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In railway system construction, maintaining a reasonable cost of the system implementation has always been difficult without a well-defined strategy. This article describes the application of a switching algorithm to accommodate a reduction in cost drivers when performing a preinstallation electromagnetic compatibility measurement to identify interference from external environments along the railway alignment. The verification was exercised using measurement evidence through activity-based costing (ABC) analysis more accurately to reflect the budget against the traditional volume-based costing (VBC) method. The results were demonstrated through the total costs following the ABC and VBC breakdowns of the practical data, as well as the optimum number of test volumes for the opportunity of gaining high profit margins through the break-even analysis. By implementing the switch unit, an additional decrease in operation costs is achievable. The maximum total reduction is recorded at 28.55%, thus providing credibility to the proposed scheme. The outcomes can provide railway industries with financial benefits and are a useful reference for cost-effective on-site activities in dealing with safety impacts from external electromagnetic environments.

Operating under both supply-side and demand-side uncertainties, a mobile-promotion platform conducts advertising campaigns for individual advertisers. Campaigns arrive dynamically over time, which is divided into seasons; each campaign requires the platform to deliver a target number of mobile impressions from a desired set of locations over a desired time interval. The platform fulfills these campaigns by procuring impressions from publishers, who supply advertising space on apps via real-time bidding on ad exchanges. Each location is characterized by its win curve, that is, the relationship between the bid price and the probability of winning an impression at that bid. The win curves at the various locations of interest are initially unknown to the platform, and it learns them on the fly based on the bids it places to win impressions and the realized outcomes. Each acquired impression is allocated to one of the ongoing campaigns. The platform’s objective is to minimize its total cost (the amount spent in procuring impressions and the penalty incurred due to unmet targets of the campaigns) over the time horizon of interest. Our main result is a bidding and allocation policy for this problem. We show that our policy is the best possible (asymptotically tight) for the problem using the notion of regret under a policy, namely the difference between the expected total cost under that policy and the optimal cost for the clairvoyant problem (i.e., one in which the platform has full information about the win curves at all the locations in advance): The lower bound on the regret under any policy is of the order of the square root of the number of seasons, and the regret under our policy matches this lower bound. We demonstrate the performance of our policy through numerical experiments on a test bed of instances whose input parameters are based on our observations at a real-world mobile-promotion platform. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4441 .

Problem Definition: Problem Definition: We develop a framework to plan capacity for ambulatory surgery centers (ASCs) typically consisting of three stages. The problem is to determine the capacity in each stage that efficiently covers possible daily patient demand and to coordinate the three stages of patient visits. The objective is to minimize the total cost incurred in satisfying the daily patient demand, where the total cost is defined as the sum of overtime cost and amortized construction cost for the three stages. Academic/Practical Relevance: Timely capacity adjustment is important for ASC practitioners, but related research is limited. We explicitly consider three sequential stages that are typical in ASCs: the pre-operative at holding room (HR), intra-operative at operating room (OR), and post-operative at post-anesthesia care unit (PACU). Such ASCs can be modeled as a hybrid flow shop (HFS) in the scheduling literature. The interdependence of activities and uncertainties in patient-mix as well as their durations pose a significant challenge to manage the capacity of each activity and achieving a smooth patient flow. Methodology: In contrast to the traditional top-down approach to capacity planning, our approach contributes by proposing a bottom-up strategy based on optimization methods combined with analytics that are informed by operational-level archival patient data. Specifically, we use analytics tools to classify patients into groups, which reduces the complexity of the capacity planning problem and improves the model's practicality. Later, we develop several mathematical formulations and heuristics based on scheduling theory to derive the most cost-efficient capacity solution. Results: Given the trade-off between overtime cost and amortized capacity construction cost, this study relaxes the fixed capacity assumption of traditional HFS problems. Because of the computational complexity of the exact HFS model, we develop a heuristic that is straightforward and easy to implement to find cost-efficient capacities for the three stages. Our computational study examines how uncertain business parameters, e.g., patient-mix, service durations, overnight-stay probabilities, affect the capacity planning decision. Managerial implications: This study highlights the benefit of considering multiple stages together in capacity planning, rather than focusing solely on the operating rooms exclusively. We expect our approach to guide the more than 5,000 ASCs in the U.S., performing 23 million surgeries annually, to make appropriate investments that will improve ASC operations via capacity adjustment and patient scheduling.

In this paper, we first propose a multi-objective task-scheduling optimization problem that minimizes both the makespans and total costs in a fog-cloud environment. Then, we suggest an optimization model based on a Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II) to deal with the discrete multi-objective task-scheduling problem and to automatically allocate tasks that should be executed either on fog or cloud nodes. The NSGA-II algorithm is adapted to discretize crossover and mutation evolutionary operators, rather than using continuous operators that require high computational resources and not able to allocate proper computing nodes. In our model, the communications between the fog and cloud tiers are formulated as a multi-objective function to optimize the execution of tasks. The proposed model allocates computing resources that would effectively run on either the fog or cloud nodes. Moreover, it efficiently organizes the distribution of workloads through various computing resources at the fog. Several experiments are conducted to determine the performance of the proposed model compared with a continuous NSGA-II (CNSGA-II) algorithm and four peer mechanisms. The outcomes demonstrate that the model is capable of achieving dynamic task scheduling with minimizing the total execution times (i.e. makespans) and costs in fog-cloud environments

In this paper, we address the Multi-Component Application Placement Problem (MCAPP) in Mobile Edge Computing (MEC) systems. We formulate this problem as a Mixed Integer Non-Linear Program (MINLP) with the objective of minimizing the total cost of running the applications. In our formulation, we take into account two important and challenging characteristics of MEC systems, the mobility of users and the network capabilities. We analyze the complexity of MCAPP and prove that it is NP-hard, that is, finding the optimal solution in reasonable amount of time is infeasible. We design two algorithms, one based on matching and local search and one based on a greedy approach, and evaluate their performance by conducting an extensive experimental analysis driven by two types of user mobility models, real-life mobility traces and random-walk. The results show that the proposed algorithms obtain near-optimal solutions and require small execution times for reasonably large problem instances.

