The flow refueling location problem with load flow control

Battery electric vehicles as well as renewable energy are two key factors that can contribute significantly to sustainable development within the transportation and the energy sector. However, the market introduction of these technologies results in new challenges, especially with regard to the interaction between both sectors. So far, neither location models for charging stations nor load flow models for the electrical grid consider these interactions sufficiently. Thus, an integration of planning problems from both sectors is needed in order to exploit potential synergies and to avoid negative impacts.In this paper, we present such an integrated planning approach to locate charging infrastructure for battery electric vehicles considering interactions with the electrical grid. Herein, we combine a charging station location model and a power flow model with integrated energy stores. We aim at determining a network configuration that satisfies the charging demand of battery electric vehicles, herein maximizing the benefits and minimizing the negative impacts resulting from the interactions of the two sectors. To demonstrate the benefit of our integrated planning approach, we apply it to an illustrative case and present results of a sensitivity analysis. We derive managerial insights regarding the interdependencies of the number of sited charging stations and the installed storage capacity based on renewable energy generation and charging demand.     

MIXED ASSEMBLY AND DISASSEMBLY OPERATIONS FOR REMANUFACTURING

In this paper we consider the problem of designing a mixed assembly‐disassembly line for remanufacturing. That is, parts from the disassembly and repair of used products can be used to build “new” products. This is a problem common to many OEM remanufacturers, such as Xerox or Kodak. We study two main configurations, under the assumption that the disassembly sequence is exactly the reverse of the assembly sequence. Under a parallel configuration, there exist two separate dedicated lines, one for assembly and one for disassembly, which are decoupled by buffers—from both disassembly operations, which have preference, as well as parts from an outside, perfectly reliable supplier. Under a mixed configuration, the same station is used for both disassembly and assembly of a specific part. The problem is studied using GI/G/c networks, as well as simulation. Due to a loss of pooling, we conclude that the parallel configuration outperforms the mixed line only when the variability of both arrivals and processing time are significantly higher for disassembly and remanufacturing than for assembly. Via a simulation, we explore the impact of having advanced yield information for the remanufacturing parts. We find that advanced yield information generally improves flow times; however, there are some instances where it lengthens flow times.     

Optimizing the Staffing and Routing of Small‐Size Hierarchical Call Centers

Multiple‐skill call centers propagate rapidly with the development of telecommunications. An abundance of literature has already been published on call centers. Here, we want to focus on centers that would typically occur in business‐to‐business environments; these are call centers that handle many types of calls but where the arrival rate for each type is low. To find an optimal configuration, the integrality of the decision variables is a much more important issue than for larger call centers. The present paper proposes an approach that uses elements of combinatorial optimization to find optimal configurations. We develop an approximation method for the evaluation of the service performance. Next, we search for the minimum‐cost configuration subject to service‐level constraints using a branch‐and‐bound algorithm. What is at stake is to find the right balance between gains resulting from the economies of scale of pooling and the higher cost or cross‐trained agents. The article shows that in most cases this method significantly decreases the staffing cost compared with configurations with only cross‐trained or dedicated operators.     

An estimation of distribution algorithm for lot-streaming flow shop problems with setup times

Lot-streaming flow shops have important applications in different industries including textile, plastic, chemical, semiconductor and many others. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and no-idling production cases. The objective is to minimize the maximum completion time or makespan. To solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation. In the proposed EDA, an efficient initialization scheme based on the NEH heuristic is presented to construct an initial population with a certain level of quality and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search towards good solutions by taking into account both job permutation and similar blocks of jobs. A simple but effective local search is added to enhance the intensification capability. A diversity controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up method is presented to reduce the computational effort needed for the local search technique and the NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms from the literature. The results show that the proposed EDA is very effective in comparison after comprehensive computational and statistical analyses.     

Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services

Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling as few design points from the design space as possible is desired. However, in the case of multiple objectives, traditional simulation optimization methods are ineffective to uncover the efficient frontier. We propose a framework for multi-objective simulation optimization that combines the power of genetic algorithm (GA), which can effectively search very large design spaces, with data envelopment analysis (DEA) used to evaluate the simulation results and guide the search process. In our framework, we use a design point's relative efficiency score from DEA as its fitness value in the selection operation of GA. We apply our algorithm to determine optimal resource levels in surgical services. Our numerical experiments show that our algorithm effectively furthers the frontier and identifies efficient design points.     

