In manual picker-to-part systems, the order-picking process involves a set of interrelated decisions, including order batching, assignment, sequencing, and picker routing (OBASR). In this study, a comprehensive mixed-integer linear programming (MILP) model is formulated to address the joint order batching, assignment, sequencing, and picker routing problem under time-window due date constraints (JOBASRTWD) in an offline system, aiming to maximize customer service level by minimizing the earliness and tardiness of customer orders. Finding solutions is challenging because the fully integrated formulation of batching, assignment, sequencing, and routing decisions substantially increases the search space. To obtain a high-quality solution in a reasonable time, this work proposes an efficient hybrid algorithm, namely H-GAVND, based on a hybrid-coded genetic algorithm (GA) and variable neighbourhood descent (VND). The H-GAVND algorithm simultaneously searches for near-optimal solutions to all OBASR decisions. Various simulation experiments are performed across different sizes and with different instances to validate the proposed mathematical model and assess the performance of the hybrid algorithm. The proposed mathematical model effectively solves small-scale instances using the CPLEX solver, while on a real-world scale, the H-GAVND outperforms. Moreover, the performance of the proposed method is evaluated against a population-based metaheuristic, GA, and a solution-based local search approach, VND. The results indicate that the H-GAVND algorithm outperforms the standalone GA and VND approaches, owing to the effective combination of the global exploration capability of the population-based algorithm and the strong intensification of the solution-based local search method.

Yield uncertainty is an important issue in various industries such as agriculture, food, and textile where the production output relies on uncontrollable factors and fluctuating raw material quality. To systematically leverage data to deal with uncertainty cost-effectively, distributionally robust optimization combines the strengths of stochastic programming and robust optimization by optimizing the expected costs against an ambiguity set that defines possible distributions. In this work, we leverage a data-driven robust optimization framework and formulate a mixed-integer distributionally robust multi-item lot-sizing model with uncertain production yield to determine a robust production plan. To this end, we use a scenario-wise formulation that partitions the available data into scenarios that define different patterns influencing the quality of the product and production process. In addition, we apply the proposed approach to real-world data of a case study to demonstrate the effectiveness of the proposed framework in dealing with yield uncertainty. Our experimental results show that distributionally robust plans lead to more effective cost-saving strategies and decreased risk of stock-outs. Additionally, our findings suggest that the proposed model exhibits lower sensitivity to variations in production yield realizations and it is more proficient in incorporating historical data into the decision-making process. This results in a more effective response to challenges encountered within the production system under yield uncertainty. Note to Practitioners–In a production context with various sources of uncertainty for which the mathematical estimation of the uncertain parameter can be complex or hard to perform, it would be better to use the proposed robust approach. Here, any information, accurate or not, new or historical, can be integrated into the system to improve the quality of the obtained production plan, yet still robust and mitigate nervousness. For the reduction of conservatism, the proposed approaches indicate how the manufacturer risk aversion could be integrated into the decision models to respond to the strategic need for robustness for production planning under uncertainty.

Effective replenishment planning and inventory control are essential for the smooth operation and adaptability of supply chains. These aspects play a pivotal role in upholding a company’s competitiveness and triumph in today’s fiercely competitive markets. Supply chain planners encounter significant hurdles in choosing the most appropriate suppliers in diverse scenarios, reducing average inventory levels, and determining optimal safety lead times. This research tackles these challenges by examining and evaluating a multi-period replenishment planning issue within the framework of dynamic demand and multiple suppliers. The suppliers are pre-selected and defined by procurement costs, with lead times considered as independent discrete random variables with known and limited probability distributions. The goal is to optimize the distribution of order quantities among these pre-selected suppliers while minimizing the anticipated total cost. Two strategies and corresponding linear models are suggested to investigate the impact of dividing orders between suppliers, order crossover, and order flexibility. Numerical experiments provide evidence that concurrently considering splitting and flexibility yields benefits in terms of cost optimization.

This article studies a bi-objective stochastic optimization problem for multi-period multi-sourcing supply planning. The formulated problem accounts for stochastic lead times, degressive pricing, holding and backlog costs, delivery flexibility costs, as well as both holding and transportation carbon footprint. The first objective is to minimize the expected total cost, while the second objective is to minimize the expected total footprint. These objectives must be achieved while adhering to suppliers’ capacity constraints and meeting customer demand. In this paper, the proposed stochastic integer linear program is detailed, and the ϵ-constraint method used to solve it is described. The first results of experiments are presented and discussed.

In order to meet item demands, end-of-life (EOL) product and subassembly ordering and disassembly schedules are determined by disassembly lot sizing, which is the subject of this study. We take into consideration a stochastic version with undetermined ordering lead time (OLT). In this case, OLT stands for the amount of time that passes between placing and receiving an order (we can only order EOL products). Throughout the planning horizon, scenarios are used to model the stochasticity. The objective is to reduce the expected total of setup, purchasing, inventory, and backlog expenses. This is achieved by expressing the problem as a two-stage mixed integer linear programming (2S-MILP) model across all potential scenarios. The 2S-MILP is unsolvable since it is predicated on every scenario conceivable. A Simultaneous Backward Reduction approach is proposed to make it tractable. To confirm the suggested method’s efficacy, it is assessed in a variety of environments.

Support vector clustering (SVC) has been proposed in the literature as a data-driven approach to build uncertainty sets in robust optimization. Unfortunately, the resulting SVC-based uncertainty sets induces a large number of additional variables and constraints in the robust counterpart of mathematical formulations. We propose a two-phase method to approximate the resulting uncertainty sets and overcome these tractability issues. This method is controlled by a parameter defining a trade-off between the quality of the approximation and the complexity of the robust models formulated. We evaluate the approximation method on three distinct, well-known optimization problems. Experimental results show that the approximated uncertainty set leads to solutions that are comparable to those obtained with the classic SVC-based uncertainty set with a significant reduction of the computation time. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Funding: This work was supported by the German-French Academy for the Industry of the Future [Data-driven collaboration in Industrial Supply Chains project].

