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Integrated production-delivery control policy for an unreliable manufacturing system and multiple retailers

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2022年第0期245卷 108383-108383页

作者： Dhahri, Akrem Gharbi, Ali Ouhimmou, Mustapha Univ Quebec Syst Engn Dept Prod Syst Design & Control Lab Ecole Technol Super 1100 Notre Dame St West Montreal PQ H3C 1K3 Canada CIRRELT Interuniv Res Ctr Enterprise Networks Log Montreal PQ Canada

Production control policies have been studied in the literature for different types of unreliable manufacturing systems. But only few have been addressed to the unreliable manufacturer-retailer systems. We consider the integrated production and delivery control for an unreliable manufacturing system and multiple retailers. The retailers, supplied by the manufacturer through a common warehouse, are located in different sites and face different demand rates. We determine the optimal joint production and delivery control policy with the objective of minimizing the total cost, which comprises holding/backlog in addition to transportation costs. The stochastic optimal control and the impulse control theory are used to develop the optimal control policy (policy 1). The production is controlled by a hedging point policy and the delivery policy is governed by a state dependent economic order quantity. The optimal parameters of the proposed policy are determined by adopting a simulation-based optimization approach. A sensitivity analysis is conducted to verify the robustness of the results. An improved policy (policy 2) involving priority rules for delivery is proposed using a simulation-based approach. We conduct a comparative study of the new policies (policy 1 and 2) and the most relevant policy found in the literature (policy 3), and the results show that the new proposed improved policy (policy 2) outperforms others.

关键词： Unreliable manufacturing system Feedback production planning Economic order quantity Vendor managed inventory One-vendor multiple-buyer system Simulation stochastic dynamic programming

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Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 202卷 106994-106994页

作者： Yan, Tao Lei, Yaguo Wang, Biao Han, Tianyu Si, Xiaosheng Li, Naipeng Xi An Jiao Tong Univ Educ Minist Modern Design & Rotor Bearing Syst Key Lab Xian 710049 Shaanxi Peoples R China

Joint maintenance and spare parts inventory optimization has attracted increasing attention in recent years because of its capability in addressing the maintenance planning and the spare parts provisioning of industrial systems simultaneously. However, imperfect maintenance (IM) actions are either neglected or over-simplified as constant improvements in existing studies, which reduces their practicality in industrial applications. To tackle this limitation, this paper investigates the joint maintenance and spare parts inventory optimization for multi-unit systems with the consideration of IM actions as random improvement factors. First, a two-step approximate derivation method is proposed, which overcomes the derivation difficulties of replacement numbers due to the introduction of random improvement factors and enables the construction of the inventory level transition relationship. Then based on the inventory level transition relationship, an expected total cost model is formulated via the finite horizon stochastic dynamic programming (FHSDP). The decision variables are optimized by the joint use of enumeration and the FHSDP. Finally, a numerical simulation of a wind farm is carried out for illustration. Sensitivity analyses are further conducted to study the influences of critical parameters.

关键词： Imperfect maintenance Joint optimization stochastic dynamic programming Multi-unit systems

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An analysis of voluntary recycled content claims under demand benefit and uncertain recycled input supply

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OPERATIONS RESEARCH LETTERS 2020年第3期48卷 249-256页

作者： Vedantam, Aditya Iyer, Ananth, V SUNY Buffalo Dept Operat Management & Strategy Buffalo NY 14260 USA Purdue Univ Krannert Sch Management Susan Bulkeley Butler Chair Operat Management W Lafayette IN 47907 USA

We focus on the problem of choosing the optimal recycled content claim under stochastic local recycled content availability under two claim types - period specific (when claims have to hold each period) and average (when claims are evaluated across periods). We show conditions under which specific claims are higher than average claims, and explore cases where the optimal claims and profits are aligned to be in the same direction. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Recycled content claims Supply uncertainty Flexibility Inventory management stochastic dynamic programming

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New Exact Algorithm for the Vehicle Routing Problem with stochastic Demands

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TRANSPORTATION SCIENCE 2020年第4期54卷 1073-1090页

作者： Florio, Alexandre M. Hartl, Richard F. Minner, Stefan Univ Vienna Dept Business Decis & Analyt A-1010 Vienna Austria Tech Univ Munich Sch Management D-80333 Munich Germany