On the performance of Scatter Search for post-enrolment course timetabling problems

This study presents an investigation of enhancing the capability of the Scatter Search (SS) metaheuristic in guiding the search effectively toward elite solutions. Generally, SS generates a population of random initial solutions and systematically selects a set of diverse and elite solutions as a reference set for guiding the search. The work focuses on three strategies that may have an impact on the performance of SS. These are: explicit solutions combination, dynamic memory update, and systematic search re-initialization. First, the original SS is applied. Second, we propose two versions of the SS (V1 and V2) with different strategies. In contrast to the original SS, SSV1 and SSV2 use the quality and diversity of solutions to create and update the memory, to perform solutions combinations, and to update the search. The differences between SSV1 and SSV2 is that SSV1 employs the hill climbing routine twice whilst SSV2 employs hill climbing and iterated local search method. In addition, SSV1 combines all pairs (of quality and diverse solutions) from the RefSet whilst SSV2 combines only one pair. Both SSV1 and SSV2 update the RefSet dynamically rather than static (as in the original SS), where, whenever a better quality or more diverse solution is found, the worst solution in RefSet is replaced by the new solution. SSV1 and SSV2 employ diversification generation method twice to re-initialize the search. The performance of the SS is tested on three benchmark post-enrolment course timetabling problems. The results had shown that SSV2 performs better than the original SS and SSV1 (in terms of solution’s quality and computational time). It clearly demonstrates the effectiveness of using dynamic memory update, systematic search re-initialization, and combining only one pair of elite solutions. Apart from that, SSV1 and SSV2 can produce good quality solutions (comparable with other approaches), and outperforms some approaches reported in the literature (on some instances with regards to the tested datasets). Moreover, the study shows that by combining (simple crossover) only one pair of elite solutions in each RefSet update, and updating the memory dynamically, the computational time is reduced.     

The Effect of Geographic Proximity to Unconventional Oil and Gas Development on Public Support for Hydraulic Fracturing

With the rapid growth of unconventional oil and natural gas development transforming the U.S. economic and physical landscape, social scientists have increasingly explored the spatial dynamics of public support for this issue—that is, whether people closer to unconventional oil and gas development are more supportive or more opposed. While theoretical frameworks like construal‐level theory and the “Not in My Backyard” (or NIMBY) moniker provide insight into these spatial dynamics, case studies in specific locations experiencing energy development reveal substantial variation in community responses. Larger‐scale studies exploring the link between proximity and support have been hampered by data quality and availability. We draw on a unique data set that includes geo‐coded data from national surveys (nine waves; n  = 19,098) and high‐resolution well location data to explore the relationship between proximity and both familiarity with and support for hydraulic fracturing. We use two different measures of proximity—respondent distance to the nearest well and the density of wells within a certain radius of the respondent's location. We find that both types of proximity to new development are linked to more familiarity with hydraulic fracturing, even after controlling for various individual and contextual factors, but only distance‐based proximity is linked to more support for the practice. When significant, these relationships are similar to or exceed the effects of race, income, gender, and age. We discuss the implications of these findings for effective risk communication as well as the importance of incorporating spatial analysis into public opinion research on perceptions of energy development.     

A three-phased local search approach for the clique partitioning problem

This paper presents a three-phased local search heuristic CPP-P\(^{3}\) for solving the Clique Partitioning Problem (CPP). CPP-P\(^{3}\) iterates a descent search, an exploration search and a directed perturbation. We also define the Top Move of a vertex, in order to build a restricted and focused neighborhood. The exploration search is ensured by a tabu procedure, while the directed perturbation uses a GRASP-like method. To assess the performance of the proposed approach, we carry out extensive experiments on benchmark instances of the literature as well as newly generated instances. We demonstrate the effectiveness of our approach with respect to the current best performing algorithms both in terms of solution quality and computation efficiency. We present improved best solutions for a number of benchmark instances. Additional analyses are shown to highlight the critical role of the Top Move-based neighborhood for the performance of our algorithm and the relation between instance hardness and algorithm behavior.     

The triggers of local and distant search: Relative magnitude and persistence in explaining acquisition relatedness

Research on problemistic search has assumed negative attainment discrepancy to be the trigger of both local and distant search. Extending this research, we present and compare two additional triggers: (1) relative attainment discrepancy, which reflects how much a firm's attainment discrepancy deviates from its past negative attainment discrepancies; and (2) persistent attainment discrepancy, which reflects how often the firm experiences below-aspirations performance. Our triggers for distant search model a behavioral explanation for the timing and relatedness of acquisitions. We find support for baseline arguments of problemistic search whereby firms increase both industry- and skill-related acquisitions when they perform below aspirations. When they persistently perform below aspirations, however, this likelihood is reduced and firms engage in acquisitions that are more unrelated, thereby providing support for the notion of expanding search boundaries from local to distant search. Of the two triggers of distant search proposed, relative attainment discrepancy does not induce firms to expand search boundaries. Our results indicate that persistent attainment discrepancy is a key construct to consider when studying the expansion of search boundaries.     