Supply chains are complicated dynamical systems due to many factors, e.g. the competition between companies, the globalization, demand fluctuations, sales forecasting. Hence, they must react to changes in order to adapt quickly the network. In this paper we focus on a two echelon supply chain problem dealing with supplier selection issue during periods in a highly flexible context. How to select suppliers is the principle question we try to answer in this research. A suggested approach based on dynamic optimization is highlighted to solve this problem.

The four papers in this special section are devoted to the topic of radio frequency identification (RFID), new areas of technological development, and applications for its use.

We focus on one of the simple assembly line balancing problems known as SALBP-2 which consists in assigning a set of elementary operations V = {1, 2, ... , n} to the m linearly ordered stations with respect to the precedence constraints and aims in minimizing the line cycle time c. The processing times of operations tj, j ¿ V may vary during the life cycle of assembly line for manual operations (represented by set V¿ ) and be fixed for automated operations (set V \ V¿ ). The goal of this paper is to derive necessary and sufficient condition (so-called qualitative analysis) of the stability of an optimal balance found for a given vector of operations times t = (t1, t2, ... , tn) with regard to possible independent perturbations of the processing times of the operations from set .

Distributed control architectures in manufacturing are becoming increasingly popular because their modularity makes them easy to install, configure, and modify. These benefits do not come for free. For creating the manufacturing systems of the future, engineers need to dare a leap in their ways of thinking. Considering a simple multi-agent model in use in a distributed flexible manufacturing project, it is shown that the dynamics of such multi-agent systems without appropriate design control can be chaotic, strongly dependent on some parameters inherent to the agentpsilas negotiation rules. In order to develop appropriate multi-agent models, the dynamic behavior of the system is investigated using concepts and methods of the theory of nonlinear dynamical systems, and suggests some ways to control chaotic behaviours.

A problem of inventory control for assembly systems is considered where the component lead times are random. A periodic ldquolot for lotrdquo policy for component supplying is studied. The decision variables are component planned lead times. The aim is to minimize the average holding cost for components while keeping a high customer service level for the finished product. Some properties and a lower bound on the cost function are proved. These results can be useful for the development of efficient exact optimization algorithms, as Branch and Bound, for example. This articlepsilas models can be used for the following MRP parameterization: the calculation the planned lead time for each component under lead time uncertainties.

A multilevel assembly system with one type of finished product and several types of components is considered. The component lead times for components at each period are independent random variables, and the demand of finished product per period is constant. Finished products should be delivered by the end of each period. Otherwise a backlogging cost is incurred. Components necessary to assemble the same semi-finished product that are delivered in advance will cause holding cost. The objective is to find the optimal release dates (safety lead times which is measured in number of periods) for the components in order to minimize the total expected costs composed of the finished product backlogging cost and the component holding cost. We propose a model that gives the optimal release dates values for the components.

The transfer lines dealt with in this paper are equipped with multifunctional spindle heads, capable to realize several operations in the same time (in parallel) on the same work-piece. Each such multifunctional spindle head is called a block and is fully described by its set of operations, operating time and cost. The reconfigurability concerns here the possibility of realizing a family of products with the same equipment. The blocks and the product family features are supposed known. The problem is to assign the blocks to workstations such as to minimize the total cost of the line and to meet a set of time and technological constraints for the whole product family. This problem is formulated as a linear program (LP) and solved by using the Cplex ILOG optimization software. An example is provided to illustrate the steps of the proposed solving method.

This paper deals with a transfer line optimal design. In transfer lines, operations of the same block are executed simultaneously. Blocks are assigned to machines and they can be activated in mixed order. The set of all available blocks is given beforehand. The line investment cost is defined by the sum of blocks costs and stations costs. In addition to the standard line balancing problem, precedence and cycle time constraints, blocks compatibility and parallelism constraints must be taken into account. The problem is to assign all operations grouped into blocks that all constraints are respected and line investment cost is minimum. This paper is focused on solving the problem by a branch-and-bound algorithm. A new approach for obtaining a lower bound is offered. It is based on a reduction of the transfer line balancing problem to a set partitioning problem. Computational experiments provides that the proposed approach is efficient to solve practical transfer line design problems

The paper focuses on the enhancement of automatic robot programming techniques for laser cutting application. Its particular contribution lies in the area of multiobjective optimisation of robot motions via graph representation of the search space and dynamic programming procedures. It have been developed algorithms that allow to generate smooth manipulator trajectories within acceptable time, simultaneously considering kinematic, collision and singularities constraints of the robotic system, as well as the limitations of the robot control units. The presented results are implemented in a commercial software package and verified for real-life applications in automotive industry.

We address the simple assembly line balancing problem: Minimize the number of stations in for processing n partially ordered operations V={1, 2,..., n} within the given cycle time c. The processing time t/sub i/ is given for each operation i/spl isin/V but cannot be changed only for the operations from the subset of automated and semi-automated operations V/spl bsol/V/spl tilde/. If i/spl isin/V/spl bsol/V/spl tilde/, then operation time t/sub i/ is strictly positive real number, which is fixed during the life cycle of the assembly line. Subset V/spl tilde/ of set V includes manual operations, for which it is hard or even impossible to fix processing time for the whole life cycle of the assembly line. We assume that if j/spl isin/V/spl tilde/, then given operation time t/sub j/ can be different for different cycles of production process. For the optimal line balance b of passed assembly line, we investigate its stability radius. In particular, we derive necessary and sufficient conditions when optimal line balance b is stable (in other words, when b has strictly positive stability radius).