This paper considers the vehicle routing problem with stochastic demands under optimal restocking. We develop an exact algorithm that is effective for solving instances with many vehicles and few customers per route. In our experiments, we show that in these instances, solving the stochastic problem is most relevant (i.e., the potential gains over the deterministic equivalent solution are highest). The proposed branch-price- and-cut algorithm relies on an efficient labeling procedure, exact and heuristic dominance rules, and completion bounds to price profitable columns. Instances with up to 76 nodes could be solved in less than five hours, and instances with up to 148 nodes could be solved in long runs of the algorithm. The experiments also allowed new findings on the problem. The solution to the stochastic problem is up to 10% less costly than the deterministic equivalent solution. Opening new routes reduces restocking costs and in many cases results in solutions with less transportation costs. When the number of routes is not fixed, the optimal solutions under detour-to-depot and optimal restocking are nearly equivalent. However, when the number of routes is limited and the expected demand along a route is allowed to exceed the vehicle capacity, optimal restocking may be significantly more cost-effective than the detour-to-depot policy.

关键词： stochastic vehicle routing optimal restocking branch price and cut stochastic dynamic programming

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A Condition-Based Maintenance Model Including Resource Constraints on the Number of Inspections

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IEEE TRANSACTIONS ON RELIABILITY 2020年第3期69卷 1165-1176页

作者： Alvarez, Claudio Lopez-Campos, Monica Stegmaier, Raul Mancilla-David, Fernando Schurch, Roger Angulo, Alejandro Univ Tecn Federico Santa Maria Dept Ind Engn Valparaiso 1680 Chile Univ Colorado Dept Elect Engn Denver CO 80217 USA Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 1680 Chile

This article presents a stochastic dynamic programming model for a condition-based maintenance application. The approach seeks to determine the most opportune moment to inspect and execute preventive maintenance over each component of a nonredundant system, where the number of inspections to be performed simultaneously during each period is limited due to resource constraints. The model minimizes the total maintenance cost per unit of time, considering failure, maintenance, and inspection costs. Unlike most related literature, the model proposed herein allows nonperiodic inspections;it does not require to predefine a maintenance threshold and does not necessarily connect inspections to maintenance actions. Also, the criticality of each component is not static through time, or defined beforehand, but dynamically determined according to the available resources and the risk of failure. A numerical example illustrates the performance of the proposed model in comparison to three traditional maintenance models, namely corrective maintenance, age-based maintenance, and condition-based maintenance with periodic inspections. Results suggest that the proposed model yields the best solution among the studied policies and is more efficient, with a significant reduction of 90% in inspection resources.

关键词： Inspection Degradation Numerical models Integrated circuit modeling stochastic processes Preventive maintenance Condition-based maintenance reliability resource constraints stochastic dynamic programming

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Lower cost departures for airlines: Optimal policies under departure metering

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年第0期111卷 531-546页

作者： Chen, Heng Solak, Senay Univ Nebraska Coll Business Lincoln NE 68588 USA Univ Massachusetts Isenberg Sch Management Amherst MA 01003 USA

Departure metering is an airport surface management procedure that limits the number of aircraft in the runway queue by holding aircraft either at a predesigned metering area or at gates. Field tests of the procedure have shown significant fuel savings, implying that the procedure can play an important role in the Next Generation Air Transportation System being implemented in the U.S. In this paper we study optimal departure operations at airports in the context of departure metering. More specifically, we develop a stochastic dynamic programming framework for tactical management of pushback operations at gates and for determining the optimal number of aircraft to be directed to the runway queue from the metering areas. We introduce four easy-to-implement departure metering policies, and perform comparative analyses between these practical policies and the numerical-optimization based policies. In addition, from a strategic perspective, we identify optimal capacities for metering areas to be used as part of departure metering implementations. Overall, we find that the annual fuel and operating savings for airlines could be around $1.7 million if our proposed policies are implemented at the Detroit Metropolitan Wayne County Airport. Such policies can also be adapted by other airports to improve the overall efficiency of surface traffic management and departure operations.

关键词： Airline operations Airport operations stochastic dynamic programming Departure metering

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Production Campaign Planning Under Learning and Decay

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2020年第3期22卷 615-632页

作者： Jahandideh, Hossein Rajaram, Kumar McCardle, Kevin Univ Calif Los Angeles Anderson Sch Management Los Angeles CA 90095 USA

