Showing papers in "Annals of Operations Research in 2016"
TL;DR: Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation as discussed by the authors, where discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise, etc.
Abstract: Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise—various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in SO as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.
284 citations
TL;DR: This paper examines the order quantity of the retailer and sustainability investment of the manufacturer for the decentralized supply chain with one retailer and one manufacturer and the centralized case, and finds that the sustainability investment efficiency has a significant impact on the optimal solutions.
Abstract: Carbon emission abatement is a hot topic in environmental sustainability and cap-and-trade regulation is regarded as an effective way to reduce the carbon emission. According to the real industrial practices, sustainable product implies that its production processes facilitate to reduce the carbon emission and has a positive response in market demand. In this paper, we study the sustainability investment on sustainable product with emission regulation consideration for decentralized and centralized supply chains. We first examine the order quantity of the retailer and sustainability investment of the manufacturer for the decentralized supply chain with one retailer and one manufacturer. After that, we extend our study to the centralized case where we determine the production quantity and sustainability investment for the whole supply chain. We derive the optimal order quantity (or production quantity) and sustainability investment, and find that the sustainability investment efficiency has a significant impact on the optimal solutions. Further, we conduct numerical studies and find surprisingly that the order quantity may be increasing in the wholesale price due to the effects of the sustainability and emission consideration. Moreover, we investigate the achievability of supply chain coordination by various contracts, and find that only revenue sharing contract can coordinate the supply chain whereas the buyback contract and two-part tariff contract cannot. Important insights and managerial implications are discussed.
264 citations
TL;DR: This paper derives insights into the current state of knowledge in each area of the literature and identifies some associated challenges with a discussion of some specific models of MV models for supply chain risk analysis.
Abstract: Pioneered by Nobel laureate Harry Markowitz in the 1950s, the mean-variance (MV) formulation is a fundamental theory for risk management in finance. Over the past decades, there is a growing popularity of applying this ground breaking theory in analyzing stochastic supply chain management problems. Nowadays, there is no doubt that the mean-variance (MV) theory is a well-proven approach for conducting risk analysis in stochastic supply chain operational models. In view of the growing importance of MV approach in supply chain management, we review a selection of related papers in the literature that focus on MV analytical models. By classifying the literature into three major areas, namely, single-echelon problems, multi-echelon supply chain problems, and supply chain problems with information updating, we derive insights into the current state of knowledge in each area and identify some associated challenges with a discussion of some specific models. We also suggest future research directions on topics such as information asymmetry, supply networks, and boundedly rational agents, etc. In conclusion, this paper provides up-to-date information which helps both academicians and practitioners to better understand the development of MV models for supply chain risk analysis.
242 citations
TL;DR: Examination of various factors associated with the implementation of lean in SMEs in the U.S. suggests that most of SMEs have a relatively accurate understanding of lean concept and philosophy, and provides evidences that major lean barriers are encountered by SMEs regarding management or people related factors as well as key knowledge and know-how.
Abstract: Lean as a business strategy is used to improve quality and service, eliminate waste, reduce time and costs, and enhance overall organizational effectiveness. Heightening challenges in competition in recent years have prompted many small and medium-sized enterprises (SMEs) to adopt lean to enhance firms’ competitiveness. This paper attempts to present an all-inclusive study and it examines various factors associated with the implementation of lean in SMEs in the U.S. The findings suggest that most of SMEs have a relatively accurate understanding of lean concept and philosophy. The primary reasons to implement lean are mainly internal, including reduce cost, improve profit margin, improve utilization of plant/facility, and maintain competitive position. A hierarchical cluster analysis was conducted to investigate lean status. It was discovered that both advanced adopters and beginners of lean are discovered. ANOVA test results show that there exist quite significant differences in terms of the degrees of lean implementation in SMEs. Varied lean tools and programs have been applied and they are positively related with firms’ performance. Lastly, the paper provides evidences that major lean barriers are encountered by SMEs regarding management or people related factors as well as key knowledge and know-how.
231 citations
TL;DR: The main objective is to engage the case company with their supplier networks to diminish the greenhouse gases emissions and cost in their production process to support the selection of the best green supplier and an allocation of order among the potential suppliers.
