Ordering Policies of a Deteriorating Item in an EOQ Model under Upstream Partial Order-Quantity-Dependent Trade Credit and Downstream Full Trade CreditRead the full article
Advances in Operations Research publishes original research and review articles contributing to the theory and methodology of operational research.
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A Successful Three-Phase Metaheuristic for the Shift Minimization Personal Task Scheduling Problem
Workforce scheduling process consists of three major phases: workload prediction, shift generation, and staff rostering. Shift generation is the process of transforming the determined workload into shifts as accurately as possible. The Shift Minimization Personnel Task Scheduling Problem (SMPTSP) is a problem in which a set of tasks with fixed start and finish times must be allocated to a heterogeneous workforce. We show that the presented three-phase metaheuristic can successfully solve the most challenging SMPTSP benchmark instances. The metaheuristic was able to solve 44 of the 47 instances to optimality. The metaheuristic produced the best overall results compared to the previously published methods. The results were generated as a by-product when solving a more complicated General Task-based Shift Generation Problem. The metaheuristic generated comparable results to the methods using commercial MILP solvers as part of the solution process. The presented method is suitable for application in large real-world scenarios. Application areas include cleaning, home care, guarding, manufacturing, and delivery of goods.
A Parallelized Variable Fixing Process for Solving Multistage Stochastic Programs with Progressive Hedging
Long time horizons, typical of forest management, make planning more difficult due to added exposure to climate uncertainty. Current methods for stochastic programming limit the incorporation of climate uncertainty in forest management planning. To account for climate uncertainty in forest harvest scheduling, we discretize the potential distribution of forest growth under different climate scenarios and solve the resulting stochastic mixed integer program. Increasing the number of scenarios allows for a better approximation of the entire probability space of future forest growth but at a computational expense. To address this shortcoming, we propose a new heuristic algorithm designed to work well with multistage stochastic harvest-scheduling problems. Starting from the root-node of the scenario tree that represents the discretized probability space, our progressive hedging algorithm sequentially fixes the values of decision variables associated with scenarios that share the same path up to a given node. Once all variables from a node are fixed, the problem can be decomposed into subproblems that can be solved independently. We tested the algorithm performance on six forests considering different numbers of scenarios. The results showed that our algorithm performed well when the number of scenarios was large.
A New Green Efficiency-Based Carbon Taxing Policy and Its Effects on a Production-Inventory System with Random Carbon Emissions and Green Investment
In this study, the author proposes a new carbon taxing policy. This proposed carbon tax has two tax components. The first component is constant, and the second component depends on the green efficiency of production. The green efficiency of production is measured by the average amount of emissions per unit production in an assessment year. The green efficiency-based tax component can be reset every year. Lesser average emission rate indicates better green efficiency. The second component of this proposed carbon tax forces the firm to improve the green efficiency of production, which results in cleaner production. The author incorporates this new carbon tax policy in a production-inventory system with a price-sensitive demand rate. A rule is provided for the implementation of this new tax. Emissions during setup, production, and storage are considered as independent random variables. The firm has the opportunity of investing in green technologies to improve green efficiency. A profit maximization policy is adopted to solve the developed model. A solution algorithm is also provided. The model is illustrated by numerical examples with randomly generated model parameters. The results of numerical examples show the environmental benefits of the proposed carbon tax.
Operations Research/Management Science Research in Europe: A Bibliometric Overview
This paper provides a bibliometric analysis of the articles in the field of operations research or management science (OR/MS) published in the years 1980–2018 by European researchers. The analysis’s objective is to identify and examine the current state of OR/MS studies in Europe, which publishes about 38% of the papers published worldwide. The analysis was based on the data from the Web of Science (WoS) databases. We found a total of 65,352 papers in 148 different journals in the OR/MS field. The results provide a general picture of the studies, which are classified according to the most influential authors, institutions, papers, and journals. The study revealed that the ratio of OR/MS studies having at least one European author has steadily increased over the decades from 28.27% in the 1980 s to 41.29% in the 2010 s. The analysis also provides citation statistics of the European OR/MS articles. The study concluded that the impact of European publications is less than the impact of U.S. publications. The bibliometric analysis of the studies showed that only a small portion of the countries/regions, institutions, and even authors published a substantial portion of the papers, as indicated by the Pareto rule. The research trends have been identified through an analysis of keyword usage over the years. In keyword analysis, which subcategories are studied together is also identified. In the paper, collaboration among countries and institutions is also identified and depicted by using VOS viewer.
Optimizing Wiener and Randić Indices of Graphs
Wiener and Randić indices have long been studied in chemical graph theory as connection strength measures of graphs. Later, these indices were used in different fields such as network analysis. We consider two optimization problems related to these indices, with potential applications to network theory, in particular to epidemiological networks. Given a connected graph and a fixed total edge weight, we investigate how individual weights must be assigned to edges, minimizing the connection strength of the graph. In order to measure the connection strength, we use the weighted Wiener index and a modified version of the ordinary Randić index. Wiener index optimization is linear, while Randić index optimization turns out to be both nonlinear and nonconvex. Hence, we adopt the technique of separable programming to generate solutions. We present our experimental results by applying relevant algorithms to several graphs.
Route Optimization of Electric Vehicle considering Soft Time Windows and Two Ways of Power Replenishment
Under the background of severe air pollution and energy shortage, electric vehicles (EVs) are promising vehicles to support green supply chain and clean production. In the world, the renewal of EVs has become a general trend. Therefore, the concern about EVs is a hot issue at present, but EVs have the characteristics of limited driving distance and long charging time. When the EVs are used in logistics transportation, these characteristics have a significant impact on the vehicle routing problems. Therefore, based on the research experience of traditional vehicle routing optimization, combining with the characteristics of EVs, this paper presents an optimal problem of electric vehicle routes with time windows based on two charging methods and it also designs a mathematical model which was caused by early and late arrival as the objective function to minimize the transportation cost, vehicle use cost, power supply cost, and penalty cost. The model is solved using an ant colony algorithm. Finally, the ant colony algorithm is tested and analysed with an example.