We model the creation of a new venture with a novel drift-variance diffusion control framework in which the state of the venture is captured by a diffusion process. The entrepreneur creating the venture chooses costly controls, which determine both the drift and the variance of the process. When the process reaches an upper boundary, the venture succeeds and the entrepreneur receives a reward. When the process reaches a lower boundary, the venture fails. The entrepreneur can choose between two different controls and wishes to determine the policy that maximizes the expected total reward minus total cost. We consider two variations of the model: one in which both boundaries are fixed, and one in which only the upper boundary is fixed but the lower is free. We derive closed-form expressions under which the optimal policy will be dynamic versus static and we prove that when the policy is dynamic it switches between the two controls at most once. The results reveal a subtle trade-off between the cost of the two controls, their drift and their variances, in which controls that are more expensive may be utilized more than controls that are less expensive. We also demonstrate that in the fixed boundary case the entrepreneur may wastefully use a more expensive control near the lower boundary to avoid hitting that boundary. This implies that efficient utilization of the two controls cannot happen when the entrepreneur does not have the freedom to choose when to abandon the venture.

We consider a system with a finite lifespan and a single critical component that is subject to random failures. An age-based replacement policy is applied to preventively replace the component before its failure. The components used for replacement come from either a weak population or a strong population, referred to as population heterogeneity. However, the true population type is unknown to the decision maker. By considering that the decision maker has a belief on the probability of having a weak population, we build a partially observable Markov decision process model with the objective of minimizing the total cost over the lifespan of the system. The resulting optimal policy updates the belief variable in a Bayesian fashion by using the data obtained over the course of the system lifespan, and it denotes when to execute preventive replacement. It optimally balances the trade-off between the cost of learning the true population type (via deliberately delaying the preventive replacement time to better learn the population type) and the cost of maintenance activities. By addressing this so-called exploration-exploitation trade-off, we generate insights on the optimal policy and compare its performance with existing heuristic approaches from the literature. We also characterize a lower bound to the optimal cost, allowing us to determine the value of resolving the uncertainty on the population type.

Motivated by the remanufacturing systems at Daimler AG and other automobile, aerospace and heavy machinery parts dealers/manufacturers, we develop a hybrid manufacturing/remanufactu-ring system with controlled two-way substitution between new and remanufactured products. We develop Markov decision processes for management on product-level as well as on component-level. We show that the optimal policy on product-level has the switching-curve structure under the assumption that the lost sales costs for new and remanufactured products are identical. Based on this structural result, we develop an efficient, close-to-optimal heuristic for the general product-level model and for the component-level model. Our numerical experiments indicate that two-way substitution on component level significantly reduces total cost. We also extend our model to strategic cost that includes environmental aspects in the objective function. For this model, we find that substitution appears to be a phenomenon primarily beneficial for profit-oriented firms. Sustainability-oriented firms require significantly less frequent product substitution.

Active electricity distribution systems (AEDS) play an important role on the social welfare that is highly relies on the reliability of these systems. In this regard, maintenance management tries to decrease component failures via repairing component that are more possible to be failed in operation time. In this manuscript, a stochastic MINLP maintenance scheduling model is proposed in which the reliability of AEDS's equipment is considered through employing a three-state Markov model. This model helps us to embed the uncertainty of AEDS's equipment service restoration time by considering online monitoring system for it and FLISR functions in AEDS. In this paper, minimizing the total cost of AEDS is determined as objective function considering total customer interruption cost, total cost of energy not supplied, total cost of prevented maintenance actions, total corrective maintenance actions cost implemented in the AEDS and total cost of implementing online monitoring for AEDS's component in maintenance scheduling. The proposed framework is applied in two different cases, Case I is maintenance scheduling without online monitoring system and Case II is maintenance scheduling with online monitoring system. The aforementioned cases are implemented in an IEEE Test System as well as a real scale distribution network. Moreover, several sensitivity analyses are implemented for indicating the efficiency of the proposed model.

Structural balance in signed social networks has become a research hotspot for the task of finding the optimal network structure, which is able to make social systems reach a relatively stable and harmonious state. Usually, such a structural balance problem can be modeled as an optimization problem. The typical way of existing optimization methods for structural balance is to only minimize the total cost of changing the unbalanced edges in the whole network, but it neglects the load balance of changing edges between different nodes in signed social network, which may not fit the nature of the social systems. In this paper, we propose a novel structural balance model, which incorporates the total cost information of the changed edges in the whole network and the balance information of the changed edges of each single node. To optimize the novel structural balance model, we propose a Judgment-Rule-based Evolutionary Algorithm, called JREA, based on two significant essential features of the structural balance problem, i.e., the determination of node attribute depending on node’s degree and the attributes of its neighbour nodes. Extensive experiments are conducted on the four generated signed social networks and the three real signed social networks. Experimental results demonstrate the effectiveness and efficiency of the algorithm.

This paper examines an inventory system of a retailer who uses a continuous-review base-stock policy to manage inventory. We consider two types of demands: Markov-modulated fluid demands with linear rates and batch-size demands that arrive according to a Markov arrival process (MAP). In addition, we consider two types of supply: (i) a regular supply that follows an ( S , s ) policy with stochastic lead times under a lost-sales assumption and (ii) an additional emergency supply that brings the inventory up to level 0 S e ≤ S when stockout. We assume that such an emergency supply has a negligible lead time but incurs an extra cost. Hence, we analyze four models that are each adequate for a different demand-supply pattern. Our cost structure includes variable and fixed costs for each type of order, together with holding and lost-sales costs. By integrating a multidimensional martingale technique and hitting-time transforms for fluid models and MAPs, we derive explicit formulas for the cost components that aim to minimize the expected discounted total cost. A numerical study provides insights into the best parameters and how they depend on different factors. For example, we show that the MAP-type arrivals yield higher control values; increasing the lead time (average and variance) and the lost cost increases these values, and eventually leads to the ( S , s = S − 1 ) policy. We further show that increasing the discount factor decreases s and the fluid models are less sensitive to the timing. By comparing the models, we provide managers with a framework for assessing when dual sourcing is more beneficial than single sourcing.

This work is geared toward a real-world manufacturing planning (MP) task, whose two objectives are to maximize the order fulfillment rate and minimize the total cost. More important, the requirements and constraints in real manufacturing make the MP task very challenging in several aspects. For example, the MP needs to cover many production components of multiple plants over a 30-day horizon, which means that it involves a large number of decision variables. Furthermore, the MP task's two objectives have extremely different magnitudes, and some constraints are difficult to handle. Facing these uncompromising practical requirements, we introduce an interactive multiobjective optimization-based MP system in this article. It can help the decision maker reach a satisfactory tradeoff between the two objectives without consuming massive calculations. In the MP system, the submitted MP task is modeled as a multiobjective integer programming (MOIP) problem. Then, the MOIP problem is addressed via a two-stage multiobjective optimization algorithm (TSMOA). To alleviate the heavy calculation burden, TSMOA transforms the optimization of the MOIP problem into the optimization of a series of single-objective problems (SOPs). Meanwhile, a new SOP solving strategy is used in the MP system to further reduce the computational cost. It utilizes two sequential easier SOPs as the approximator of the original complex SOP for optimization. As part of the MP system, TSMOA and the SOP solving strategy are demonstrated to be efficient in real-world MP applications. In addition, the effectiveness of TSMOA is also validated on benchmark problems. The results indicate that TSMOA as well as the MP system are promising.