Abstract: The low-carbon supply chain is one of the predominant topics towards a green economy and it establishes the opportunity to reduce carbon emissions across the product value chain. This paper focuses on recycling and optimized sourcing in the paper industry as a case company. The main objective is to engage the case company with their supplier networks to diminish the greenhouse gases (GHG) emissions and cost in their production process. It proposes a model to support the selection of the best green supplier and an allocation of order among the potential suppliers. The proposed model contains a two-phase hybrid approach. The first phase presents the rating and selection of potential suppliers by considering economics (cost), operational factors (quality and delivery), and environmental criteria (recycle capability and GHG emission control) using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) methodology. The second phase presents the order allocation process using multi-objective linear programming in order to minimize cost, material rejection, late delivery, recycle waste and $$\mathrm{CO}_{2}$$
emissions in the production process. A case study from a paper manufacturing industry is presented to elucidate the effectiveness of the proposed model. The results demonstrate a 26.2 % reduction of carbon emission by using recycle products in the production process. The firm benefits by forming a systematic methodology for green supplier evaluation and order allocation. Finally, a conclusion and a suggested direction of future research are introduced.
156 citations
TL;DR: A survey is presented which attempts to identify the common features of WSRP scenarios and the solution methods applied when tackling these problems and a study on the computational difficulty of solving these type of problems is presented.
Abstract: In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of scenarios include nurses visiting patients at home, technicians carrying out repairs at customers’ locations and security guards performing rounds at different premises, etc. We refer to these scenarios as workforce scheduling and routing problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time at the locations where tasks need to be performed. The first part of this paper presents a survey which attempts to identify the common features of WSRP scenarios and the solution methods applied when tackling these problems. The second part of the paper presents a study on the computational difficulty of solving these type of problems. For this, five data sets are gathered from the literature and some adaptations are made in order to incorporate the key features that our survey identifies as commonly arising in WSRP scenarios. The computational study provides an insight into the structure of the adapted test instances, an insight into the effect that problem features have when solving the instances using mathematical programming, and some benchmark computation times using the Gurobi solver running on a standard personal computer.
137 citations
TL;DR: A hybrid algorithm that combines a genetic algorithm with a variable neighborhood search method (VNSGA) is proposed to solve the sequence-dependent disassembly line balancing problem (SDDLBP), and the performance of VNSGA was compared with the best known metaheuristic methods reported in the literature.
Abstract: For remanufacturing or recycling companies, a reverse supply chain is of prime importance since it facilitates in recovering parts and materials from end-of-life products. In reverse supply chains, selective separation of desired parts and materials from returned products is achieved by means of disassembly which is a process of systematic separation of an assembly into its components, subassemblies or other groupings. Due to its high productivity and suitability for automation, disassembly line is the most efficient layout for product recovery operations. A disassembly line must be balanced to optimize the use of resources (viz., labor, money and time). In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence dependent time increments. A hybrid algorithm that combines a genetic algorithm with a variable neighborhood search method (VNSGA) is proposed to solve the SDDLBP. The performance of VNSGA was thoroughly investigated using numerous data instances that have been gathered and adapted from the disassembly and the assembly line balancing literature. Using the data instances, the performance of VNSGA was compared with the best known metaheuristic methods reported in the literature. The tests demonstrated the superiority of the proposed method among all the methods considered.
131 citations
TL;DR: The results show that the proposed PSO is an effective method to solve the multi-depot vehicle routing problem, and the carton heterogeneous vehicle routing Problem with a collection depot, and is feasible with a saving of about 28 % in total delivery cost.
Abstract: In this paper, a carton heterogeneous vehicle routing problem with a collection depot is presented, which can collaboratively pick the cartons from several carton factories to a collection depot and then from the depot to serve their corresponding customers by using of heterogeneous fleet. Since the carton heterogeneous vehicle routing problem with a collection depot is a very complex problem, particle swarm optimization (PSO) is used to solve the problem in this paper. To improve the performance of the PSO, a self-adaptive inertia weight and a local search strategy are used. At last, the model and the algorithm are illustrated with two test examples. The results show that the proposed PSO is an effective method to solve the multi-depot vehicle routing problem, and the carton heterogeneous vehicle routing problem with a collection depot. Moreover, the proposed model is feasible with a saving of about 28 % in total delivery cost and could obviously reduce the required number of vehicles when comparing to the actual instance.
