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Purpose: A supply chain consists of raw material suppliers, manufacturers and retailers where inventory of raw materials and finished goods are involved, respectively. Therefore, it is important to find optimal solutions, which are beneficial for both supplier, manufacturer and retailer. Design/methodology/approach: This research focuses on a semi-continuous manufacturing facility by assuming that the production of succeeding cycle starts immediately after the production of preceding cycle. In reality, the inventory of a supply chain system may not be completely empty. A number of products may be left over after the deliveries are made. These leftover inventories are added to the next shipment after the production of required amount to makeup a complete batch for shipment. Therefore, it is extremely important to search for an optimal strategies for these types production facilities where leftover finished goods inventory remains after the final shipment in a production cycle. Considering these scenarios, an inventory model is developed for an imperfect matching condition where some finished goods remains after the shipments. Findings: Based on the previous observation, this research also considers a single facility that follows JIT delivery and produces multiple products to satisfy customers' demand. For this problem a rotational cycle model is developed to optimize the facility operations. Both problems are categorized as mixed integer non-linear programming problems which are to be solved to find optimum number of orders, shipments and rotational cycle policy for multiple products. Also, this solution will lead to estimate the optimum production quantity and minimum total system cost. Research limitations: This research considers the supply chain based on manufacturers point of view and it does not consider the transportation cost associated with supply chain. Next study will be focused on issues with joint decision making, information sharing, and transportation decision. Practical implications: This study will help the managers of refinery and paper industries in making their operation smooth by applying optimizing techniques and robust decision making. Originality/value: Based on the literature, no research was found on continuous production system supply chain and its optimization with JIT delivery. This research will definitely provide a direction for such problem to the researchers.

Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem has been addressed to enable on-time completion of customer orders in a lean manufacturing environment. The problem is optimizing the partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components. Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid excess final product inventory. The first model is a non-linear integer programming model whereas the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented. Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times compared to the other two models. It has also been showed that the alternative model is able to solve moderate sized real-world problems. Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature owing to the inclusion of new circumstances that are present in real-world applications. Those are: customer orders consisting of multi-products made of multi-parts, processing of all parts of the same product from different orders in the same batch, and delivering the orders only when all related products are completed. This research also contributes to the literature of batch scheduling problem by presenting new optimization models.

In this paper, we study an e-grocer’s tactical problem of differentiated time slot pricing in attended home delivery. The purpose of differentiating delivery prices is to influence customers’ choice behavior concerning the offered time slots, such that cost-effective delivery schedules on an operational level can be expected and overall profit is maximized. We present a mixed-integer linear programming formulation of the problem, in which delivery costs are anticipated by explicitly incorporating routing constraints, and we model customer behavior by a general nonparametric rank-based choice model. Concerning cost anticipation, we also propose a model-based approximation that enables application to real-world problem sizes. In a setup inspired by an industry partner operating in urban areas, we then perform a comprehensive computational study that reveals the value of the model-based approximation as a supporting instrument for an e-grocer’s pricing decisions in practice. In particular, we demonstrate the superiority of the model-based approximation for real-world problem sizes to several benchmark approaches applied in the scientific literature and in practice (e.g., a unit price approach and other standard pricing heuristics). The online appendix is available at https://doi.org/10.1287/trsc.2017.0738 .

We consider the transport of containers through a fleet of ships. Each ship has a capacity constraint limiting the total number of containers it can carry and each ship visits a given set of ports following a predetermined route. Each container has a release date at its origination port, and a due date at its destination port. A container has a size 1 or size 2; size 1 represents a 1 TEU (20‐foot equivalent unit) and size 2 represents 2 TEUs. The delivery time of a container is defined as the time when the ship that carries the container arrives at its destination port. We consider the problem of minimizing the maximum tardiness over all containers. We consider three scenarios with regard to the routes of the ships, namely, the ships having (i) identical, (ii) nested, and (iii) arbitrary routes. For each scenario, we consider different settings for origination ports, release dates, sizes of containers, and number of ports; we determine the computational complexity of various cases. We also provide a simple heuristic for some cases, with its worst case analysis. Finally, we discuss the relationship of our problems with other scheduling problems that are known to be open.

