A branch-and-price algorithm for the robust single-source capacitated facility location problem under demand uncertainty

An iterated tabu search heuristic for the Single Source Capacitated Facility Location Problem

We present a very large-scale neighborhood (VLSN) search algorithm for the capacitated facility location problem with single-source constraints. The neighborhood structures are induced by customer multi-exchanges and by facility moves. We consider both traditional single-customer multi-exchanges, detected on a suitably defined customer improvement graph, and more innovative multicustomer multi-exchanges, detected on a facility improvement graph dynamically built through the use of a greedy scheme. Computational results for some benchmark instances are reported that demonstrate the effectiveness of the approach for solving large-scale problems. A further test on real data involving an Italian factory is also presented.

We address the problem of accelerating column generation (CG) for set-covering formulations via dual optimal inequalities (DOIs). We study two novel classes of DOIs, which are referred to as Flexible DOIs (F-DOIs) and Smooth-DOIs (S-DOIs), respectively (and jointly as SF-DOIs). F-DOIs provide rebates for covering items more than necessary. S-DOIs describe the payment of a penalty to permit the undercoverage of items in exchange for the overinclusion of other items. Unlike other classes of DOIs from the literature, the S-DOIs and F-DOIs rely on very little problem-specific knowledge and, as such, have the potential to be applied to a vast number of problem domains. In particular, we discuss the application of the new DOIs to three relevant problems: the single-source capacitated facility location problem, the capacitated p-median problem, and the capacitated vehicle-routing problem. We provide computational evidence of the strength of the new inequalities by embedding them within a column-generation solver for these problems. Substantial speedups can be observed as when compared with a nonstabilized variant of the same CG procedure to achieve the linear-relaxation lower bound on problems with dense columns and structured assignment costs.

Simulation modeling combined with decision control can offer important benefits for analysis, design, and operation of semiconductor supply-chain network systems. Detailed simulation of physical processes provides information for its controller to account for (expected) stochasticity present in the manufacturing processes. In turn, the controller can provide (near) optimal decisions for the operation of the processes and thus handle uncertainty in customer demands. In this paper, we describe an environment that synthesizes discrete-event system specification (DEVS) with model predictive control (MPC) paradigms using a knowledge interchange broker (KIB). This environment uses the KIB to compose discrete event simulation and model predictive control models. This approach to composability affords flexibility for studying semiconductor supply-chain manufacturing at varying levels of detail. We describe a hybrid DEVS/MPC environments via a knowledge interchange broker. We conclude with a comparison of this work with another that employs the Simulink/MATLAB environment.

Due to increased traffic congestion and carbon emissions, Bike Sharing Systems (BSSs) are adopted in various cities for short distance travels, specifically for last mile transportation. The success of a bike sharing system depends on its ability to have bikes available at the "right" base stations at the "right" times. Typically, carrier vehicles are used to perform repositioning of bikes between stations so as to satisfy customer requests. Owing to the uncertainty in customer demand and day-long repositioning, the problem of having bikes available at the right base stations at the right times is a challenging one. In this paper, we propose a multi-stage stochastic formulation, to consider expected future demand over a set of scenarios to find an efficient repositioning strategy for bike sharing systems. Furthermore, we provide a Lagrangian decomposition approach (that decouples the global problem into routing and repositioning slaves and employs a novel DP approach to efficiently solve routing slave) and a greedy online anticipatory heuristic to solve large scale problems effectively and efficiently. Finally, in our experimental results, we demonstrate significant reduction in lost demand provided by our techniques on real world datasets from two bike sharing companies in comparison to existing benchmark approaches.

This study aims to apply the PDCA (Plan, Do, Check, Act) method as a systematic strategy in inventory control for phase-out products to reduce deadstock at PT XYZ. The background of the study shows a significant increase in the amount of scrapping in the last three years due to the accumulation of uncontrolled deadstock. The research uses a qualitative descriptive approach with data collection techniques through observation, interviews, and historical company data. Through the PDCA stages, it was found that the main causes of deadstock stemmed from the inconsistency of phase-out control methods, manual monitoring processes by the PIC, and uncertainty in customer demand. The implementation of solutions in the form of standardizing control methods, digitizing inventory systems, and implementing an early warning system yielded significant results. The evaluation results showed that the improvement strategy was able to reduce the potential for deadstock by up to 80.7% in the sample product model. Thus, the PDCA method proved to be effective as a continuous improvement approach in inventory control during the product phase-out. This research is recommended to be applied more widely in inventory management in companies-.

