A Comprehensive Risk Management Tool Based on Multi-Agents and System Dynamics for Traditional and E-Commerce Supply Chain

Supply chain management paradigms are becoming increasingly common management perspectives all over the world due to violent global competition of trade organizations and rapid changes in technology. In recent years, thanks to the communication improvements, customers have become more conscious about purchasing goods or services. Furthermore, organizations have to be customer oriented and more flexible against the dynamism of supply chain environment which increases uncertainties in supply chain parameters. Although a considerable amount of risk factors appearing in supply chain operations, this study concentrates on detecting key supply chain risks which could cause abnormalities and occur from rapid changes in customer demand, unpredictable price fluctuations, defect variations and delivery delays and provides the correction of these problems automatically. Thus, a system dynamics model is established for determining risks. This combined approach would be helpful for integrated supply chain risk management.

An Integrated Methodology for Order Release and Scheduling in Hybrid Manufacturing Systems

There are three main problems that could impact the performance of a Hybrid Manufacturing System (HMS): (1) order release (OR), (2) batch scheduling and (3) worker assignment. This paper deals with these three main problems hierarchically for an HMS. Three different mathematical models are developed to describe the problems more clearly. A novel methodology is proposed to adopt a holistic approach to these problems and find an effective solution. Implementation of the proposed methodology permits integrating batch scheduling and worker timetabling. Feasible solutions in the best-known Pareto front are evaluated as alternative solutions. The goal is to select a preferred solution that satisfies worker constraints, creates effective worker teams in cells, minimizes the number of utility workers, and the average flow time. The study also presents several improvements, which are made following the application of the proposed methodology to a real company that produces expansion joints.

Hub Location Allocation Problems and Solution Algorithms

The Hub Location-Allocation Problem is one of the most important topics in industrial engineering and operations research, which aims to find a form of distribution strategy for goods, services, and information. There are plenty of applications for hub location problem, such as Transportation Management, Urban Management, locating service centers, Instrumentation Engineering, design of sensor networks, Computer Engineering, design of computer networks, Communication Networks Design, Power Engineering, localization of repair centers, maintenance and monitoring power lines, and Design of Manufacturing Systems. In order to define the hub location problem, the present chapter offers two different metaheuristic algorithms, namely Particle Swarm Optimization or PSO and Differential Evolution. The presented algorithms, then, are applied to one of the hub location problems. Finally, the performances of the given algorithms are compared in term of benchmarking.

Metaheuristic Approaches for Extrusion Manufacturing Process

Optimization, basically, is a method used to find solutions for a particular problem without neglecting the existing boundaries or limitations. Flower Pollination Algorithm (FPA) is one of the recently developed nature inspired algorithms, based on the intriguing process of flower pollination in the world of nature. The main aim of this study is to utilize FPA in optimizing cold forward extrusion process in order to obtain optimal parameters to produce workpiece with the minimum force load. It is very important to find the most optimal parameters for an extrusion process in order to prevent waste from happening due to trial and error method in determining the optimal parameters and thus, FPA is used to replace the traditional trial and error method to optimize the cold forward extrusion process. The optimization performance of the FPA is then compared with the particle swarm optimization (PSO), in which the FPA shows comparable performance in this regard.

Data Analytics in Industry 4.0

The new form of future generation machines and automated systems could be synchronized by IoT adaptation. By this way, a very large size data can be carefully stored in data repositories and have to be analyzed for extracting knowledge. Thus, optimization techniques are becoming invaluable tools for finding patterns from parallel distributed machines. On the other hand, statistical methods and optimization models could not be utilized efficiently due to excessive dimension of data. Additionally, data analytics should be applied and results should be gathered by using practical approaches especially for security, access control and fault detection issues. In this study, optimization techniques are evaluated in the perspective of big data analytics and both mathematical and statistical methods will be extensively analyzed for different versions of problem solving and decision making in Industry 4.0 era.

