Proposed Solution Framework Research Articles

A Metaheuristic Framework for Energy-Intensive Industries With Batch Processing Machines

Batch processing machines, which operate multiple jobs at a time, are commonly used in energy-intensive industries. A significant amount of energy can be saved in such industries using production scheduling as an approach to enhance efficiency. This study deals with an energy-aware scheduling problem for parallel batch processing machines with incompatible families and job release times. In such an environment, a machine may need to wait until all the jobs in the next batch become ready. During waiting time, a machine can be switched <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> or kept <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> standby for more energy-efficient scheduling. We first present a mixed-integer linear programming (MILP) model to solve the problem. However, the presented MILP model can only solve small problem instances. We therefore propose an energy-efficient tabu search (ETS) algorithm for solving larger problem instances. The proposed solution framework incorporates multiple neighborhood methods for efficient exploration of the search space. An energy-related heuristic is also integrated into the ETS for minimizing energy consumption during the waiting time. The performance of our proposed ETS algorithm is validated by comparing it with CPLEX for small problem instances and with two other heuristic algorithms for larger problem instances. The contribution of different components in ETS is also established in our experimental studies. The proposed solution framework is expected to bring many benefits in energy-intensive industries both economically and environmentally.

Integrated train timetabling and rolling stock rescheduling for a disturbed metro system: A hybrid deep reinforcement learning and adaptive large neighborhood search approach

For a metro line with high density and short trip time, the original train timetable and rolling stock circulation face infeasible risks under unexpected disturbances. This paper focuses on integrated train timetabling and rolling stock rescheduling for a disturbed metro system to reduce the negative impact. The problem is formulated as a multi-objective master model, in which required operational constraints and dispatching strategies are taken into account. To satisfy the real-time requirement, an innovative solution framework consisting of an independent rescheduling process and a cooperative rescheduling process is proposed. In the independent rescheduling process, the master model is decomposed into a series of submodels in the unit of train service. The submodel can be transformed into a Markov Decision Process (MDP) with well-defined fundamental elements (i.e., state, action, and reward function). Based on the MDP, a hierarchical policy is developed by introducing deep reinforcement learning to generate the initial solution, including the lower-level policy for train timetable and the higher-level policy for flexible rolling stock circulation. In the cooperative rescheduling process, the selfishness of agents that appears after the model decomposition is overcome by an adaptive large neighborhood search algorithm, which can improve the solution quality. Finally, two sets of numerical experiments are conducted to demonstrate the performance of the proposed solution framework. The experimental results show that a near-optimal solution can be obtained in a short time, which is better than the currently used practical rules in the automatic train supervision system, especially during peak hours. Furthermore, the effects of different parameter settings are analyzed.

A new solution framework for time-dependent reliability-based design optimization

Time-dependent reliability-based design optimization (TRBDO) has attracted intensive research attentions in recent years by virtue of its unique ability to allow consideration of dynamic uncertainties caused by stochastic processes and material property degradation. However, existing TRBDO methods are generally too intricate to be practically applicable for practical engineering application. On top of that, extremely high computational cost for complex TRBDO problems further hinders its practicability. To facilitate smooth implementation via enhancing computational efficiency in solving TRBDO problems, this study proposes an innovative and efficient solution framework. The strategy is that time-dependent performance function in each probabilistic constraint is discretized into a series of instantaneous performance functions to transform the original TRBDO problem into a RBDO problem. The reliability of each probabilistic constraint in the transformed RBDO problem is then considered under extreme value condition. With engagement of the first-order reliability method, the RBDO problem is transformed into two different triple-loop timeindependent RBDO problem. However, the issue of expensive computational cost still persists due to the triple-loop structure and identification of temporal variables under extreme value condition. To this gap, a decoupled strategy is adopted to resolve the triple-loop structure into a series of cycles of double-loop reliability analyses and deterministic optimization. Two numerical examples and three engineering applications are employed to demonstrate the supreme computational performance of the currently proposed solution framework. Results show that the proposed framework is capable of achieving a reliable optimal design at a fast convergence speed.

