Engineer-to-order (ETO) production planning and control: an empirical framework for machinery-building companies

This paper illustrates the results of an empirical study involving 21 engineer-to-order (ETO) companies, operating in the machinery-building industry. The study investigates the needs and requirements of such companies in terms of software support for governing the businesses, with particular emphasis on production planning and control (PPC) processes. An empirical analysis investigated two main aspects: (i) the set of business activities performed by the companies in the analysed industry and (ii) the relevant, high-level software functionalities required for the execution of such activities. As an answer to the observed compelling need for reviewing the general approaches to PPC in machinery-building companies, we develop an empirical, high-level production planning and scheduling reference framework, encompassing all the activities involved in the order fulfilment process.     

Modeling cost-delivery trade-offs for distribution logistics by a generalized PVRP model

This article provides a mathematical model to support management in making decisions about cost-delivery trade-offs in the case of cost-delivery flexibilities in distribution logistics. The optimization problem can be modeled as a bi-objective periodic vehicle routing problem, which is known as NP-hard. In the periodic vehicle routing problem considered in this study, no delivery patterns are pre-defined, instead the patterns are the result of the optimization process of the model. In addition, delivery time flexibilities are incorporated in the model. A heuristic solution method for realistic problem sizes is based on the tabu search procedure, and a real case study illustrates the applicability of the solution concept.     

Assessing social vulnerability and identifying spatial hotspots of flood risk to inform socially just flood management policy

This study presents the first nationwide spatial assessment of flood risk to identify social vulnerability and flood exposure hotspots that support policies aimed at protecting high-risk populations and geographical regions of Canada. The study used a national-scale flood hazard dataset (pluvial, fluvial, and coastal) to estimate a 1-in-100-year flood exposure of all residential properties across 5721 census tracts. Residential flood exposure data were spatially integrated with a census-based multidimensional social vulnerability index (SoVI) that included demographic, racial/ethnic, and socioeconomic indicators influencing vulnerability. Using Bivariate Local Indicators of Spatial Association (BiLISA) cluster maps, the study identified geographic concentration of flood risk hotspots where high vulnerability coincided with high flood exposure. The results revealed considerable spatial variations in tract-level social vulnerability and flood exposure. Flood risk hotspots belonged to 410 census tracts, 21 census metropolitan areas, and eight provinces comprising about 1.7 million of the total population and 51% of half-a-million residential properties in Canada. Results identify populations and the geographic regions near the core and dense urban areas predominantly occupying those hotspots. Recognizing priority locations is critically important for government interventions and risk mitigation initiatives considering socio-physical aspects of vulnerability to flooding. Findings reinforce a better understanding of geographic flood-disadvantaged neighborhoods across Canada, where interventions are required to target preparedness, response, and recovery resources that foster socially just flood management strategies.     

A “maximum node clustering” problem

In this note we introduce a graph problem, called Maximum Node Clustering (MNC). We prove that the problem (which is easily shown to be strongly NP-complete) can be approximated in polynomial time within a ratio arbitrarily close to 2. For the special case where the graph is a tree, the problem is NP-complete in the ordinary sense; for this case we present a pseudopolynomial algorithm based on dynamic programming, and a related Fully Polynomial Time Approximation Scheme (FPTAS). Also, the tree case is shown to be exactly solvable in time, where n is the number of nodes.