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Communication games and the revelation principle in supply chain management
(12-10-2015) Zissis, Dimitris; Athens University of Economics and Business, Department of Management Science and Technology; Burnetas, Apostolos; Tarantilis, Christos D.; Mourtos, Yiannis; Kritikos, Emmanouil; Androutsopoulos, Konstantinos; Repoussis, Panagiotis; Ioannou, George
The aim of this PhD dissertation is the in-depth study of supply chain and how the nodes could coordinate their strategies in a decentralized system. We provide the nodes the opportunity to communicate with each other, without any restrictions. Therefore, we propose a “free'' communication system, including all the possible ways of communication among the nodes, and examine how communication leads to the system-wide coordination and, thus, reduces costs, eliminates inefficiencies, and results in better individual profits for all the participants. We study supply chains with rational nodes, which have to make private decisions in order to maximize their utility functions. These decisions are related to the order quantity, quantity discounts, product prices, inventory levels, etc. Furthermore, these decisions are usually competitive, because every node has different preferences and different information. However, there are cases in which some nodes have incentives to build a coalition; therefore they act as a single entity. As each node is a distinct decision maker and has private information and different preferences, we model the supply chain as a game using tools of Game Theory. Our core objective is to examine how each node decides on his strategy in a decentralized system.An increasing body of literature in the area of Supply Chain Management addresses the way in which the nodes of a chain can act in a cross-linked mode, in order to reduce both their own costs and the total cost of the chain. Key research work has been published in premier archival journals tackling problems associated with supply chain coordination; however, examination of the recent literature reveals that almost all the papers have restrictive (e.g., sign of contracts) or unrealistic assumptions (e.g., all the nodes possess the same information). Thus, there are many open issues deserving attention. It would be ideal if we could propose ways of coordination without restrictive and unrealistic assumptions to align the individual incentives of the nodes with the incentives of the whole chain.In this regard, we allow nodes to communicate with each other; with respect to any private information they may possess. Obviously, opportunities for mutual benefits cannot be found, unless the nodes share their private information. To proceed in sharing private information, nodes should be provided with appropriate incentives. It is worth to investigate how information sharing could be achieved. We consider that all the possibilities for communication are assumed to be entirely controlled by a mediator. The fundamental idea is the framework proposed by Gibbard (1973) and Myerson (1979, 1982), the Revelation Principle. The extended Revelation Principle's framework, by Myerson, Hurwicz, and Maskin, was awarded the 2007 Nobel Prize in economics. Intuitively, the Revelation Principle states that the mediator could design a mechanism to enforce all the nodes to reveal their private information and obey his suggestions about their actions, because it is in their self-interest. Therefore, by using credible mediator, coordination is attainable.
Optimization methods for complex vehicle routing and scheduling problems
Repoussis, Panagiotis P.; Athens University of Economics and Business, Department of Management Science and Technology; Tarantilis, Christos D.; Prastacos, Gregory P.; Ioannou, George
This dissertation is entitled \Optimization Methods for Complex Vehicle Routing and Scheduling problems". The term \optimization methods" refers to hybrid methodologies that combine heuristic and metaheuristic algorithms to produce effective and robust solution methods for combinatorial optimization problems. The term \complex" denotes the computational complexity and the large-scale nature of the problems considered. Finally, the term \vehicle routing and scheduling problems" describes a subclass of problems emanating from the broader family of Vehicle Routing Problems (VRP). The VRP is the problem of finding a set of optimal routes for a fleet of vehicles to serve a given set of customers. The vehicles are often assumed to have a common home base, called the depot. The cost of traveling between each pair of customers and between the depot and each customer is given. The goal is to find the optimum \assignment" and service \schedule" of customers for each vehicle route, such that the total traveling cost is minimized and all customers are served by exactly one vehicle. Typically the solution has to satisfy several other restrictions, such as total capacity of vehicles, route durations and other operational constraints. In this thesis, the term VRP is used to describe a broad class of problems and not a specific problem with a limited set of restrictions or constraints; thus, in the remainder of the thesis the term VRP includes all problems that involve creating one or more routes, starting and ending in one or more common depots or at predefined start and end terminals, in contrast to most archival literature where VRP is mainly used for the Capacitated Vehicle Routing Problem (CVRP). A subclass of VRPs is the Vehicle Routing Problem with Time Windows (VRPTW); this is the core problem studied in this thesis along with its variants. In the VRPTW, a desired visit time (time window) at each customer is given. Also, a number of additional constraints are often enforced, e.g. heterogeneous fleets of vehicles, open routes, multiple depots, etc. The VRPTW is a classical and well-studied vehicle routing problem. It is an NP-hard combinatorial optimization problem that has attracted the interest of both researchers and practitioners. For many of the problem instances considered in this thesis, the set of feasible solutions is so large that even if we had a computer that in a systematic way could construct and evaluate the cost of a trillion (1012) solutions per second, and we had started that computer right after the big bang, 14 billion years ago, it would still not have evaluated all the feasible solutions today. Consequently we have to resort to other methods and forget simple enumeration. Engineering and technology have been continuously providing examples of difficult optimization problems, with practical as well as theoretical importance. Optimization problems are concerned with the search for the \best" configuration of a set of variables to achieve some goals. A huge collection of optimization techniques has been suggested by several researchers from different fields; an immense number of refinements has made these techniques work for specific types of applications. All these procedures are based on some common ideas, which are being characterized by additional specific features. However, for most optimization problems no procedure is known, in general, to get directly an \optimal" solution. Three types of solution methods are typically employed to solve problems such as the VRPTW: Heuristics, Approximate Algorithms and Exact Methods. Heuristics are solution methods that relatively quickly can _nd a feasible solution with reasonable quality. Approximation algorithms are special heuristics that can provide a solution and an errorquality guarantee. Exact methods guarantee that the optimal solution is found subject to sufficient time and memory space. Although in recent years significant algorithmic achievements have been reached and highly sophisticated exact methods have been proposed, heuristics remain the only reliable and viable approach for the solution of practical large-scale instances. Towards this end, a special class of heuristics that has received special attention during the last two decades is the so-called metaheuristics. Such algorithms provide general frameworks for heuristics that can be applied to different problems, while high quality solutions are often obtained within reasonable computational burdens. This PhD thesis focuses on the exploration of the computational power of metaheuristics, not only in terms of solving effectively and efficiently the VRPTW and other real-life industrial variants of the problem in a limited amount of time, but also developing cooperative and competitive optimization methods that are characterized by simplicity, exibility and robustness. The problems studied have been inspired from real-world applications and the last part of the thesis describes the application of some of the solution methods developed in a real-life industrial environment.

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