Saadettin Erhan Kesen

A fuzzy AHP approach to personnel selection problem

Due to the increasing competition of globalization and fast technological improvements, world markets demand companies to have quality and professional human resources. This can only be achieved by employing potentially adequate personnel. In this paper, we proposed a personnel selection system based on Fuzzy Analytic Hierarchy Process (FAHP). The FAHP is applied to evaluate the best adequate personnel dealing with the rating of both qualitative and quantitative criteria. The result obtained by FAHP is compared with results produced by Yagers weighted goals method. In addition to above-mentioned methods, a practical computer-based decision support system is introduced to provide more information and help manager make better decisions under fuzzy circumstances.

A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.

Analyzing the behaviors of virtual cells (VCs) and traditional manufacturing systems: Ant colony optimization (ACO)-based metamodels

The aim of this paper is two fold. First we investigate the three different types of systems, namely cellular layout (CL), process layout (PL) and virtual cells (VCs). VCs are addressed by using family-based scheduling rule, developed by a part allocation algorithm in a PL. Simulation is used to compare three types of systems under the performance metrics such as mean flow time and mean tardiness. Results indicate that VCs have better responsiveness in terms of the performance metrics. Second we develop a new ant colony optimization-based metamodels fed by existing simulation runs to represent the prospective simulation runs, which require a lot of time and effort. Regression metamodels, which allow us to obtain much faster results, are seen to be promising to estimate the systems behaviors.

New product design using FDMS and FANP under fuzzy environment

Quality function deployment (QFD) is a customer-oriented design approach in processing new product developments in order to reach maximum customer satisfaction. Design requirements (DRs) and part characteristics are important decision making problems during QFD activity processes for new product development. Here a Fuzzy decision-making system (FDMS) considering customer needs (CNs) as factors is proposed to formulate the problem. The structure of the FDMS is based on fuzzy control rules. Thus, CNs are determined as input variables and fuzzified using membership function concept. Weights of these factors are then fuzzified to ensure the consistency of the decision maker when assigning the importance of each factor over another. By applying IF-THEN decision rules, DRs of the firm are scored. This paper also uses Fuzzy analytic-network process (FANP) to determine the fulfillment levels of DRs of the firm. The comparison of FDMS with Fuzzy analytical network process (FAHP) is also presented.

The coordinated production and transportation scheduling problem with a time-sensitive product: a branch-and-cut algorithm

In many supply chain scenarios in which short lifespan products are considered, production and transportation decisions must be made in a coordinated manner with no inventory stage. Hence, a solution to this problem conveys information about production starting times of each product lot at facility and delivery times of the lots to various customer-sites located in different geographic regions. In this paper, we study a variant of the problem that single product with limited shelf life is produced at single facility. Once produced, production lot is directly distributed to the customers with non-ignorable transportation time by single vehicle having limited capacity before the lifespan. Objective is to determine the minimum time required to produce and deliver all customer demands. To this end, we develop a branch-and-cut (B&C) algorithm using several valid inequalities adopted from the existing literature to improve lower bounds and applying a local search based on simulated annealing approach to improve upper bounds. On test problems available in the literature, we evaluate the performance of the B&C algorithm. Results show the promising performance of the B&C algorithm.

Job Scheduling in Virtual Manufacturing Cells with Lot-Streaming Strategy: A New Mathematical Model Formulation and a Genetic Algorithm Approach

This paper discusses the job scheduling problem in virtual manufacturing cells (VMCs) with the objective of makespan minimization. In the VMC scheduling problem, each job undergoes different processing routes and there is a set of machines to process any operation. Jobs are produced in lot and lot-streaming is permitted. In addition, machines are distributed through the facility, which raises the travelling time issue. For this reason, the decisions are machine assignments, starting times and sub-lot sizes of the operations. We develop a new Mixed Integer Linear Programming (MILP) formulation that considers all aspects of the problem. Owing to the intractability matter, it is unlikely that the MILP could provide solutions for big-sized instances within a reasonable amount of time. We therefore present a Genetic Algorithm (GA) with a new chromosome structure for the VMC environment. Based on a wide range of examinations, comparative results show that GA is quite favourable and that it obtains the optimum solution for any of the instances in the case where sub-lot number equals 1.

Capacity-constrained supplier selection model with lost sales under stochastic demand behaviour

In today’s market conditions, volume of demand is quite uncertain and thus it is hard to estimate. In many cases, buyer is prone to use supply chain flexibility rather than inventory holding strategy to withstand demand uncertainty. We assume that the buyer releases a replenishment order to the supplier for each cycle (or period) under the contract which is mainly composed of four parameters: (1) supply cost per unit, (2) minimum order quantity, (3) order quantity reduction penalty and (4) maximum capacity of the supplier. Based on these parameters, there are two flexibility options that buyer should evaluate in the order of cycle (1) issue an order smaller than the minimum order quantity and pay the related penalty and (2) place no order and lose the sales. Hence, Qlost emerges as a critical buyer decision, the order quantity, below which no order is placed. Total expected supply cost plus lost sales, as a function of Qlost is presented. We derive the optimal Qlost that minimises the total cost function. Since capacity of each supplier is finite, we then develop a supplier selection model with total cost minimisation over the suppliers subject to capacity constraint that has a stochastic nature stemming from demand behaviour. Linearisation on the model is performed using chance-constrained programming approach. From a given set of supply bids from the potential supply chain partners, the buyer is able to make a quantifiable choice.

How important is the batch splitting activity in scheduling of virtual manufacturing cells (VMCs)

Especially developed for small-to-medium companies that perform batch production, virtual manufacturing cells (VMCs) have attracted some researchers’ close attention as they provide greater flexibility associated with flexible manufacturing systems (FMSs) and some of the scheduling simplicity related to cellular manufacturing systems (CMSs) without incurring much additional system cost. In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically located in a contiguous area. Batch splitting (also called lot streaming) activity emerges as a key element that management must decide whether to apply it. In this paper, we consider the case where there are multiple jobs with different processing sequences. There are multiple machine types, each of which has several identical machines and identical machines are distributed through the shop floor. Two scheduling objectives are makespan minimisation and total travelling distance minimisation. Under these circumstances, we developed two mixed integer linear programming (MILP) formulations with and without batch splitting. On a broad range of test problems, performances of the models are compared with each other and computational results show that batch splitting leads to more desirable system results while it increases the computational time. The decision maker or manager should make a trade-off between system performance and computational time efficiency.

Adaptability of Just-in-time (JIT) Philosophy to Service Systems: A Case Study

Although a great deal of successful research has been carried out in just-in-time (JIT) philosophy, few researchers have focused on the adaptation of JIT philosophy in service systems. Despite the recent improvements in the service systems, few studies have been carried out which eXamine the effects of the JIT philosophy on service systems which have a different structure to production systems. As a result of the benefits obtained from successful application of the JIT philosophy in the production systems, we have adapted the JIT philosophy for the service systems and eXamined the obtainable benefits. For this purpose, observation and time-scaling studies were made in an Adult Emergency Service in Turkey. Within the framework of the data acquired in this study, the eXisting system was modeled. By analyzing the simulation results, an alternative system has been proposed which can be applied to the system. Results show that the proposed system under the principle of JIT philosophy outperforms the eXisting system.