Operational Planning Level Research Articles

A multi-level production-inventory-distribution system under mixed make to stock, make to order, and vendor managed inventory strategies: An application in the pulp and paper industry

In today's competitive environment, most companies tend to adopt a hybrid manufacturing strategy to provide more reliable services to a wide range of customers. Based on a real-world case study, we investigate a customer-wise production-inventory-distribution system in a multi-level supply chain. Within this system, at a tactical planning level, the decision concerning the production strategies including make to stock (MTS), make to order (MTO), or vendor managed inventory (VMI) strategy, to choose for each customer is made, and then at the operational planning level, the production, inventory, and distribution activities are coordinated according to the strategy assigned to the customers. The objective is to optimize the total cost while the VMI customers are timely satisfied and the target service level for MTO customers is respected. Furthermore, the purchase acceptance rate for MTS customers can be controlled to enhance capacity utilization. We develop a rolling horizon planning approach to deal with the dynamics of customers' demands under different forecasting scenarios. We perform numerical experiments under both static and rolling horizons based on adapted data from a real-world application from a Canadian pulp and paper industry. The results reveal that customers placing smaller orders are classified within the VMI strategy to mitigate potential delays. Conversely, MTS customers are predominantly characterized by larger order quantities. A sensitivity analysis of key problem parameters demonstrates that the backorder cost for MTO customers can effectively influence the strategy allocation. Moreover, our results underscore the notable influence of sales prices on the distribution of customers among strategies.

Information models of tactical and operational planning levels in energy construction

The strategic, tactical and operational levels of energy construction planning are considered. The purpose of the article is to present information models for solving multi-criteria tasks of forming schedules of various types. For the first information model based on vector representations of aggregations, a multilevel classification of criteria and ranking algorithms for multi-criteria tasks is proposed. A structural analysis of criteria and ranking algorithms for multi-criteria tasks is carried out. Schemes of hierarchies of multiple inclusion of criteria and ranking algorithms for information planning models are presented. Recursive definitions of criteria and ranking algorithms are obtained. For the second information model, a heuristic paradigm of centralized scheduling is described for various types of presentation of source data, namely: sets of independent applications, sets of application vectors and sets of hierarchical or network structures of applications. The scheduling paradigm is based on the use of two schemes for generating schedules and priority rules. Using the first constructive scheme, the initial schedule is cyclically formed.

A semantic web-based risk assessment framework for collaborative planning to enhance overall supply chain effectiveness for semiconductor industry

Semiconductor supply chain uncertainty is increasingly complicated due to shortening product life cycles, demand fluctuation and a series of collaborative decisions, yet little research focuses on risk assessment from operational planning level to supply chain planning level. Limitations of existing approaches can be traced in part to the lack of a framework within which the decisions involved in different levels can be integrated and aligned in light of supply chain dynamics. This study aims to develop a semantic web based risk assessment framework to integrate collaborative planning and enable the interpretability among real world and simulation modelling. This study starts by proposing a performance elevation metric from interpreting planning level for overall supply chain effectiveness. This study further constructs an ontology for planning and control decisions to support building the risk assessment simulation model to ensure information system interoperability and link domain knowledge for semiconductor supply chain network resilience. For validation, a simulation model is constructed for backend production from a leading semiconductor company in Germany. The contribution of the proposed semantic web-based framework provides the interoperability for supporting supply chain management and reduces the gaps between simulation modelling and physical setting to enhance overall supply chain effectiveness.

Data quality issues in production planning and control – Linkages to smart PPC

In the era of Industry 4.0 and digitalization, planning solutions need to co-exist with each other and be able to manage higher complexity and with a higher performance. As the concept smart production planning and control is a part of industry 4.0, it is highly relevant to study and is in this paper explored on the four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning). This paper provides a framework for linking the four elements of smart PPC with data quality issues in state-of-the-art production planning and control environments. Maintaining a high standard of data quality in the business processes aids the organization to stay competitive in its market. Hence, our assumption is that a high level of data quality is needed in production planning and control for a high-performance outcome. The empirical part of our study results in a bar-chart of seven data quality problems and their occurrences together with their causes in PPC. According to the empirical data results, inaccurate data entries is the most common data quality problem related to PPC. The causes of the inaccurate data entries can be linked to human resources and organizational control. Future research should strengthen the validity of the proposed linkages between data quality problems and elements of smart PPC and implications on strategic, tactical, and operational planning levels.

