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The purpose of this paper is to develop a multi-item economic production quantity (EPQ) strategy under grey environment and space constraint. Since the “demand” cannot be predicted with certainty, it is assumed that data behave under grey environment and compare the proposed inventory model with other studies using crisp or fuzzy environments.

This paper is to optimise the cycle time and total cost of the multi-item EPQ inventory model. For this purpose, the Lagrangian coefficient is used to solve the constrained optimisation problem. The grey relational analysis approach and grey data are applied in developing the EPQ inventory model.

The results are compared with the analysis using crisp and fuzzy data. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The results of the study demonstrate that crisp data outperform the other two data in all scales problems in terms of cycle time and cost; grey data perform better in all scales problems than fuzzy data.

The contribution of this research is the use of grey data in developing the EPQ inventory model with space constraint.

The oil and gas industry is a crucial economic sector for both developed and developing economies. Delays in extraction and refining of these resources would adversely affect industrial players, including that of the host countries. Supplier selection is one of the most important decisions taken by managers of this industry that affect their supply chain operations. However, determining suitable suppliers to work with has become a phenomenon faced by these managers and their organizations. Furthermore, identifying relevant, critical and important criteria needed to guide these managers and their organizations for supplier selection decisions has become even more complicated due to various criteria that need to be taken into consideration. With limited works in the current literature of supplier selection in the oil and gas industry having major methodological drawbacks, the purpose of this paper is to develop an integrated approach for supplier selection in the oil and gas industry.

To address this problem, this paper proposes a new uncertain decision framework. A grey-Delphi approach is first applied to aid in the evaluation and refinement of these various available criteria to obtain the most important and relevant criteria for the oil and gas industry. The grey systems theoretic concept is adopted to address the subjectivity and uncertainty in human judgments. The grey-Shannon entropy approach is used to determine the criteria weights, and finally, the grey-EDAS (evaluation based on distance from average solution) method is utilized for determining the ranking of the suppliers.

To exemplify the applicability and robustness of the proposed approach, this study uses the oil and gas industry of Iran as a case in point. From the literature review, 21 criteria were established and using the grey-Delphi approach, 16 were finally considered. The four top-ranked criteria, using grey-Shannon entropy, include warranty level and experience time, relationship closeness, supplier’s technical level and risks which are considered as the most critical and influential criteria for supplier evaluation in the Iranian oil and gas industry. The ranking of the suppliers is obtained, and the best and worst suppliers are also identified. Sensitivity analysis indicates that the results using the proposed methodology are robust.

The proposed approach would assist supply chain practicing managers, including purchasing managers, procurement managers and supply chain managers in the oil and gas and other industries, to effectively select suitable suppliers for cooperation. It can also be used for other multi-criteria decision-making (MCDM) applications. Future works on applying other MCDM methods and comparing them with the results of this study can be addressed. Finally, broader and more empirical works are required in the oil and gas industry.

This study is among the first few studies of supplier selection in the oil and gas industry from an emerging economy perspective and sets the stage for future research. The proposed integrated grey-based MCDM approach provides robust results in supplier evaluation and can be used for future domain applications.

This study aims to develop a measurement model of the halal supply chain resilience (HSCRES) index, which represents the capability of the supply chain (SC) to handle disruption caused by halal risks. A case study is conducted to apply the HSCRES index in the halal chicken SC in Yogyakarta, Indonesia, to test the proposed methodology.

A literature synthesis was conducted to establish the main capability and vulnerability factors and their relevant indicators. The indicators were validated using the confirmatory factor analysis approach. Then, applying an analytical hierarchy process involving ten experts – practitioners and academicians – the weight of each indicator was obtained. A survey of 20 employees of slaughterhouses, 35 sellers and 100 consumers was conducted to obtain the value of each indicator. Finally, the HSCRES index was calculated by comparing the total weighted capability value to vulnerability.

The results revealed that the resilience of halal chicken SC in Yogyakarta is at a good level, with an index of 3.459, and “halal team” is the most significant indicator. The findings also revealed several capabilities that need improvement, including dedicated halal facilities, employees’ halal competence and halal regulation. However, the lack of a halal certification board, lack of management commitment and packaging contamination were found as vulnerability indicators that need to be reduced.

The case of this study is limited to the halal chicken SC in Yogyakarta, Indonesia. As a consequence, the obtained results are limited to a specific context. The application of this method to different areas and objects enables the establishment of different capability and vulnerability indicators.

The halal resilience measurement model offers a comprehensive understanding of the strengths and weaknesses of the HSC. The findings can help stakeholders improve preparedness for halal risks, deal with halal risks better and recover more quickly. Measuring the HSCRES index can be particularly useful for policymakers in developing evidence-based strategies to increase HSCRES.

The current study is the first to define and classify the contributing halal resilience attributes and also to calculate the halal resilience index.

Finding efficient disaster recovery center location-allocation-routing (DRCLAR) network schemes play a vital role in the disaster recovery logistics network (DRLN) design. The purpose of this paper is to propose and demonstrate how to design efficient DRCLAR network schemes under the risk of facility disruptions as a part of the disaster relief activities.

A goal programming (GP) model is formulated to consider four performance measures simultaneously for the DRCLAR design. The cross-evaluation based-super efficiency data envelopment analysis (DEA) approach is applied to better evaluate the DRCLAR network schemes generated by solving the GP model so that more efficient network schemes can be identified.

The proposed approach identifies more efficient DRCLAR network schemes consistently among various network schemes generated by GP. We find that combining these two methods compensates for each method's weaknesses and enhances the discriminating power of the DEA method for effectively identifying and ranking the network schemes.

This study presents how to generate balanced DRCLAR network schemes and how to evaluate various network schemes for identifying efficient ones. The proposed procedure of developing and evaluating them could be extended for designing some disaster recovery/relief supply chain systems with conflicting performance measures.