We obtain the optimal execution strategy for two sequential trades in the presence of a transient price impact. We first present a novel and general solution method for the case of a single trade (a metaorder) that is executed as a sequence of sub-trades (child orders). We then analyze the case of two sequential metaorders, including the case where the size and direction of the second metaorder are uncertain at the time the first metaorder is initiated. We obtain the optimal execution strategy under two different cost functions. First, we minimize the total cost when each metaorder is benchmarked to the price at its initiation, the total separate costs approach widely used by practitioners. Although simple, we show that optimizing total separate costs can lead to a significant understatement of the real costs of trading whilst also adversely impacting order scheduling. We overcome these issues by introducing a new cost function that splits the second metaorder into two parts, one that is predictable when the first metaorder is initiated and a residual that is not. The predictable and residual parts of the second metaorder are benchmarked using the initiation prices of the first and second metaorders, respectively. We prove existence of an optimal execution trajectory for linear instantaneous price impact and positive definite decay, and derive the explicit form of the minimizer in the special case of exponentially decaying impact. Various numerical examples are included for illustration, each of which admits a unique optimal solution, however uniqueness in general remains unproven.

In the multi-access edge computing environment, app vendors deploy their services and applications at the network edges, and edge users offload their computation tasks to edge servers. We study the user-perceived delay-aware service placement and user-allocation problem in edge environment. We model the MEC-enabled network, where the user-perceived delay consists of computing delay and transmission delay. The total cost in the offloading system is defined as the sum of service placement, edge server usage and energy consumption cost, and we need to minimize the total cost by determining the overall service-placing decision and user-allocation decision, while guaranteeing that the user-perceived delay requirement of each user is fulfilled. Our considered problem is formulated as a Mixed Integer Linear Programming problem, and we prove its NP-hardness. Due to the intractability of the considered problem, we propose a LOCal-search based algorithm for USer-perceived delay-aware service placement and user-allocation in edge environment, named LOCUS, which starts with a feasible solution and then repeatedly reduces the total cost by performing local-search steps. After that, we analyze the time complexity of LOCUS and prove that it achieves provable guaranteed performance. Finally, we compare LOCUS with other existing methods and show its good performance through experiments.

Many real applications use the transportation problems for finding the optimal solution for product shipment from supply to demand. The transportation problem (TP) is related to delivering some products from the supply to the demand. The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. However, the existing methods of IBFS do not always provide a good feasible solution which can reduce the number of iterations to find the optimal solution. Therefore, it is still challenging to develop a better method of IBFS. In this paper, a new method to obtain the IBFS of transportation problem called Bilqis Chastine Erma method (BCE) was proposed. The thirty-five numerical examples were used to evaluate the performance of the proposed method. It was compared to other IBFS methods, namely: Vogel’s Approximation Method (VAM), Total Differences Method 1 (TDM1), Total opportunity cost matrix – Minimal total (TOCM-MT), and Juman & Hoque Method (JHM). The experiment results indicate that BCE obtained lower total minimal cost than VAM, TDM1, TOCM-MT, and JHM and reached the fastest solving time. It reached its optimal solution for thirty-one of thirty-five numerical examples (88.57%).

This paper develops a model for a cold chain operated among a single product manufacturer and multiple retailers to minimize the total cost of the chain. A distinguishing feature of the cold chain is that the quality of the product degrades with time. The storage temperature and time influence the degradation rate. In this context, quality can be measured using multiple distinct indices. A global stability index is used to quantify the overall quality. The non-Arrhenius model is exclusively applied to measure the quality degradation during the transportation and at a retailer's storage as the product moves in the cold chain. Optimal replenishment times for the manufacturer and retailers are decision variables and the cold chain's total cost includes the cooling cost, carbon emission cost, and loss of value in addition to the traditional supply chain costs. It is found that for a given manufacturer's replenishment time, the total cost is convex on each retailer's replenishment time. Hence, an algorithm is developed based on this property to find the optimal replenishment times for the manufacturer and retailers. This paper also investigates how specific cold chain parameters influence optimal decisions and the total cost. The analyses show that the resulting costs will be significantly higher if quality degradation is ignored in a cold chain.

We consider a variant of the vehicle routing problem (VRP) where each customer has a unit demand and the goal is to minimize the total cost of routing a fleet of capacitated vehicles from one or multiple depots to visit all customers. We propose two parallel algorithms to efficiently solve the column-generation-based linear-programming relaxation for this VRP. Specifically, we focus on algorithms for the “pricing problem,” which corresponds to the resource-constrained elementary shortest path problem. The first algorithm extends the pulse algorithm for which we derive a new bounding scheme on the maximum load of any route. The second algorithm is based on random coloring from parameterized complexity which can be also combined with other techniques in the literature for improving VRPs, including cutting planes and column enumeration. We conduct numerical studies using VRP benchmarks (with 50–957 nodes) and instances of a medical home care delivery problem using census data in Wayne County, Michigan. Using parallel computing, both pulse and random coloring can significantly improve column generation for solving the linear programming relaxations and we can obtain heuristic integer solutions with small optimality gaps. Combining random coloring with column enumeration, we can obtain improved integer solutions having less than 2% optimality gaps for most VRP benchmark instances and less than 1% optimality gaps for the medical home care delivery instances, both under a 30-minute computational time limit. The use of cutting planes (e.g., robust cuts) can further reduce optimality gaps on some hard instances, without much increase in the run time. Summary of Contribution: The vehicle routing problem (VRP) is a fundamental combinatorial problem, and its variants have been studied extensively in the literature of operations research and computer science. In this paper, we consider general-purpose algorithms for solving VRPs, including the column-generation approach for the linear programming relaxations of the integer programs of VRPs and the column-enumeration approach for seeking improved integer solutions. We revise the pulse algorithm and also propose a random-coloring algorithm that can be used for solving the elementary shortest path problem that formulates the pricing problem in the column-generation approach. We show that the parallel implementation of both algorithms can significantly improve the performance of column generation and the random coloring algorithm can improve the solution time and quality of the VRP integer solutions produced by the column-enumeration approach. We focus on algorithmic design for VRPs and conduct extensive computational tests to demonstrate the performance of various approaches.