VEHICLE SEQUENCING STRATEGIES IN OPTIMIZATION OF FORESTRY LOGISTICS

Timber transportation is a hard task for any forest company. There are several efforts to reduce and control these costs, considering efficient equipment and optimization frameworks. Due to the pattern of combinatorial problems, the deterministic methods have a high computational effort of processing time, which generally makes their use unfeasible. An alternative procedure is applying approximation algorithms, which have efficient searches for finding feasible solutions. The present study evaluated algorithms for solving the forest transport sequencing problem. The Simulated instances were designed to highlight three instances for 10, 20, and 30 stands with a realistic pattern. According to the operational set, four solving strategies were proposed, considering three algorithms (Simulated Annealing, Greedy, and Greedy-Simulated Annealing). The computational code, processing, and analysis of the data were performed using RStudio software. The results show feasible solutions from all tested algorithms, highlighting the hybrid Greedy-Simulated Annealing algorithm. With proven normality and homogeneity of variance, the algorithms were tested with the Tukey test at a level of 5%. The blocks, stand random selection, and multiple flow strategies produced the best results of the tested instances. As a conclusion, the proposed algorithms are efficient for solving the forest transportation problem and may be helpful at the operational planning level.

The value of stochastic crowd resources and strategic location of mini-depots for last-mile delivery: A Benders decomposition approach

Crowd-shipping is an emergent solution to avoid the negative effects caused by the growing demand for last-mile delivery services. Previous research has studied crowd-shipping typically at an operational planning level. However, the study of support infrastructure within a city logistics framework has been neglected, especially from a strategic perspective. We investigate a crowd-sourced last-mile parcel delivery system supported by a network of strategically located mini-depots and present a two-stage stochastic network design problem with stochastic time-dependent arc capacity to fulfill stochastic express deliveries. The first-stage decision is the location of mini-depots used for decoupling flows allowing more flexibility for crowd–demand matching. The second stage of the problem is the demand allocation of crowd carriers or professional couriers for a finite set of scenarios. We propose an exact Benders decomposition algorithm embedded in a branch-and-cut framework. To enhance the algorithm, we use partial Benders decomposition, warm-start, and non-dominated cuts. We perform computational experiments on networks inspired by the public transportation network of Munich. The proposed solution method outperforms an off-the-shelf solver by solving instances 3.6 to 19 times faster. The results show the potential to exploit the stochastic crowd flows to deliver packages with deadlines of 3 or 8 h. The crowd can transport 8.3% to 32.5% of the total demand by using between 4% to 24% of the crowd capacity, and we observe average daily savings of 2.1% to 7.6% of the total expected operational cost. The results show values of the stochastic solution of at least 1% and up to 10%.

Composing Vessel Fleets for Maintenance at Offshore Wind Farms by Solving a Dual-Level Stochastic Programming Problem Using GRASP

Background: Dual-level stochastic programming is a technique that allows modelling uncertainty at two different levels, even when the time granularity differs vastly between the levels. In this paper we study the problem of determining the optimal fleet size and mix of vessels performing maintenance operations at offshore wind farms. In this problem the strategic planning spans decades, while operational planning is performed on a day-to-day basis. Since the operational planning level must somehow be taken into account when making strategic plans, and since uncertainty is present at both levels, dual-level stochastic programming is suitable. Methods: We present a heuristic solution method for the problem based on the greedy randomized adaptive search procedure (GRASP). To evaluate the operational costs of a given fleet, a novel fleet deployment heuristic (FDH) is embedded into the GRASP. Results: Computational experiments show that the FDH produces near optimal solutions to the operational day-to-day fleet deployment problem. Comparing the GRASP to exact methods, it produces near optimal solutions for small instances, while significantly improving the primal solutions for larger instances, where the exact methods do not converge. Conclusions: The proposed heuristic is suitable for solving realistic instances, and produces near optimal solution in less than 2 h.