Min-energy broadcast in mobile ad hoc networks with restricted motion

This paper concerns about energy-efficient broadcasts in mobile ad hoc networks, yet in a model where each station moves on the plane with uniform rectilinear motion. Such restriction is imposed to discern which issues arise from the introduction of movement in the wireless ad hoc networks. Given a transmission range assignment for a set of n stations S, we provide an polynomial O(n ²)-time algorithm to decide whether a broadcast operation from a source station could be performed or not. Additionally, we study the problem of computing a transmission range assignment for S that minimizes the energy required in a broadcast operation. An O(n ³log?n)-time algorithm for this problem is presented, under the assumption that all stations have equally sized transmission ranges. However, we prove that the general version of such problem is NP-hard and not approximable within a (1?o(1))ln?n factor (unless NP?DTIME(n ^O(log?log?n))). We then propose a polynomial time approximation algorithm for a restricted version of that problem.     

电动汽车物流配送系统的换电站选址与路径优化问题研究

在能源、环境形势日益严重的今天,电动汽车因其清洁、节能的显著优势,已经逐步成为物流配送公司重要的新能源交通工具,优化物流配送网络成为电动汽车作为物流工具普及的一个重要问题。本文提出了电动汽车物流配送系统的换电站选址与配送路径优化问题,建立了整数规划模型,并设计禁忌搜索-改进Clarke-Wright 节省的两阶段启发式算法来求解该模型,提出了两种不同的禁忌准则,并且通过算例对这两种准则进行了比较。为了证明算法的有效性,还将该算法的结果同CPLEX的计算结果进行了比较,结果表明该算法更加有效和可靠。最后,对车辆的装载容量、电池续航里程和单位建站成本做敏感性分析,发现总成本随着装载容量的增加而显著降低,电池续航里程的提升有助于降低建站成本并降低目标函数值,而单位建站成本的增加可能减少建站个数,增加运输成本,但由于续航里程的限制,建站个数也可能保持不变。     

Optimal mixed-model sequencing for balanced assembly lines

This paper describes an algorithm for solving optimally, the mixed-model sequencing problem when assembly line stations are balanced for each model. An optimal sequence is obtained with the minimization of the overall assembly line length for zero station idle time.The algorithm incorporates two basic steps. The first involves a search procedure that generates all cycle sequences; i.e. sequences having identical ‘start’ and ‘finish’ positions and whose work content can be executed within a defined station length. The second step uses integer programming (IP) to determine the number and combination of the various cycle sequences, such that the production demand is satisfied.     

Cell-to-switch level planning in mobile wireless networks for efficient management of radio resources

A major step in effectively managing radio resources in a cellular network is to design an appropriate scheme for assigning cells to a location area (LA), serviced by a switch, and allocate resources for individual switches. However, this assignment is already proven in the literature to be an NP-hard problem [Merchant A, Sengupta B. Assignment of cells to switches in PCS networks. IEEE/ACM Transactions on Networking 3(5) (1995) 521–6] that requires efficient heuristic search techniques for obtaining real-time solutions. This work presents a state-space search technique, which is a variant of best first search heuristic technique. The algorithm called the block depth first search (BDFS), allocates cells to switches during switch level resource planning. Under various simulated performance criteria, we compare the performance of the proposed technique with other similar procedures in the literature. Our results indicate that the BDFS outperforms the meta-heuristic procedures in terms of both efficiency and quality of solutions. Hence, we conclude that our proposed technique can be effectively used for switch level planning leading to an efficient management of scarce radio resource in cellular networks.     

Computing globally optimal (s,S,T) inventory policies

We consider a single-echelon inventory installation under the (s,S,T) periodic review ordering policy. Demand is stationary random and, when unsatisfied, is backordered. Under a standard cost structure, we seek to minimize total average cost in all three policy variables; namely, the reorder level s, the order-up-to level S and the review interval T. Considering time to be continuous, we first model average total cost per unit time in terms of the decision variables. We then show that the problem can be decomposed into two simpler sub-problems; namely, the determination of locally optimal solutions in s and S (for any T) and the determination of the optimal T. We establish simple bounds and properties that allow solving both these sub-problems and propose a procedure that guarantees global optimum determination in all policy variables via finite search. Computational results reveal that the usual practice of not treating the review interval as a decision variable may carry severe cost penalties. Moreover, cost differences between (s,S,T) and other standard periodic review policies, including the simple base stock policy, are rather marginal (or even zero), when all policies are globally optimized. We provide a physical interpretation of this behavior and discuss its practical implications.     

