Environmentally responsible manufacturing, green supply chain management (GSCM), and related principles have become important strategies for companies to achieve profit and gain market share by lowering their environmental impacts and increasing their efficiency. As environment has become a key strategic consideration in supply chains, this study examines the components and elements of GSCM and suggests a novel GSCM evaluation framework. It also provides a real-case study of Ford Otosan, one of the pioneering companies about environmental subjects in Turkey, to illustrate the industrial application of our theoretical assessment model. The identified components are integrated into a strategic assessment and evaluation tool using analytical network process (ANP). The dynamic characteristics and complexity of the GSCM analysis environment make the ANP technique a suitable tool for this study. Moreover, to cope with ambiguity and vagueness of the decision maker's evaluations, the fuzzy extension of the ANP method is preferred. 相似文献
Multiprocessor open shop makes a generalization to classical open shop by allowing parallel machines for the same task. Scheduling of this shop environment to minimize the makespan is a strongly NP-Hard problem. Despite its wide application areas in industry, the research in the field is still limited. In this paper, the proportionate case is considered where a task requires a fixed processing time independent of the job identity. A novel highly efficient solution representation is developed for the problem. An ant colony optimization model based on this representation is proposed with makespan minimization objective. It carries out a random exploration of the solution space and allows to search for good solution characteristics in a less time-consuming way. The algorithm performs full exploitation of search knowledge, and it successfully incorporates problem knowledge. To increase solution quality, a local exploration approach analogous to a local search, is further employed on the solution constructed. The proposed algorithm is tested over 100 benchmark instances from the literature. It outperforms the current state-of-the-art algorithm both in terms of solution quality and computational time.
Incorporation of the behavioral issues of the decision maker (DM) is among the aspects that each Multicriteria Decision Making
(MCDM) method implicitly or explicitly takes into account. As postulated by regret theory, the feelings of regret and rejoice
are among the behavioral issues associated with the entire decision making process. Within the context of MCDM, the DM may
feel regret, when the chosen alternative is compared with another one having at least one better criterion value. PROMETHEE
II is a widely known MCDM method that makes no explicit incorporation of regret attitude of the DM. In this paper, we elaborate
on the applicability of regret theory to MCDM context. In particular, we investigate the findings of regret theory and explore
the parallel between regret theory and PROMETHEE II method. Relying on the concepts of regret theory, we demonstrate how a
decision that is made using a PROMETHEE II based outranking method conforms to the regret attitude of the DM. 相似文献