CONCEPTUAL FRAMEWORK FOR HEAVY-DUTY VEHICLE PLATOONING IN PHYSICAL INTERNET SYSTEMS

One of today's most significant challenges is sustainability, which is closely linked to environmentally friendly solutions and resource efficiency. As a solution to these goals, the concept of the Physical Internet emerged, defining the logistics network of the future as a global, open, and interconnected system. Concerning the conditions of vehicles based on Physical Internet-based systems, we cannot ignore the latest vehicle technology innovations that appear more and more intensively in parallel. The framework proposes planning at the strategic, tactical, and operational levels. Different levels of coordination implement different approaches to platoon coordination in line with the network architecture of PI-based logistics systems. We recommend the highest level of offline design in fixed π-hubs. The tactical level involves designing π-hubs online. We propose the implementation of speed-based solutions at the operational planning level.

An integrated multi-criteria decision analysis and optimization modeling approach to spatially operational road repair decisions

In this study, we developed a generic cost-effective approach for spatially explicit decision support involving the allocation of road repair treatments. The approach begins with an assessment of the existing road condition to identify the extent of environmental impacts and to determine road repair regimes in a subjective manner using group-decision making efforts. An integer programming model is then formulated by combining expert opinions with operational costs to guide repair schedules required for each road segment at the operational planning level. To demonstrate model performance, we applied it to a 400 km2 landscape consisting of 289 km of paved roads in the mountainous region of the Hyrcanian forests in Iran. We assessed sensitivity of the inputs, such as weight verification, budgetary limitations, and rehabilitation weights. The results of the subjective analysis show that 76% of the roads analyzed in these forests must be prioritized to receive treatments as intended for logistical purposes. Incorporating the extent of environmental dimensions into operational costs allows us to generate an optimal tradeoff curve by selecting an appropriate treatment for segments of a road network. The approach demonstrated here can be used to design detailed alternative solutions for addressing spatially-informed road decisions under various terrain conditions.

A Systematic literature review on maritime transportation optimization using linear programming

Maritime transportation has a decisive position in world trade. Along with the development of science and technology, maritime transportation faced more complex and robust problems in term of their optimization. In this paper, we purpose a Systematic Literature Review (SLR) on Maritime Transportation Optimization (MTO) in three-level decision making that is the strategic planning level, tactical planning level, and operational planning level. We also have identified Linear Programming (LP) as a proper preference to study MTO. This SLR using a structured approach that involves bibliometric and network analytics from the articles published during 1976 – 2019. Some of the key findings in this study as follows, (i) research and citation in MTO using LP is rising and reached its peak in 2018; (ii) three countries with the highest number of articles are the United States, Norway, and China; (iii) shows a great opportunity for future research to combine strategic planning, tactical planning, and operational planning.

Same-Day delivery with pickup stations and autonomous vehicles

Same-day delivery has become a very important challenge for e-commerce providers. However, effective delivery concepts are still not established. In this paper, we analyze the potential of combining parcel pickup stations and autonomous vehicles for same-day delivery. This combination may allow for consolidation of goods, automated delivery processes, and reduced operation costs. We consider the problem on the operational planning level. A depot, a set of pickup stations, and an autonomous delivery fleet are given. Customers dynamically order goods to a preferred pickup station and expect fast service. The goods need to be delivered from the depot to the preferred pickup station or another pickup station in the close neighborhood. Autonomous vehicles are directly dispatched between the depot and the stations. To decide where and when to dispatch a vehicle and about the corresponding goods to load, we present a policy function approximation (PFA). Our intuitive PFA allows real-time decision making while balancing the tradeoff between fast delivery and consolidation. We conduct an extensive computational study for the pickup station network of the city of Braunschweig. We show how our delivery concept allows fast delivery for up to 100 delivered goods per vehicle and day. We further pair the PFA with a value function approximation to account for heterogeneity in demand. We finally derive important managerial insights for strategical and tactical planning and present an extensive outlook on promising future research directions.

Assessment of timber extraction distance and skid road network in steep karst terrain

Abstract: This study aims to define a simple and effective method to calculate skidding distances on steep karst terrain, rich in ground obstacles (stoniness and rockiness) to support decision planning of secondary and primary forest infrastructure network for timber extraction in productive selective cut forests. Variations between geometrical extraction distances and actual distances were highlighted on the operational planning level (i.e., compartment level) through GIS-related calculation models, focusing on cable skidder timber extraction. Automation in defining geometrical and real extraction distances, as well as relative forest openness were achieved by geo-processing workflows in GIS environment. Due to variation of extraction correction factors at the compartment level from a minimum of 1.19 to a maximum of 5.05 in the same management unit, it can be concluded that planning harvesting operations (timber extraction) at operational level should not include the use of correction factors previously obtained for entire terrain (topographical) categories, sub-categories or even management units.

