On a New Class of Parallel Sequencing Situations and Related Games

This paper considers a special class of sequencing situations with two parallel machines in which each agent has precisely two jobs to be processed, one on each machine. The costs of an agent depend linearly on the final completion time of his jobs. We describe a procedure that provides an optimal processing order of the jobs for some particular classes. Furthermore, we study cooperative games arising from these sequencing situations. Our main result will be on the balancedness of these games.     

Processing games with shared interest

This paper introduces processing problems with shared interest as an extension of processing situations with restricted capacities (Meertens, M., et al., Processing games with restricted capacities, 2004). Next to an individual capacity to handle jobs, each player now may have interest in the completion of more than one job, and the degrees of interest may vary among players. By cooperating the players can bundle their capacities and follow an optimal processing scheme to minimize total joint costs. The resulting cost allocation problem is analyzed by considering an associated cooperative cost game. An explicit core allocation of this game is provided.     

Sequencing games without initial order

In this note we study uncertainty sequencing situations, i.e., one-machine sequencing situations in which no initial order is specified. We associate cooperative games with these sequencing situations, study their core, and provide links with the classic sequencing games introduced by Curiel et al. (Eur J Oper Res 40:344–351, 1989). Moreover, we propose and characterize two simple cost allocation rules for uncertainty sequencing situations with equal processing times.     

Sequencing Games with Slack Due Windows and Group Technology Considerations

We study sequencing situations in which the customers are initially sequenced to be served by a single server. We consider both slack due windows and group technology simultaneously. We introduce two division rules to divide among the customers the cost saving from resequencing the customers to follow the optimal sequence and characterize the rules axiomatically. Applying cooperative game theory to analyze the sequencing games corresponding to the sequencing situations, we use the theory’s solution concepts to solve the games.     

Congestion games with failures

We introduce a new class of games, congestion games with failures (CGFs), which allows for resource failures in congestion games. In a CGF, players share a common set of resources (service providers), where each service provider (SP) may fail with some known probability (that may be constant or depend on the congestion on the resource). For reliability reasons, a player may choose a subset of the SPs in order to try and perform his task. The cost of a player for utilizing any SP is a function of the total number of players using this SP. A main feature of this setting is that the cost for a player for successful completion of his task is the minimum of the costs of his successful attempts. We show that although CGFs do not, in general, admit a (generalized ordinal) potential function and the finite improvement property (and thus are not isomorphic to congestion games), they always possess a pure strategy Nash equilibrium. Moreover, every best reply dynamics converges to an equilibrium in any given CGF, and the SPs’ congestion experienced in different equilibria is (almost) unique. Furthermore, we provide an efficient procedure for computing a pure strategy equilibrium in CGFs and show that every best equilibrium (one minimizing the sum of the players’ disutilities) is semi-strong. Finally, for the subclass of symmetric CGFs we give a constructive characterization of best and worst equilibria.     

Processing games with restricted capacities

This paper analyzes processing problems and related cooperative games. In a processing problem there is a finite set of jobs, each requiring a specific amount of effort to be completed, whose costs depend linearly on their completion times. The main feature of the model is a capacity restriction, i.e., there is a maximum amount of effort per time unit available for handling jobs. There are no other restrictions whatsoever on the processing schedule.Assigning to each job a player and letting each player have an individual capacity for handling jobs, each coalition of cooperating players in fact faces a processing problem with the coalitional capacity being the sum of the individual capacities of the members. The corresponding processing game summarizes the minimal joint costs for every coalition. It turns out that processing games are totally balanced. The proof of this statement is constructive and provides a core element in polynomial time.     

On interactive sequencing situations with exponential cost functions

This paper addresses interactive one-machine sequencing situations in which the costs of processing a job are given by an exponential function of its completion time. The main difference with the standard linear case is that the gain of switching two neighbors in a queue is time-dependent and depends on their exact position. We illustrate that finding an optimal order is complicated in general and we identify specific subclasses, which are tractable from an optimization perspective. More specifically, we show that in these subclasses, all neighbor switches in any path from the initial order to an optimal order lead to a non-negative gain. Moreover, we derive conditions on the time-dependent neighbor switching gains in a general interactive sequencing situation to guarantee convexity of the corresponding cooperative game. These conditions are satisfied within our specific subclasses of exponential interactive sequencing situations.     