This paper proposes an optimization model for the joint planning of sectionalizing switches (SSs), including remote-controlled switches (RCSs) and manual switches (MSs), and tie lines among distribution feeders. Considering a flexible installation of SSs and tie lines (i.e., deployment of SSs on both sides of a line and construction of external and internal tie lines on of a feeder), this model is conducive to developing an economic investment scheme while preserving the expected energy not supplied (EENS) reliability criterion. First, the minimum total cost of investment, maintenance and penalty for EENS is chosen as the objective function. Next, a route analysis method is introduced to analyze particular performances of different deployment schemes for RCSs, MSs, external tie lines and internal tie lines. Then, the joint planning problem is formulated as a mixed-integer linear programming (MILP) model with the aid of algebraic operations to calculate the global optimal solution. The effectiveness of the proposed model is verified for several cases in a 54-node distribution system. Numerical results indicate that the proposed planning solution merits higher economy and lower EENS as compared to the schemes offered by other prevalent methods

We consider a generalization of the Minimum Cost Spanning Tree (MCST) model, called the Minimum Cost Connection Network (MCCN) model, where network users have connection demands in the form of a pair of nodes they want connected directly or indirectly. For a fixed network, which satisfies all connection demands, the problem consists of allocating the total cost of the network among its users. Thereby every MCCN problem induces a cooperative cost game where the cost of every coalition of users is the cost of an efficient network satisfying the demand of the users in the coalition. Unlike the MCST-model, we show that the core of the induced cost game in the MCCN-model can be empty even when all locations are demanded. We therefore consider sufficient conditions for non-empty core. It is shown that: when the efficient network and the demand graph (i.e. the graph consisting of the direct connections between the pairs of demanded nodes) consist of the same components, the induced cost game has non-empty core (Theorem 1); and, when the demand graph consists of at most two components, the induced cost game has non-empty core (Theorem 2).

We study a novel chemical tanker port call optimization framework to improve the efficiency of waterway traffic control, in which chemical tankers load and unload cargoes while visiting multiple terminals. We formulate this problem as a mixed-integer program to minimize the total cost of vessel operations while compressing the schedule of cargo operations. The port call optimization framework integrates decentralized scheduling efforts by different stakeholders to minimize the traffic congestion satisfying policy, operational, and technical requirements, while embracing the current competitive scheduling discipline. Solving real-world problems can be computationally challenging. Hence we propose efficient solution techniques to promote fast convergence to optimum solutions. As we compare the vessel operational schedule from the proposed port call optimization model with monthly transit data in the past, terminal utilization is improved by more than 250% and the number of individual tanker’s transit is reduced by 28%. Suggested computational techniques ensured the solving of real-world problems in approximately 10 minutes.

This paper proposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard and realistic networks considering different objective functions (OFs). The proposed IBBA has two modifications to enhance the solution quality based on multi V-shaped transfer function and adaptive search space (ASS). The IBBA is applied to solve the static TNEP problem. A two-stage procedure is employed to solve the dynamic TNEP problem. In stage-1, the adaptive neuro-fuzzy inference system (ANFIS) is utilized to find the long-term load forecasting (LTLF) up to 2039. In stage-2, the IBBA is used to solve the dynamic TNEP problem. Two OFs are considered for solving TNEP problems. The first OF achieves the investment cost reduction. The second OF aims to minimize the total costs, which include the investment cost and the total costs of energy losses and reactive power compensation (RPC). The proposed procedure is applied to Garver’s 6-bus system and the West Delta Network (WDN) as a part of the Unified Egyptian Transmission Network (UETN) to solve TNEP problems. The obtained results are compared with other methods to show the robustness of the proposed procedure for solving TNEP problems.

At present, city logistics mostly adopts a two-echelon dispatching model which combines distribution centers located in suburbs and fixed satellites located in urban areas for distribution. However, both expensive rental fees and daily changes of customer demand in metropolitan areas make dispatching route generated by fixed satellites inefficient. Moreover, the existing mobile depot model needs a large investment for facilities. In this paper, we propose a two-echelon city dispatching model with mobile satellites (2ECD-MS) which locations of mobile satellites change according to demands of customers to ensure the efficiency of delivery routes in every day. A cluster-based variable neighborhood search scheduling algorithm is proposed to determine locations of mobile satellites and dispatching routes of trucks and tricycles. Then, the 2ECD-MS is extended to 2ECD-MS-TDD to allow trucks dispatching directly (TDD) for further cost reduction. Experimental results show that the 2ECD-MS significantly reduces the total cost against the model using fixed satellites mode by 3.5% while the 2ECD-MS-TDD further reduces the total cost against the 2ECD-MS significantly by 3.25% in 54 cases with different customer scales, geographical scopes, and distribution types. These show the superiority of the proposed methods in cost reduction for city logistics in comparison to the traditional fixed model.

This paper proposes a reliable hypercube queuing problem where servers (e.g., vehicles) can be dispatched either individually or jointly as cooperative units, and these servers are subjected to probabilistic disruptions. The probability of a server disruption depends on the amount of resources (e.g., firefighter staffing number) allocated to this server. If any server in a cooperation unit is disrupted, a less preferred (e.g., based on capabilities and source station) but functioning and available unit will be dispatched instead. This paper derives formulas for steady-state system performance metrics for this new hypercube queuing system. The queuing model is embedded into a mixed-integer non-linear program to optimize the allocation of resources so as to minimize the total costs under server disruptions. An empirical case study is used to compare the system performance measures under a range of implementation scenarios, and sensitivity analyses are conducted to reveal insights.

International audience; Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.

Reductions in defense expenditure require holistic and coordinated planning of two critical and interconnected defense capabilities, namely a fleet of assets and the workforce required. In this paper, we model and solve a joint problem of strategic workforce planning and fleet renewal in a military context. The joint problem studied involves addressing a trade-off among several costs (e.g., workforce, maintenance, operating, etc.) and operational readiness (availability) of the fleet. To make such trade-offs, the decisions associated with workforce planning (i.e., the recruitment and the career progression of the workforce) and fleet renewal (i.e., the timing of asset replacements) strategies have to be simultaneously considered and optimized. We develop a simulation-optimization approach by coupling a system dynamics (SD) simulation model and a genetic algorithm (GA) to solve the joint problem. In the developed approach, the GA generates candidate workforce planning and fleet renewal strategies to find the best joint strategy. Then, the candidate workforce planning and renewal strategies are passed to the SD model which simulates both the career progression of the workforce and the life-cycle of assets to evaluate the total cost. We illustrate the applicability and effectiveness of the joint model on a realistic case study motivated by the recent modernization efforts of the Royal Australian Navy. The results obtained indicate that this approach leads to a considerable cost reduction and identifies the causes of inferior performance. We also test the robustness of the optimized strategies under uncertainty by sensitivity and scenario discovery analyses to infer further insights.