A hybrid optimization algorithm for surgeries scheduling

This paper deals with the Operating Room (OR) planning problem at an operational planning level. The problem addressed consists of two interrelated sub-problems usually referred to as “advance scheduling” and “allocation scheduling”. In the first sub-problem, the decisions considered are the assignment of a surgery date and an OR block to a set of patients to be operated on over a given planning horizon. The second aims at determining the sequence of selected patients in each OR and day. We assume that the duration of surgeries are random variables with known probability distributions. For each sub-problem an integer linear stochastic formulation is given. A hybrid two-phase optimization algorithm which exploits the potentiality of neighborhood search techniques combined with Monte Carlo simulation is developed to solve the overall problem. The approach developed searches for a feasible and robust solution designed to balance the trade-off arising between the hospital and patient perspectives, i.e. maximizing the OR utilization and minimizing the number of patient cancellations. The contribution of this paper is twofold. The former, more methodological, is to provide an efficient algorithmic framework to solve the joint advance and allocation scheduling problem taking into account the inherent uncertainty of surgery durations. The latter, more practical, is to provide a tool to develop robust offline OR schedules which consider the trade-off between reducing surgery cancellations and postponements while maximizing the operating theater utilization. To evaluate the efficiency of the proposed algorithmic approach, in terms of quality of solutions and solution time, we provide a computational analysis on a set of instances based on real data.

Operational planning level of development in production enterprises in the machine building industry and its impact on the effectiveness of production

This article examines the interdependence between the operational planning level of development and the effectiveness of the production process. The operational planning level of development has been measured by the scale of six intensity levels (1–6) to evaluate each of the eight targeted tasks (planning raw and materials, work planning, planning tools and equipment, capacity planning, termination of production, scheduling by work centres, drafting and launching of work documentation and various records terms and performance), while the effectiveness of the production process has been measured by the following indicators: productivity, stock turnover rate, capacity utilisation rate, delivery time and fallout ratio. The results have shown that enterprises with higher operational planning level of development present better effectiveness of the production process in comparison to enterprises with less operational planning level of development.

Business process management and IT management: The missing integration

The importance of business processes and the centrality of IT to contemporary organizations’ performance calls for a specific focus on business process management and IT management. Despite the wide scope of business process management covering both business and IT domains, and the profound impact of IT on process innovations, the association between business process management and IT management is under-explored. Drawing on a literature analysis of the capabilities of business process and IT governance frameworks and findings from a case study, we propose the need for horizontal integration between the two management functions to enable strategic and operational business—IT alignment. We further argue that the role of IT in an organization influences the direction of integration between the two functions and thus the choice of integration mechanisms. Using case study findings, we propose that IT as a business enabler respectively calls for sequential and reciprocal integrations at strategic and operational planning levels. Drawing on logical reasoning, we suggest that IT as a strategic driver necessitates reciprocal integration at both levels.

A fuzzy multi-criteria approach for robust operating room schedules

Operating room schedules are regularly influenced by uncertain demands such as unknown surgery durations or randomly arriving emergency patients. The performance of these schedules depends on the information available about these uncertainties when designing the schedules. We focus on an offline operational planning level which assigns patients to days and rooms without focusing on the intra-day sequence. A sufficient amount of time per day is to be reserved for elective and emergency surgeries. At the same time we observe that the performance of a particular schedule influences several stakeholders’ interests. We therefore combine the aspects of uncertain planning parameters and multiple stakeholders’ interests and investigate the performance of schedules for operating rooms using a dedicated robust multi-criteria optimisation approach. We compute a robust compromise schedule focusing on stochastic surgery times and different objectives and simultaneously reserve time windows dedicated to randomly arriving emergency demand. In order to evaluate the schedule’s quality, we perform an extensive simulation study and demonstrate to what extent each robust schedule achieves the mentioned goals. In a second step, we perform a sensitivity analysis in order to investigate how significant changes in assumptions about the stochastic model parameters affect the level of achievement of the different objectives.

