Elsevier

Computers & Operations Research

Volume 98, October 2018, Pages 301-321
Computers & Operations Research

Heuristic modeling for sustainable procurement and logistics in a supply chain using big data

https://doi.org/10.1016/j.cor.2017.05.008Get rights and content

Abstract

Drastic climate change has enforced business organizations to manage their carbon emissions. Procurement and transportation is one of the supply chain business operations where carbon emissions are huge. This paper proposes an environmentally sustainable procurement and logistics model for a supply chain. The proposed models are of MINLP (Mixed Integer Non Linear Program) and MILP (Mixed Integer Linear Program) form requiring a variety of the real time parameters from buyer and supplier side such as costs, capacities, lead-times and emissions. Based on real time data, the models provide an optimal sustainable procurement and transportation decision. It is also shown that large sized problems possessing essential 3V's of big data, i.e., volume, variety and velocity consume non-polynomial time and cannot be solved optimally. Therefore, a heuristic (H-1) is also proposed to solve the large sized problems involving big data. T-test significance is also conducted between optimal and heuristic solutions obtained using 42 randomly generated data instances possessing essential characteristics of big data. Encouraging results in terms of solution quality and computational time are obtained.

Introduction

The global concern over environmental threats caused by various business operations has led researchers and practitioners to explore variety of approaches to reduce overall carbon footprint of a firm. Therefore, the business organizations have started reframing their strategic and operational policies to improve the environmental performance of the products or/and overall manufacturing processes starting from procurement of products till the delivery of finished goods. Hence, a complete integration and successful coordination among all the members of supply chain including raw material suppliers, manufacturers, distributors, and users is required (Fahimnia and Jabbarzadeh, 2016). Low carbon approach has been becoming the trend of the world economy. Carbon emission regulatory policies such as carbon cap, carbon tax, carbon offset, carbon cap and trade are being increasingly applied to various business organizations all over the globe. The globalization of business activities have led to increased demand of products and services worldwide. Therefore, the production, transportation, storage and consumption of increasing demand of products and services have further added to environmental problems.

In this information age, lot of data generates at both supplier and buyer side. However, most of the supply chain decisions still do not incorporates the big data characteristics into the decision making models. Therefore, it is important to jointly consider big data in supply chain modeling. Data available at supplier and buyer's side are mostly voluminous and also possesses variety and velocity characteristics of big data. In view of this, for effective and efficient decision making these available data should be utilized considering big data while modeling. Hence, supply chain modeling using big data provides a competitive edge to the business organization and makes the supply chain resilient and sustainable (Brown et al., 2011; Wamba et al., 2015). As much the big data is essential for decision making in highly volatile and competitive markets, it is equally challenging to store and analyse big data. This is the major reason that despite the huge scope of big data, there are very few attempts made so far to develop models using big data in supply chain modelling (Tayal and Singh, 2016; Lamba and Singh, 2016).

This paper proposes a joint sustainable procurement and logistics model for a carbon sensitive supply chain. The model considers the emissions caused during ordering, holding and logistics. The proposed model tends to obtain optimal decision by simultaneously minimizing procurement cost and carbon emissions cost. The proposed model is of MINLP type. MINLP is further linearized to MILP using Axioms. The model is solved using exact approach for big data possessing 3V's, i.e., volume, variety and velocity. It is observed that model takes non-polynomial time to solve in presence of big data. Therefore, a heuristic (H-1) is proposed to solve the model having big data. The optimal and heuristic solutions are also compared. T-test has been also conducted for statistical significance between heuristic and optimal solutions.

The structure of the paper is organized as follows. Detailed literature review is presented in Section 2. The joint sustainable procurement and logistics model is proposed in Section 3. The solution methodology using big data is provided in Section 4. The numerical illustrations are analyzed in Section 5. The conclusions and future scope of work are summarized in Section 6.

Section snippets

Literature review

The section provides detailed exhaustive review on the recent development on sustainable procurement and its logistics in supply chain. The past review is sub-divided into two sections where the first section focus on the modeling of sustainable procurement and logistics where as the later section present the big data application in modeling of sustainable procurement and logistics. Lastly, a section is provided to link big data with sustainable procurement and logistics in supply chain.

Problem statement

The procurement and logistics problem is considered here under a carbon cap and trade scenario, where a mandatory cap over the carbon emissions of a firm is kept. However, carbon emissions saved or exceeded are traded. The problem is multi-period, multi-product, multi-supplier and multi-carrier. In addition, the emissions caused during ordering, holding items, transporting through carriers are also considered in the problem. The problem is to optimize the order allocation among set of available

Solution methodology

In this section, the solution methodology to solve proposed sustainable procurement and logistics is proposed. The proposed model involves cost, capacity and emission parameters on the real time possessing essential big data characteristics (3V's). To incorporate big data characteristics into the joint procurement and logistics problem, Lamba and Singh (2016) proposed a big data framework for procurement and logistics. However, the demonstration of the proposed framework for real applications

MINLP

The MINLP model is coded and solved in LINGO 10. All executions are carried out in a machine with Windows 7 operating system, Intel core i7 processor and 8 Gb RAM. The model code is shown in Appendix A. Forty two computational experiments are carried out on randomly generated problem instances possessing essential big data characteristics (3V's). It is observed from the computational experiments optimal solution is obtained up to problem instances of T= 3. Moreover, it is also observed that the

Computational experiments

This section illustrates the proposed solution methodology using two illustrations from the forty two computational experiments already discussed in Section 4. In this section two illustrations are solved and discussed in detail. The two examples of different data sizes are discussed. The nomenclature of discussed examples follows (T-P-S-M) structure, where T, P, S, M stands for time periods, products, suppliers and carriers respectively. The illustrative examples are discussed in following

Conclusion and future scope of work

This paper proposes a joint procurement and logistics model for a sustainable supply chain. The model is able to provide a joint decision for lot sizing, supplier and carrier selection. The model considers carbon trading policy to account and manage total emissions caused during procurement and logistics, where excess/saved emissions are directly linked to the objective function in terms of carbon cost. Hence, an effective and optimal trade-off between the economic gains of a firm and its

Acknowledgment

Authors thank anonymous referee for their valuable comments which has improved the quality of the manuscript.

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