Recently, the closed-loop supply chain (CLSC) and its application to various fields have been an area of great interest. Despite the importance of CLSC, there remains a paucity of evidence on agriculture in this area. In this work, a CLSC network for the avocado industry is firstly designed by developing a bi-objective model considering the costs of the avocado industry and the social factor of job employment opportunities. The two objectives are the total costs minimization and job employment maximization in various opened locations. To validate the proposed model, a real case study in Puebla, Mexico, is addressed. The GAMS software and its CPLEX solver are utilized to find the best optimum solutions and determine the best locations to open different centers. The applicability of the proposed network is verified by conducting several sensitivity analyses on the important parameters of the problem. According to the obtained results, demand has the most effect on this network in which that a 25 percent decrease in demand can increase the total cost (the first objective) up to 40 percent and improve employment efficiency (the second objective) up to around 30 percent, simultaneously.

The hydrocarbon supply chain (HCSC) is a significant part of the world's energy sector. The energy market has experienced erratic behavior over the last few years results in financial risks such as exceeding certain limits of the budget or not achieving the desired levels of cash in-flow, i.e., revenue. In this work, robust optimization and multi-objective mathematical programming are used to develop a model that eliminates or at least mitigates the impact of uncertain market behavior. Robust optimization provides tactical plans that are feasible and robust over market scenarios. The model assesses the trade-offs between alternatives and guides the decision-maker towards the effective management of the HCSC. The economic objectives are to minimize total cost and maximize revenue, while the non-economic objective is to minimize the depletion rate. The model considers the environmental aspect by limiting the emission of CO2 and the sustainability aspect by reducing the depletion rate of natural resources. Uncertain behavior of the oil market is modeled on scenario representation. A case study based on real data from Saudi Arabia HCSC is provided to demonstrate the model's practicality, and a sensitivity analysis is conducted to provide some managerial insights. The results indicate that Saudi Arabia can cover its entire expenditure, break-even-point, by producing oil at 7.18 MMbbld and gas at 3,543.48 MMcftd. Besides, the robust approach provides a preferred plan with the highest cash inflow and the lowest sustainability over other approaches, e.g., deterministic, stochastic, and risk-based. The differences show that the robust model increases oil production to compensate for the variability of the scenario.

Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously. An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained (total cost can be reduced at least 2%).

In the current socio-economic situation, the daily demand for essential goods in the business sector is always changing owing to various unavoidable reasons. As a result, choosing the right method for profitable business has become quite tricky. This study introduces different business strategies based on constant and fuzzy demands. There are two types of constraints considered in this model to avoid the backorder cost. However, combining the service-level constraints with the constant and fuzzy demand, this study compares the total costs, and finally, the best strategy is established. Moreover, investing a small amount, this model improves the quality of the products and reduces the vendor’s setup cost. Depending on the number of transported products, this model follows the transportation discount policy for hassle-free delivery of the products with a minimum delivery rate. The Kuhn-Tucker optimization technique is employed, and global optimality is verified numerically, analytically using the Hessian matrix. This model’s robustness is discussed through a comparative study, numerical examples, sensitivity analysis, graphical representation, and managerial insights. Finally, some concluding remarks along with future extensions are discussed.

An algorithm for optimum design of nonlinear viscous dampers with the goal of achieving minimum total cost is presented. An appropriate cost model is developed to determine the initial cost of equipping structures with viscous dampers, and the expected costs of the studied structures related to the possible earthquakes over their life time are estimated using life cycle cost analysis (LCCA). The results of this analysis are used in a multi-objective optimization algorithm with the aim of achieving minimum initial and life cycle cost. To evaluate the seismic behavior of structures, the Endurance Time (ET) method is used as a dynamic response-history analysis method which requires much less computational effort in comparison with conventional time history methods. To this end, the optimum arrangement of nonlinear viscous dampers along with their damping exponent (Alpha) for two weak steel moment frames with 4 and 8 stories are acquired using multi-objective genetic algorithm. Results from the proposed method are compared with two classic methods; uniform and stiffness proportional damping methods. With the aim of proposing an optimal Alpha value, a detailed discussion is provided on its effects on the overall performance of studied structures in terms of life cycle cost.

The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. 2-s2.0-85119326153 WOS:000720710800001 Q1

This paper, for the first time, studied a new extension of the flexible job shop scheduling problem by assuming the acceptance and rejection of machines and orders. The flexible job shop problem was extended to implement production without a factory in natural environments. Therefore, the mixed-integer linear programming (MILP) model was developed for this problem aiming to minimize total costs, including the fixed cost of machine selection, variable operational cost, transportation cost, and order rejection cost. Due to the high complexity of this problem, a heuristic algorithm was employed to find an acceptable solution. For algorithm performance evaluation, 40 samples were randomly generated and solved using the mathematical model and the proposed algorithm. The results of analyzing random samples showed a negligible error rate indicating algorithm efficiency.

This study addresses a humanitarian supply chain network that includes locating relief centers, pre-positioning and distributing relief items, and providing medical treatment in affected regions to help people suffering. Also, the role of non-governmental organizations along with governmental organizations and disruption in relief centers is examined. The objective of the model is to minimize total costs. The problem is formulated as a mixed-integer linear mathematical model. A two-stage mixed fuzzy-stochastic approach is developed to capture the uncertainty. To solve the model on a large scale, grasshopper optimization algorithm is utilized, and its performance is examined through computational experiments. To assess the applicability of the proposed model, it is applied to Tehran as a real case study. According to the numerical examples and sensitivity analysis, fruitful managerial insights are provided to help decision-makers increase relief operations' efficiency. The results indicate that non-governmental organizations' contribution significantly enhances the performance of the humanitarian supply chain while disruption has an adverse impact.

