A Problem-Specific and Effective Metaheuristic for Flexibility Design

Matching uncertain demand with capacities is notoriously hard. Operations managers can use mix-flexible resources to shift excess demands to unused capacities. To find the optimal configuration of a mix-flexible production network, a flexibility design problem (FDP) is solved. Existing literature on FDPs provides qualitative structural insights, but work on solution methods is rare. We contribute the first metaheuristic which integrates these structural insights and is specifically tailored to solve FDPs. Our genetic algorithm is compared to commercial solvers on instances of up to 15 demand types, resources, and 500 demand scenarios. Experimental evidence shows that in the realistic case of flexible optimal configurations, it dominates the comparison methods regarding runtime and solution quality.Flexibility, Metaheuristic, Network Design

Division of Labour and Social Coordination Modes : A simple simulation model

This paper presents a preliminary investigation of the relationship between the process of functional division of labour and the modes in which activities and plans are coordinated. We consider a very simple production process: a given heap of bank-notes has to be counted by a group of accountants. Because of limited individual capabilities and/or the possibilities of mistakes and external disturbances, the task has to be divided among several accountants and a hierarchical coordination problem arises. We can imagine several different ways of socially implementing coordination of devided tasks. 1) a central planner can compute the optimal architecture of the system; 2) a central planner can promote quantity adjustments by moving accountants from hierarchical levels where there exist idle resources to levels where resources are insufficient; 3) quasi-market mechanisms can use quantity or price signals for promoting decentralized adjustments. By means of a simple simulation model, based on Genetic Algorithms and Classifiers Systems, we can study the dynamic efficiency properties of each coordination mode and in particular their capability, speed and cost of adaptation to changing environmental situations (i.e. variations of the size of the task and/or variations of agents' capabilities). Such interesting issues as returns to scale, specialization and workers exploitation can be easily studied in the same model

Supply Chain Contracting in the Presence of Supply Uncertainty and Store Brand Competition

In today\u27s complex business environment, manufacturers are striving to maintain a competitive advantage over their supply chain partners. Manufacturers\u27 profitability is tightly linked to their strategic interactions with other entities in the supply chain. While numerous studies have been conducted to investigate such interactions in supply chains, certain issues remain unresolved. We apply a game-theoretic framework to analyze two distinct supply chain structures in the presence of supply uncertainty and store brand competition in two essays, respectively. In the first chapter, we study a decentralized assembly supply chain under supply uncertainty. In a decentralized assembly supply chain, one assembler assembles a set of $n$ components, each produced by a different supplier, into a final product to meet an uncertain market demand. Each supplier faces an uncertain production capacity such that only the lesser of the planned production quantity and the realized capacity can be delivered to the assembler. We assume that the suppliers\u27 random capacities and the random demand can follow an arbitrary continuous multivariate distribution. We formulate the problem as a two-stage Stackelberg game. The assembler and the suppliers adopt a so-called Vendor-Managed-Consigned-Inventory (VMCI) contract. We analytically characterize the equilibrium of this game, based on which we obtain several managerial insights. Surprisingly, we show that when a supplier\u27s production cost increases or when his component salvage value decreases, it hurts all other members and the entire supply chain, but it might sometimes benefit this particular supplier. Similarly, when the suppliers do not have supply uncertainty, it benefits the assembler but it does not necessarily benefit the suppliers. Furthermore, we demonstrate that when the suppliers\u27 capacities become more positively correlated, the assembler is always better off, but the suppliers might be better or worse off. Later in the chapter, we also solve the game under the conventional wholesale-price contract. We find that the assembler always prefers the VMCI contract, and the suppliers always prefer the wholesale price contract. In addition, we illustrate that the VMCI contract is more efficient than the wholesale price contract for this decentralized assembly supply chain. In the second chapter, we consider a two-tier decentralized supply chain with a national brand supplier and a retailer. The national brand supplier (she) distributes her products to consumers through the retailer. Meanwhile, the retailer (he) intends to develop and produce his own store brand through a manufacturing source that is different from the national brand supplier. The retailer holds the store brand production unit cost as private information, for which the national brand supplier only has a subjective assessment. Given a supply contract offered by the national brand supplier, the retailer simultaneously decides whether to accept the contract and whether to produce the store brand. The national brand supplier aims to design an optimal menu of contracts to maximize her expected profit as well as extract the retailer\u27s private cost information. We formulate the problem as a two-stage screening game to analyze the strategic interaction between the two players. Despite the inherent computational complexity, we are able to derive the optimal menu of contracts for the national brand supplier, of which the format depends on the national brand supplier\u27s unit production cost. Furthermore, we investigate how the model parameters affect the value of information for each member in the supply chain. We show that the retailer\u27s private cost information becomes less valuable to both the national brand supplier and the retailer when the national brand unit production cost increases. We also illustrate that when the gap between the two possible cost values increases, the private cost information becomes more valuable to the national brand supplier, however the value of information to the retailer himself can either increase or decrease. Finally, we demonstrate that when the perceived quality of the national brand increases, the value of information to the retailer first decreases then increases, but the impact on the value of information to the national brand supplier can be either positive or negative

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii

Optimal scope of supply chain network & operations design

The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are significant and that has attracted considerable research attention since the late 1990s. This doctoral thesis focuses on developing manageable and realistic optimization models for solving four contemporary and interrelated supply chain network and operations design problems. Each requires an integrated decision-making approach for advancing supply chain effectiveness and efficiency. The first model formulates the strategic robust downsizing of a global supply chain network, which requires an integrated decision-making on resource allocation and network reconfiguration, given certain financial constraints. The second model also looks at the strategic supply chain downsizing problem but extends the first model to include product portfolio selection as a downsizing decision. The third model concerns the redesign of a warranty distribution network, which requires an integrated decision-making on strategic network redesign and tactical recovery process redesign. The fourth model simultaneously determines the operational-level decisions on job assignment and process sequence in order to improve the total throughput of a production facility unit

RMS capacity utilisation: product family and supply chain

yesThe paper contributes to development of RMS through linkage with external stakeholders such as customers and suppliers of parts/raw materials to handle demand fluctuations that necessitate information sharing across the supply chain tiers. RMS is developed as an integrated supply chain hub for adjusting production capacity using a hybrid methodology of decision trees and Markov analysis. The proposed Markov Chain model contributes to evaluate and monitor system reconfigurations required due to changes of product families with consideration of the product life cycles. The simulation findings indicate that system productivity and financial performance in terms of the profit contribution of product-process allocation will vary over configuration stages. The capacity of an RMS with limited product families and/or limited model variants becomes gradually inoperative whilst approaching upcoming configuration stages due to the end of product life cycles. As a result, reconfiguration preparation is suggested quite before ending life cycle of an existing product in process, for switching from a product family to a new/another product family in the production range, subject to its present demand. The proposed model is illustrated through a simplified case study with given product families and transition probabilities