Last-mile logistics is both a source and cause of problems in urban areas, especially problems related to traffic congestion, unsustainable delivery modes, and limited parking availability. In this context, multiple sustainable logistics solutions have been proposed. We focus on micro-depots (MDs), which can function as a consolidation center and a collection-and-delivery point for business-to-consumer (B2C) small parcels. This paper presents a new research idea that extends the existing MD solution by introducing the concept of a shared MD network with parcel lockers. Such networks enable multiple logistics service providers (LSPs) and/or business partners to use an MD while minimizing their individual costs and optimizing the use of urban space. We present case studies of such shared MD networks operating in the cities of Helsinki and Helmond. We provide a framework for auxiliary businesses that can exploit the existing MD structure to offer services to the surrounding population. Finally, we define metrics for evaluating the success of shared MD networks while considering social, environmental and economic objectives. The case studies highlight the complexity of implementing such a solution; it requires stakeholders’ involvement and collaboration. In particular, deciding on the location for a shared MD network is a critical phase, since local authorities have their own regulations, and residents’ preferences are usually different than LSPs’ ones. Nevertheless, if these challenges are overcome, this sustainable last-mile logistics solution has a promising future.

In certain types of processes, verification of the quality of the output units is possible only after the entire batch has been processed. We develop a model that prescribes which units should be inspected and how the units that were not inspected should be disposed of, in order to minimize the expected sum of inspection costs and disposition error costs, for processes that are subject to random failure and recovery. The model is based on a dynamic programming algorithm that has a low computational complexity. The study also includes a sensitivity analysis under a variety of cost and probability scenarios, supplemented by an analysis of the smallest batch that requires inspection, the expected number of inspections, and the performance of an easy to implement heuristic.

Industrial projects are plagued by uncertainties, often resulting in both time and cost overruns. This research introduces an innovative approach, employing Reinforcement Learning (RL), to address three distinct project management challenges within a setting of uncertain activity durations. The primary objective is to identify stable baseline schedules. The first challenge encompasses the multimode lean project management problem, wherein the goal is to maximize a project’s value function while adhering to both due date and budget chance constraints. The second challenge involves the chance-constrained critical chain buffer management problem in a multimode context. Here, the aim is to minimize the project delivery date while considering resource constraints and duration-chance constraints. The third challenge revolves around striking a balance between the project value and its net present value (NPV) within a resource-constrained multimode environment. To tackle these three challenges, we devised mathematical programming models, some of which were solved optimally. Additionally, we developed competitive RL-based algorithms and verified their performance against established benchmarks. Our RL algorithms consistently generated schedules that compared favorably with the benchmarks, leading to higher project values and NPVs and shorter schedules while staying within the stakeholders’ risk thresholds. The potential beneficiaries of this research are project managers and decision-makers who can use this approach to generate an efficient frontier of optimal project plans.

This paper details collaboration between the distribution organization within the Yedioth Group, the largest media group in Israel, Technion—Israel Institute of Technology in Haifa, Israel, and the Massachusetts Institute of Technology in Cambridge, Massachusetts. This collaboration has led to fundamental changes in how Yedioth distributes print magazines and newspapers. In our work, we developed and implemented decision support tools that are based on new models and algorithms, which were enabled through an electronic data interchange system, and in the future will be enabled with a specialized radio-frequency identification technological solution. The underlying concept is to use real-time information about the sales at the retail outlets to enable pooling of inventory in the network. In particular, we leverage this information to implement an additional redistribution period during the week, delaying the distribution of some magazines until after we receive partial sales data. We model the system as a two-stage stochastic optimization problem. Moreover, we show that the resulting cost is jointly convex in the decision variables. This approach gives rise to a multidimensional dynamic program. By formulating the second-stage subproblem as a linear program, we develop an innovative stochastic gradient-based optimization algorithm that finds the optimal solution in a matter of seconds. The changes resulting from this collaboration generated substantial cost savings at Yedioth—from both a reduction in magazine production levels and a reduction in the return levels. These savings were achieved while maintaining the same sales levels.

This research focuses on CONWIP, a closed production control system where all containers traverse a circuit incorporating the entire production line. We develop estimates, for an important level of work in process inventory, for four important performance measures: the means and variances of time between departures and flowtime. We develop our estimates through the concept of a “conceptual bottleneck machine”. This concept enables us to develop an analogy between deterministic and stochastic systems. This concept also allows us to handle migrating bottlenecks, an issue generally neglected. The model is widely applicable, assuming only finite means and variances of the processing time distributions. We test our model computationally, both against existing models and on a wide range of randomly generated problems. Finally we detail insights, obtained from our analytical model, into how CONWIP production systems operate. These insights enable us to explain the sources of the values of our performance measures, thus aiding system design and modification.