130 citations
TL;DR: The present article gives an overview over a second strand of the recent literature, namely methods that preserve the multi-objective nature of the problem during the computational analysis, including publications assuming a risk-neutral decision maker, but also articles addressing the situation where the decision maker is risk-averse.
Abstract: Currently, stochastic optimization on the one hand and multi-objective optimization on the other hand are rich and well-established special fields of Operations Research. Much less developed, however, is their intersection: the analysis of decision problems involving multiple objectives and stochastically represented uncertainty simultaneously. This is amazing, since in economic and managerial applications, the features of multiple decision criteria and uncertainty are very frequently co-occurring. Part of the existing quantitative approaches to deal with problems of this class apply scalarization techniques in order to reduce a given stochastic multi-objective problem to a stochastic single-objective one. The present article gives an overview over a second strand of the recent literature, namely methods that preserve the multi-objective nature of the problem during the computational analysis. We survey publications assuming a risk-neutral decision maker, but also articles addressing the situation where the decision maker is risk-averse. In the second case, modern risk measures play a prominent role, and generalizations of stochastic orders from the univariate to the multivariate case have recently turned out as a promising methodological tool. Modeling questions as well as issues of computational solution are discussed.
124 citations
TL;DR: For the uniform-speed case, in which all jobs have arbitrary power demands and must be processed at a single uniform speed, it is proved that the non-preemptive version of this problem is inapproximable within a constant factor unless P = NP, and for the speed-scalable case, this problem can be solved in polynomial time.
Abstract: We consider the problem of scheduling jobs on a single machine to minimize the total electricity cost of processing these jobs under time-of-use electricity tariffs. For the uniform-speed case, in which all jobs have arbitrary power demands and must be processed at a single uniform speed, we prove that the non-preemptive version of this problem is inapproximable within a constant factor unless $$\text {P} = \text {NP}$$
. On the other hand, when all the jobs have the same workload and the electricity prices follow a so-called pyramidal structure, we show that this problem can be solved in polynomial time. For the speed-scalable case, in which jobs can be processed at an arbitrary speed with a trade-off between speed and power demand, we show that the non-preemptive version of the problem is strongly NP-hard. We also present different approximation algorithms for this case, and test the computational performance of these approximation algorithms on randomly generated instances. In addition, for both the uniform-speed and speed-scaling cases, we show how to compute optimal schedules for the preemptive version of the problem in polynomial time.
118 citations
TL;DR: The main focus of this survey is to show analytic results for queue length distributions, waiting time distribution, and tail asymptotics for the queue length and waiting time distributions.
Abstract: Retrial queueing systems have been extensively studied because of their applications in telephone systems, call centers, telecommunication networks, computer systems, and in daily life. This survey deals with various retrial queueing models. The main focus of this survey is to show analytic results for queue length distributions, waiting time distributions, and tail asymptotics for the queue length and waiting time distributions. This survey also considers the stability analysis of retrial queueing models.
TL;DR: The problem of what characterises decision-aiding for public policy making problem situations is addressed, and the need to expand the concept of rationality which is expected to support the acceptability of a public policy is shown.
Abstract: This paper aims at addressing the problem of what characterises decision-aiding for public policy making problem situations. Under such a perspective it analyses concepts like “public policy”, “deliberation”, “legitimation”, “accountability” and shows the need to expand the concept of rationality which is expected to support the acceptability of a public policy. We then analyse the more recent attempt to construct a rational support for policy making, the “evidence-based policy making” approach. Despite the innovation introduced with this approach, we show that it basically fails to address the deep reasons why supporting the design, implementation and assessment of public policies is such a hard problem. We finally show that we need to move one step ahead, specialising decision-aiding to meet the policy cycle requirements: a need for policy analytics.
TL;DR: This paper defines tactical SC flexibility and identifies tactical flexibility measures and options for development of flexible SC planning models to identify research gaps in the current literature and provide insights for future modeling and research efforts in the field.
Abstract: Supply chains (SCs) can be managed at many levels. The use of tactical SC planning models with multiple flexibility options can help manage the usual operations efficiently and effectively, whilst improve the SC resiliency in response to inherent environmental uncertainties. This paper defines tactical SC flexibility and identifies tactical flexibility measures and options for development of flexible SC planning models. A classification of the existing literature of SC planning is introduced that highlights the characteristics of published flexibility inclusive models. Additional classifications from the reviewed literature are presented based on the integration of flexibility options used, solution methods utilized, and real world applications presented. These classifications are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.
