Quantum Heat Engines with Complex Working Media,Complete Otto Cycles and Heuristics

Quantum thermal machines make use of non-classical thermodynamic resources, one of which include interactions between elements of the quantum working medium. In this paper, we examine the performance of a quasi-static quantum Otto engine based on two spins of arbitrary magnitudes subject to an external magnetic field and coupled via an isotropic Heisenberg exchange interaction. It has been shown earlier that the said interaction provides an enhancement of cycle efficiency, with an upper bound that is tighter than the Carnot efficiency. However, the necessary conditions governing engine performance and the relevant upper bound for efficiency are unknown for the general case of arbitrary spin magnitudes. By analyzing extreme case scenarios, we formulate heuristics to infer the necessary conditions for an engine with uncoupled as well as coupled spin model. These conditions lead us to a connection between performance of quantum heat engines and the notion of majorization. Furthermore, the study of complete Otto cycles inherent in the average cycle also yields interesting insights into the average performance.     

Analysis of heuristics for finding a maximum weight planar subgraph

The problem of identifying a planar subgraph of maximum total weight in an edge-weighted graph has application to the layout of facilities in a production system and elsewhere in industrial engineering.This problem is NP-hard, and so we confine our attention to polynomial-time heuristics. In this paper we analyse the performance of some heuristics for this problem.     

A comparison of average and discounted cost models for the dynamic lot size inventory problem

It is a common practice in the inventory literature to use average cost models as approximations to the theoretically correct discounted cost models. An average cost model minimizes the average undiscounted cost per period, while a discounted cost model minimizes the total discounted cost over the problem horizon. This paper attempts to answer an important question: How good are the results (the total discounted costs) for the average cost models compared to those for the discounted cost models? This question has been conclusively answered for the simplest inventory model where the demand rate and other parameters are assumed to remain constant in time. This paper addresses this issue for the first time for the case where demand rates are allowed to be nonstationary in time.A discounted cost model has been developed in the paper to carry out this comparison. It is shown that a simple dynamic programming algorithm can be used to find optimal order policies for the discounted cost model.The effect of the varying interest rates and other parameters on the relative performance of the average cost model has been studied by developing an insightful analysis and also by doing a computational study. The results show that, while the average cost model can cost as much as about 26% more than the discounted cost model in extreme cases, this increase is not significant for the parameter values in the range of the common interest.     

A large scale machine loading problem in flexible assembly

Flexible manufacturing is characterized by versatile work stations with minimum change over times and a versatile material handling system. The loading problem in flexible manufacturing is to assign tools, material, operations and jobs to work stations in order to minimize the total number of job-to-work station assignments. In this paper, we describe a special case of the general loading problem applied to flexible assembly and develop a discrete optimization model. We then discuss approaches for obtaining good heuristic solutions and present results for a large scale study.     

A five facets frame for the design of heuristics

The author suggested to distinguish between the ‘engineering approach’ and the ‘mathematical approach’ in connection with the design of heuristics. Stainton and Papoulias extended the scope by suggesting the ‘relational approach’. Based upon this extension, a five facets frame is presented here which is suggested to precede and accompany the ‘technical’ design of the heuristic. The facets are: coverage by participation, experience by doing, abstraction by structuring, extension by comparison, exploration by creativity.     

Approximation schemes for the subset-sum problem: Survey and experimental analysis

Given a set of n positive integers w₁,…,w_n and a positive integer W, the Subset-Sum Problem is to find that subset whose sum is closest to, without exceeding, W. The most important heuristics for this problem are approximation schemes based on a worst-case analysis. We survey them and experimentally analyze their statistical behaviour on a large number of test problems.     

Production scheduling with applications in the printing industry

This paper presents a technique for the solution of scheduling problems encountered in a printing company that can be applied to a range of practical industrial problems. The aim of the research was to develop a due date scheduling algorithm within the framework of the scheduling system already in use by the company. The objective was to enable jobs to be scheduled as close to their due dates as possible, while ensuring that the resultant schedule was feasible with respect to work centre capacities and earliest start date constraints.     

Decomposition strategies for large-scale continuous location-allocation problems

^** Email: nenad.mladenovic{at}brunel.ac.uk The continuous location–allocation problem requires findingsites for m new facilities in the plane in order to serve nusers such that the total transportation costs are minimized.Several heuristics have been developed to solve this well-studiedglobal optimization problem. However, little work has been doneusing decomposition techniques. In this paper, we examine afew new ways to decompose the location–allocation problem.These strategies are then embedded in a variable neighbourhooddecomposition search heuristic and tested on large instancesof this problem.     

Solving the continuous space p-centre problem: planning application issues

^** Email: wei.97{at}osu.edu^*** Email: murray.308{at}osu.edu^**** Email: xiao.37{at}osu.edu The Voronoi diagram heuristic has been proposed for solvingthe p-centre problem in continuous space. However, importantassumptions underlie this heuristic and may be problematic forpractical applications. These simplifying assumptions includeuniformly distributed demand, representing a region as a rectangle;analysis of a simple Voronoi polygon in solving associated one-centreproblems and no restrictions on potential facility locations.In this paper, we explore the complexity of solving the continuousspace p-centre problem in location planning. Considering theissue of solution space feasibility, we present a spatiallyrestricted version of this problem and propose methods for solvingit heuristically. Theoretical and empirical results are provided.