Anticipatory Service Network Design of Bike Sharing Systems

Today‘s conurbations suffer from inefficiency in transportation systems. Bike sharing systems (BSS) combine the advantages of public and private transportation to better exploit the given transportation infrastructure. They provide bikes for short-term trips at automated rental stations. However, spatio-temporal variation of bike rentals leads to imbalances in the distribution of bikes, causing full or empty stations in the course of the day. Ensuring the reliable provision of bike and free bike racks is crucial for the viability of these systems. On the tactical planning level, target fill levels of bikes at stations are determined to provide reliability in service. On the operational planning level, the BSS operator relocates bikes in vehicles among stations based on target fill levels. A recent approach in tactical service network design (SND) anticipates relocation operations of BSS by means of a dynamic transportation model yielding the required demand of relocation services (RS). A RS is described by pickup and return station, time period, and the number of relocated bikes. RS represent the design decision for implementing a service between two stations in each period for each day of the system operation. The output of the SND model are the time-dependent target fill levels at stations and the set of cost-efficient RS to facilitate these target fill levels. However, the existing approach neglects the sequence of RS into tours, thus leading to a weak anticipation of operational decisions. We extend an existing SND approach by including the concept of service tours (ST). RS are sequenced in ST which start and end at the depot. Experiments shows that the ST obtained by the extended SND yield a stronger anticipation of operational decisions.

Evaluation of Robust Operating Room Schedules Considering Multiple Stakeholders

Operating room (OR) schedules are regularly influenced by uncertain demands such as surgery durations or randomly arriving emergency patients. The performance of these schedules depend on the information about these uncertainties when designing the schedules. We focus on an oine operational planning level at which assignments of patients to days and rooms are made without focusing on the intra-day sequence. At this level a sucient amount of time is to be reserved for every elective surgery. At the same time we observe that the performance of a given schedule influences several stakeholders’ interests. We therefore combine the aspects uncertain planning parameters and multiple interests and investigate the performance of schedules for ORs using a dedicated robust multi-criteria optimization approach. We compute dierent schedules focusing on fluctuating surgery times, randomly arriving emergency demand, and dierent objectives. In order to evaluate the schedules’ quality, we perform a simulation study and demonstrate to what extend robust schedules achieve the mentioned goals. The consequences of significant changes in assumptions about possible deviations in surgery times and how they aect the level of achievement of dierent objectives is investigated in a second step.

Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions

This paper reviews studies from the past 30 years that use operations research methods to tackle containership routing and scheduling problems at the strategic, tactical, and operational planning levels. These problems are first classified and summarized, with a focus on model formulations, assumptions, and algorithm design. The paper then gives an overview of studies on containership fleet size and mix, alliance strategy, and network design (at the strategic level); frequency determination, fleet deployment, speed optimization, and schedule design (at the tactical level); and container booking and routing and ship rescheduling (at the operational level). The paper further elaborates on the needs of the liner container shipping industry and notes the gap between existing academic studies and industrial practices. Research on containership routing and scheduling lags behind practice, especially in the face of the fast growth of the container shipping industry and the advancement of operations research and computer technology. The purpose of this paper is to stimulate more practically relevant research in this emerging area.

Development of an integrated tactical and operational planning model for supply of feedstock to a commercial‐scale bioethanol plant

AbstractIn this paper, a new modeling approach is proposed to integrate the tactical and operational planning levels in the biomass supply chain. The proposed approach includes an optimization model and a simulation model. The integration is made between these models (i) to assure the fulfillment of the daily biomass demand year‐round for a commercial‐scale cellulosic ethanol plant and (ii) to reduce biomass delivery costs. The optimization model prescribes the design of the supply area in a way that the annual biomass demand is met at a minimum delivery cost for a five‐year planning horizon. Given the design of the supply area, the simulation model schedules the flow of multi‐biomass in the supply chain to meet the daily biomass demand of the ethanol plant subject to the dynamics and uncertainties in the supply chain. If the daily demand cannot be met, the outputs of the simulation model are used to adjust the design in the optimization model to assure the fulfillment of the daily demand. The application of the integrated model to a proposed commercial‐sized bioethanol plant shows the efficiency of the integrated approach to design the supply area in a way that the daily biomass demand is met at the minimum delivery cost possible. The results of the sensitivity analysis reveal that the most influential parameter on the design is biomass yield. In addition, bale bulk density, in‐farm and road transportation operations, and farmer participation rates have the highest impact on delivery cost compared to other input parameters. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd