EURO Journal on Decision Processes最新文献

Most policies suffer from what we call the “inherent vices” of policy design – attributes which increase policy volatility and risks of failure. These risks of uncertainty, maliciousness and non-compliance often emerge from the human or behavioural aspects of policy processes, such as when target populations fail to comply with rules designed to achieve a specific public value, or when policy-makers fail to design policies that achieve such values in the first place. Soft OR, developed on the assumption that problems are perceived by stakeholders through different lenses based on their social, cultural and psychological constructs, presents useful policy tools for assessing and dealing with these risks. We critically assess the use of Soft OR methods as tools for improving public policy-making.

The $q$-rung orthopair fuzzy sets ($q$-ROFs) which are eminent extensions of the intuitionistic fuzzy sets and the pythagorean fuzzy sets can be considered as an efficient tool for modeling real life decision making problems involving uncertainty of information. In this paper, in order to reflect the correlation among the attributes of real decision making problems, the Choquet integral operator is extended to develop the $q$-rung orthopair fuzzy Hamacher generalized Shapley Choquet integral ($q$-ROFHGSCI) operator under the $q$-rung orthopair fuzzy environment. Furthermore, some important properties and special cases of the $q$-ROFHGSCI operator are discussed. An approach for multiattribute decision making based on $q$-ROFHGSCI operator is developed. Finally, a numerical example is provided to illustrate the proposed approach.

More and more literature and practice recommend involving the public at the early stages of the policy cycle, i.e. issue identification, definition of the policy objectives and policy design. Policy design involves, among others, identifying solutions, ideas or alternatives which may address the policy objectives. Three main arguments are often put forward to advocate for the involvement of stakeholders, or the public, in policy design: a “user-centered” argument (i.e. for the policy to better meet people's priorities), an innovation argument (i.e. to conceive new solutions) and a collective argument (i.e. to identify collective actions and better tackle environmental problems). However, in both research and practice these arguments have been challenged. Research has insufficiently generated evidence of the influence of large-scale participation in policy design on resulting proposed actions. The objective of this paper is to analyze whether a large-scale participatory process leads to action proposals that fit people's priorities and that are innovative and collective. It draws from a land management and rural development policy design experiment conducted in six vulnerable areas of Tunisia. 4,300 direct participants were involved and 11,583 action proposals were collected. Our results highlight the influence of the local circumstances on innovation and the interest towards collective actions. Our results also show that whether policy design is made individually or in group influences the outcomes. The results also suggest that appropriate facilitation can help fostering more collective and innovative actions. We conclude the paper by opening up the idea of hybridizing policy design methods with methods from political and agricultural sciences in order to better understand the drivers and rationalities behind participants’ action proposals.

“Ivrea, industrial city of the 20th century”, is the 54th UNESCO site in Italy, and it has been in the World Heritage List since July 2018. Its inclusion was the result of a complex decision process, which needs to be reactivated to enhance this large, unitary urban system of great historical, architectural and environmental value. The huge dimensions and extreme heterogeneity of the built heritage, the weakness of the socio-economic context, and the dynamics of the public and private organisations have generated a complex situation, which hinders the enhancement of the whole UNESCO site. A methodology that integrates two decision aid approaches can deal with and reduce these complexity factors, to guide the incremental development of knowledge and to foster relationships that facilitate the process reactivation. A Problem Structuring method was used to structure the decision problems and logically synthesise fragmented knowledge. Multicriteria models and methods were used to analytically describe the space of action and propose examples of how alternative policies, enhancement activities or projects can be analysed and evaluated, to be ranked or selected. The paper describes the problem situation and how these methods were integrated and used. The study aims to underline the complementarity of the different decision aid approaches and to show how logical and analytical models, applications of tools and results can be proposed to create an effective communication process, both at technical level and with stakeholders who are willing to participate in preparatory workshops.

Incorporating consideration of causal mechanisms of complex policy issues and goals is critical for policy design, but tools to support exploration of the interconnections, trade-offs and unintended consequences of a focused policy issue are limited. Understanding how to undertake systems-based policy design is crucial for designing effective policy interventions. Through a case study with two housing associations (HAs) in England, this paper explores how group model building (GMB) workshops, as a systems thinking tool, can elicit complex causal mechanisms to inform policy design. The paper presents a causal loop diagram (CLD) describing English HAs’ decision-making around sustainable and healthy housing in response to housing policies. The CLD illustrates how frequent policy changes and disjointed objectives can create disruptive challenges for HA's long-term decision-making, increasing short-term decision-making, and compromising the delivery of housing policy goals as an unintended consequence. We argue that the systems perspective of the interlinkages between policy design, specifically inconsistencies and changes, and housing organisations’ reactions highlights the importance of the systems thinking approach of policy design to support HAs’ organisational decision-making for sustainability and social issues. Policy design elements that facilitate HAs’ long-term decision-making are discussed. Through the case study, we contribute to the housing policy literature by explicitly showing how policy changes affect HA's decision-making. We advance the integration of policy design and soft operational research fields by describing the systems thinking approaches are used not only on the content of policy design to enhance a particular policy, but also on increasing our understanding of its process, by generating insights about the nature of decision-making dynamics and challenges faced. Limitations and implications for future research are discussed.

