Generalization of the convex-hull-and-line traveling salesman problem

Two instances of the traveling salesman problem, on the same node set (1,2 n} but with different cost matrices C and C, are equivalent iff there exist {a, hi: -1, n} such that for any 1 _i, j _n, j, C(i, j) C(i,j) q-a -t-bj [7]. One ofthe well-solved special cases of the traveling salesman problem (TSP) is the convex-hull-and-line TSP. We extend the solution scheme for this class of TSP given in [9] to a more general class which is closed with respect to the above equivalence relation. The cost matrix in our general class is a certain composition of Kalmanson matrices. This gives a new, non-trivial solvable case of TSP

Group Projects in an Introductory Statistics Course

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Improvement to an existing multi-level capacitated lot sizing problem considering setup carryover, backlogging, and emission control

This paper presents a multi-level, multi-item, multi-period capacitated lot-sizing problem. The lot-sizing problem studies can obtain production quantities, setup decisions and inventory levels in each period fulfilling the demand requirements with limited capacity resources, considering the Bill of Material (BOM) structure while simultaneously minimizing the production, inventory, and machine setup costs. The paper proposes an exact solution to Chowdhury et al. (2018)\u27s[1] developed model, which considers the backlogging cost, setup carryover & greenhouse gas emission control to its model complexity. The problem contemplates the Dantzig-Wolfe (D.W.) decomposition to decompose the multi-level capacitated problem into a single-item uncapacitated lot-sizing sub-problem. To avoid the infeasibilities of the weighted problem (WP), an artificial variable is introduced, and the Big-M method is employed in the D.W. decomposition to produce an always feasible master problem. In addition, Wagner & Whitin\u27s[2] forward recursion algorithm is also incorporated in the solution approach for both end and component items to provide the minimum cost production plan. Introducing artificial variables in the D.W. decomposition method is a novel approach to solving the MLCLSP model. A better performance was achieved regarding reduced computational time (reduced by 50%) and optimality gap (reduced by 97.3%) in comparison to Chowdhury et al. (2018)\u27s[1] developed model

Some problems in one-operator scheduling

A flexible workforce or a versatile machine is employed to perform various types of operations. Often these resources are associated with setups. Whenever a worker or machine switches from processing one type of operation to another a setup time may be required although several operations of a same type can be processed in succession after a single setup. The presence of setups gives rise to the problem of choosing batch sizes that are neither too large nor too small. In the last one and a half decade, many researchers have addressed the problem of scheduling with batching. A majority of articles assumes that there is only one type of scarce resource, which is typically machine. Often there can be two scarce resources such as a worker and a machine or a machine and a tool. We propose a resource constrained scheduling model with a single operator and two or more machines. Whenever the operator changes machine, a setup time is required that may be sequence dependent or sequence independent. We consider the two cases of an open shop and a flow shop. In the open shop case, the order in which a job visits the machines is unrestricted. In the flow shop case, every job must visit the machines in the same order. We consider various scheduling objectives. For variable number of machines, many cases are intractable. We discuss some dominance properties that narrow down the search for an optimal schedule. We present a dynamic programming approach which solves a large number of cases. The running time of the dynamic program is polynomial for a fixed number of machines. For the case of two machines, we show that the dominance properties have a nice interpretation. We develop some algorithms and justify their use by establishing running times, comparing the running times with those of the existing algorithms, and testing the performance of the algorithms

Composite Index Creation Using AHP: Efficiency Optimization for the Health Care Industry

The growth of the health care industry, both in medicine and administration, increases the awareness of determining the efficiency of the services, utilities, and overall performance. The objective of this paper is to present a conceptual creation of an index that will quantify the efficiency of centers in the health care industry. Its proposed use will be as a determinant tool, where the users are able to pinpoint areas of inefficiency that require further action for improvement. The creation of a composite index to measure the health care industry’s efficiency simplifies both internal and external decisions made based on specific centers. It will be able to aid in public policy, which tasks itself with defining the optimal amount of hospitals capable of fulfilling the health care needs of designated areas. The indices allow for appropriate comparisons between hospitals or clinics, respectively, and their effectiveness on all levels of functionality. A modified version of the Analytical Hierarchy Process is the methodology incorporated in the creation of the composite index. AHP itself is an eigenvector method of calculating priorities which researchers have found to be a superior methodology for the weighing of a composite index. The index integrates sub-indices that are aggregated and weighted to determine overall efficiency. This method is evaluated using the local health care industry in Windsor, Canada