Practical Solution Approach Research Articles

Clean India Mission: Building Nation through Sustainable Waste Management Practices

The case study underscores an operational concern faced by Praveen Vishwakarma, the Senior Manager — Operations at Eco Pro Environmental Services in Indore, in January 2020, pertaining to the daily waste collection in the city. Working in close cooperation with the civic administration, the company focuses on daily waste management of Indore, a commercial hub in Central India. The city of Indore has been declared as the cleanest city in India for three consecutive years according to the cleanliness survey conducted by Government of India under the aegis of clean Indian mission. Clean India mission, launched in 2014, encompassing sustainable development forms the backdrop. The company has been facing huge transportation costs for operating waste collection vehicles and there has been severe violations in the timelines in serving two of the wards. The protagonist is at the crossroads of operational efficiency and service effectiveness. The case highlights the key decision making which involves redesigning of the collection routes through an optimization problem and uses a practical solution approach to resolve the multi-faceted dilemma. The case study is helpful for civic administrators and players involved in waste management by providing actionable guidelines.

Min–max version of single-machine scheduling with generalized due dates under scenario-based uncertainty

This article considers the min–max version of a single-machine scheduling problem with generalized due dates under processing time uncertainty. The objective is to minimize the maximum number of tardy jobs over all scenarios. For a problem with a common due date, denoted d, it is shown that the case with a fixed number of scenarios is weakly NP-hard and has no fully polynomial time approximation scheme, although it has a polynomial time approximation scheme. Furthermore, it is shown that the case with an arbitrary number of scenarios has no α-approximation algorithm for any constant . For a problem with identical due date intervals, it is shown that the case with two scenarios is strongly NP-hard and has no α-approximation algorithm for any constant . As a practical solution approach, a genetic algorithm is proposed and numerical experiments are conducted to verify its performance.

Nonlinear flexure mechanics of beams: stress gradient and nonlocal integral theory

In order to study the intrinsic size-effects, the stress gradient theory is implemented to a nano-scale beam model in nonlinear flexure. The nonlocal integral elasticity model is considered as a suitable counterpart to examine the softening behavior of nano-beams. Reissner variational principle is extended consistent with the stress gradient theory and applied to establish the differential, constitutive and boundary conditions of a nano-sized beam in nonlinear flexure. The nonlinear integro-differential and boundary conditions of inflected beams in the framework of the nonlocal integral elasticity are determined utilizing the total elastic strain energy formulation. A practical series solution approach in terms of Chebyshev polynomials is introduced to appropriately estimate the kinematic and kinetic field variables. A softening structural behavior is observed in the flexure of the stress gradient and the nonlocal beam in terms of the characteristic parameter and the smaller-is-softer phenomenon is, therefore, confirmed. The flexural response associated with the stress gradient theory is demonstrated to be in excellent agreement with the counterpart results of the nonlocal elasticity model equipped with the Helmholtz kernel function. The nonlocal elasticity theory endowed with the Error kernel function is illustrated to underestimate the flexural results of the stress gradient beam model. Detected numerical benchmark can be efficiently exploited for structural design and optimization of pioneering nano-engineering devices broadly utilized in advanced nano-electro-mechanical systems.

Hierarchical group decision‐making approach for information technology project evaluation and prioritization

AbstractThe purpose of this study is to define a novel hierarchical group decision‐making (HGDM) problem. For this problem, a practical solution approach is proposed for information technology (IT) project evaluation and prioritization. HGDM is a decision‐making problem with a large number of decision makers (DMs) or experts organized in a hierarchical structure. For this problem, a practical solution approach based on pairwise comparisons is developed. For evaluating the projects in organizations, specifically for the IT projects, a hierarchy of many DMs make project proposals and assess proposed projects using various sets of criteria. The proposed HGDM approach is applied to the evaluation and prioritization of IT projects. A problem with 22 DMs and experts in a three‐level hierarchy, 29 criteria and 15 projects is solved using the proposed HGDM approach. The HGDM approach can deal with more complex real‐life problems such as problems where more than 500 projects, 100 DMs and experts, and 100 criteria exist. The originality of the study is the introduction of HGDM in the group decision‐making environment.