A multi-agency coordination resource allocation and routing decision-making problem: A coordinated truck-and-drone DSS for improved wildfire detection coverage

This study proposes a novel coordinated truck-drone system as a decision support system to improve fire suppression operations. The problem in this study is formulated as a bi-objective mathematical model to minimize total monitoring cost and time, considering trucks as mobile drone depots and that drones can fly at various altitudes and access hard-to-reach areas. In addition, time and cost parameters in the mathematical model are deemed uncertain, rendering the model more realistic. Accelerated Benders' decomposition (ABD) is utilized to solve the model rapidly. Furthermore, a column-and-constraint generation (CCG) algorithm is employed, which is an effective method for solving scenario-based models under uncertainty. Two criteria are subsequently used to evaluate and compare proposed robust optimization approaches (risk averse and min-max relative regret). The results indicate that the proposed system can assist firefighters in determining the optimal number of drones and trucks to patrol and the time and cost required to visit all areas. Moreover, the findings demonstrate that the min-max relative regret approach can outperform other methods during the hot season when the risk of wildfire is elevated. In contrast, when the fire risk is lower during the cold season, time and cost can be effectively managed, and a risk-averse strategy can be implemented. Finally, the proposed solution framework can facilitate optimal strategic organizational decision planning.

An Approximation Algorithm for k-Depot Split Delivery Vehicle Routing Problem

A multidepot capacitated vehicle routing problem aims to serve customers’ demands using a fleet of capacitated vehicles located in multiple depots, such that the total travel cost of the vehicles is minimized. We study a variant of this problem, the k-depot split delivery vehicle routing problem (or k-DSDVRP in short), for the situation where each customer’s demand can be served by more than one vehicle, and the total number of depots, denoted by [Formula: see text], is a fixed constant. This is a challenging problem with broad applications in the logistics industry, for which no constant ratio approximation algorithm is known. We develop a new approximation algorithm for the k-DSDVRP, ensuring an approximation ratio of [Formula: see text] and a polynomial running time for any fixed constant [Formula: see text]. To achieve this, we propose a novel solution framework based on a new relaxation of the problem, a cycle splitting procedure, and a vehicle assignment procedure. To further enhance its efficiency for practical usage, we adapt the newly developed approximation algorithm to a heuristic, which runs in polynomial time even when k is arbitrarily large. Experimental results show that this heuristic outperforms a commercial optimization solver and a standard vehicle routing heuristic. Moreover, our newly proposed solution framework can be applied to developing new constant ratio approximation algorithms for several other variants of the k-DSDVRP with [Formula: see text] being a fixed constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search &amp; Approximation Algorithms. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971177, 71725001, U1811462], Research Grants Council of Hong Kong SAR, China [Grant 15221619], and Guangdong Basic and Applied Basic Research Foundation [Grant 2023A1515030260]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0193 . The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0193 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0193 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Multiscale Analysis of Permeable and Impermeable Wall Models for Seawater Reverse Osmosis Desalination

In recent years, high permeability membranes (HPMs) have attracted wide attention in seawater reverse osmosis (SWRO) desalination. However, the limitation of hydrodynamics and mass transfer characteristics for conventional spiral wound modules defeats the advantage of HPMs. Feed spacer design is one of the effective ways to improve module performance by enhancing permeation flux and mitigating membrane fouling. Herein, we propose a multiscale modeling framework that integrates a three-dimensional multi-physics model with a permeable wall and an impermeable wall, respectively, at a sub-millimeter scale and a system-level model at a meter scale. Using the proposed solution framework, a thorough quantitative analysis at different scales is conducted and it indicates that the average errors of the friction coefficient and the Sherwood number using the impermeable wall model are less than 2% and 9%, respectively, for commercial SWRO membrane (water permeability 1 L m−2 h−1 bar−1) and HPMs (3 L m−2 h−1 bar−1, 5 L m−2 h−1 bar−1 and 10 L m−2 h−1 bar−1) systems, compared to the predictions using the permeable wall model. Using both the permeable and impermeable wall models, the system-level simulations, e.g., specific energy consumption, average permeation flux, and the maximum concentration polarization factor at the system inlet are basically the same (error &lt; 2%), while the impermeable wall model has a significant advantage in computational efficiency. The multiscale framework coupling the impermeable wall model can be used to guide the efficient and accurate optimal spacer design and system design for HPMs using, e.g., a machine learning approach.