Stream processing is a new memory computing paradigm that deals with dynamic data streams efficiently. Storm is one of the stream processing frameworks, but the default stream processing scheduler of storm also has some problems. For example, it does not consider reducing the cost in the cloud environment while ensuring the performance requirements. In this paper, a cost-efficient scheduling algorithm for Storm framework (CE-Storm) is proposed to reduce cost while satisfying deadline constrain. First, a new cost-efficient model (including resources usage cost, energy cost and communication cost) based on Storm framework is built. Then, based on the cost model, a cost-efficient scheduling algorithm which integrated resource monitoring module and communication detection module is designed. The nodes in the cluster are prioritized according to the cost-efficient information, and the nodes with higher priority are assigned tasks first to minimize the total cost of the cluster. Furthermore, this algorithm also reduces the communication cost between nodes and improves the cost effectiveness of the Storm cluster. We have performed extensive experiments on Storm clusters using Hibench’s workloads in cloud environment. The result shows that the cost consumption of Storm clusters in cloud environment is reduced by 19.25% on average compared with the traditional scheduling algorithm. In others words, the proposed algorithms effectively improve the cost efficiency of Storm cluster in the cloud environment while satisfying the performance constrains.

With the large-scale grid connection of wind and photovoltaic power, the contradiction between renewable energy and thermal power is becoming more and more serious. To solve this problem, this paper comprehensively considers the construction and operation costs of power plants, and constructs an optimal generation expansion planning model of a combined thermal–wind–photovoltaic​ power system with the objective of minimizing total cost. The planning is calculated under the boundary conditions of ensuring the safe operation of the power grid and taking into account the share requirement, utilization requirements and construction requirements for renewable energy. The model is applied to the self-use and supporting power plants in Xinjiang. The results show that the proposed generation expansion plan can realize the coordinated development of three types of power sources under multiple boundary conditions, which has good applicability.

We consider a power system with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve their customers. All the entities in the system are foresighted : each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g., energy generation costs and the aggregators’ costs) by dispatching the energy production among the generators. The decision making of the foresighted entities is complicated for two reasons. First, the information is decentralized among the entities, namely each entity does not know the others’ states. Second, an aggregator’s current decision affects its future costs due to the coupling introduced by the energy storage. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its locally-available information. We prove that the proposed framework can achieve the social optimum despite being decentralized and involving complex coupling. Simulation results show that the proposed foresighted demand side management achieves significant reduction in the total cost, compared to the optimal myopic demand side management (up to 60% reduction), and the foresighted demand side management based on the Lyapunov optimization framework (up to 30% reduction).

Arranging an optimal distribution system is a challenging step in every company who runs logistics activities because it is one of the crucial factors to achieve high customers’ satisfaction level. This problem becomes more complex during the high or special seasons such as Christmas, new year, etc. In Indonesia, high season for logistics usually during the Eid season. During that special season, the demand increases while the resource decreases due to long national holiday. Failed to find an optimal distribution system might lead to a huge loss for the company. Therefore, to overcome this problem, logistics company should optimize the distribution system including optimizing the route and vehicles utilization. This paper aims to help a logistics company based in Bali, Indonesia, to find an optimal distribution route during the Eid Season. This company find difficulty to fulfil the customer demands especially during the Eid Season. To find an optimal route for the company, this paper applies Saving Matrix and Nearest Insert methods. These two algorithms are chosen because both of them have a simple procedure. Thus, it can save the computational time. Besides, a simple procedure will help the company to reuse the proposed methods for the next application. Data collection and observation in this paper were carried out during one week of Eid season in 2019. The computational results show that the optimal route is able to deliver all items before the due date with total distribution time 7 hours 16 minutes. Compare with the existing route, the proposed route reduces the total cost by 30%.

This paper focuses on designing multi-objective biomass supply chain planning models that aim to simultaneously minimize the total cost and the carbon footprint from the transportation. Stochastic and fuzzy models were developed for making strategic (optimal plant locations) and tactical decisions (material flows, truck types, etc.), while capturing the uncertainty of the demand. An epsilon-constraint method was applied to generate optimal solutions from these models. Managerial insights are provided based on a practical case study at a biomass plant in the Lower Northern region of Thailand; nine biomass plant candidates, nine rice husks suppliers and eight trucks were considered in the case study. A sensitivity analysis has been conducted to compare the results from the two models. The stochastic model can take into account all the scenarios of demand, while the fuzzy model can handle only a certain level of demand defined by a centroid value. The stochastic model needs to take into account a large number of variables and constraints, and therefore, requires more runtime to define an optimal solution, as compared to the fuzzy model. The trade-off between the operating cost and carbon emissions from both the models are provided with managerial insights.

We study the joint scheduling of deferrable demands (e.g., the charging of electric vehicles) and storage systems in the presence of random supply, demand arrivals, processing costs, and subject to processing rate limit constraint. We formulate the scheduling problem as a dynamic program so as to minimize the expected total cost, the sum of processing costs, and the noncompletion penalty (incurred when a task is not fully processed by its deadline). Under mild assumptions, we characterize an optimal index-based priority rule: Tasks with less laxity should be processed first, and for two tasks with the same laxity, the task with a later deadline has the priority. Based on the established optimal control policy characterizations (on resource allocation among multitasks and storage operation), we propose to apply data-driven reinforcement learning (RL) methods to make energy procurement decisions. Numerical results show that the proposed approach significantly outperforms existing RL methods combined with the earliest deadline first priority rule (by reducing 26%–32% of system cost).

This paper introduces a new charging service mode that is emerging in the market for electric vehicles called the valet charging service. Through this service mode, a company provides free valet charging services to users that are far away from charging stations, and users can request the fee-based service when they are unwilling to charge electric vehicles themselves. The valet charging service is a new service mode that promises to alleviate users’ worries regarding the lack of nearby charging stations. However, whether such a mode truly benefits a specified system remains open to careful study. We build the charging station locating and sizing model with the valet charging service under demand uncertainty. We first formulate this problem as a two-stage stochastic mixed-integer model (TSMIP). Then, considering that the demand probability distribution is difficult to estimate, we reformulate TSMIP into a risk-averse two-stage stochastic mixed-integer model (RTSMIP) with the ambiguity set. Next, we propose an SAA-based hybrid-cut L-shaped algorithm to solve this model. By comparing the total cost of the system with and without the valet charging service under different scenarios through hypothetical numerical experiments and a realistic case study in Shanghai, meaningful insights are provided that include identifying the conditions in which the valet charging service works well and how it influences the location and capacity of charging stations. These insights provide some understanding of this new service mode that can help companies make rational decisions.

