An integrated method using MACBETH and EDAS methods for evaluating steam boiler alternatives

The evaluation of steam boiler alternatives is a Multi-Criteria Decision Making (MCDM) process and includes various alternatives and conflicting criteria. As selecting the most appropriate steam boiler affects the operating cost of the textile company, this decision process is crucial for the dyehouse. In the literature, various MCDM methods are proposed for evaluating the alternatives and selecting the most appropriate one among them. In this paper, the steam boiler alternatives that will be used in a dyehouse of a textile company are evaluated with an integrated MCDM method. This integrated method based on MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) and EDAS (Evaluation based on Distance from Average Solution) methods. MACBETH is used to determine the weights of the criteria and alternatives are ranked with EDAS method. At the end, the best steam boiler alternative is determined for the dyehouse of the textile company. © 2018 John Wiley & Sons, Ltd

An alternative approach based on fuzzy PROMETHEE method for the supplier selection problem

In this paper, an alternative version of the fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) method is proposed. Differently from other studies, preference functions used in PROMETHEE method are handled in terms of fuzzy distances between alternatives with respect to each criterion. In order to indicate the applicability of this method, it is applied for a supplier selection problem in the literature. Ranking results are similar obtained by TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and fuzzy ELECTRE (ELimination Et Choix Traduisant la REalité) methods. The implementation of the proposed method indicates that the amount of computations is decreased and decision makers can easily reach to desirable solution. © 2016 Growing Science Ltd. All rights reserved

Heuristic Search in LegalTech: Dynamic Allocation of Legal Cases to Legal Staff

We investigate an allocation problem inspired by the process of assigning legal cases (matters) to staff in law firms. Addressing this problem is important as it can prevent issues around unbalanced workloads and over-recruitment, thus decreasing costs. This initial study on the topic frames the problem as a combinatorial dynamic single-objective problem (minimising tardiness) with constraints modelling staff-client relationships, staff capacities, and earliest start dates of matters. The paper motivates the allocation problem and puts it in context with the literature. Further contributions include: (i) a formal problem definition, (ii) the proposal and validation of a feature-rich problem generator to create realistic test cases, (iii) an initial analysis of the performance of selected heuristics (a greedy approach, a nature-inspired approach, and random search) on different test instances, and finally (iv) a discussion on directions for future research.</p

Decision support tool for dynamic scheduling

Production scheduling in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamic-event module that combines dispatching rules that best fit the performance measures pre-classified by Kano’s model. The proposed tool was tested in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.- (undefined