Drop out monotonic rules for sequencing situations

This note introduces a new monotonicity property for sequencing situations. A sequencing rule is called drop out monotonic if no player will be worse off whenever one of the players decides to drop out of the queue before processing starts. This intuitively appealing property turns out to be very strong: we show that there is at most one rule satisfying both stability and drop out monotonicity. For the standard model of linear cost functions, the existence of this rule is established.     

On games corresponding to sequencing situations with ready times

This paper considers the special class of cooperative sequencing games that arise from one-machine sequencing situations in which all jobs have equal processing times and the ready time of each job is a multiple of the processing time.By establishing relations between optimal orders of subcoalitions, it is shown that each sequencing game within this class is convex.This author is financially supported by the Dutch Organization for Scientific Research (NWO).     

Simultaneous Job Scheduling and Resource Allocation on Parallel Machines

Most deterministic production scheduling models assume that the processing time of a job on a machine is fixed externally and known in advance of scheduling. However, in most realistic situations, apart from the machines, it requires additional resources to process jobs, and the processing time of a job is determined internally by the amount of the resources allocated. In these situations, both the cost associated with the job schedule and the cost of the resources allocated should be taken into account. Therefore, job scheduling and resource allocation should be carefully coordinated and optimized jointly in order to achieve an overall cost-effective schedule. In this paper, we study a parallel-machine scheduling model involving both job processing and resource allocation. The processing time of a job is non-increasing with the cost of the allocated resources. The objective is to minimize the total cost including the cost measured by a scheduling criterion and the cost of all allocated resources. We consider two particular problems of this model, one with the scheduling criterion being the total weighted completion time, and the other with that being the weighted number of tardy jobs. We develop a column generation based branch and bound method for finding optimal solutions for these NP-hard problems. The method first formulates the problems as set partitioning type formulations, and then solves the resulting formulations exactly by branch and bound. In the branch and bound, linear relaxations of the set partitioning type formulations are decomposed into master problems and single-machine subproblems and solved by a column generation approach. The algorithms developed based on this method are capable of solving the two problems with a medium size to optimality within a reasonable computational time.     

Minimizing the Range of Order Completion Times with Multiple Job Classes

A number of important contributions have been made toward the problem of minimizing the difference in ‘customer’ treatment on a single machine. However, so far, all the research has been to solve the problem of minimizing the ‘job’ difference. That is, it is assumed implicitly that each customer places only one job, and hence minimizing the difference in customer treatment is equivalent to minimizing the difference in job treatment. In many practical situations, a customer may place an order which consists of several jobs. The jobs usually can be grouped into different classes. Thus, the range, i.e. the difference between the maximum and the minimum, of order completion times becomes an appropriate performance measure for treating customers equally. In this paper, a dynamic programming (DP) algorithm is developed to provide the optimal solution. Due to the exponential time complexity of the DP algorithm, two heuristic methods are also proposed to solve large-sized problems. Computational results for the heuristics are provided.     

A value for games withn players andr alternatives

We study value theory for a class of games called games withn players andr alternatives. In these games, each of then players must choose one and only one of ther alternatives. A linear, efficient value is obtained using three characterizations, two of which are axiomatic. This value yields an a priori evaluation for each player relative to each alternative.     

A unifying model for matrix-based pairing situations

We present a unifying framework for transferable utility coalitional games that are derived from a non-negative matrix in which every entry represents the value obtained by combining the corresponding row and column. We assume that every row and every column is associated with a player, and that every player is associated with at most one row and at most one column. The instances arising from this framework are called pairing games, and they encompass assignment games and permutation games as two polar cases. We show that the core of a pairing game is always non-empty by proving that the set of pairing games coincides with the set of permutation games. Then we exploit the wide range of situations comprised in our framework to investigate the relationship between pairing games that have different player sets, but are defined by the same underlying matrix. We show that the core and the set of extreme core allocations are immune to the merging of a row player with a column player. Moreover, the core is also immune to the reverse manipulation, i.e., to the splitting of a player into a row player and a column player. Other common solution concepts fail to be either merging-proof or splitting-proof in general.     