Energy is one of the most important social and economic infrastructures and human life is strongly depends on it. In recent years, the occurrence of natural disasters such as drought as a high-impact low-probability event is undeniable. So, energy planning in the disruption situation is necessary in order to anticipate and maintain the performance of the energy system in optimal condition. This paper focuses on developing a hybrid long-term framework for evaluating the energy system resilience based on sustainability concept against drought. The model bridges the gap between long-term energy system planning in macro level and short-term switching operations or micro level modeling​ approach in response to drought resulted by climate change. The new resilience indexes are used to maximize the social welfare, to minimize the energy needed but not supplied (ENNS), to recover the system performance rapidly and minimum the total cost of the energy system after occurring drought. When disaster happens, the modeling of energy systems will do through two stages because the behavior of the system should be considered, compare and analyzed before and after disruption events. On the other hand, the system uncertainties, possible futures and different strategies can change the optimal path of the energy needed supply, which is examined by scenario analysis in the proposed model. So, the developed model is a two-stage optimization model with linear programming approach and scenario analyzing model with a combination of back-casting and forward future approaches Numerical model is performed on UTOPIA energy system and its results demonstrate the effectiveness of the proposed method.

Global warming has been a critical issue in recent years. Many leaders and politicians have talked about replacing fossil fuels with Renewable Energy Resources (RES). Some of them even went further and are talking about running a country with 100% RES soon. Is that economically viable? What would be the estimated cost of such a system? In this paper, a city in west Texas with a 100 MW peak load has been assumed to operate with the wind, solar, and Battery Energy Storage System (BESS) totally disconnected from the grid for a cost evaluation. Real generation and load time series data are used to achieve an optimum combination of the installed capacity of those resources by minimizing the total overnight cost, respecting a maximum of 24 h of faults in which the system cannot provide the load during the period. A Monte Carlo simulation is applied to the previous results to evaluate the influence of faulty devices and increase the robustness of the system. The results for both cases are analyzed and compared. Finally, the total cost of install capacity will be compared with other non-renewable resources.

Patient unpunctuality causes perturbations in healthcare operations, compromising productivity and service quality. In this paper, we propose an approach that mitigates the negative impacts of unpunctuality using both appointment scheduling and real-time sequencing taking into account patient unpunctuality, no-shows, random service durations, and multiple providers. The objective is to minimize the total cost incurred by patient waiting and provider overtime. An optimal real-time sequencing strategy is established to serve the waiting patient with the smallest “LAR” index, which is defined as the Larger of Appointment time and Real arrival time for a patient. The optimal appointment schedule is determined by a simulation optimization approach with unbiased gradient estimators. Sample path discontinuities are smoothed by smoothed perturbation analysis. Properties of the optimal real-time sequencing strategies are used for the efficient sample path gradient estimation. Extensive experiments demonstrate the effectiveness of the proposed algorithm. Using real data, numerical experiments illustrate that the optimal appointment schedule depends on the system parameters and differs significantly from those of the existing literature. Specifically, the pattern of the appointment schedule is determined by the number of providers and the real-time sequencing strategy. The length of the appointment intervals is sensitive to the degree of unpunctuality and no-shows. Compared with the schedules in the previous studies, our schedule can achieve a significant cost reduction. Further, the optimal real-time sequencing strategy outperforms the commonly-used strategies in practice (e.g., appointment order, arrival order). Managerial insights are also provided for hospital managers to schedule unpunctual patients.

Management strategies of complex energy systems composed by different technologies is mandatory to exploit optimally the characteristics of each power generator, to reduce the cost of energy, the impact of greenhouse gases emissions and to increase the penetration of mini- and micro-grids into energy systems. To this purpose, optimization methods and algorithms have to be developed to assess the unit commitment of generators and to suggest decision variables in the definition of the emission costs. In this paper, a novel Mixed Integer Linear Programming (MILP) optimization algorithm has been developed to compute the optimal management of a micro-energy grid composed either by four Internal Combustion Generators (ICGs), or three ICGs and a Micro Gas Turbine (MGT). The algorithm optimizes a multi objective function that takes in consideration the total cost, the NO x and the CO2 emissions of the system, while setting some technological constraints, like start-ups and transients that are typically neglected. Moreover, different fuelling of the devices is evaluated. The model proved the importance of including an accurate model of the greenhouse gases emissions as they can significantly affect the optimization results. Furthermore, it proved to be very flexible and to be a proper basis to be adopted in more complex systems embedding energy storage devices and renewable energy systems.

Lack of electricity networks in remote areas and also the high cost of connecting these areas to national networks due to the unfavorable geographical situation of the region leads to the use of other energy sources independent of the network in these areas. Today, the use of distributed generation sources is increasing due to the reduction of fossil fuel sources and heat problems. The output power of distributed generation sources, especially renewable energy sources, fluctuates under the influence of atmospheric conditions. The main purpose of the present study is to present a new optimal configuration for a hybrid photovoltaic/diesel generator/battery system to refine the load demand of a rural area in Hoxtolgay, China. The idea is to minimize the CO2 emissions value, the annualized cost, and the loss of load probability of the system. To simplify this system, e -constraint method is performed. The problem has been solved by an adaptive version of Marine Predators Algorithm (AMPA) to improve the optimization results. The total ACS for the optimal system is achieved 8224.15 $. Also, an optimal value of 451.30 kW for diesel generator, 4266$ for battery storage, and 2670.53 $ for PV generator are achieved. The initial capital cost of the system is achieved 47940 $ that is less than the NPC. In this case, the maximum cost of the system with 43.12% belongs to the PV system of total cost of the system. Finally, CO2 emission by the suggested AMPA with 1624 kg/year shows the minimum value with cleanest result among the others. Final results of the proposed technique are compared with PSO method and HOMER-based optimization to indicate its effectiveness. Sensitivity analysis has been also performed to determine the system efficiency.

Purpose Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon. Design/methodology/approach The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems. Findings The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs. Originality/value On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

The closed-loop supply chain model with an assumption of perfect produced items is used commonly in the literature. We consider the imperfect quality production to provide meaningful solutions to practical problems. In this novel model, we assume that the screening is not always perfect, and inspection errors are more likely to take place in practice. The model considers multiple periods, echelons, and uncertainties. Moreover, the proposed robust multi-objective mixed integer linear programming model considers the optimization of three objectives simultaneously. The first objective function is optimized to minimize the total cost of the supply chain. The second objective function seeks to minimize the environmental influence, and the third objective function is optimized to maximize the social benefits. The augmented weighted Tchebycheff method is used to aggregate the objective functions into one objective and produce the set of efficient solutions. Robust optimization, based on the extended Mulvey et al. (1995) approach, is used to obtain a set of solutions that are robust against the future fluctuation of parameters. Finally, numerical examples have been presented to test and analyze the tradeoff between solution robustness and model robustness.