Train Control and Schedule Integrated Optimization With Reversible Substations

The Carbon Neutralization is a grand strategy to deal with global climate change. As a major energy consumer, urban rail transit (URT) system should contribute corresponding solutions. This paper proposes an energy-efficient train control and schedule integrated optimization method under DC traction power supply system (TPSS) with reversible substation (RSS). The dynamic coupling mechanism between the train movement process and the RSS working mode is analyzed and modeled. Aiming to minimize the total RSS energy consumption for the train traveling over multi-interstation with a given total trip time, the train trajectory and schedule optimization problem is integrated in the same level, and is formulated as a multi-phase optimal control problem (MOCP) to be solved by a pseudo-spectral method (PSM). As a supplement, a customized dynamic programming (DP) method for the problem is introduced to verify the validity of the proposed solution framework. Simulation results under the models of the RSS energy together with conventional rectifier substation energy and vehicle mechanical energy are presented and compared, indicating that the proposed methodology has great energy saving performance for the new-type DC railway system.

A Unified MILP Solution Framework for Adaptive Robust Scheduling Problems With Mixed-Integer Recourse Objective

To mitigate the high computational cost of adaptive robust optimization with a mixed-integer recourse objective in real-world power scheduling, a unified mixed-integer linear programming (MILP) solution framework is proposed in this paper. Numerical tests verify the superiority and practicability of the proposed solution framework over the existing decomposition-based solution algorithms.

A Simulation-Based Model for Continuous Network Design Problem Using Bayesian Optimization

This paper investigates the continuous network design problem (CNDP) and proposes a simulation-based bi-level model and solution framework based on Bayesian optimization. In the bi-level model, the upper level minimizes the total system cost, and the lower level assigns traffic under an approximated dynamic equilibrium condition corresponding to the given network design strategy. A simulation tool integrated micro- and macro- traffic dynamics, namely SUMO, is employed to solve the lower-level problem. The embedded high-fidelity, non-linear understanding of traffic in the simulator oftentimes gains additional complexity due to the lack of tractable mathematical representation. Thus, a Bayesian machine learning technique is utilized to build the link between simulation and optimization. The proposed solution framework takes both the advantages of fine-grained simulations and the efficiency of surrogate-based optimization techniques. Moreover, lane width expansion is innovatively proposed as the decision variable of CNDP to bridge the gap between theory and practice. The relationship between link free-flow speed (FFS) and lane width is also explored based on real data and established to accurately calibrate the simulation input. For demonstrative purposes, numerical experiments on the optimal lane width expansion design were conducted in the inner city of Suzhou, China. The results show that with proper parameter settings, the proposed method is capable to find the global optimal solution within a very tight computational budget, which makes the simulation-based framework an encouraging option for policymakers to enhance transportation network performance.

Maintenance scheduling optimisation of Reverse Osmosis Networks (RONs) via a multistage Optimal Control reformulation

State-of-the-art approaches for membrane cleaning scheduling have focused on the Mixed-Integer Nonlinear Programming (MINLP) formulation so far, a strategy leading to a combinatorial problem that does not capture accurately the dynamic behaviour of the system. In this work, the Reverse Osmosis (RO) cleaning scheduling problem is solved using a novel approach based on the Multistage Integer Nonlinear Optimal Control Problem (MSINOCP) formulation. The approach produces an automated solution for the membrane cleaning scheduling, which also obviates the need for any form of combinatorial optimisation.Two different simulations, for 26 and 52 periods of operation (each period with a duration of one week), are carried out to illustrate the application of the proposed framework and the total cost is 1.17 and 2.48107€, respectively. The RO network configuration considers 2 stages, each with 3 individual RO modules. The results show evidently that the new proposed solution framework can solve successfully this type of problems, even for large scale configurations, long time horizons and arbitrary realistic complexity of the underlying dynamic model of the RO process considered.

Community logistics and dynamic community partitioning: A new approach for solving e-commerce last mile delivery

Last mile delivery shows an increasingly tough challenge for logistics service providers due to the rapidly expanding e-commerce sales around the globe. To ease the implementation of last mile delivery, an effective delivery strategy is to predetermine the service regions of vehicles before optimizing their delivery routes. On this ground, this paper proposes a new delivery strategy named Community Logistics (CL) to generate vehicle service region and departure time dynamically. Through adopting this new delivery strategy, we transform the original last mile delivery to a new type of research problem, namely dynamic community partitioning problem (DCPP), with an aim to strike a balance between vehicle service region range, order delay time and vehicle capacity usage based on the real-time order arrivals and vehicle availability status. We present a Markov decision process (MDP) model for the DCPP and develop a heuristic solution approach to solve this MDP model. Numerical results demonstrate significant benefits of the proposed solution framework and delivery strategy.

Optimal Control of Network-Coupled Subsystems: Spectral Decomposition and Low-Dimensional Solutions

In this article, we investigate the optimal control of network-coupled subsystems with coupled dynamics and costs. The dynamics coupling may be represented by the adjacency matrix, the Laplacian matrix, or any other symmetric matrix corresponding to an underlying weighted undirected graph. Cost couplings are represented by two coupling matrices which have the same eigenvectors as the coupling matrix in the dynamics. We use the spectral decomposition of these three coupling matrices to decompose the overall system into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$({L+1})$</tex-math></inline-formula> systems with decoupled dynamics and cost, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> is the number of linearly independent eigendirections associated with nonzero eigenvalue triples of the three coupling matrices. Furthermore, the optimal control input at each subsystem can be computed by solving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$({L_\text{dist}+1})$</tex-math></inline-formula> decoupled Riccati equations, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_\text{dist}\,(L_\text{dist}\leq L)$</tex-math></inline-formula> is the number of distinct nonzero eigenvalue triples of the three coupling matrices. A salient feature of the result is that, given the spectral decompositions of the couplings, the solution complexity does not directly depend on the number of subsystems. Therefore, the proposed solution framework provides a scalable method for synthesizing and implementing optimal control laws for large-scale network-coupled subsystems.

Deep Reinforcement Learning for Distributed Flow Shop Scheduling with Flexible Maintenance

A common situation arising in flow shops is that the job processing order must be the same on each machine; this is referred to as a permutation flow shop scheduling problem (PFSSP). Although many algorithms have been designed to solve PFSSPs, machine availability is typically ignored. Healthy machine conditions are essential for the production process, which can ensure productivity and quality; thus, machine deteriorating effects and periodic preventive maintenance (PM) activities are considered in this paper. Moreover, distributed production networks, which can manufacture products quickly, are of increasing interest to factories. To this end, this paper investigates an integrated optimization of the distributed PFSSP with flexible PM. With the introduction of machine maintenance constraints in multi-factory production scheduling, the complexity and computation time of solving the problem increases substantially in large-scale arithmetic cases. In order to solve it, a deep Q network-based solution framework is designed with a diminishing greedy rate in this paper. The proposed solution framework is compared to the DQN with fixed greedy rate, in addition to two well-known metaheuristic algorithms, including the genetic algorithm and the iterated greedy algorithm. Numerical studies show that the application of the proposed approach in the studied production-maintenance joint scheduling problem exhibits strong solution performance and generalization abilities. Moreover, a suitable maintenance interval is also obtained, in addition to some managerial insights.