Dynamic optimization of natural gas networks under customer demand uncertainties

With the increasingly competitive market environment nowadays, the uncertainty of customer demand has been raised step by step; meanwhile, in order to build up more close supply chain partnership, the collaboration of logistics service supply chain (LSSC) presents multiply periodic features. Consequently, the study on the quantity coordination of capability collaboration for multi-period-oriented LSSC under uncertain conditions would have more practical significance compared with that of single-period. By considering the two-echelon supply chain structure within multiply period consisting of a functional logistics service provider (FLSP) and a logistics services integrator (LSI), this paper proposed the LSI’s cost function and the FLSP’s profits function under stochastic conditions, and established the multi-period capability quantity coordination model with Stackelberg decision-making. With the software Matlab7.0, numerical simulation was conducted, and the parameters among three collaboration strategies and two LSI decision modes were compared. The results indicated that under multiply periodic conditions, the optimal collaboration strategy was that the satisfaction degree for the initial capability of the providers was higher than the standardized satisfaction value set by LSI, at this time the LSI should take rapid decision mode, and reduce the unit penalty value so as to decrease the LSI’s cost and increase FLSP’s profit. Key words: Logistics service supply chain (LSSC), two-echelon collaboration, quantity coordination, multi-period, uncertainty.

An inventory-theory-based interval stochastic programming method and its application to Beijing’s electric-power system planning

In this paper, a multi-product, multi-period, multi-site supply chain production and transportation planning problem under customer demand uncertainty is considered. The problem is modelled using a multi-stage stochastic programming model taking into account the production amount, the inventory and backorder levels as well as the quantity of products to be transported between the different plants and customers in each period. Risk management is also addressed by incorporating a risk metric into the stochastic programming model which leads to a multi-objective optimisation model. The objective aims simultaneously to maximise the expected profit as well as the worst case profit. A real example from textile and apparel industry is studied in order to illustrate the effectiveness of the proposed model and the robustness of the supply chain planning solutions.

To minimize waste and scrap, reworking becomes an integral part of many manufacturing companies. In this study, the integrated production-quality planning in the tactical horizon considering the customer demand uncertainty is presented. A mixed-integer programming model is developed to find optimal dynamic planning production which minimizes costs and reduces the scrap. After each production cycle, the quality indicators and relative costs for good and poor quality are performed. These indicators are introduced into the developed model to determine the optimal production orders and stock levels considering the reworked and scrapped products. A real case study is presented to illustrate the applicability of the proposed model. By comparing the obtained results with real results from studied company, we proved that this new model gives an optimal production plan.

In practice, the parameters of the vehicle routing problem are uncertain, which is called the uncertain vehicle routing problem (UVRP). Therefore, a data‐driven robust optimization approach to solve the heterogeneous UVRP is studied. The uncertain parameters of customer demand are introduced, and the uncertain model is established. The uncertain model is transformed into a robust model with adjustable parameters. At the same time, we use a least‐squares data‐driven method combined with historical data samples to design a function of robust adjustable parameters related to the maximum demand, demand range, and given vehicle capacity to optimize the robust model. We improve the deep Q‐learning‐based reinforcement learning algorithm for the fleet size and mix vehicle routing problem to solve the robust model. Through test experiments, it is proved that the robust optimization model can effectively reduce the number of customers affected by the uncertainty, greatly improve customer satisfaction, and effectively reduce total cost and demonstrate that the improved algorithm also exhibits good performance.

Quadratically adjustable robust linear optimization with inexact data via generalized S-lemma: Exact second-order cone program reformulations

In this paper, a bi-objective Vehicle Routing Problem (bi-RVRP) with uncertainty in both demands and travel times is studied by means of robust optimization. Uncertain demands per customer are modeled by a discrete set of scenarios representing the deviations from an expected demand, while uncertain travel times are independent from customer demands. Then, traffic records are considered to get discrete scenarios to each arc of the transportation network. Here, the bi-RVRP aims at minimizing the worst total cost of traversed arcs and minimizing the maximum total unmet demand over all scenarios. As far as we know, this is the first study for the bi-RVRP which finds practical applications in urban transportation, e.g. , serving small retail stores. To solve the problem, different variations of solution approaches, coupled with a local search procedure are proposed: the Multiobjective Evolutionary Algorithm (MOEA) and the Non-dominated Sorting Genetic Algorithm (NSGAII). Different metrics are used to measure the algorithmic performance, the convergence, as well as the diversity of solutions for the different methods.

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