Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes

[EN] One of the problems when conducting research in mathematical programming models for operations planning is having an adequate database of experiments that can be used to verify advances and developments with enough factors to understand different consequences. This paper presents a test bed generator and instances database for a rolling horizons analysis for multiechelon planning, multiproduct with alternatives processes, multistroke, multicapacity with different stochastic demand patterns to be used with a stroke-like bill of materials considering production costs, setup, storage and delays for operations management. From the analysis of the operations planning obtained from this test bed, it is concluded that a product structure with an alternative process obtains the lowest total cost and the highest service level. In addition, decreasing seasonal demand could present a lower total cost than constant demand, but would generate a worse service level. 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Production and distribution research center

Issued as Annual report, Project no. E-24-62

Multiperiod optimisation and strategic planning for chemical processes

Il tema è l'ottimizzazione multiperiodale applicata a processi chimici, in particolare ottimizzazione dei livelli di scorte e pianificazione strategica

Efficient neighborhood evaluations for the vehicle routing problem with multiple time windows

In the vehicle routing problem with multiple time windows (VRPMTW), a single time window must be selected for each customer from the multiple time windows provided. Compared with classical vehicle routing problems with only a single time window per customer, multiple time windows increase the complexity of the routing problem. To minimize the duration of any given route, we present an exact polynomial time algorithm to efficiently determine the optimal start time for servicing each customer. The proposed algorithm has a reduced worst-case and average complexity than existing exact algorithms. Furthermore, the proposed exact algorithm can be used to efficiently evaluate neighborhood operations during a local search resulting in significant acceleration. To examine the benefits of exact neighborhood evaluations and to solve the VRPMTW, the proposed algorithm is embedded in a simple metaheuristic framework generating numerous new best known solutions at competitive computation times

An Optimization Framework for a Dynamic Multi-Skill Workforce Scheduling and Routing Problem with Time Windows and Synchronization Constraints

This article addresses the dynamic multi-skill workforce scheduling and routing problem with time windows and synchronization constraints (DWSRP-TW-SC) inherent in the on-demand home services sector. In this problem, new service requests (tasks) emerge in real-time, necessitating a constant reevaluation of service team task plans. This reevaluation involves maintaining a portion of the plan unaltered, ensuring team-task compatibility, addressing task priorities, and managing synchronization when task demands exceed a team's capabilities. To address the problem, we introduce a real-time optimization framework triggered upon the arrival of new tasks or the elapse of a set time. This framework redesigns the routes of teams with the goal of minimizing the cumulative weighted throughput time for all tasks. For the route redesign phase of this framework, we develop both a mathematical model and an Adaptive Large Neighborhood Search (ALNS) algorithm. We conduct a comprehensive computational study to assess the performance of our proposed ALNS-based reoptimization framework and to examine the impact of reoptimization strategies, frozen period lengths, and varying degrees of dynamism. Our contributions provide practical insights and solutions for effective dynamic workforce management in on-demand home services

A new mixed-integer programming model for harvest scheduling subject to maximum area restrictions

Forest ecosystem management often requires spatially explicit planning because the spatial arrangement of harvests has become a critical economic and environmental concern. Recent research on exact methods has addressed both the design and the solution of forest management problems with constraints on the clearcut size, but where simultaneously harvesting two adjacent stands in the same period does not necessarily exceed the maximum opening size. Two main integer programming approaches have been proposed for this area restriction model. However, both encompass an exponential number of variables or constraints. In this work, we present a new integer programming model with a polynomial number of variables and constraints. Branch and bound is used to solve it. The model was tested with both real and hypothetical forests ranging from 45 to 1,363 polygons. Results show that the proposed model’s solutions were within or slightly above 1% of the optimal solution and were obtained in a short computation time

Inventory routing problem with backhaul considering returnable transport items collection

The Inventory Routing Problem (IRP) has been highlighted as a valuable strategy for tackling routing and inventory problems. This paper addresses the IRP but considers the forward delivery and the use of Returnable Transport Items (RTIs) in the distribution strategy. We develop an optimization model by considering inventory routing decisions with RTIs collection (backhaul customers) of a Closed-Loop Supply Chain (CLSC) within a short-term planning horizon. RTIs consider reusable packing materials such as trays, pallets, recyclable boxes, or crates. The RTIs represent an essential asset for many industries worldwide. The solution of the model allows concluding that if RTIs are considered for the distribution process, the relationship between the inventory handling costs of both the final goods and RTIs highly determines the overall performance of the logistics system under study. The obtained results show the efficiency of the proposed optimization scheme for solving the combined IRP with RTIs, which could be applied to different real industrial cases

Petroleum refinery scheduling with consideration for uncertainty

Scheduling refinery operation promises a big cut in logistics cost, maximizes efficiency, organizes allocation of material and resources, and ensures that production meets targets set by planning team. Obtaining accurate and reliable schedules for execution in refinery plants under different scenarios has been a serious challenge. This research was undertaken with the aim to develop robust methodologies and solution procedures to address refinery scheduling problems with uncertainties in process parameters. The research goal was achieved by first developing a methodology for short-term crude oil unloading and transfer, as an extension to a scheduling model reported by Lee et al. (1996). The extended model considers real life technical issues not captured in the original model and has shown to be more reliable through case studies. Uncertainties due to disruptive events and low inventory at the end of scheduling horizon were addressed. With the extended model, crude oil scheduling problem was formulated under receding horizon control framework to address demand uncertainty. This work proposed a strategy called fixed end horizon whose efficiency in terms of performance was investigated and found out to be better in comparison with an existing approach. In the main refinery production area, a novel scheduling model was developed. A large scale refinery problem was used as a case study to test the model with scheduling horizon discretized into a number of time periods of variable length. An equivalent formulation with equal interval lengths was also presented and compared with the variable length formulation. The results obtained clearly show the advantage of using variable timing. A methodology under self-optimizing control (SOC) framework was then developed to address uncertainty in problems involving mixed integer formulation. Through case study and scenarios, the approach has proven to be efficient in dealing with uncertainty in crude oil composition

A Decision Support System for Land Allocation under Multiple Objectives in Public Production Forests in the Brazilian Amazon

Logging in natural forests is a vital economic activity in the Brazilian Amazon. However, illegal and unplanned logging is exhausting forests rapidly. In 2006, a new forestry law in Brazil (Lei 11,284/2006) established the legal framework to develop state and national public forests for multiple uses. To support public forest planning efforts, we combine spatially explicit data on logging profits, biodiversity, and potential for community use for use within a forest planning optimization model. While generating optimal land use configurations, the model enables an assessment of the market and nonmarket tradeoffs associated with different land use priorities. We demonstrate the model's use for Faro State Forest, a 636,000 ha forest embedded within a large mosaic of conservation units recently established in the state of Pará. The datasets used span the entire Brazilian Amazon, implying that the analysis can be repeated for any public forest planning effort within the region