Osman Kulak

A decision support system for fuzzy multi-attribute selection of material handling equipments

Effective use of labor, providing system flexibility, increasing productivity, decreasing lead times and costs are some of the most important factors influencing selection of material handling equipment. In this study, a decision support system (FUMAHES: fuzzy multi-attribute material handling equipment selection) considering these factors for material handling equipment selection is developed. FUMAHES consists of a database, a rule-based system and multi-attribute decision making modules. This database includes detailed data about equipment types and their properties. The rule-based system module provides rules, which are utilized by inference engine for determining the most proper material handling equipment type. Ultimately, a final decision is made for the most proper equipment among the alternatives of the same type using the information axiom of axiomatic design principles. Evaluation of alternatives is made for the cases of both complete and incomplete information. This paper also introduces a fuzzy information axiom approach and uses it in the selection of material handling equipment in a real case.

Review: Applications of axiomatic design principles: A literature review

Although there have been quite a number of theoretical and practical studies where axiomatic design (AD) principles have been used in the last few years, there is a lack of a comprehensive literature survey which evaluates and classifies these papers. This study provides a recognizable overview of literature on AD principles from the past 20 years and introduces a novel classification scheme covering 63 papers. Each article was classified into four main groups, namely the type of the axiom, the application area, the method, and the evaluation type. Findings of our paper indicate that most of the studies in the literature are application-based papers which use typically the independence axiom. While product design is put forward in the application area, the crisp approach is widely used as an evaluation type. A rise in the fuzzy evaluation based research studies using the information axiom for multi-attribute decision making problems has also been noticed.

Conceptual design evaluation using interval type-2 fuzzy information axiom

Concept selection is the most critical part of the design process as it determines the direction of subsequent design stages. In addition, it is a difficult task because available information for decision-making at this stage is imprecise and subjective. This necessitates the need for fuzzy decision models for selecting the best conceptual design among a set of alternatives. Although ordinary fuzzy sets cover uncertainties of linguistic words to some extent, it is recommended to use interval type-2 fuzzy sets (IT2FS) to capture potential uncertainties of words. This paper presents a new concept selection methodology that extends the fuzzy information axiom (FIA) approach to incorporate IT2FSs. The proposed methodology is called interval-type-2 fuzzy information axiom (IT2-FIA). IT2-FIA method is also enriched by using ordered weighted geometric aggregation operator to include the decision makers attitude during the aggregation process. A case study is given to demonstrate the potential of the methodology.

PCB assembly scheduling for collect-and-place machines using genetic algorithms

In printed circuit board (PCB) assembly, collect-and-place machines, which use a revolver-type placement head to mount electronic components onto the board, represent one of the most popular types of assembly machinery. The assignment of feeders to slots in the component magazine and the sequencing of the placement operations are the main optimisation problems for scheduling the operations of an automated placement machine. In this paper, we present different genetic algorithms (GAs) for simultaneously solving these highly interrelated problems for collect-and-place machines in PCB assembly. First we consider single-gantry machines as the basic type of machinery. In the conventional GA approach all placement operations and the feeder-slot assignment are represented by a single chromosome. In order to increase the efficiency of the genetic operators, we present a novel GA approach, which integrates a clustering algorithm for generating sub-sections of the PCB and grouping the corresponding placement operations. It is shown that the proposed GAs can be extended to schedule dual-gantry placement machines, which are equipped with two independent placement heads and two dedicated component magazines. Hence, component feeders have to be allocated between the two magazines. To solve this allocation problem, two different heuristic strategies are proposed. Finally, detailed numerical experiments are carried out to evaluate the performances of the proposed GAs.

A perturbation based variable neighborhood search heuristic for solving the Vehicle Routing Problem with Simultaneous Pickup and Delivery with Time Limit

The Vehicle Routing Problem with Simultaneous Pickup and Delivery with Time Limit (VRPSPDTL) is a variant of the basic Vehicle Routing Problem where the vehicles serve delivery as well as pick up operations of the clients under time limit restrictions. The VRPSPDTL determines a set of vehicle routes originating and terminating at a central depot such that the total travel distance is minimized. For this problem, we propose a mixed-integer mathematical optimization model and a perturbation based neighborhood search algorithm combined with the classic savings heuristic, variable neighborhood search and a perturbation mechanism. The numerical results show that the proposed method produces superior solutions for a number of well-known benchmark problems compared to those reported in the literature and reasonably good solutions for the remaining test problems.

Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem

We developed efficient hybrid genetic algorithms for dynamic job shop scheduling.A new KK heuristic is proposed and it is combined with genetic algorithm.The problem includes new job arrival, machine breakdown and changes in processing time.In conclusion, proposed methodologies generate outstanding solutions. Job shop scheduling has been the focus of a substantial amount of research over the last decade and most of these approaches are formulated and designed to address the static job shop scheduling problem. Dynamic events such as random job arrivals, machine breakdowns and changes in processing time, which are inevitable occurrences in production environment, are ignored in static job shop scheduling problem. As dynamic job shop scheduling problem is known NP-hard combinatorial optimization, this paper introduces efficient hybrid Genetic Algorithm (GA) methodologies for minimizing makespan in this kind of problem. Various benchmark problems including the number of jobs, the number of machines, and different dynamic events are generated and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies. The numerical results indicate that the proposed methods produce superior solutions for well-known benchmark problems compared to those reported in the literature.

A new multi criteria decision making approach for medical imaging systems considering risk factors

The flowchart of the RFAD approach. Effective and good quality imaging is important for medical decision-making and can reduce unnecessary costs and procedures. Therefore, decision making regarding any technology can present serious problems for healthcare centers with multi criteria decision making problems (MCDM). This paper is the first to develop the fuzzy axiomatic design with risk factors (RFAD) approach and to use it in multi attribute comparisons of medical imaging systems in a university hospital. Although most MCDM approaches in the literature treat risk factors as separate criteria, in real life every alternative has its own risks related to each criterion. The proposed approach integrates the risk factors in each criterion and calculates the information content to compare alternatives. This paper applies three different approaches to MCDM problems related to the selection of medical imaging systems for a university hospital.

A GA-based solution approach for balancing printed circuit board assembly lines

Printed circuit board (PCB) assembly lines consist of a number of different machines for mounting electronic components onto PCBs. While high-speed placement machines are employed to assemble standard components, so-called fine-pitch placement machines are used to mount complex electronic components with high precision and by use of specific nozzles. In this paper, we investigate a typical mass production environment where a single type of PCB is assembled in a line comprising high-speed as well as high-precision placement machines. The PCB assembly line balancing problem consists of assigning component feeders, each holding a specific electronic component type, and the corresponding placement operations to machines in the line so as to minimize the assembly cycle time. To solve this problem, a two-stage solution procedure based on genetic algorithm (GA) is proposed. In the first stage, component feeders are assigned to the placement machines with the objective of balancing the workload within the assembly line. A number of candidate solutions are then transmitted to the second stage, where specific machine optimization algorithms are applied to determine the feeder-slot assignment in the component magazine of the machines and the placement sequence of the various components. As a result, fine-tuned placement operation times are achieved which reflect the individual operation mode and the actual component setup of the placement machines. Finally, from the candidate solutions the one which minimizes the actual PCB assembly time is selected.

Evaluation of product design concepts using grey-fuzzy information axiom

Evaluation of product design concepts is an important and critical problem, considering incomplete and imprecise information in the early stages of product design process. In order to solve this problem, grey theory, fuzzy sets and information axiom are combined in this study under the name of grey-fuzzy information axiom for solving product concept evaluation problem for the first time. The information axiom has the capability to solve multi-attribute evaluation problems. Grey and fuzzy set theories are complementary methods for quantification uncertainty. Applicability of the proposed method is demonstrated on the evaluation of dishwasher design concepts. It has been shown that grey-fuzzy information axiom is an appropriate tool to be used for the concept evaluation problem in case of having different types of uncertainties.

A New Fuzzy Multi-criteria Decision Making Approach: Extended Hierarchical Fuzzy Axiomatic Design Approach with Risk Factors

In recent years, Axiomatic Design (AD) has been widely used as a multi criteria decision making approach. AD approach compares the design objects and system capabilities in a framework and then selects the best alternative based on these comparisons. Some researchers then include fuzziness in the AD approach which helps to evaluate alternatives in fuzzy environments. The main advantage of fuzzy AD approach is the ability to evaluate both crisp and fuzzy values at the same time during decision process. However, these approaches are not appropriate for hierarchical decision problems. Therefore, these are extended to solve the hierarchical decision problems and Hierarchical Fuzzy Axiomatic Design Approach (HFAD) is presented. In this study, HFAD is extended to include risk factors for the first time in literature and a new approach called RFAD is proposed. Moreover, the application of the new approach is shown on a real world supplier selection problem and the results are compared to the other widely used decision making approaches in literature.