TL;DR: Four different types of combinations of metaheuristics, including exact methods from mathematical programming approaches, and constraint programming approaches developed in the artificial intelligence community are considered.
Abstract: During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
TL;DR: In this article, a tabu search-based approach is proposed to solve the problem of integrated inventory and routing problems with perishable goods in a distributed manner, where a depot, a set of customers and a homogeneous fleet of capacitated vehicles are considered.
Abstract: Most of the research on integrated inventory and routing problems ignores the case when products are perishable. However, considering the integrated problem with perishable goods is crucial since any discrepancy between the routing and inventory cost can double down the risk of higher obsolescence costs due to the limited shelf-life of the products. In this paper, we consider a distribution problem involving a depot, a set of customers and a homogeneous fleet of capacitated vehicles. Perishable goods are transported from the depot to customers in such a way that out-of-stock situations never occur. The objective is to simultaneously determine the inventory and routing decisions over a given time horizon such that total transportation cost is minimized. We present a new “arc-based formulation” for the problem which is deemed more suitable for our new tabu search based approach for solving the problem. We perform a thorough sensitivity analysis for each of the tabu search parameters individually and use the obtained gaps to fine-tune the parameter values that are used in solving larger sized instances of the problem. We solve different sizes of randomly generated instances and compare the results obtained using the tabu search algorithm to those obtained by solving the problem using CPLEX and a recently published column generation algorithm. Our computational experiments demonstrate that the tabu search algorithm is capable of obtaining a near-optimal solution in less computational time than the time required to solve the problem to optimality using CPLEX, and outperforms the column generation algorithm for solving the “path flow formulation” of the problem in terms of solution quality in almost all of the considered instances.
TL;DR: The study shows that the effective relief effort carried out by private enterprises in responding to Hurricane Katrina in 2005 attributes to the combination of proactive response method and logistic expertise and the proactive insourcing strategy is good enough for a relief supply chain involving only imperishable relief supplies.
Abstract: Private enterprises, such as Wal-Mart and Home Depot, have implemented effective relief efforts in responding to Hurricane Katrina in 2005. From the perspective of operation research, the factors of this success have been addressed as quick response, pre-position and logistic expertise. However, the waste made by FEMA for stockpiling food and ice in anticipation of a busy hurricane season in 2006 and several other cases indicate otherwise. In this paper, we introduce the private enterprise into a relief supply chain and propose an outsourcing framework to uncover the reason of Wal-Mart’s success and provide explanations to the counter-cases. Our study shows that the effective relief effort carried out by private enterprises in responding to Hurricane Katrina in 2005 attributes to the combination of proactive response method and logistic expertise. Our findings advocate adopting different strategies to deal with different types of relief supplies. Specifically, the proactive insourcing strategy is good enough for a relief supply chain involving only imperishable relief supplies. For perishable goods, the proactive outsourcing strategy can make a relief supply more efficient.
TL;DR: There is actually a common theme to these strategies, and underpinning the entire field remains the fundamental algorithmic strategies of value and policy iteration that were first introduced in the 1950’s and 60s.
Abstract: Approximate dynamic programming has evolved, initially independently, within operations research, computer science and the engineering controls community, all search- ing for practical tools for solving sequential stochastic optimization problems. More so than other communities, operations research continued to develop the theory behind the basic model introduced by Bellman with discrete states and actions, even while authors as early as Bellman himself recognized its limits due to the "curse of dimensionality" inherent in discrete state spaces. In response to these limitations, subcommunities in computer science, control theory and operations research have developed a variety of methods for solving dif- ferent classes of stochastic, dynamic optimization problems, creating the appearance of a jungle of competing approaches. In this article, we show that there is actually a common theme to these strategies, and underpinning the entire field remains the fundamental algo- rithmic strategies of value and policy iteration that were first introduced in the 1950's and 60's. Dynamic programming involves making decisions over time, under uncertainty. These problems arise in a wide range of applications, spanning business, science, engineering, economics, medicine and health, and operations. While tremendous successes have been achieved in specific problem settings, we lack general purpose tools with the broad applica- bility enjoyed by algorithmic strategies such as linear, nonlinear and integer programming. This paper provides an introduction to the challenges of dynamic programming, and describes the contributions made by different subcommunities, with special emphasis on computer science which pioneered a field known as reinforcement learning, and the opera- tions research community which has made contributions through several subcommunities, including stochastic programming, simulation optimization and approximate dynamic pro- gramming. Our presentation recognizes, but does not do justice to, the important contribu- tions made in the engineering controls communities.
TL;DR: An EOQ model with down-stream partial delayed payment and up-streampartial prepayment under three different scenarios (without shortage, with full backordering and with partial backordering) is presented and the optimal solutions are derived.
Abstract: In a competitive market, the retailers, in order to encourage the customers to increase their orders, give them the opportunity to pay a fraction of the purchasing cost after delivery of the ordered items (i.e., down-stream partial delayed payment). On the other hand, the suppliers, in order to reduce the risk of cancellations of orders from buyers, may ask the retailers to pay a portion of the purchasing cost before delivery of products (i.e., up-stream partial prepayment). In this paper, an EOQ model with down-stream partial delayed payment and up-stream partial prepayment under three different scenarios (without shortage, with full backordering and with partial backordering) is presented. In order to find the optimal solutions of the models developed for different scenarios, the convexity of the objective functions (i.e., total cost functions) are proved and then closed-form optimal solutions are derived. Also, a solution algorithm is proposed for the model of the third scenario. To demonstrate the applicability of the framework, some numerical examples are presented. Finally, sensitivity analyses are made on several key parameters, in order to gain some managerial insight.
TL;DR: A transportation problem in which costs are triangular intuitionistic fuzzy numbers, in which accuracy function using score functions for membership and non membership functions of triangular intuitionism fuzzy numbers is proposed.
Abstract: In solving real life transportation problem, we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To deal with uncertainty and hesitation many authors have suggested the intuitionistic fuzzy representation for the data. In this paper, we formulate a transportation problem in which costs are triangular intuitionistic fuzzy numbers. We have defined accuracy function using score functions for membership and non membership functions of triangular intuitionistic fuzzy numbers. Then ordering of triangular intuitionistic fuzzy numbers using accuracy function has been proposed. We have utilized this ordering to develop methods for finding starting basic feasible solution in terms of triangular intuitionistic fuzzy numbers. Also the same ordering is utilized to develop intuitionistic fuzzy modified distribution method for finding the optimal solution. Finally the method is illustrated by a numerical example which is followed by graphical representation and discussion of the finding.
TL;DR: A multi-period, multi-product closed-loop supply chain network with stochastic demand and price in a Mixed Integer Linear Programming (MILP) structure is proposed and the acceptability of proposed solution approach for the Stochastic model is revealed.
Abstract: Analyzing current trends in supply chain management, lead to find unavoidable steps toward closing the loop of supply chain. In order to expect best performance of Closed-Loop Supply Chain (CLSC) network, an integrated approach in considering design and planning decision levels is necessary. Further, real markets usually contain uncertain parameters such as demands and prices of products. Therefore, the next important step is considering uncertain parameters. In order to cope with designing and planning a closed-loop supply chain, this paper proposes a multi-period, multi-product closed-loop supply chain network with stochastic demand and price in a Mixed Integer Linear Programming (MILP) structure. A multi criteria scenario based solution approach is then developed to find optimal solution through some logical scenarios and three comparing criteria. Mean, Standard Deviation (SD), and Coefficient of Variation (CV), which are the mentioned criteria for finding the optimal solution. Sensitivity analyses are also undertaken to validate efficiency of the solution approach. The computational study reveals the acceptability of proposed solution approach for the stochastic model. Finally, a real case study in an Indian manufacturer is evaluated to ensure applicability of the model and the solution methodology.
TL;DR: Improved cut generation strategies and primal heuristics are proposed and evaluated and the optimality of the vast majority of instances was proved, the best known solutions were improved by up to 15 % and strong dual bounds were obtained.
Abstract: This work presents integer programming techniques to tackle the problem of the International Nurse Rostering Competition. Starting from a compact and monolithic formulation in which the current generation of solvers performs poorly, improved cut generation strategies and primal heuristics are proposed and evaluated. A large number of computational experiments with these techniques produced the following results: the optimality of the vast majority of instances was proved, the best known solutions were improved by up to 15 % and strong dual bounds were obtained. In the spirit of reproducible science, all code was implemented using the Computational Infrastructure for Operations Research.