Optimizing N-Tier Application Scalability in the Cloud

An effective cloud computing environment requires both good performance and high efficiency of computing resources. Through extensive experiments using a representative n-tier benchmark application (Rice University Bulletin Board System (RUBBoS)), we show that the soft resource allocation (e.g., thread pool size and database connection pool size) in component servers has a significant impact on the overall system performance, especially at high system utilization scenarios. Concretely, the same software resource allocation can be a good setting in one hardware configuration and then becomes an either under- or over-allocation in a slightly different hardware configuration, causing a significant performance drop. We have also observed some interesting phenomena that were caused by the non-trivial dependencies between the soft resources of servers in different tiers. For instance, the thread pool size in an Apache web server can limit the total number of concurrent requests to the downstream servers, which surprisingly decreases the Central Processing Unit (CPU) utilization of the Clustered Java Database Connectivity (C-JDBC) clustering middleware as the workload increases. To provide a globally optimal (or near-optimal) soft resource allocation of each tier in the system, we propose a practical iterative solution approach by combining a soft resource aware queuing network model and the fine-grained measurement data of every component server. Our results show that to truly scale complex distributed systems such as n-tier web applications with expected performance in the cloud, we need to carefully manage soft resource allocation in the system.

A decentralized channel assignment and power control approach using noncooperative game and Vickrey auction in relay‐aided MC‐CDMA IoT networks

AbstractInternet of things (IoT) is a global infrastructure, providing ubiquitous intelligence by enabling seamless data flows between physical and virtual objects based on existing and evolving interoperable communication technologies. It is expected that the demand for wireless resources will rapidly increase in future IoT networks, and hence, the utilization of the wireless medium needs to be highly improved by efficiently allocating the resources. In this work, we investigate the problem of allocating wireless network resources in a multicarrier code‐division multiple access relay‐aided wireless IoT network, considering both channel assignment and power control, to satisfy the signal‐to‐interference ratio requirement of the devices with the objective of supporting as many devices as possible. The primary problem, named power control and channel assignment, is formulated as a mixed integer linear programming problem. Due to its complexity, three practical suboptimal solution approaches with different applications are developed. First, the Lagrangian relaxation and the dual decomposition methods are employed, and the problem is divided into two pure integer programming and linear programming subproblems, which are then solved using an iterative subgradient‐based algorithm that converges to a near‐optimal solution. Next, a greedy heuristic algorithm based on the Gaussian elimination method is proposed to efficiently find a suboptimal solution. In addition, a two‐phase second‐price auction‐based channel assignment and noncooperative game‐based power control algorithm is proposed to allocate resources in an iterative, distributed, and scalable manner. In the distributed scheme, no information about path gains is required. The numerical results show that the difference between the optimal solution and our approaches is negligible, so they can be efficiently employed in real‐world scenarios.

Daily berth planning in a tidal port with channel flow control

This paper studies an operational-level berth allocation and quay crane assignment problem (daily berth planning) considering tides and channel flow control constraints. An integer programming model is proposed for this problem. Then a column generation solution approach is developed on a set partitioning based reformulation of the original model. Computational study is conducted on 30 test cases constructed from real-world data to validate efficiency of the proposed solution approach. Results show that this simple but practical solution approach can optimally solve the daily berthing planning problem instances with up to 80 vessels, 40 berths, and 120 quay cranes within one hour, which is reasonable and acceptable for the real-world applications. The proposed decision model and the solution approach could be potentially useful for some tidal ports with (or without) navigation channels.

A practical solution approach for the green vehicle routing problem

Green vehicle routing (GVRP) is an active research area that is concerned with the development and analysis of distribution activities with eco-friendly vehicles. We propose a practical solution approach for the GVRP that uses a mixed-integer linear formulation and a reduction procedure. The newly formulation offers two significant advantages: compactness and flexibility. We provide empirical evidence that the formulation and the reduction procedure enable to derive optimal solutions for medium-sized instances using a general-purpose solver. We show that the proposed exact approach consistently outperforms a state-of-the-art branch-and-cut algorithm and constitutes an appealing and practical alternative for optimally solving GVRPs.

A Decomposition-Based Practical Approach to Transient Stability-Constrained Unit Commitment

Traditional security-constrained unit commitment (SCUC) considers only static security criteria, which may however not ensure the ability of the system to survive dynamic transition before reaching a viable operating equilibrium following a large disturbance, such as transient stability. This paper proposes a tractable mathematical model for transient stability-constrained unit commitment (TSCUC) and a practical solution approach. The problem is modeled without explicit differential-algebraic equations, reducing the problem size to one very similar to a conventional SCUC. The whole problem is decomposed into a master problem for UC and a range of subproblems for steady-state security evaluation and transient stability assessment (TSA). Additional constraints including Benders cut and so-named stabilization cut are generated for eliminating the security/stability violations. The extended equal-area criterion (EEAC) is used for fast TSA and analytically deriving the stabilization cut, wherein multiple contingencies having common instability mode can be simultaneously stabilized by one cut. The proposed approach is demonstrated on the New England 10-machine system and the IEEE 50-machine system, reporting very high computational efficiency and high-quality solutions.

