Combining data envelopment analysis and multi-objective model for the efficient facility location\u2013allocation decision

Jae-Dong Hong,Ki-Young Jeong

doi:10.1007/s40092-018-0294-2

Abstract

This paper proposes an innovative procedure of finding efficient facility location–allocation (FLA) schemes, integrating data envelopment analysis (DEA) and a multi-objective programming (MOP) model methodology. FLA decisions provide a basic foundation for designing efficient supply chain network in many practical applications. The procedure proposed in this paper would be applied to the FLA problems where various conflicting performance measures are considered. The procedure requires that conflicting performance measures classified as inputs to be minimized, or outputs to be maximized. Solving an MOP problem generates diverse alternative FLA schemes along with multi-objective values. DEA evaluates these schemes to generate a relative efficiency score for each scheme. Then, using stratification DEA, all of these FLA schemes are stratified into several levels, from the most efficient to the most inefficient levels. A case study is presented to demonstrate the effectiveness and efficiency of the proposed integrating method. We observe that the combined approach in this paper performs well and would provide many insights to academians as well as practitioners and researchers.

Highlights

The facility location–allocation (FLA) decision is often considered the most important factor leading to the success of a private- or public-sector organization. Daskin (2013) emphasizes the importance of facility location problems by asserting in his recent book that in short, the success or failure of both private and public-sector facilities depends in part on the locations chosen for those facilities
Since the design of efficient supply chain networks starts from efficient FLA decisions, the FLA models have been widely used in practical life as well as in many academic disciplines
Decision-makers may want to consider DMU251 generated with α = (0.1, 0, 0, 0, 0.9) rather than DMU10, since they can observe that switching from DMU10 to DMU251 would reduce expected number of non-disrupted supplies (ENDS) by 133 K (= 4027–3894 K) (3.3%), but all other inputs are significantly saved: total logistics cost (TLC), maximum coverage distance (MCD), maximum demand‐weighted coverage distance (MDWCD), and nCDE are reduced by $14,034 K (29%), 68 miles (31%), 28282 K miles (45%), 636 K (19%), respectively

Summary

Introduction

The facility location–allocation (FLA) decision is often considered the most important factor leading to the success of a private- or public-sector organization. Daskin (2013) emphasizes the importance of facility location problems by asserting in his recent book that in short, the success or failure of both private and public-sector facilities depends in part on the locations chosen for those facilities. The objective of this paper is to present and demonstrate how to combine DEA and MOP techniques for the efficient FLA decisions and patterns to help practitioners as well as decision-makers who are responsible for the strategic and operational decision plans. Considering each generated alternative option for a given set of weight as a DMU, we evaluate all alternative options by utilizing DEA technique to find the efficiency of each alternative option and identify the most efficient FLA schemes. In this way, decision-makers evaluate and identify efficient and robust FLA decisions without any subjective judgment. We demonstrate our proposed method by MOFLA formulation and DEA evaluation through a case study using actual data in South Carolina, followed by conclusions

Literature review

Objective

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Conclusions