Distributionally robust portfolio optimization with linearized STARR performance measure

We study the distributionally robust linearized stable tail adjusted return ratio (DRLSTARR) portfolio optimization problem, in which the objective is to maximize the worst-case linearized stable tail adjusted return ratio (LSTARR) performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties, named uncertain probabilities and continuum of realizations, associated with the losses of assets. We account for two typical combinatorial trading constraints, called buy-in threshold and diversification constraints, to reflect stock market restrictions. Leveraging conic duality theory to tackle the distributionally robust worst-case expectation, the proposed problems are reformulated into mixed-integer linear programming problems. We carry out a series of empirical tests to illustrate the scalability and effectiveness of the proposed solution framework, and to evaluate the performance of the DRLSTARR-constructed portfolios. The cross-validation results obtained using a rolling-horizon procedure show the superior out-of-sample performance of the DRLSTARR portfolios under an uncertain continuum of realizations.

BIM implementation in Sarawak construction industry: Awareness, readiness and challenges

Lack of coordination and fragmentations have always existed among the stakeholders and parties involved in the implementation of planning, design and construction process of construction projects, especially in the aspects of information transfer. In the recent trends, Building Information Modelling, BIM has been introduced to address the fragmentation issues. The BIM framework is based on the notion of coordination between stakeholders in digital format for information exchange throughout the construction progress. Although BIM technology has been implemented in Malaysia for several years, low adoption is found within the Sarawak construction industry. This paper aims to assess the awareness and readiness for BIM implementation in the Sarawak construction industry. The study was carried out using questionnaire survey across the districts in the Southern Region, Central Region and Northern Region of Sarawak. There are in total 404 responses received, and it is revealed that only 14% of the participants have experience in BIM-related projects. This is still far from the goals set by the Construction Industry Development Board (CIDB) Malaysia. Meanwhile, 63% of respondents had heard of BIM and 46% of them had attended some seminar or program related on BIM. The investigation of awareness, readiness and challenges of BIM implementation has been further inferred and compiled as a foundation guideline for the proposed solution framework to increase BIM usage in the state. It is hopeful that proposed solution framework can help in overcoming BIM implementation barriers in the Sarawak Construction Industry.

Design of Prototype Information System for Tracking & Tracing Fish Distribution Based on Mobile Agent

The National Fish Logistics System or often called SLIN is an Indonesian Ministry of Maritime Affairs and Fisheries program that aims to maintain the stability of the production and marketing systems and control the disparity in national fish prices. Central Sulawesi is the Province that becomes the main corridor of this program. The inefficient distribution monitoring process generally causes several problems in the field of fisheries distribution management that still often occur today by the regional Ministry of Maritime Affairs and Fisheries. It indicates that SLIN is not yet running optimally. This study purpose a prototype design of fish distribution tracking based on a mobile agent that can use to help consumers to track distribution channels and get information about the origin of the fish to be purchased. The data will further process for monitoring fish distribution in a real-time manner by the regional Ministry of Maritime Affairs and Fisheries. A proper monitoring mechanism will undoubtedly help the government in making policies and conducting supervision to make the SLIN implementation successful in Central Sulawesi. By the research, we found that the proposed method can gather data from every level fish distribution agent then processed the data to inform about distribution line and the origin of the fish for the consumer. The proposed solution framework could be implemented and nearly fit with current implementation criteria. The framework later can be a base framework for developing a more advanced information system for SLIN in Central Sulawesi Region.