A production scheduling problem with uncertain sequence-dependent set-up times and random yield

A scheduling problem in a real production line with uncertain sequence-dependent set-up times and a random yield is considered. The production line can produce multiple product types as production lots, each of which is composed of a number of products of the same product type. To changeover product types, a sequence-dependent set-up operation should be performed, and only the lower and upper bounds are known for the sequence-dependent set-up times. Moreover, the processing time to produce the required number of product for each production lot is uncertain due to the random yield. For the objective of minimising the average tardy probability of given production lots, a systematic approximation scheme to estimate tardy probabilities of lots in any given production sequence is developed by taking not only the uncertainties but also the computational efficiency into account. As practical solution approaches, a simulated annealing and a discrete particle swarm optimisation algorithms using the approximation scheme are developed, and their performance are evaluated by computational experiments. Since there has been no research on the scheduling problems with uncertain sequence-dependent set-up times and random yield, the authors expect this research will make an excellent contribution to develop practical scheduling methodologies in uncertain scheduling environments.

A BI‐LEVEL DECISION MODEL FOR CUSTOMER CHURN ANALYSIS

This paper develops a bi‐level decision model and a solution approach to optimizing service features for a company to reduce its customer churn rate. First, a bi‐level decision model, together with its modeling approach, are developed to describe the gaming relationship between decision makers in a company (service provider) and its customers. Then, a practical solution approach to reaching solutions for the bi‐level‐modeled customer churn problem is developed. Finally, experiments and case studies are conducted to illustrate the bi‐level decision model and the solution approach.

Frequency-based similarity for parameterized sequences: Formal framework, algorithms, and applications

Computing sequence similarity is a problem emerging in several areas of research. Current solution algorithms are often based on alignment methods under the assumption that matching symbols, or at least a substitution schema among them, are known in advance. This is natural for sequences defined over the same alphabet of symbols. However, for sequences defined over different alphabets and in absence of an appropriate background knowledge, sequence similarity can be conveniently reconsidered from a different perspective where determining the best substitution schema is also part of the computation problem. The basic idea is that any symbol of a sequence can be correlated with many symbols of another, provided each correlation frequently occurs over the various positions of the alignment. This novel setting is formalized and relevant application domains fitting its peculiarities are illustrated. Moreover, the computational complexity of the alignment problems arising therein is analyzed, and practical solution approaches are proposed and validated over synthetic and real datasets.

Randomized Local Search for Real-Life Inventory Routing

In this paper, a new practical solution approach based on randomized local search is presented for tackling a real-life inventory routing problem. Inventory routing refers to the optimization of transportation costs for the replenishment of customers' inventories: based on consumption forecasts, the vendor organizes delivery routes. Our model takes into account pickups, time windows, drivers' safety regulations, orders, and many other real-life constraints. This generalization of the vehicle-routing problem was often handled in two stages in the past: inventory first, routing second. On the contrary, a characteristic of our local search approach is the absence of decomposition, made possible by a fast volume assignment algorithm. Moreover, thanks to a large variety of randomized neighborhoods, a simple first-improvement descent is used instead of tuned, complex metaheuristics. The problem being solved every day with a rolling horizon, the short-term objective needs to be carefully designed to ensure long-term savings. To achieve this goal, we propose a new surrogate objective function for the short-term model, based on long-term lower bounds. An extensive computational study shows that our solution is effective, efficient, and robust, providing long-term savings exceeding 20% on average, compared to solutions built by expert planners or even a classical urgency-based constructive algorithm. Confirming the promised gains in operations, the resulting decision support system is progressively deployed worldwide.

A practical, distribution-free approach to the classical newsboy inventory stocking problem for highly right-skewed but otherwise unknown demand distributions

The reliable approximation of the demand distributions is critically important for most of the existing solution approaches to the classical newsboy inventory problem. This is rendered especially diff icult in the absence of adequate historical sales information especially for newly established businesses. Such approximation, as previous research has shown, is also not entirely hazard-free even for businesses that have a sales history. So a closed-form, distribution-free approach defi nitely warrants exploration and has indeed been the subject of previous research. However, the distribution-free approaches that currently exist in the literature work optimally in idealized situations where demand distributions are approximately normal. But many real-life business situations; where the newsboy-type inventory model can be relevantly applied; face highly right-skewed demand distributions. We posit a very simple and practical solution approach for the classical newsboy-type inventory stocking problem that is designed for situations where the demand distributions are highly right-skewed but are otherwise totally unknown. To demonstrate its versatility, we give an illustrative sample of Poisson demand distributions which if assumed, yield optimal solutions that agree exactly with our posited approach.