The main purpose of the study is to examine how the global economic crisis has affected the hospital productivity growth and quality performance of public hospitals in Greece. Through the nonparametric estimation of a quality-adjusted metafrontier Malmquist index, the study accounts the heterogeneity of hospitals according to the hierarchical levels of the healthcare system (primary, secondary and tertiary care) to examine the trends of efficiency change, innovation change, and leadership change during the period 2009-2013. The proposed approach relies on a novel methodological framework for measuring quality into a separate component of the metafrontier Malmquist. To understand the effect of the economic crisis in healthcare the study further explores the association between productivity and quality in the hospital sector. The results show that the implemented austerity measures have had a negative impact particularly on the quality of the tertiary hospitals that exhibited an overall productivity regress.

Deposit return systems have started making their reappearance as more environmentally conscious consumers seek ways to effectively reduce their carbon footprint. An example is the management of refillable glass bottles which requires a well-organized collection network with inventory management. A collection planning with an efficient algorithm and information system has to be applied. This paper investigates, using integer linear programming, a vehicle routing problem with time constraints to provide flexibility as well as priority rules to avoid inventory saturation at collection points. The model presented, based on a real-life application in the city of Lyon and surrounding areas, includes several objectives with specific assumptions. The result of the optimization is a vehicle routing plan with flexible scheduling based on time slots. Numerical experiments are conducted on instances of different scales making it possible to model the current problem as well as its future evolution. These experiments consider several instances, using a single vehicle among three vehicle types (cargo-bicycle, car and van) and a network composed of 20 stores/clients to collect bottles from. The results show the impacts of the priority rules on the solution obtained and additional indicators are proposed in order to analyze more precisely the quality of the solution in terms of financial cost and environmental impact. The proposed model and program will help make appropriate decisions in planning and scheduling the routes of the vehicles for the refillable glass bottle collection, especially in urban areas.

There has been an ongoing debate in research regarding the use of heuristics in decision-making. Advocators have succeeded in showing that applying heuristics not only reduces effort but can even be more accurate than analytical approaches under certain conditions. Others point out the biases and cognitive distortions inherent in disregarding information. Researchers have used both simulations and experiments to study how the use of heuristics affects the decision's outcome. However, a good decision is determined by the process and not a lucky outcome. It is a conscious reflection on the decision-maker's information and preferences. Therefore, a heuristic must be assessed by its ability to match a structured decision processing all available information. Thus, the question remains: how often does the reduction of information considered in heuristic decisions lead to a different recommended alternative? We applied different heuristics to a dataset of 945 real, personal decisions. We have found that by using heuristics instead of a fully developed decision structure, in 60.34% of cases, a different alternative would have been recommended to the decision-maker leading to a mean relative utility loss for the deviating decisions of 34.58%. This shows that a continuous effort to reflect on the weighing of objectives and alternatives leads to better decisions.

Given the dynamics of today’s markets, decision support based on advanced analytics is required to help market players keep their top position. This paper presents an approach to help business decision-makers gain market share by providing competitive tender offers for Full Truck Load (FTL) services. In particular, we compare operating a fleet of full charter trucks (FCT), using Spot-Market (SM) capacity and a mixture of both options against each other. A Pickup and Delivery Problem is modeled, and solved using an Adaptive Large Neighborhood Search heuristic. Computational results indicate strong service benefits combining FCT and SM usage. Numerical experiments are presented in detail to support the findings. Additionally, a real-life case study originating from DB Schenker is presented.

Significant increase in environmental concerns has led organizations to consider the necessary measures to apply green supply chain management to improve their environmental and economic performance. An important way to implement green supply chain management is to revise the methods of purchasing and selecting green suppliers. In this paper, a new fuzzy Data Envelopment Analysis (DEA) model for green supplier selection by collecting expert votes is presented. Then a way to improve the cross-efficiency method to achieve a complete ranking is suggested. In the improved method, a new secondary goal model based on the concepts of the Fuzzy COmbinative Distance-based ASsessment (CODAS) method will be presented. The proposed models were then used to select a green supplier in an automotive group. In the present case study, a complete ranking for green suppliers is obtained.