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The design of a biomass supply chain is a problem where multiple stakeholders with often conflicting objectives are involved. To accommodate the aspects stakeholder, the supply chain design should incorporate multiple objectives. In addition to the supply chain design, the management of energy from biomass is a demanding task, as the operation of production of biomass products needs to be aligned with the rest of the operations of the biomass supply chain. The purpose of the paper is to propose a mathematical framework for the optimal design of biomass supply chain.

An integrated mathematical framework that models biomass production, transportation and warehousing throughout the nodes of a biomass supply chain is presented. Owing to conflicting objectives, weights are imposed on each aspect, and a 0-1 weighted goal programming mixed-integer linear programming (WGP MILP) programming model is formulated and used for all possible weight representations under environmental, economic and social criteria.

The results of the study show that emphasis on the environmental aspect, expressed with high values in the environmental criterion, significantly reduces the level of CO₂ emissions derived from the transportation of biomass through the various nodes of the supply chain. Environmental and economic criteria seem to be moving in the same direction for high weight values in the corresponding aspect. From the results, social criterion seems to move to the opposite direction from environmental and economic criteria.

An integrated mathematical framework is presented modeling biomass production, transportation and warehousing. To the best of the authors’ knowledge, such a model that integrates multiple objectives with supply chain design has not yet been published.

The selection of security configurations for complex information systems is a cumbersome process. Decision-making regarding the choice of security countermeasures has to take into consideration a multitude of, often conflicting, functional and non-functional system goals. Therefore, a structured method to support crucial security decisions during a system’s design that can take account of risk whilst providing feedback on the optimal decisions within specific scenarios would be valuable.

Secure Tropos is a well-established security requirements engineering methodology, but it has no concepts of Risk, whilst Constrained Goal Models are an existing method to support relevant automated reasoning tasks. Hence we bridge these methods, by extending Secure Tropos to incorporate the concept of Risk, so that the elicitation and analysis of security requirements can be complimented by a systematic risk assessment process during a system’s design time and supporting the reasoning regarding the selection of optimal security configurations with respect to multiple system objectives and constraints, via constrained goal models.

As a means of conceptual evaluation, to give an idea of the applicability of the approach and to check if alterations may be desirable, a case study of its application to an e-government information system is presented. The proposed approach is able to generate security mechanism configurations for multiple optimisation scenarios that are provided, whilst there are limitations in terms of a natural trade-off of information levels of risk assessment that are required to be elicited.

The proposed approach adds additional value via its flexibility in permitting the consideration of different optimisation scenarios by prioritising different system goals and the automated reasoning support.

The paper deals with a model of decision‐making given in a form of a fuzzy relational equation with relationships between imprecisely defined goals and constraints modelled by means of a fuzzy relation. The usefulness of the model and its novelty is clearly pointed out. Some basic problems concerned, e.g. with a construction of the relation of the model and ordering of goals and constraints with respect to their importance (viz. their influence on a fuzzy decision), are also discussed in a detailed manner.

The purpose of this paper is to formulate a model which not only determines minimum level of maintenance requirements but also satisfies expected reliability level.

A multi‐objective decision making (MODM) model has been developed by viewpoint of decreasing wastes and increasing system reliability. Wastes that have been minimized are maintenance requirements (i.e. labor, spare parts, reserve system and productive maintenance activities) and maintenance system reliability that has been maximized is a function of maintenance requirements. This paper proposed goal programming model for decision‐making aid and a real case in an existing Iranian automobile manufacturing company was studied.

Output of the proposed model was optimum level of maintenance requirements which satisfies the expected reliability level. For example, outputs of model for man hour approximately are the same as available maximum level and inspection, service and test and adjustment activities are not necessary 12 times a year for all the machineries.

This model is useful for managers because they could use that in any maintenance systems and it would ensure them in achieving minimum level of maintenance requirements and the expected reliability level.