Lean Manufacturing

Lean has become a new mantra in today's manufacturing sector. In this millennium, companies are facing a challenge to be economically competitive in manufacturing. Many of them have realized that the old style of mass manufacturing is no longer successful. Hence, lean manufacturing is being embraced by the companies to simultaneously achieve a competitive edge and economic growth. Many studies have shown that lean organizations are capable of meeting customer's expectations consistently, at each step of the production systems. Lean manufacturing also implies efficient use of non-renewable resources in order to maintain a sustainable environment. To reach the full potential of an organization, lean must be embraced as a holistic business strategy. In this chapter, the history of lean innovation will be briefly discussed, followed by the principles of lean manufacturing and various tools in implementing lean practices. Examples of organizations that have experienced significant improvements once transformed to lean manufacturing will also be cited.

Mathematical Optimization Models for the Maintenance Policies in Production Systems

This chapter initially presents a brief information about production systems. At these systems, different types of maintenance policies are developed to cope with wear out failures. Mainly used maintenance policies can be classified as corrective, preventive, and condition-based maintenance. In the corrective maintenance, repair or replacement is applied whenever components of the machine breakdown. In the preventive maintenance approach maintenance activities are applied to the critical components on a periodic basis. On the other hand, maintenance activities are applied whenever critical reliability level is reached or exceeded. These types of maintenance policies are modeled using mathematical modeling techniques such as linear programming, goal programming, dynamic programming, and simulation. A review of current literature about the mathematical models, the simulation-based optimization studies examining these maintenance policies are categorized and explained. Besides, the solution methodologies are discussed. Finally, the opportunities for future research are presented.

Optimal Strategy Selection in a Supply Chain

Uncertainties and unpredictability in the market force companies to develop strategies which enable them to perform better than their competitors. Developing proper strategies for a supply chain is crucial. Strategies are affected by the nature of the firm's products or services, customer preferences, operations, process design of the firm, etc. Companies should form adaptive supply chain strategies which enable them to be resilient and flexible enough in the flow of materials, products, information, and money along the supply chain. There are many studies about supply chain management and supplier selection in the literature. However, the number of studies about the selection of the right supply chain strategy are very limited. This study presents the components which help to constitute a supply chain strategy and classify the supply chain strategies described in the literature. Lastly, it offers a strategy and criteria matrix which can be used as a road map for selecting the most appropriate supply chain strategy by firms.

Differential Return on Investment Optimization

A two-echelon supply chain is studied that involves a retailer who faces demand from two or more market segments and enable to set different prices and marketing expenditures and a supplier who desires to find optimal number of shipments through an integrated system. A new mixed-integer non-linear fractional programming (MINLFP) model is developed. In order to solve the resultant MINLFP model, the constrained non-linear programming model is reformulated as an unconstrained one using penalty terms. Two meta-heuristics, namely simulated annealing (SA) and imperialist competitive algorithm (ICA), are applied to solve the relaxed unconstrained model. Numerical results show that ICA can reach better solutions in comparison with SA. However, SA has the ability of providing more robust solutions which are converged to a good solution. The chapter concludes with superiority of SA.

Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System

Problems encountered in real manufacturing environments are complex to solve optimally, and they are expected to fulfill multiple objectives. Such problems are called multi-objective optimization problems(MOPs) involving conflicting objectives. The use of multi-objective evolutionary algorithms (MOEAs) to find solutions for these problems has increased over the last decade. It has been shown that MOEAs are well-suited to search solutions for MOPs having multiple objectives. In this chapter, in addition to comprehensive information, two different MOEAs are implemented to solve a MOP for comparison purposes. One of these algorithms is the non-dominated sorting genetic algorithm (NSGA-II), the effectiveness of which has already been demonstrated in the literature for solving complex MOPs. The other algorithm is fast Pareto genetic algorithm (FastPGA), which has population regulation operator to adapt the population size. These two algorithms are used to solve a scheduling problem in a Hybrid Manufacturing System (HMS). Computational results indicate that FastPGA outperforms NSGA-II.