Trade-offs between target hardening and overarching protection

Defenders concerned about protecting multiple targets can either protect them individually (through target hardening), or collectively (through overarching protections such as border security, public health, emergency response, or intelligence). Decision makers may find it relatively straightforward to choose which targets to harden, but are likely to find it more difficult to compare seemingly incommensurate forms of protection – e.g., target hardening, versus a reduction in the likelihood of weapons being smuggled across the border. Unfortunately, little previous research has addressed this question, and fundamental research is needed to provide guidance and practical solution approaches. In this paper, we first develop a model to optimally allocate resources between target hardening and overarching protection, then investigate the factors affecting the relative desirability of target hardening versus overarching protection, and finally apply our model to a case study involving critical assets in Wisconsin. The case study demonstrates the value of our method by showing that the optimal solution obtained using our model is in some cases substantially better than the historical budget allocation.

Imprecision and Flexible Constraints in Fuzzy Vehicle Routing Problem

The Vehicle Routing Problem (VRP) is a well-known combinatorial optimization problem. The classical definitions of vehicle routing problems often lack handling of uncertain parameters and flexibility of constraints. The most popular approaches to these aspects involve probability distributions for uncertainty and penalty-based goal function for flexibility. In this paper a different approach is proposed which involves fuzzy sets framework, modeling both imprecision and flexibility, and analyzing practical solution approaches for VRP with uncertain information. Several variants of the basic problem and fuzzy optimization problem formulations are described. The fuzzy VRP is obtained when some of the elements in the formulation are fuzzy. The main versions of VRP that have appeared in the literature are reviewed and the standard approaches for solving the corresponding models are described.

A practical solution approach for the cyclic inventory routing problem

Abstract Vendor managed inventory (VMI) is an example of effective cooperation and partnering practices between up- and downstream stages in a supply chain. In VMI, the supplier takes the responsibility for replenishing his customers’ inventories based on their consumption data, with the aim of optimizing the over all distribution and inventory costs throughout the supply chain. This paper discusses the challenging optimization problem that arises in this context, known as the inventory routing problem (IRP). The objective of this IRP problem is to determine a distribution plan that minimizes average distribution and inventory costs without causing any stock-out at the customers. Deterministic constant customer demand rates are assumed and therefore, a long-term cyclical approach is adopted, integrating fleet sizing, vehicle routing, and inventory management. Further, realistic side-constraints such as limited storage capacities, driving time restrictions and constant replenishment intervals are taken into account. A heuristic solution approach is proposed, analyzed and evaluated against a comparable state-of-the-art heuristic.

A two-phase approach for multi-objective programming problems with fuzzy coefficients

In this study, a two-phase procedure is introduced to solve multi-objective fuzzy linear programming problems. The procedure provides a practical solution approach, which is an integration of fuzzy parametric programming (FPP) and fuzzy linear programming (FLP), for solving real life multiple objective programming problems with all fuzzy coefficients. The interactive concept of the procedure is performed to reach simultaneous optimal solutions for all objective functions for different grades of precision according to the preferences of the decision-maker (DM). The procedure can be also performed to obtain lexicographic optimal and/or additive solutions if it is needed. In the first phase of the procedure, a family of vector optimization models is constructed by using FPP. Then in the second phase, each model is solved by FLP. The solutions are optimal and each one is an alternative decision plan for the DM.

Designing distribution patterns for long-term inventory routing with constant demand rates

This paper proposes a practical solution approach for the challenging optimization problem of minimizing overall costs in an integrated distribution and inventory control system. Constant customer demand rates are assumed and therefore a long-term, cyclic planning approach is adopted. The concept of distribution patterns, consisting of vehicles performing multiple tours with possibly different frequencies, is used to extend the traditional concept of a single tour per vehicle. A heuristic is proposed that is capable of solving a cyclical distribution problem involving real-life features, such as customer capacity restrictions, loading and unloading extra times and prespecified minimum times between consecutive deliveries.

Simultaneous optimal configuration, design and operation of batch distillation

AbstractA methodology for simultaneous determination of optimal batch distillation configuration, design and operation is presented. The configuration design methodology utilizes a mixed integer dynamic optimization (MIDO) formulation approach, where the optimal batch distillation system is obtained automatically, based on its maximum overall profitability for a given separation duty. Using rigorous models, the MIDO problem is solved using a practical stochastic solution approach of genetic algorithm and penalty function. The feasibility of this methodology is demonstrated for both binary and multicomponent separation scenarios. In the binary separation case study, the effect of feed composition for different binary mixtures on the optimal configurations, that is, regular vs. inverted columns, is investigated and discussed. The regular column was found to be more profitable for feeds with a high fraction of the light component, whereas the inverted column is optimal for heavier feeds. The optimality of a particular configuration over another is, however, case study specific, depending on, for example, how easy the mixture is to separate. In the multicomponent separation case study, the results obtained highlight the superiority of the multivessel configuration over the regular and inverted configurations. © 2005 American Institute of Chemical Engineers AIChE J, 2005