The affect heuristic

This paper introduces a theoretical framework that describes the importance of affect in guiding judgments and decisions. As used here, “affect” means the specific quality of “goodness” or “badness” (i) experienced as a feeling state (with or without consciousness) and (ii) demarcating a positive or negative quality of a stimulus. Affective responses occur rapidly and automatically—note how quickly you sense the feelings associated with the stimulus word “treasure” or the word “hate”. We argue that reliance on such feelings can be characterized as “the affect heuristic”. In this paper we trace the development of the affect heuristic across a variety of research paths followed by ourselves and many others. We also discuss some of the important practical implications resulting from ways that this heuristic impacts our daily lives.     

The service allocation problem at the Gioia Tauro Maritime Terminal

The Service Allocation Problem (SAP) is a tactical problem arising in the yard management of a container transshipment terminal. The objective is the minimization of the container rehandling operations inside the yard. This study of the SAP was undertaken for the Gioia Tauro port which is located in Italy and is the main hub terminal for container traffic in the Mediterranean Sea. The SAP can be formulated as a Generalized Quadratic Assignment Problem (GQAP) with side constraints. Two mixed integer linear programming formulations are presented. The first one exploits characteristics of the yard layout at Gioia Tauro where the berth and the corresponding yard positions extend along a line. The second formulation is an adaptation of a linearization for the GQAP. In both cases only small instances can be solved optimally. An evolutionary heuristic was therefore developed. For small size instances the heuristic always yields optimal solutions. For larger sizes it is always better than a truncated branch-and-bound algorithm applied to the exact formulations.     

A pattern-directed approach to FMS scheduling

Scheduling in flexible manufacturing systems (FMS) must take into account the shorter lead time, the multiprocessing environment, and the dynamically changing states. In this paper, a pattern-directed approach is presented which incorporates a nonlinear planning method developed in the artificial intelligence field. The scheduling system described here is knowledge-based and utilizes both forward-and backward-chaining for generating schedules (treated as state-space plans). The pattern-directed approach is dynamically adjustable and thus can handle scheduling requirements unique to the FMS environment, such as dynamic scheduling, failure-recovery scheduling, or prioritized scheduling for meeting deadlines.     

Stochastic scheduling of parallel queues with set-up costs

We consider the problem of allocating a single server to a system of queues with Poisson arrivals. Each queue represents a class of jobs and possesses a holding cost rate, general service distribution, and a set-up cost. The objective is to minimize the expected cost due to the waiting of jobs and the switching of the server. A set-up cost is required to effect an instantaneous switch from one queue to another. We partially characterize an optimal policy and provide a simple heuristic scheduling policy. The heuristic's performance is evaluated in the cases of two and three queues by comparison with a numerically obtained optimal policy. Simulation results are provided to demonstrate the effectiveness of our heuristic over a wide range of problem instances with four queues.     

P-枢纽航线网络设计问题的一种启发式算法

针对无容量限制的多重分派枢纽中位问题(UMApHMP),提出了一种基于禁忌搜索和最短路算法的新的启发式算法。利用CAB基准数据对该算法进行了验证,计算结果表明所提算法具有较强寻优能力和较快的求解效率。     

Optimal model for warehouse location and retailer allocation

Warehouse location and retailer allocation is a high‐level strategic decision problem that is commonly encountered by logisticians and supply chain managers, especially during the supply chain design phase. Considering the product distribution cost and warehouse capital cost trade‐offs, this paper models the warehouse location and retailer allocation problem as a 0–1 integer programming problem and provides an efficient two‐stage set covering heuristic algorithm to solve large‐sized problems. Finally, concluding remarks and some recommendations for further research are also presented. Copyright © 2007 John Wiley & Sons, Ltd.     

Lock-Gain Based Graph Partitioning

We propose a new heuristic for the graph partitioning problem. Based on the traditional iterative improvement framework, the heuristic uses a new type of gain in selecting vertices to move between partitions. The new type of gain provides a good explanation for the performance difference of tie-breaking strategies in KL-based iterative improvement graph partitioning algorithms. The new heuristic performed excellently. Theoretical arguments supporting its efficacy are also provided. As the proposed heuristic is considered a good candidate for local optimization engines in metaheuristics, we combined it with a genetic algorithm as a sample case and obtained a surprising result that even the average results over 1,000 runs equalled the best known for most graphs.     

Investigation of the Fitness Landscapes in Graph Bipartitioning: An Empirical Study

An empirical study is performed on the local-optimum space in graph bipartitioning. We examine some statistical features of the fitness landscape and the local properties of the landscape. They include the distributions of local optima, their cost-distance correlations, their attraction powers, the properties around the central area of local optima, etc. The study reveals some new notable results about the properties of the fitness landscape. For example, the central area yielded good quality in local-optimum space, the local-optimum space had the self-similar structure of global convexity, local optima showed clusters in more than one place, etc. We also provide a simple experiment on whether it is worth to exploit the area around the Euclidean center of the problem space.     

Ordinal algorithms for packing with target center of gravity

The problem of arranging N unit length weights on a line segment of length N, given a target center of gravity on this line segment, is examined under the assumption that the only information we have about the weights is their order, i.e., a ₁ a ₂ ... a _N . Lower bounds on worst case performance of algorithms for this problem are developed, and algorithms (permutations) which come close to achieving these bounds are provided.This work was partially supported under Natural Sciences and Engineering Research Council (NSERC) of Canada research grant number OGP0002507.     

An efficient heuristic algorithm for minimum matching

We give a new heuristic algorithm for minimum matching problems and apply it to the Euclidean problem with random vertices in 2 dimensions. The algorithm is based on simulated annealing and performs in practice faster than previous heuristic algorithms yielding suboptimal solutions of the same good quality. From configurations with up toN=20.000 vertices in the unit square we estimate that the length of a minimum matching scales asymptotically asLN with (=0.3123±0.0016.

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Zusammenfassung Wir stellen einen neuen heuristischen Algorithmus für minimale Matching-Probleme vor und wenden diesen auf das euklidische Problem mit zufÄlliger Punkteverteilung in 2 Dimensionen an. Auf Simulated Annealing basierend lÄuft der Algorithmus schneller als frühere heuristische Algorithmen und erreicht dabei suboptimale Lösungen gleich guter QualitÄt. Aus Konfigurationen mit bis zuN=20.000 Punkten im Einheitsquadrat schÄtzen wir, da\ für die LÄnge des minimalen Matchings asymptotischLN mit=0.3123±0.0016 gilt.