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This paper aims to overcome some of the limitations of previous works regarding automated supply chain formation (SCF). Hence, it proposes an algorithm for automated SCF using multiple contract parameters. Moreover, it proposes a decision-making mechanism that provides means for incorporating risk in the decision-making process. To better emphasize the features of the proposed decision-making mechanism, the paper provides some insights from the petroleum industry. This industry has a strategic position, as it is the base for other essential activities of the economy of any country. The petroleum industry is faced with volatile feed-stock costs, cyclical product prices and seasonal final products demand.

The authors have modeled the supply chain in terms of a cluster graph where the nodes are represented by clusters over the contract parameters that suppliers/consumers are interested in. The suppliers/consumers own utility functions and agree on multiple contract parameters by message exchange, directly with other participant agents, representing their potential buyer or seller. The agreed values of the negotiated issues are reflected in a contract which has a certain utility value for every agent. They consider uncertainties in crude oil prices and demand in petrochemical products and model the decision mechanism for a refinery by using an influence diagram.

By integrating the automated SCF algorithm and a mechanism for decision support under uncertainty, the authors propose a reliable and practical decision-making model with a practical application not only in the petroleum industry but also in any other complex industry involving a multi-tier supply chain.

The limitation of this approach reveals in situations where the parameters can take values over continuous domains. In these cases, storing the preferences for every agent might need a considerable amount of memory depending on the size of the continuous domain; hence, the proposed approach might encounter efficiency issues.

The current paper makes a step forward to the implementation of digital supply chains in the context of Industry 4.0. The proposed algorithm and decision-making mechanism become powerful tools that will enable machines to make autonomous decisions in the digital supply chain of the future.

The current work proposes a decentralized mechanism for automated SCF. As opposed to the previous decentralized approaches, this approach translates the SCF optimization problem not as a profit maximization problem but as a utility maximization. Hence, it incorporates multiple parameters and uses utility functions to find the optimal supply chain. The current approach is closer to real life scenarios than the previous approaches that were using only cost as a mean for pairwise agents because it uses utility functions for entities in the supply chain to make decision. Moreover, this approach overcomes the limitations of previous approaches by providing means to incorporate risk in the decision-making mechanism.

The design of tests is a very important step in the software development process because it allows us to match the users’ expectations with the finished product. Considered as a cumbersome activity, efforts have been made to automatize and alleviate the burden of test generation, but it is still a largely neglected step. The study aims to propose taking advantage of existing requirement artifacts, like scenarios that describe the dynamic of the domain in a very early stage of software development, to obtain tests from them.

In particular, the approach proposed complement the scenarios that are textually described with a glossary, the language extended lexicon. Thus, a set of rules to derive tests from scenarios is also proposed. The tests are then described using the task/method model.

The main findings of this study consist of an extension of a previously presented set of rules. And, a tool based on a media wiki platform that makes possible to record scenarios and the language extended lexicon and implement the rules to obtain the tests.

The main originality of this study is the glossary which complements scenarios, the semantic support to obtain tests and the tool to automatize the approach.

This article proposes a strategy to make the testing step easier, generating user acceptance tests (UATs) in an automatic way from requirements artifacts.

This strategy is based on two modeling frameworks: scenarios and task/method paradigm. Scenarios are a requirement artifact used to describe business processes and requirements, and task/method paradigm is a modeling paradigm coming from the artificial intelligence field. The proposed strategy is composed of four steps. In the first step, scenarios are described through a semantic wiki website. Then scenarios are automatically translated into a task/method model (step two). In the third step, the task/method model obtained in step two is executed in order to produce and store all possible achievements of tasks and thus scenarios. The stored achievements are saved in a data structure called execution tree (ET). Finally, from this ET (step four), the UATs are generated.

The feasibility of this strategy is shown through a case study coming from the agriculture production systems field.

Generally, test design approaches deal with a small number of variables describing one specific situation where a decision table or workflow is used to design tests. The proposed approach can deal with many variables because the authors rely on scenarios that can be composed in order to obtain a tree with all the testing paths that can arise from their description.

The purpose of this paper is to discuss the advantage of a combinatory methodology presented in this study. The paper suggests that the comparison with results of previously developed methods is in high agreement.

This paper introduces a combined compromise decision-making algorithm with the aid of some aggregation strategies. The authors have considered a distance measure, which originates from grey relational coefficient and targets to enhance the flexibility of the results. Hence, the weight of the alternatives is placed in the decision-making process with three equations. In the final stage, an aggregated multiplication rule is employed to release the ranking of the alternatives and end the decision process.

The authors described a real case of choosing logistics and transportation companies in France from a supply chain project. Some comparisons such as sensitivity analysis approach and comparing to other studies and methods provided to validate the performance of the proposed algorithm.

The algorithm has a unique structure among MCDM methods which is presented for the first time in this paper.