TL;DR: An updated survey of the main methods that allow the use of multi-objective schemes for single-Objective optimization and some possible paths of future work in this area are identified.
Abstract: In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers an updated survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.
TL;DR: A general nonlinear binary multi-objective mathematical model is proposed, which takes into account all the most important factors mentioned in the literature related with Project Portfolio Selection and Scheduling.
Abstract: Decision makers usually have to face a budget and other type of constraints when they have to decide which projects are going to be undertaken (to satisfy their requirements and guarantee profitable growth). Our purpose is to assist them in the task of selecting project portfolios. We have approached this problem by proposing a general nonlinear binary multi-objective mathematical model, which takes into account all the most important factors mentioned in the literature related with Project Portfolio Selection and Scheduling. Due to the existence of uncertainty in different aspects involved in the aforementioned decision task, we have also incorporated into the model some fuzzy parameters, which allow us to represent information not fully known by the decision maker/s. The resulting problem is both fuzzy and multiobjective. The results are complemented with graphical tools, which show the usefulness of the proposed model to assist the decision maker/s.
TL;DR: It is evident that hyper-heuristics are effective at solving educational timetabling problems and have the potential of advancing this field by providing a generalized solution toeducational timetabling as a whole.
Abstract: Educational timetabling problems, namely, university examination timetabling, university course timetabling and school timetabling, are combinatorial optimization problems requiring the allocation of resources so as to satisfy a specified set of constraints. Hyper-heuristics have been successfully applied to a variety of combinatorial optimization problems. This is a rapidly growing field which aims at providing generalized solutions to combinatorial optimization problems by exploring a heuristic space instead of a solution space. From the research conducted thus far it is evident that hyper-heuristics are effective at solving educational timetabling problems and have the potential of advancing this field by providing a generalized solution to educational timetabling as a whole. Given this, the paper provides an overview and critical analysis of hyper-heuristics for educational timetabling and proposes future research directions, focusing on using hyper-heuristics to provide a generalized solution to educational timetabling.
TL;DR: The emergence of the recent field of analytics and how it may impact public policy making is reviewed and current applications of, and future possibilities for, new analytic methods that can be used to support public policy problem-solving and decision processes are exposed.
Abstract: Working from a description of what policy analysis entails, we review the emergence of the recent field of analytics and how it may impact public policy making. In particular, we seek to expose current applications of, and future possibilities for, new analytic methods that can be used to support public policy problem-solving and decision processes, which we term policy analytics. We then review key contributions to this special volume, which seek to support policy making or delivery in the areas of energy planning, urban transportation planning, medical emergency planning, healthcare, social services, national security, defence, government finance allocation, understanding public opinion, and fire and police services. An identified challenge, which is specific to policy analytics, is to recognize that public sector applications must balance the need for robust and convincing analysis with the need for satisfying legitimate public expectations about transparency and opportunities for participation. This opens up a range of forms of analysis relevant to public policy distinct from those most common in business, including those that can support democratization and mediation of value conflicts within policy processes. We conclude by identifying some potential research and development issues for the emerging field of policy analytics.
TL;DR: The proposed hybrid MCDM model comprises fuzzy analytical hierarchy process (FAHP), grey relational analysis (GRA), and technique for order preference by similarity to ideal solution (TOPSIS) technique, which is used to compute the criteria weights whereas GRA–TopsIS is used for determining the ranking of alternatives.
Abstract: In today’s competitive environment, industries are required to increase reliability and safety level of their equipment with reasonable cost. Consequently, it is essential to select the appropriate maintenance strategy. In the paper industry, pumping of cooked pulp requires abrasion- and sometimes corrosion-resistant pumps that are able to handle up to 70 % solids content. But the maintenance engineer and supervisor often face failures of the pump. Hence, this article describes the application of multi criteria decision-making (MCDM) technique for the selection of optimum maintenance strategy for pumps used in the paper industry. The proposed hybrid MCDM model comprises fuzzy analytical hierarchy process (FAHP), grey relational analysis (GRA), and technique for order preference by similarity to ideal solution (TOPSIS) technique. The FAHP is used to compute the criteria weights whereas GRA–TOPSIS is used for determining the ranking of alternatives. This study discusses four maintenance strategies: corrective maintenance, predictive maintenance, time-based preventive maintenance, and condition-based maintenance. Four main criteria—safety, cost, added value, and feasibility—have been used to evaluate the optimum maintenance strategy.