Since lean concept has appeared, many works have been done on decreasing or even eliminating of waste such as extra inventory. Although these studies have not taken into account expected reliability seriously, this paper considers these two issues together.

In order to accomplish the goals of maintaining an effective supply chain, the firm needs to establish and maintain long-term relationships with its suppliers. Most supply chain problems inherently involve multiple conflicting criteria. Risks in the supply chain are dynamic in nature. While some risks can be either reduced or eliminated, new ones will appear.

This chapter will focus on the development of a mufti-criteria model using meta-goal programming to develop a supplier selection process. The goals of the model include (1) the minimization of the net cost for all items, (2) the minimization of the defective items, and (3) the minimization of late-delivered items.

Taints of corruption in public procurement (PP) exist in both developed and developing countries alike- though in different scales and with different characteristics and impacts. Attempts to achieve a taint-free procurement regulation have failed even in the most robust and mature jurisdictions due to an inherent complexity and difficulty given the paradigms used. PP systems today remain fragile to various shocks2 coming mainly from markets and corruption. This paper proposes a paradigm shift in the way in which a PP System (PPS) should be designed and practiced rendering it as “antifragile”3 as possible to benefit from shocks, stresses and disorder. Antifragile PPS design revolutionizes not only the regulations but also the frameworks and institutional setups and the whole practice of the public procurement profession in a manner that permits growth and evolution at times of stress or distress. This paradigm shift is based on a design of the PPS as a complex system.

Goal programming is an extension of linear programming. Suggests that, although linear programming is used widely in decision‐making processes, it has a major limitation which restricts the users of the technique to narrowing their problems to a single objective function. Goal programming was developed to handle problems with multiple objectives. Proposes that used properly, this technique can be excellent in strategic planning. Shows how the improper structure of a goal programming model induces misleading results.

Many of the financial instruments used in classic investment analysis do not apply to typical information technology investment decision making because of the multi‐criteria and multi‐objective nature of the problem. This is particularly true when integrating strategic, tactical, and operations planning objectives in the decision. One approach for making IT investment decisions is a multi‐objective goal programming (GP) model proposed by Talluri in 2000. The purpose of our paper is twofold: to demonstrate how a simple ranking/scoring method can be used in place of the more involved Talluri GP modeling approach if only a solution is required; and, in decision situations where solution justification is desired, to explain how GP extension methodologies can be incorporated into the analysis to generate information to determine a solution’s reliability and identify economic tradeoffs that can be used to improve an existing solution.

The purpose of this paper is to explore the relationship between effective strategies to improve the quality and quality management of allocated resources for the successful implementation of the strategies. For this purpose, three quality management resources (human, organizational and technological) and eight different strategies related to quality are considered.

The paper employs the fuzzy analytic network process (FANP) to prioritize and model the interactions between eight strategies, the three types of resources (human, organizational and technological) needed for effective strategy implementation and the ability to enhance quality. Then, Goal Programming (GP) is formulated by the output of the FANP to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources.

The first three priorities of strategies identified by the FANP include continuous management of quality system, continuous use of human knowledge and continuous approach toward target, and the order of resources is as follows: human resources, organizational resources and technological resources. The results obtained showed the largest share of human resources and its crucial role in improving the quality of the products. The contribution of organizational resources amounts to half of the contribution of human resources.

The main contribution of this paper is to employ the FANP to prioritize, whereas in prior studies in this area, priorities were conducted as definitive, and uncertainty in the opinion of experts was not considered. In this paper, the FANP–GP combined method is used.

Establishing trust within groups or teams of scientists and technicians working on large‐scale projects is considered an essential critical success factor when working in a virtual environment. To build trust among team members for any project requires a selection decision on which trust factors will best achieve the goals of the project. This paper presents application results of using two analytic methodologies used to evaluate trust survey information from the Spallation Neutron Source (SNS), the USA’s largest science project. The two decision methodologies produced results that helped support SNS management’s judgment that uniquely selected trust categories should be emphasized to build trust on this project.