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Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.

A multi‐criteria model is developed for analyzing the land‐use strategies for reducing the future social and economic costs in an area with potential natural hazard. A multi‐criteria decision‐making procedure consists of generating alternatives, establishing criteria, assessment of criteria weights and application of the compromise ranking method (VIKOR). The alternatives are the scenarios of sustainable hazard effects mitigation, generated in the form of comprehensive land‐use plans. The alternative plans should consider the redevelopment of urban areas and infrastructures, multi‐purpose land use, including restrictions on building in hazardous areas. The evaluation of alternatives is implicated with imprecision (or uncertainty) of established criteria, and the fuzzy multi‐criteria model is developed to deal with “qualitative” (unquantifiable or linguistic) or incomplete information. The planning goal is to compromise competitive land uses through choice of the best (compromise) combination of uses. An application of this model is illustrated with the post‐earthquake regional planning problem in central Taiwan.

Supplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.

The concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.

This study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.

This research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.

This work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.

In this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

The purpose of this paper is to formulate the process of measuring and benchmarking the performance of sustainability development of organizations as a multi-criteria analysis problem and presents an objective approach for solving the problem in a simple manner.

An objective approach is developed for benchmarking the sustainability development performance of individual organizations in the context of multi-criteria analysis. The relative importance of the sustainability indicators is determined independent of the subjective preferences of the decision maker using the concept of information entropy. A modified technique for order preference by similarity to ideal solutions is used for effectively incorporating the objective indicator weights into the process of determining the overall performance of sustainability development of each organization. As a result, an unbiased overall ranking of individual organizations on the performance of their sustainability development can be obtained.

The proposed approach is applicable for measuring and benchmarking the performance of organizational sustainability development through the presentation of an example.

The originality of the paper is on the development of the objective approach within the context of multi-criteria analysis for measuring and benchmarking the performance of sustainability development of individual organizations.

Multi‐criteria decision making exists in the daily lives with broad application backgrounds. Sometimes because of incomplete information, the weights can only be estimated subjectively, which leads to an unsatisfactory result. The purpose of this paper is to describe an interactive technique to decide multi‐criteria weights by multiple decision makers in the condition of incomplete information by means of virtual environment.

The procedure assumed a problem with n criteria and r decision makers. The algorithms employed are presented.

It was found that the proposed framework is an effective weight‐deciding tool; the procedure quickly locates excellent compromise weights in a series of test problems.

The methodology helps decision makers determine a most preferred final solution by the virtual environment especially when there is incomplete information. The procedures provide a mechanism to guide the decision makers towards an acceptable compromise solution, without requiring excessive decision maker input. Studies show that the procedure performs well in terms of solution quality, simplicity, computational requirements, convergence and flexibility.

In this paper, the authors investigated a proposed radio-frequency identification (RFID)-based meat supply chain to monitor quality and safety of meat products we purchase from supermarkets. The supply chain consists of farms, abattoirs and retailers. The purpose of this paper is to determine a cost-effective trade-off decision obtained from a developed multi-criteria optimization model based on three objectives. These objectives include customer satisfaction in percentage of product quantity as requested by customers, product quality in numbers of meat products and the total implementation cost. Furthermore, this work was aimed at determining the number and locations of farms and abattoirs that should be established and quantities of products that need to be transported between entities of the proposed supply chain.

To this aim, a tri-criteria optimization model was developed. The considered criteria were used for minimizing the total implementation cost and maximizing customer satisfaction and product quality. In order to obtain Pareto solutions based on the developed model, four solution approaches were employed. Subsequently, a new decision-making algorithm was developed to select the superior solution approach in terms of values of the three criteria.

A case study was applied to examine the applicability of the developed model and the performance of the proposed solution approaches. The computational results proved the applicability of the developed model in obtaining a trade-off among the considered criteria and solving the RFID-based meat supply chain design problem.

The developed tri-criteria optimization model can be used by decision makers as an aid to design and optimize food supply chains.

This paper presents a development of first, a cost-effective optimization approach for a proposed RFID-based meat supply chain seeking a trade-off among three conflicting criteria; and second, a new decision-making algorithm which can be used for any multi-criteria problem to select the best Pareto solution.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

The purpose of this paper is to present a new hybrid approach based on criticality analysis and optimisation to deal with spare parts inventory management in the initial provisioning phase in the mining industry. Spare parts represent a significant part of mining companies' expenditures, so it is important to develop new approaches to reduce the total inventory value of these items.

This hybrid approach combines qualitative and quantitative methods based on VED (vital, essential and desirable) analysis, analytical hierarchical process (AHP), and e-constraint optimisation method to obtain the spare parts to be stocked. The study was applied to a large mining company. The mineral sector was chosen due to the great importance to the emerging Brazilian economy and the lack of researches in this sector. In addition, the spare parts have a relevant weight on the total inventory cost.

Present a novel approach combining multi-objective optimisation and multi-criteria evaluation approaches to tackle the inventory decision in spare parts management. This work also defines and classifies relevant criteria for spare parts management in the mineral sector validated by specialists. The proposed approach achieves an average increase of 20.2% in the criticality and 16.6% in the number of items to be stocked compared to the historical data of the surveyed company.

This paper applies the proposed approach to a mining company in Brazil. Future research in other companies or regions should analyse the adequacy of the criticality criteria, hierarchy and weights adopted in this paper.

The proposed approach is useful for mining industries that deal with a large variety of resource constraints as it helps in formulating appropriate spare part strategies to rationalise financial resources at both tactical and strategic levels.

The paper presents a new hybrid method combining the AHP a multi-criteria decision making (MCDM) approach coupled with e-constraint optimisation to deal with spare parts inventory management allowing for a better spare parts inventory analysis in the initial provisioning phase and providing managers with a systematic tool to analyse the trade-off between spare parts criticality and total inventory value.

The aim of this paper is to introduce a newly developed multi-criteria analysis for the comparison of two grid expansion alternatives, conventional and voltage-regulated distribution transformer. The case study comprises environmental, economic, technical and social aspects.

The newly developed method decision condition Preference Ranking Organization METHod for Enrichment Evaluation (DC-PROMETHEE) combines scenario planning with the multi-attribute decision-making method PROMETHEE. DC-PROMETHEE supports the decision-maker to evaluate the total potential of an alternative considering a large number of decision conditions. The calculated performance indicator supports the decision-maker to select the best alternative.

The voltage-regulated distribution transformer shows a high overall potential in the present case study. This leads to the recommendation to the investigated distribution system operator to include the voltage-regulated distribution transformers as a grid expansion measure.

The DC-PROMETHEE can be applied to other distribution system operators by considering their individual grid topology and preferences. Other fields of application are infrastructure investments in the service area, in which expansion alternatives are evaluated in a large number of decision conditions. Examples include telecommunication, gas supply, water supply, sewage and rail networks.

This paper develops the DC-PROMETHEE approach. The DC-PROMETHEE enables the multi-criteria evaluation of a few alternatives in a large number of decision conditions.