TL;DR: Four proofs that the Gittin index priority rule is optimal for alternative bandit processes are studied, including Gittins’ original exchange argument, Weber's prevailing charge argument, Whittle's Lagrangian dual approach, and Bertsimas and Niño-Mora’s proof based on the achievable region approach and generalized conservation laws.
Abstract: We study four proofs that the Gittins index priority rule is optimal for alternative bandit processes. These include Gittins’ original exchange argument, Weber’s prevailing charge argument, Whittle’s Lagrangian dual approach, and Bertsimas and Nino-Mora’s proof based on the achievable region approach and generalized conservation laws. We extend the achievable region proof to infinite countable state spaces, by using infinite dimensional linear programming theory.
TL;DR: A simpler resilience schema is proposed, which better reflects an active system for providing humanitarian aid in post-disaster operations, similar to the model focused in this work.
Abstract: In this study, we propose a mathematical model and heuristics for solving a multi-period location-allocation problem in post-disaster operations, which takes into account the impact of distribution over the population. Logistics restrictions such as human and financial resources are considered. In addition, a brief review on resilience system models is provided, as well as their connection with quantitative models for post-disaster relief operations. In particular, we highlight how one can improve resilience by means of OR/MS strategies. Then, a simpler resilience schema is proposed, which better reflects an active system for providing humanitarian aid in post-disaster operations, similar to the model focused in this work. The proposed model is non-linear and solved by a decomposition approach: the master level problem is addressed by a non-linear solver, while the slave subproblem is treated as a black-box coupling heuristics and a Variable Neighborhood Descent local search. Computational experiments have been done using several scenarios, and real data from Belo Horizonte city in Brazil.
TL;DR: Both the bank and the manufacturer are better off due to the use of trade credit insurance, but contrary to what one might expect, the bank prefers giving a higher interest rate to the manufacturer when the premium rate is in a reasonable region, which indicates that the manufacturer cannot use the insurance to negotiate better financing terms.
Abstract: The manufacturer who is a supplier of trade credit may face non-payment risk from customers and a capital shortage problem simultaneously. Trade credit insurance, as one of the most important risk management tools, has been widely used in companies’ daily operation. In this study, the manufacturer who allows customers to delay payment for goods already delivered purchases trade credit insurance to transfer and reduce non-payment risk and borrows money from a bank to accommodate the capital constraint problem. The Stackelberg game and loss-averse theory are used to establish a newsboy model including trade credit insurance, and the optimal insurance coverage and total sales of the manufacturer are thereby investigated. Subsequently, the interest rate decision of the bank under different risk-averse situations is also characterized. We find that the interest rate set by a loss-averse bank is equal to or greater than that given by a risk-neutral bank. The use of trade credit insurance can help the manufacturer expand sales and dramatically reduce its default risk. Both the bank and the manufacturer are better off due to the use of trade credit insurance, but contrary to what one might expect, the bank prefers giving a higher interest rate to the manufacturer when the premium rate is in a reasonable region, which indicates that the manufacturer cannot use the insurance to negotiate better financing terms.
TL;DR: A multi-start algorithm based on biased randomization of routing and packing heuristics is proposed and shown to show its efficiency in the two-dimensional loading capacitated vehicle routing problem with heterogeneous fleet.
Abstract: This paper discusses the two-dimensional loading capacitated vehicle routing problem (2L-CVRP) with heterogeneous fleet (2L-HFVRP). The 2L-CVRP can be found in many real-life situations related to the transportation of voluminous items where two-dimensional packing restrictions have to be considered, e.g.: transportation of heavy machinery, forklifts, professional cleaning equipment, etc. Here, we also consider a heterogeneous fleet of vehicles, comprising units of different capacities, sizes and fixed/variable costs. Despite the fact that heterogeneous fleets are quite ubiquitous in real-life scenarios, there is a lack of publications in the literature discussing the 2L-HFVRP. In particular, to the best of our knowledge no previous work discusses the non-oriented 2L-HFVRP, in which items are allowed to be rotated during the truck-loading process. After describing and motivating the problem, a literature review on related work is performed. Then, a multi-start algorithm based on biased randomization of routing and packing heuristics is proposed. A set of computational experiments contribute to illustrate the scope of our approach, as well as to show its efficiency.