The vehicle deployment and relocation problem for electric vehicle sharing systems considering demand and parking space stochasticity

One-way electric vehicle (EV) sharing systems have received increasing attention because of their environmental, mobility, and societal benefits. In this research, we investigate the vehicle deployment and relocation problem for hybrid one-way station-based and free-floating electric vehicle sharing systems considering demand and parking space stochasticity. The problem is formulated as a two-stage stochastic program. In the first stage, the EVs are deployed into a sharing system based on historical customer data to maximize the resulting profit. The second stage maximizes the expected profit by relocating the EVs in the system using relocation staff in response to an uncertain demand. To solve the resulting complicated problem, an integer L-shaped method is proposed so that the cuts can efficiently fathom the solution space and identify solutions with a reasonable optimality gap. The proposed solution framework is empirically applied to cases of various sizes. The empirical results demonstrate the effectiveness of the proposed solution scheme.

Development and analysis of priority decision rules using MCDM approach for a flexible job shop scheduling: A simulation study

Flexible job shop scheduling problem (FJSSP) is a critical operational planning step in manufacturing that ensures on-time delivery of goods leading to customer satisfaction. The process of assigning jobs to machines has a larger solution space and consumes a huge amount of time and effort. This research focuses on solving FJSSP using the discrete event simulation (DES) model and integrates it with Composite Dispatching Rules (CDRs) and multi-criteria decision making (MCDMs) based priority rules. Performance evaluation of the flexible job shop is carried out to minimize the Makespan, mean Flow-Time, mean Tardiness, and maximum Tardiness. Best performing CDRs and hybrid MCDM techniques are employed for minimizing the objectives considered in this study. The proposed solution framework integrates Fuzzy Analytic Hierarchy Process (FAHP) for assigning weights to the criteria and MCDM approaches namely technique for order of preference by similarity to ideal solution (TOPSIS), The Evaluation based on Distance from Average Solution (EDAS), Compromise Programming (CP), and Weighted Average Method (WAM) separately to prioritize the jobs. Benchmark problems of different sizes are tested using the best performing CDRs available in the literature and proposed MCDM approaches. The proposed approach is implemented in a real-world large-scale flexible job shop (FJS) that produces auto components handling 114 job variants with 245 operations using 28 machines. The performance analysis indicates that no single rule outperforms all the objective measures considered in this study. The MCDMs based rules perform better compared to CDRs for large-scale problems by considering realistic criteria such as demand, due date, setup time, process time, customer priority, and number of operations. The methodology proposed in this study can be tailored in terms of the criteria used for ranking and production parameters to be executable for any real-time instance.

A Bilevel Mathematical Approach for the Empty Container Repositioning Proble with a Sharing and Exchanging Strategy in Liner Shipping

In the maritime industry, the management of empty containers is a significant issue whose costs account for a large proportion of the total world fleet running costs. To reduce repositioning activities and increase cost effectiveness, this paper proposes a solution framework considering empty container sharing and exchanging strategies to coordinate the shipping lines in a maritime transportation network. The proposed solution framework considers an empty container matching platform company and a group of shipping lines in a given shipping network. Because of the interactive relationship between the two parties, the proposed problem is formulated as a bilevel programming problem, and a branch and bound-based algorithm that explores the Karush-Kuhn-Tucker (KKT) features is applied. To validate the proposed solution approach, a practical Asia-Europe-Oceania shipping network is used to conduct numerical experiments. The benefits of the proposed solution framework are shown in the results.

Unsaturated soil dynamics: Finite element solution including stress-induced anisotropy

In this paper, we present a novel framework for solving boundary and initial-value problems involving multiphase granular soils under monotonic, cyclic, and dynamic loads. To achieve this, a constitutive model was developed using the concept of loading surface that allows the development of a stress integration scheme with less algorithmic complexity compared to its counterparts developed for the models that involve a yield or bounding surface. The developed model was implemented in a fully coupled framework to tackle several challenging issues in the numerical modelling of granular soils, including induced anisotropy and the response in scenarios including elevated stress levels, cyclic loading, during wetting and drying reversals, and under impact loads. Several numerical examples are presented, providing verification of the approach, and demonstrating the capabilities of the proposed solution framework.