SC management. Indeed, it is a primary difficulty in the practical analysis of SC performance. In the absence of randomness, the problems of material and product supply are eliminated; all demands, production, and transportation behavior would be completely fixed, and therefore, exactly predictable. This work seeks to integrate strategic and operational analysis of a SC subject to uncertainty, using a performance vector designed to describe the efficiency and effectiveness of the chain.

Section snippets

Literature review

The supply chain (SC) has been viewed as a network of facilities that performs the procurement of raw material, the transformation of raw material to intermediate and end products, and the distribution of finished products to retailers or directly to customers. These facilities, which usually belong to different companies, consist of production plants, distribution centers, and end-product stockpiles. They are integrated in such a way that a change in any one of them affects the performance of

Model structure and formulation

In this research, the supply chain structure consists of four echelons: (1) suppliers, (2) plants, (3) distribution centers (DCs), and (4) customer zones (CZs). Each SC echelon has a set of control parameters that affects the performance of other components. The performance of each echelon will be optimized simultaneously at two planning levels using the strategic and operational sub-models.

Solution methodology

This section presents an iterative procedure in which the strategic-level optimization sub-model is combined with the operational-level optimization sub-model to determine the optimal SC performance vector. The steps of the algorithm are given below and illustrated in Fig. 1.

The strategic-operational optimization solution algorithm:

  • Step 1. Optimize the strategic-level sub-model for an existing or proposed SC network to obtain the initial optimal configuration, using mixed integer linear

Numerical example and model performance

The example developed here illustrates the algorithm proposed in Section 4, as well as the applicability and effectiveness of the model. The example system consists of three raw materials, two finished products, five vendors, three plants, four distribution centers, and five customer zones. This example consists of two sub-models: (1) strategic (27 binary variables and 123 continuous variables), and (2) operational (0 binary variables and 91 continuous variables, 18 of which are non-linear).

Summary and conclusions

This research developed a supply chain model that facilitates simultaneous strategic and operational planning using an iterative method. This model incorporates production, delivery, and demand uncertainty, and reduces complexity via reasonable simplifications. The model also provides an appropriate performance measure by using multi-objective analysis for the entire SC network. The model developed here aids in the design of efficient, effective, and flexible supply chains, and in the

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