In this paper, a decision-making framework for the design of redistributed manufacturing (RdM) networks is developed. Redistributed manufacturing, a manufacturing paradigm greatly empowered by the Industry 4.0 toolset, is the shift in production towards geographically dispersed interconnected facilities. The framework is context independent, accounts for the collective impact of all decision-making levels on one another in an iterative manner, and incorporates uncertainty. The framework has been applied to a case study in the aerospace spare parts production sector. Results indicated that the RdM paradigm demonstrated considerable improvements in service level when compared with a traditional centralized counterpart, while it was not as competitive with regards to total cost. This paper contributes to the literature on model-based distributed manufacturing systems design under uncertainty, and enables informed decision-making regarding the redistribution of resources and decentralization of decision-making. The novelty of this paper is the approach employed to handle complexity, nonlinear interrelationships and uncertainty, within the domain of RdM network design. These computationally demanding attributes are handled through simulation, and only their impact is passed back to an analytical model that generates the RdM network.

A district heating network has generally more than one production unit, and then there are different ways to fulfill the energy demand of the network. This paper deals with the planning problem which is a problem of optimization under constraints. The production units are modeled using MINLP (mixed integer non-linear programming), in particular the following technical aspects are considered: storage capacity (for municipal wastes and biomass), allowable operating range, allowable variation of power over time, partial load performances, minimum working time when a unit runs, and minimum waiting time when it is in stand-by mode. The environmental constraint is the annual share of renewable energies in the global energy mix (for example, at least 50% over a year). The model aims to minimize the operating cost of the network. The problem cannot be solved by a one-shot optimization, so the paper proposes a sequential approach: first, a target of renewable share is calculated for each month of the year; secondly, the planning production is determined each day according to the calculated target. The results of a case study show that the planning strategy that only aims to minimize the operating cost violates the environmental constraint. Using the developed planning model raises a little bit the cost (4.5%) but fulfills the heat demand and environmental constraint at the same time. Considering a reduced VAT eligible in France when the constraint on renewable share is met, the proposed planning is even better in term of the total cost (10% lower).

The relevant problem of guaranteed supply of high-quality raw materials to a timber processing enterprise that does not have its own sources of raw materials is considered. A method for the formation of sustainable chains of supplying raw materials to a timber processing enterprise was proposed, taking into consideration uncertainties and risks associated with the purchase of raw materials on the mercantile exchange and the implementation of the circuit of delivery to a warehouse. A dynamic model, which is a problem of stochastic nonlinear programming, the objective function of which is the cost of purchasing raw materials, was developed. The model makes it possible to form a plan for purchasing raw materials on the timber section of the mercantile exchange on a given planning horizon, taking into consideration uncertainties when it comes to the number of daily offers, their volumes, and prices. The risk of cancellation of the concluded contract due to the loss of the quality of raw materials during delivery and non-fulfillment of delivery terms was also taken into consideration. To find a solution to the model, a two-stage circuit, in which the first stage involves a procurement plan that is close to optimal, was proposed. At the second stage, a plan that is closest to the basic one in terms of the volume of purchased raw materials and minimizing the total costs is chosen for each day of implementation of a random flow of applications. The numerical solution at the first stage is found using the heuristic algorithm that uses the branch and bound method and the genetic algorithm at certain steps. At the second stage, the multi-criteria problem of mathematical programming is solved numerically. An example of the formation by a timber processing enterprise in the Far East of a suboptimal procurement plan that ensures an increase in the efficiency and sustainability of economic activity in the long term is considered, Рассматривается актуальная задача гарантированного обеспечения качественным сырьём лесоперерабатывающего предприятия, не имеющего собственных сырьевых источников. Предложен метод формирования устойчивых цепочек поставок сырья на лесоперерабатывающее предприятие с учётом неопределённостей и рисков, связанных с закупками сырья на товарно-сырьевой бирже и реализацией схемы доставки до склада. Разработана динамическая модель, представляющая собой задачу стохастического нелинейного программирования, целевой функцией которой являются затраты на приобретение сырья. Модель позволяет на заданном горизонте планирования формировать план закупок сырья на лесной секции биржи с учётом неопределённостей по количеству суточных предложений, их объёмам и ценам. Также учитывается риск отказа от заключённого договора в связи с потерей качества сырья во время доставки и невыполнением сроков доставки. Для нахождения решения модели предложена двухэтапная схема, в которой на первом этапе находится близкий к оптимальному «базовый» план закупок. На втором этапе для разыгранной для каждого дня реализации случайного потока заявок выбирается такой план, который наиболее близок по объёму закупаемого сырья к «базовому» и минимизирует суммарные затраты. Численное решение на первом этапе находится эвристическим алгоритмом, который на отдельных шагах использует метод ветвей и границ и генетический алгоритм. На втором этапе численно решается мультикритериальная задача математического программирования. Рассмотрен пример формирования лесоперерабатывающим предприятием Дальнего Востока субоптимального плана закупок, обеспечивающего повышение эффективности и устойчивости хозяйственной деятельности в долгосрочном периоде., Розглядається актуальна задача гарантованого забезпечення якісною сировиною лісопереробного підприємства, що не має власних сировинних джерел. Запропоновано метод формування стійких ланцюжків поставок сировини на лісопереробне підприємство з урахуванням невизначеностей та ризиків, пов'язаних із закупівлями сировини на товарно-сировинній біржі та реалізацією схеми доставки до складу. Розроблено динамічну модель, що представляє собою задачу стохастичного нелінійного програмування, цільовою функцією якої є витрати на придбання сировини. Модель дозволяє на заданому горизонті планування формувати план закупівель сировини на лісовій секції біржі з урахуванням невизначеностей за кількістю добових пропозицій, їх обсягами та цінами. Також враховується ризик відмови від укладеного договору в зв'язку з втратою якості сировини під час доставки та невиконанням термінів доставки. Для знаходження рішення моделі запропонована двоетапна схема, в якій на першому етапі знаходиться близький до оптимального «базовий» план закупівель. На другому етапі для розіграної для кожного дня реалізації випадкового потоку заявок вибирається такий план, який найбільш близький за обсягом закуповуваної сировини до «базової» та мінімізує сумарні витрати. Чисельне рішення на першому етапі знаходиться евристичним алгоритмом, який на окремих кроках використовує метод гілок і меж, і генетичний алгоритм. На другому етапі чисельно вирішується мультикритеріальне завдання математичного програмування. Розглянуто приклад формування лісопереробним підприємством Далекого Сходу субоптимального плану закупівель, що забезпечує підвищення ефективності та стійкості господарської діяльності в довгостроковому періоді.