A multiobjective optimization model for optimal supplier selection in multiple sourcing environment

Supplier selection is an important concern of a firm’s competitiveness, more so in the context of the imperative of supply-chain management. In this paper, we use an approach to a multiobjective supplier selection problem in which the emphasis is on building supplier portfolios. The supplier evaluation and order allocation is based upon the criteria of expected unit price, expected score of quality and expected score of delivery. A fuzzy approach is proposed that relies on nonlinear S-shape membership functions to generate different efficient supplier portfolios. Numerical experiments conducted on a data set of a multinational company are provided to demonstrate the applicability and efficiency of the proposed approach to real-world applications of supplier selectio

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Single Item Supplier Selection and Order Allocation Problem with a Quantity Discount and Transportation Costs

In this paper, we address a single item supplier selection, economic lot-sizing, and order assignment problem under quantity discount environment and transportation costs. A mixed-integer nonlinear program (MINP) model is developed with minimization of cost as its objective, while lead-time, the capacity of the supplier and demand of the product are incorporated as constraints. The total cost considered includes annual inventory holding cost, ordering cost, transportation cost and purchase cost. An efficient and effective genetic algorithm (GA) with problem-specific operators is developed and used to solve the proposed MINP model.  The  model is illustrated through a numerical example and the results show that the GA can solve the model in less than a minute. Moreover, the results of the numerical illustration show that the item cost and transportation cost are the deciding factors in selecting suppliers and allocating orders. Keywords: Supplier selection, Economic Order Quantity, Order allocation, Mixed-integer nonlinear programming

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Dynamic model of optimized supply for organizational units of armed forces (at decentralized procurement)

Efficient activity of organizational units in armed forces is impossible without comprehensive and continuous logistics. The key role in the arrangement of logistics is played by supply processes: ordering, purchase, delivery, and storage of material and technical resources (goods). The Complexity and multiplicity of implementing the logistics process assume the use of economic-mathematical modeling, as an efficient tool for supporting decisions, which ensures the selection of the most favorable supply options. This paper provides a dynamic model of optimized supply (at decentralized procurement of material and technical resources), which describes the possible options of arranging the logistics of organizational units of the armed forces. The criterion of global optimization is represented by a normalized performance indicator characterizing the level of provision of organizational units with material and technical resources. The proposed economic-mathematical model is an efficient tool for supporting decisions taken by logistics-management divisions of organizational units of armed forces – at multiple options of implementing the logistic processes and limited financial resources, which allows optimizing the level of provision of organizational units with required material and technical resources (for the entire planning period of supply, regarding change of needs, scope of funds allocated for logistics and logistic costs accompanying the supply process)