Gerald W. Evans

A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs

Todays competitive business environment has resulted in increasing cooperation among individual companies as members of a supply chain. Accordingly, third party logistics providers (3PLs) must operate supply chains for a number of different clients who want to improve their logistics operations for both forward and reverse flows. As a result of the dynamic environment in which these supply chains must operate, 3PLs must make a sequence of inter-related decisions over time. However, in the past, the design of distribution networks has been independently conducted with respect to forward and reverse flows. Thus, this paper presents a mixed integer nonlinear programming model for the design of a dynamic integrated distribution network to account for the integrated aspect of optimizing the forward and return network simultaneously. Since such network design problems belong to a class of NP hard problems, a genetic algorithm-based heuristic with associated numerical results is presented and tested in a set of problems by an exact algorithm. Finally, a solution of a network plan would help in the determination of various resource plans for capacities of material handling equipments and human resources.

Fuzzy multicriteria models for quality function deployment

Abstract An integrated formulation and solution approach to Quality Function Deployment (QFD) is presented. Various models are developed by defining the major model components (namely, system parameters, objectives, and constraints) in a crisp or fuzzy way using multiattribute value theory combined with fuzzy regression and fuzzy optimization theory. The proposed approach would allow a design team to reconcile tradeoffs among the various performance characteristics representing customer satisfaction as well as the inherent fuzziness in the system. In addition, the modeling approach presented makes it possible to assess separately the effects of possibility and flexibility inherent or permitted in the design process on the overall design. Knowledge of the impact of the possibility and flexibility on customer satisfaction can also serve as a guideline for acquiring additional information to reduce fuzziness in the system parameters as well as determine how much flexibility is warranted or possible to improve a design. The proposed modeling approach would be applicable to a wide spectrum of design problems where multiple design criteria and functional design relationships are interacting and/or conflicting in an uncertain, qualitative, and fuzzy way.

A bi-objective reverse logistics network analysis for post-sale service

Reverse logistics, induced by various forms of return, has received growing attention throughout this decade. Reverse logistics network design is a major strategic issue. This paper addresses the analysis of reverse logistic networks that deal with the returns requiring repair service. A problem involving a manufacturer outsourcing to a third-party logistics (3PLs) provider for its post-sale services is proposed. First, a bi-objective optimization model is developed. Two objectives, minimization of the overall costs and minimization of the total tardiness of cycle time, are addressed. The facility capacity option at each potential location is treated as a discrete parameter. The purpose is to find a set of non-dominated solutions to the facility capacity arrangement among the potential facility locations, as well as the associated transportation flows between customer areas and service facilities. Then, a solution approach is designed for solving this bi-objective optimization model. The solution approach consists of a combination of three algorithms: scatter search, the dual simplex method and the constraint method. Finally, computational analyses are performed on trial examples. Numerical results present the trade-off relationship between the two objectives. The numerical results also show that the optimization for the first objective function leads to a centralized network structure; the optimization for the second objective function results in a decentralized network structure.

A simulation model for evaluating personnel schedules in a hospital emergency department

This paper describes an Arena simulation model of a particular hospitals emergency department. The model allows the simulation of the process flows of 13 different types of patients, and is used to evaluate various feasible schedules for nurses, technicians, and doctors. The main performance measure used in the evaluation process is the average length-of-stay of patients in the emergency department.

Layout design using the analytic hierarchy process

Abstract Layout design is a complex problem involving multiple, conflicting objectives as well as inputs that can be either qualitative or quantitative in nature. This paper describes and illustrates an approach to the layout design problem which considers both of these difficulties. The approach involves the use of several layout design algorithms as well as manual methods to generate candidate layouts. These candidate layouts are then used as input to Saatys Analytic Hierarchy Process in order to allow the consideration of the problems multiple objectives. The approach is illustrated on a problem involving the layout of a commercial printing and binding facility.

Multicriteria design of manufacturing systems through simulation optimization

Simulation is a popular tool for the design and analysis of manufacturing systems. The popularity of simulation is due to its flexibility, its ability to model systems when analytical methods have failed, and its ability to model the time dynamic behavior of systems. However, in and of itself, simulation is not a design tool; therefore, in order to optimize a simulation model, it often must be used in conjunction with an optimum-seeking method. This paper describes an interactive (decision maker-computer) methodology for multiple response optimization of simulation models. This approach is based on a multiple criteria optimization technique called the STEP method. The proposed methodology is illustrated with an example involving the optimization of a manufacturing system. >

Comparison of global search methods for design optimization using simulation

A methodology for the application of global search methods for optimizing the results of a computer simulation is presented. Specific global optimization methods including simulated annealing, genetic algorithms, and Bayesian techniques are discussed in terms of their strengths and weaknesses as applied to this methodology. In particular, the effects of simulation time, constraints, dimensionality, and computational complexity are considered as they relate to the choice of algorithms. Simulated annealing and genetic algorithms perform similarly, yet differ in many ways from the class of Bayesian algorithms. Bayesian algorithms spend additional computation time in modeling all past values of the unknown function in an effort to minimize the number of evaluations of the function. These methods would be the algorithms of choice for determining the optimal design via simulation, provided the number of design variables is less than 10 and the time required to run a single simulation is large compared with the time it takes the algorithm to determine the next point.<<ETX>>

Multicriteria optimization of simulation models

The authors suggest a framework for the multicriteria optimization of simulation models by first discussing the unique difficulties of this problem area along with important problem characteristics, and then discussing the way that these problem characteristics would affect the choice of a particular technique. The problem of manufacturing system optimization is addressed. Various techniques, along with their advantages and disadvantages, are discussed and categorized according to the timing of the articulation of the required preference (tradeoff) information with respect to the optimization.<<ETX>>

A goal programming model for the operation of closed-loop supply chains

The implementation of closed-loop supply chains to handle the problem of increasing levels of electronic and electrical equipment waste can be beneficial both economically and ecologically. Three different problem analysis techniques—why–whats stopping analysis, fundamental objective hierarchy, and means objective network—are used to analyse the problem from various perspectives. A non-preemptive goal-programming model and solution approach have also been developed, with goals being assigned different weights according to the respective goal priorities. The model considers multiple products, as well as operations associated with the product, subassembly, part, and material levels. A major contribution of the research involves the fact that the objectives and related constraints for each member of the supply chain are explicitly modeled. The results of the analysis show both the effect of varying the priority/weight associated with a goal, and how the values of the deviational variables can aid a decision maker in model analysis.

A review of multi-criterion approaches to FMS scheduling problems

Abstract An increasing number of organizations are implementing Flexible Manufacturing Systems (FMSs) to achieve competitive advantage. The implementation process can be viewed in a hierarchical perspective: ranging from design problems at the top level to scheduling and control problems at the lower level. In recent years, researchers have focused a significant amount of attention on FMS scheduling problems. However, the major share of those studies considered FMS scheduling in the context of a single performance criterion. Though several researchers have synthesized the literature involving single criteria, the literature with multi-criterion approaches has not been consolidated and reviewed. Therefore, the purpose of this paper is to explore the applicability of multi-criterion approaches to the production scheduling problems of an FMS and review the pertinent literature on FMS scheduling involving multiple objectives. Some areas for future research are also identified.