Layouts for ocean wave energy farms: Models,properties, and optimization

Renewable energy, such as wave energy, plays a significant role in sustainable energy development. Wave energy represents a large untapped source of energy worldwide and potentially offers a vast source of sustainable energy. We present models and a heuristic algorithm for choosing optimal locations of wave energy conversion (WEC) devices within an array, or wave farm. The location problem can have a significant impact on the total power of the farm due to the interactions among the incident ocean waves and the scattered and radiated waves produced by the WECs. Depending on the nature of the interference (constructive or destructive) among these waves, the wave energy entering multiple devices, and thus the power output of the farm, may be significantly larger or smaller than the energy that would be seen if the devices were operating in isolation. Our algorithm chooses WEC locations to maximize the performance of a wave farm as measured by a well known performance measure called the q-factor, which is the ratio of the power from an array of N WECs to the power from N WECs operating independently, under the point absorber approximation. We prove an analytical optimal solution for the 2-WEC problem and, based on the properties of the 2-WEC solution, we propose an iterative heuristic optimization algorithm for the general problem.     

Learning from mixed OR method practice: The NINES case study

Despite continued interest in the use of mixed OR/MS methods, limited attention has been paid in the literature to generic lessons that could be gained from mixing methods in practice. Many organisational problems demand the use of a mixed method approach and thus recognising and sharing lessons could prove beneficial to both practitioners and researchers. This paper reports on an in-depth evaluation of a case study involving risk identification and quantification of the Northern Isles New Energy Solutions (NINES) project which sought to trial and plan a new energy system. The intervention involved a mixed method approach and client feedback on the efficacy of the approach was sought. The evaluation reported in this paper is carried out using a set of themes taken from the literature and seeks to highlight transferable lessons. The set of lessons that emerge are presented along with their implications for both general OR modelling practice and the specific situation of mixing OR/MS methods. The paper concludes by discussing the implications of the work and directions for future work which will be of interest to both practitioners and researchers interested in mixed method OR/MS work.     

Using Sustainable Production Indicators to Measure Progress in ISO 14001, EHS System and EPA Achievement Track

This case study presents results from testing a new tool — indicators of sustainable production (ISPs) — at Acushnet Rubber of New Bedford, Massachusetts. The authors demonstrate how the tool can be used to measure continual improvement and how it fits within the current system for reporting compliance to regulations; and performance under ISO 14001 (environmental management system standard), the environmental, health and safety (EHS) system, and EPA Achievement Track.Developed at the Lowell Center for Sustainable Production (LCSP), this tool includes both core (common for all companies) and supplemental (production-specific) indicators, and aims to raise awareness, support decision-making, and promote continuous improvement toward sustainability. Indicators such as energy use, water use, global warming potential, lost work time case rate, turnover rate, community spending and charitable contributions, among others, were tested at both facility and corporate levels. Results showed the company's progress in EHS as well as the areas that need improvement. The paper concludes with a summary of key lessons learned and recommendations for further use of the tool.     

Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.     

A memetic differential evolution algorithm for energy-efficient parallel machine scheduling

This paper considers an energy-efficient bi-objective unrelated parallel machine scheduling problem to minimize both makespan and total energy consumption. The parallel machines are speed-scaling. To solve the problem, we propose a memetic differential evolution (MDE) algorithm. Since the problem involves assigning jobs to machines and selecting an appropriate processing speed level for each job, we characterize each individual by two vectors: a job-machine assignment vector and a speed vector. To accelerate the convergence of the algorithm, only the speed vector of each individual evolves and a list scheduling heuristic is applied to derive its job-machine assignment vector based on its speed vector. To further enhance the algorithm, we propose efficient speed adjusting and job-machine swap heuristics and integrate them into the algorithm as a local search approach by an adaptive meta-Lamarckian learning strategy. Computational results reveal that the incorporation of list scheduling heuristic and local search greatly strengthens the algorithm. Computational experiments also show that the proposed MDE algorithm outperforms SPEA-II and NSGA-II significantly.     

Implementing Operations Strategy: How Vertical and Horizontal Coordination Interact

We study the implementation of operations strategy at six German manufacturers in mature businesses. Search theory argues that vertical coordination (i.e., unilateral top‐down adjustment of lower‐level search actions) balances stability against the improvement potential enabled by frontline search and also that horizontal coordination (i.e., bilateral adjustment among lower‐level search actions) is required to ensure compatibility among the initiatives generated in various organizational subunits. Much less is known about how vertical and horizontal coordination interact in operations strategy implementation—that is the focus of this study. We first study how horizontal and vertical coordination affect the compatibility and creativity of distributed search, triangulating our cross‐level interviews with data on the manufacturers' productivity gains and their strategic projects. We then examine whether and how vertical and horizontal coordination interact. Our case comparisons suggest that leaving either one of them “loose” and keeping the other one “tight” results in a useful balance between compatibility and creativity; in contrast, tightening both types of coordination suppresses creativity and loosening both types risks incompatibility of initiatives across units. These results lead to a theoretical framework that identifies vertical and horizontal coordination as partial substitutes for operations strategy implementation.