Problem definition: We analyze a catalyst-activated batch-production process with uncertainty in production times, learning about catalyst-productivity characteristics and decay of catalyst performance across batches. The goal is to determine the quality level of batches and to decide when to replenish a catalyst so as to minimize average costs, consisting of inventory-holding, backlogging, and catalyst-switching costs. Academic/ practical relevance: This is an important problem in a variety of process-industry sectors, such as food processing, pharmaceuticals, and specialty chemicals, but has not been adequately studied in the academic literature. This paper also contributes to the stochastic economic lot-sizing literature. Methodology: We formulate this problem as a semi-Markov decision process (SMDP) and develop a two-level heuristic to solve this problem. The heuristic consists of a lower-level problem that plans the duration of batches within the current campaign to maximize the efficiency of the catalyst while ensuring that the target attribute level for each batch is set to meet a quality specification represented by an average attribute level across all the batches in a campaign. The higher-level problem determines when to replace the costly catalyst as its productivity decays. To evaluate our heuristic, we present a lower bound on the optimal value of the SMDP. This bound accounts for all costs, as well as the randomness and discreteness in the process. We then extend our methods to multiple-product settings, which results in an advanced stochastic economic lot-sizing problem. Results: We test our proposed solution methodology with data from a leading food-processing company and show that our methods outperform current practice with an average improvement of around 22% in costs. In addition, compared with the stochastic lower bounds, our results show that the two-level heuristic attains near-optimal performance for the intractable multidimensional SMDP. Man

关键词： campaign planning batch production stochastic economic lot sizing problem Bayesian updating production learning semi-Markov processes stochastic dynamic programming

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Joint pricing and inventory management under servitisation

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2020年第6期71卷 893-909页

作者： Xu, Chao Duan, Yongrui Huo, Jiazhen Tongji Univ Sch Econ & Management Sipring Rd Shanghai 200092 Peoples R China

We analyse a problem involving the joint pricing of a product and associated service, together with the management of inventory under servitisation. For a finite planning horizon, the firm sells both a product and a product-centric service whose market size depends on the past sales of the product. At the beginning of each period, the firm simultaneously decides the price of the product, the price of the service, and the replenishment quantity. We prove that a modified base-stock list-price policy is optimal to this problem. In addition, we find that there is a trade-off between realising current profit from the product and stimulating demand to increase future profit, and that overlooking this trade-off results in overall loss of profit for the firm. Moreover, the optimal policy that takes the trade-off into account leads to a lower optimal product price compared with a myopic policy, and the optimal price increases as a function of the past sales of the product.

关键词： Pricing inventory management stochastic dynamic programming servitisation product-service system

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A dual sourcing inventory model for modal split transport: Structural properties and optimal solution

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第3期283卷 883-900页

作者： Dong, Chuanwen Transchel, Sandra ESCP Europe Berlin Campus Berlin Germany Kuehne Logist Univ Hamburg Germany

Shifting freight volumes from road to rail transport increases the economic performances of freight logistics. However, compared to road transport, rail transport generally lacks the flexibility in delivery quantity and frequency, and exhibits economies of scale in its shipment volume. This often leads to high inventory levels in the destination after deliveries. We generalize the tailored base-surge dual sourcing inventory model by introducing a fixed cost in rail transport, adding an extra decision in its delivery frequency, and relaxing the assumption of the base stock control of road transport, to support firms' modal split transport optimization. The objective is to optimize the controls of the two transport modes and the corresponding inventory management at the destination, which minimize the combined average transport and inventory costs per period in the steady state. Using stochastic dynamic programming, we find that when the delivery quantity and frequency of rail transport is fixed, the optimal shipment volume via the road transport indeed follows a base stock control. This allows to solve the relevant Bellman equation via an efficient policy iteration approach. We also find that the total cost is convex in the delivery quantity of rail transport, and a bi-section search can be applied. Finally, we analyze the sensitivity and robustness of our model using values suggested by a consumer goods firm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Inventory Modal split transport Dual sourcing stochastic dynamic programming Optimization

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Application of Markov renewal theory andsemi-Markovdecision processes in maintenance modeling and optimization of multi-unit systems

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NAVAL RESEARCH LOGISTICS 2020年第7期67卷 548-558页

作者： Salari, Nooshin Makis, Viliam Univ Toronto Mech & Ind Engn Toronto ON Canada

In this paper, a condition-based maintenance model for a multi-unit production system is proposed and analyzed using Markov renewal theory. The units of the system are subject to gradual deterioration, and the gradual deterioration process of each unit is described by a three-state continuous time homogeneous Markov chain with two working states and a failure state. The production rate of the system is influenced by the deterioration process and the demand is constant. The states of the units are observable through regular inspections and the decision to perform maintenance depends on the number of units in each state. The objective is to obtain the steady-state characteristics and the formula for the long-run average cost for the controlled system. The optimal policy is obtained using a dynamic programming algorithm. The result is validated using a semi-Markov decision process formulation and the policy iteration algorithm. Moreover, an analytical expression is obtained for the calculation of the mean time to initiate maintenance using the first passage time theory.

关键词： maintenance Markov renewal theory multi-unit system stochastic dynamic programming

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