Purpose Africa has been identified as an area where higher mortality happens due to un-accessibility to health care, drugs and other health facilities. Nigeria, as one of the African countries, is not excluded from such difficulties. This study aims to examine the determinants of efficient last-mile delivery at selected health facilities and the Kaduna State Health Supplies Management Agency (KADSHMA). Design/methodology/approach The study sourced data from KADSHMA and the health facilities’ staff, with a total of 261 observations used. Likewise, the respondents were picked from warehouses of each health facility and KADSHMA. The data was analysed using the partial least square structural equation modelling analysis to estimate the relationship among the variables of the study. Findings The study’s findings revealed that all five variables of the study (i.e. determinants) were significantly affecting the efficient last-mile delivery. Four constructs (delivery cost [DC], delivery time [DT], mode of delivery [MD] and facilities technology [FT]) have shown a positive and significant association with efficient last-mile delivery, whereas one variable (product mix [PM]) indicated a negative and significant association with efficient last-mile delivery. The study concludes that DC, DT, MD, FT and PM played significant roles in efficient last-mile delivery. Research limitations/implications The study provides that specific means of transportation should always be on standby to transport health supplies. Time schedules should always be prepared and adhered to when transporting health supplies to the facilities, and each facility should network with robust technology to ease communication in terms of order and order planning. Additionally, facilities should try as much as possible to reduce the varieties of products when ordering health supplies, as it will increase the efficiency of the delivery. Originality/value To the best of the authors’ knowledge, this study is the first of its kind that considered these five variables (DC, DT, MD, FT and PM) with impact on the last-mile delivery in one model, especially in the Nigerian case. This is a great contribution to knowledge, more importantly, to the last-mile delivery of the health sector. The result confirmed the importance of these determinants (DC, DT, FT and PM) of last-mile delivery efficiency in saving lives.

•Using large-scale transactional data, we study return rates in online retailing.•A fast delivery time significantly increases the likelihood of product returns.•Insufficient reduction of post-purchase dissonance may explain this finding.•New customers are especially likely to return a fast-delivered product.•Retailers need to balance the benefits of fast deliveries with their downsides. This study explores the impact of short delivery times on product returns in the context of online retailing. Using a large dataset from a global fashion retailer's U.S. e-commerce platform, we investigate whether fast deliveries characterized by below-average delivery times influence the likelihood of product returns. The analysis employs logistic regression to examine the relationship between delivery times and return rates, and additionally considers product characteristics and customer attributes. Our findings indicate that fast deliveries lead to a significant increase in the likelihood of returns, particularly among new customers. Insufficient post-purchase cognitive dissonance reduction may theoretically motivate this counterintuitive result as also indicated by a preliminary follow-up study reported in the online appendix. These insights challenge the prevalent assumption that the shortening of delivery times unequivocally benefits online retailers and customers, highlighting the need for a balanced management approach to order fulfillment that considers both benefits in terms of customer acquisition and downsides in terms of return costs.

In practical problems, in addition to the processing time of the job, the impact of the time required for delivering the service to customers on the cost is also considered, i.e., delivery time, where the job processing time is a simple linear function of its starting time. This paper considers the impact of past-sequence-dependent delivery times (which can be referred to as psddt) on the studied objectives in three types of due windows (common, slack and different due windows). This serves to minimize the weighted sum of earliness, tardiness, starting time and size of due window, where the weights (coefficients) are related to the location. Through the theoretical analysis of the optimal solution, it is found that these three problems can be solved in time O(NlogN), respectively, where N is the number of jobs.

PurposeProduction and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.Design/methodology/approachIn this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.FindingsThe optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.Originality/valueThis study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

This paper studies the NP-hard problem of scheduling jobs on identical parallel machines with machine-dependent delivery times to minimize the total weighted tardiness. A mixed integer linear programming formulation is presented that does not require machine-indexed variables due to a transformation of the problem. A variable neighborhood search (VNS) algorithm is proposed incorporating a local search that utilizes fast evaluation techniques (FET) to significantly improve computational efficiency of the search in four different neighborhoods. In experiments, the VNS is compared with other solution approaches on a large set of randomly generated test instances. Additionally, results for the computational benefits of our FETs are reported.