具有学习效应的排序对策

建立了具有学习效应的排序对策模型,在这类排序对策中,工件的实际加工时间不再是常数,而是关于工件位置的递减幂函数。当所有工件的正常加工时间相等时,松弛可行顺序的条件,相应的排序对策是均衡的,但不一定是凸对策。     

The core and related solution concepts for infinite assignment games

Assignment problems where both sets of agents that have to be matched are countably infinite, the so-called infinite assignment problems, are studied as well as the related cooperative assignment games. Further, several solution concepts for these assignment games are studied. The first one is the utopia payoff for games with an infinite value. In this solution each player receives the maximal amount he can think of with respect to the underlying assignment problem. This solution is contained in the core of the game. Second, we study two solutions for assignment games with a finite value. Our main result is the existence of core-elements of these games, although they are hard to calculate. Therefore another solution, the f-strong ε-core is studied. This particular solution takes into account that due to organisational limitations it seems reasonable that only finite groups of agents will eventually protest against unfair proposals of profit distributions. The f-strong ε-core is shown to be nonempty. These authors’ research is partially supported by the Generalitat Valenciana (Grant number GV-CTIDIA-2002-32) and by the Government of Spain (through a joint research grant Universidad Miguel Hernández — Università degli Studi di Genova HI2002-0032).     

An Approach to Fuzzy Noncooperative Nash Games

Systems that involve more than one decision maker are often optimized using the theory of games. In the traditional game theory, it is assumed that each player has a well-defined quantitative utility function over a set of the player decision space. Each player attempts to maximize/minimize his/her own expected utility and each is assumed to know the extensive game in full. At present, it cannot be claimed that the first assumption has been shown to be true in a wide variety of situations involving complex problems in economics, engineering, social and political sciences due to the difficulty inherent in defining an adequate utility function for each player in these types of problems. On the other hand, in many of such complex problems, each player has a heuristic knowledge of the desires of the other players and a heuristic knowledge of the control choices that they will make in order to meet their ends.In this paper, we utilize fuzzy set theory in order to incorporate the players' heuristic knowledge of decision making into the framework of conventional game theory or ordinal game theory. We define a new approach to N-person static fuzzy noncooperative games and develop a solution concept such as Nash for these types of games. We show that this general formulation of fuzzy noncooperative games can be applied to solve multidecision-making problems where no objective function is specified. The computational procedure is illustrated via application to a multiagent optimization problem dealing with the design and operation of future military operations.     

An approximation algorithm for the m-machine permutation flow shop scheduling problem with controllable processing times

The paper deals with the m-machine permutation flow shop scheduling problem in which job processing times, along with a processing order, are decision variables. It is assumed that the cost of processing a job on each machine is a linear function of its processing time and the overall schedule cost to be minimized is the total processing cost plus maximum completion time cost. A algorithm for the problem with m = 2 is provided; the best approximation algorithm until now has a worst-case performance ratio equal to . An extension to the m-machine (m ≥2) permutation flow shop problem yields an approximation algorithm with a worst-case bound equal to

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, where is the worst-case performance ratio of a procedure used, in the proposed algorithm, for solving the (pure) sequencing problem. Moreover, examples which achieve this bound for = 1 are also presented.     

Non-cooperative monopolistic games and monopolistic market games

A class of non-cooperative games is discussed in which one player (“the monopolist”) by choosing his strategy restricts the other players to subsets of their strategy sets. Examples of such games in various fields are given. In particular it is shown that some very important economic situations fall within this class of games. A solution concept is defined and sufficient conditions for its existence are derived. The question of the advantages a player derives from being a monopolist is raised and conditions are derived for him to benefit from being a monopolist.     

Random Walk Analysis in Antagonistic Stochastic Games

Abstract

This article deals with two “antagonistic random processes” that are intended to model classes of completely noncooperative games occurring in economics, engineering, natural sciences, and warfare. In terms of game theory, these processes can represent two players with opposite interests. The actions of the players are manifested by a series of strikes of random magnitudes imposed onto the opposite side and rendered at random times. Each of the assaults is aimed to inflict damage to vital areas. In contrast with some strictly antagonistic games where a game ends with one single successful hit, in the current setting, each side (player) can endure multiple strikes before perishing. Each player has a fixed cumulative threshold of tolerance which represents how much damage he can endure before succumbing. Each player will try to defeat the adversary at his earliest opportunity, and the time when one of them collapses is referred to as the “ruin time”. We predict the ruin time of each player, and the cumulative status of all related components for each player at ruin time. The actions of each player are formalized by a marked point process representing (an economic) status of each opponent at any given moment of time. Their marks are assumed to be weakly monotone, which means that each opposite side accumulates damages, but does not have the ability to recover. We render a time-sensitive analysis of a bivariate continuous time parameter process representing the status of each player at any given time and at the ruin time and obtain explicit formulas for related functionals.